Samadi Yassir
email: yassir.samadi@um5s.net.ma

Zbakh Mostapha

E-HEFT: Enhancement Heterogeneous Earliest Finish Time algorithm for Task Scheduling based on Load Balancing in Cloud Computing

Tadonki Claude

Mines ParisTech-PSL Centre de Recherche en Informatique (CRI)

Paris, France claude.tadonki@mines-paristech.fr Abstract-Cloud computing is one of the most widely spreaded platforms for executing tasks through virtual machines as processing elements. However, there are various issues that need to be addressed in order to be efficiently utilized for workflow applications. One of the fundamental issues in cloud computing is related to task scheduling. Optimal scheduling of tasks in cloud computing is an NP-complete optimization problem, and many algorithms have been proposed to solve it. Furthermore, the existing algorithms fail to either meet the user's Quality of Service (QoS) requirements such as minimizing the makespan and satisfying budget constraints, or to incorporate some basic principles of cloud computing such as elasticity and heterogeneity of computing resources. Among these algorithms, the Heterogeneous Earliest Finish Time (HEFT) heuristic is known to give good results in short time for tasks scheduling in heterogeneous systems. Generally, the HEFT algorithm yields good tasks execution time, but its drawback is that there is no load balancing. In this paper, an enhancement of Heterogeneous Earliest Finish Time (E-HEFT) algorithm under a user-specified financial constraint is proposed to achieve a well balanced load across the virtual machines while trying to minimize the makespan of a given workflow application. To evaluate the performance of the enhancement algorithm, we compare our algorithm with some existing scheduling algorithms. Experimental results show that our algorithm outperforms other algorithms by reducing the makespan and improving load balance among virtual machines.

Index Terms-scientific workflows; task scheduling; data dependency; cloud computing; load balancing

I. INTRODUCTION

Cloud computing is a kind of Internet-based computing, where virtually unlimited resources are provided on demand and as services (i.e., IaaS, PaaS and SaaS, respectively). Cloud computing has also emerged as a platform for executing scientific workflows. Under this new paradigm, services and storage resources are now delivered on a pay-as-you-go basis [START_REF] Youseff | Toward a unified ontology of cloud computing[END_REF], [START_REF] Oliveira | Towards a taxonomy for cloud computing from an e-Science perspective[END_REF], where clients only pay for resources they actually used and for the time they own them. Cloud clients are consuming and producing a huge volume of data that should be analyzed and processed, thus being considered as dataintensive experiments. This large volume of data can be found in many areas like astronomy [START_REF] Deelman | Pegasus: Mapping scientific workflows onto the grid[END_REF], high-energy physics [START_REF] Ludascher | Scientific workflow management and the Kepler system[END_REF] and bio-informatics [START_REF] Oinn | Taverna: A tool for the composition and enactment of bioinformatics workflows[END_REF], etc. Thus, scientists need to analyze terabytes of data either from existing data resources or collected from physical devices. In order to manage the execution of these complex experiments, scientific workflows can be a prominent solution. A scientific workflow [START_REF] Yu | A taxonomy of workflow management systems for grid computing[END_REF] describes the automation of scientific or general process and the set of rules (dependencies). In other words, scientific workflows are sets of elementary tasks and their dependencies. In addition, a given client is interested in reducing the makespan. So, the scheduler should efficiently fully utilize the available resources and make sure that the load is globally well balanced.

In a scheduling algorithms, scientific workflow can be viewed as a directed acyclic graph (DAG), where nodes (or tasks) represent the computation and edges represent the communication between them. The scheduler assigns a weight for each node in the DAG, which represents the computation cost, and assigns weights for edges corresponding to communication cost between nodes. In addition, scientific workflow applications have many computations and tasks that generate many intermediate large datasets with dependencies among them. So, the scheduler should also take care of precedence constraints between the set of tasks.

In its most general form, the problem of tasks scheduling of a graph onto a set of different resources is an NP-Complete problem [START_REF] Pinedo | Scheduling: theory, algorithms, and systems[END_REF]. As a result, over several years, a number of heuristic algorithms suitable for DAG scheduling on heterogeneous resources have been suggested [START_REF] Bittencourt | Dag scheduling using a lookahead variant of the heterogeneous earliest finish time algorithm[END_REF] that attempt to strike a good balance between running time, complexity and schedule quality [START_REF] Kwok | Static scheduling algorithms for allocating directed task graphs to multiprocessors[END_REF], but still a lot of work needs to be done to make scheduling in clouds more effective. The Heterogeneous Earliest Finish Time heuristic (HEFT) [START_REF] Topcuoglu | Performance effective and low-complexity task scheduling for heterogeneous computing[END_REF] has been one of the most often cited and used, with the advantage of simplicity and generating generally good schedules with a short makespan. However, HEFT algorithm lacks load balancing among the machines of the cluster. In this paper, we extend our previous work [START_REF] Samadi | Graph-based Model and Algorithm for Minimizing Big Data Movement in a Cloud Environment[END_REF], which balances the load between datacentres and afterwards minimizes the overhead of data exchanges, and propose an enhancement of HEFT algorithm. In other words, our scheduling algorithm aims to minimize the total execution time of tasks as well as to achieve a well-balanced load across all VMs in cloud environment, always obeying both budget and precedence constraints imposed by the DAG. That is, there are two objectives considered here. The first one is the minimization of the tasks execution time. The second one is to evenly distribute the workload among the virtual machines of the entire cluster.

In this paper, in order to reduce the total execution time of the workflow, we propose a scheduling approach that takes into account the variety and heterogeneity of virtual machines in a cloud computing cluster (e.g. different bandwidths, transfer rates, and processing capacities). It also takes into account the data distribution and data constraints all together within the same solution, i.e. tasks and data transfers are scheduled together by the E-HEFT. Thus, the scheduler defines the distribution of tasks and data among the virtual machines so it minimizes the total execution time and the unnecessary data transfers. Then, our scheduling algorithm is simulated using the CloudSim toolkit package [START_REF] Buyya | Modeling and simulation of scalable cloud computing environments and the cloudSim toolkit: challenges and opportunities[END_REF], [START_REF] Calheiros | CloudSim: A novel framework for modeling and simulation of cloud computing infrastructures and services[END_REF]. To evaluate the performance of the enhancement algorithm, a comparative study is done among some state-of-the-art algorithms.

The rest of the paper starts with a review of related works in section 2. In section 3, we describe the task scheduling problem, its formulation and our objective function. Section 4 presents the proposed algorithm. Section 5 assesses the applicability of our proposed solution and illustrates its performance using Cloudsim simulator. Section 6 outlines a conclusion and some future works.

II. RELATED WORK

In this section, we conduct a survey of related work on different task scheduling techniques in cloud computing. Task scheduling or module mapping for workflows has been investigated extensively in the literature in the past decade [START_REF] Bajaj | Improving scheduling of tasks in a heterogeneous environment[END_REF].

Many task scheduling algorithms have been proposed to minimize the workflow makespan in heterogeneous environment such as Opportunistic Load Balancing (OLB) [START_REF] Armstrong | The relative performance of various mapping algorithms is independent of sizable variances in run-time predictions[END_REF], Minimum Execution Time (MET) [START_REF] Freund | Scheduling resources in multi-user, heterogeneous, computing environments with SmartNet[END_REF] and Heterogeneous Earliest Finish Time (HEFT) [START_REF] Topcuoglu | Performance effective and low-complexity task scheduling for heterogeneous computing[END_REF]. OLB assigns randomly each task to the machine that is expected to be available, regardless of the tasks expected execution time on that machine [START_REF] Freund | Guest editor's introduction: Heterogeneous processing[END_REF]. This algorithm aims to keep all machines as busy as possible. One advantage of OLB is its simplicity. However, OLB does not consider expected task execution times, the mappings it finds can result in very poor makespan. In contrast to OLB, Minimum Execution Time (MET) assigns randomly each task to the machine with the best expected execution time for that task, regardless of that machines availability [START_REF] Armstrong | The relative performance of various mapping algorithms is independent of sizable variances in run-time predictions[END_REF], [START_REF] Freund | Scheduling resources in multi-user, heterogeneous, computing environments with SmartNet[END_REF]. MET aims to give each task to its best machine. This can cause a severe load imbalance across machines. Earliest Finish Time (HEFT) algorithm aims to minimize the overall execution time of a DAG application in a heterogeneous environment. While being effective at optimizing makespan, the HEFT algorithm does not consider the budget constraint and load balancing among the machines of the cluster when making scheduling decisions. In [START_REF] Wu | End-to-end delay minimization for scientific workflows in clouds under budget constraint[END_REF], Chase et al. constructed analytical models to quantify the network performance of scientific workflows using cloud-based computing resources. In this work, they designed a critical-greedy algorithm to minimize the workflow end-to-end delay by defining a global budget level (GBL) parameter and preassigns tasks with the budget-level execution cost. However, this algorithm is designed for homogeneous cloud environments, where the communication time between tasks is assumed zero, which is not the case on heterogeneous cloud computing systems.

In addition to single parameter optimization scheduling, the task scheduling problem becomes more challenging when two QoS parameters (i.e., time, cost and load balancing) are considered simultaneously. The authors in [START_REF] Oprescu | Budget estimation and control for bag-of-tasks scheduling in clouds[END_REF] have studied the problem of budget-constrained schedules of bag-of-tasks problems on clouds. Authors have presented a scheduler which is used to calculate the cost and performance of a scientific workflow on multiple different clouds under budget-constraints and offer viable options to the user. The work have focused on a statistical method for estimating costs and application makespan for a given bag on different clouds. Their work however does not consider the problem of load balancing discussed in this paper. Another multi-objective algorithm named Revised Discrete Particle Swarm Optimization (RDPSO) algorithm was proposed by Wu et al. [START_REF] Wu | A revised discrete particle swarm optimization for cloud workflow scheduling[END_REF]. The algorithm suggested by the authors, either optimizes cost or makespan based on the budget and deadline constraints. For evaluating cost, the data transmission costs and the computation costs are taken into account. Different sets of workflows are used for experimentation and comparisons are made with the standard PSO and the BRS algorithms. This multiobjective model is efficient and the results show that the RDPSO algorithm can attain much more cost savings and reduces the makespan than the existing PSO and BRS (Best Resource Selection) algorithms.

A common drawback of the aforementioned approaches is that they do not consider the load balancing among the cluster machines. The proposed E-HEFT algorithm covers all of these deficits and presents an approach that achieves well balanced load across the machines and minimizes the makespan of a given workflow application under a user-specified budget constraint.

III. SYSTEM MODEL AND PROBLEM FORMULATION A. System model

We start by refining the problem definition and then present a background about HEFT algorithm. Recall that the problem we are interested in this paper is performing the tasks of a scientific workflow in a cloud computing environment while taking into account the quality of service criteria, namely the overall execution time and load balancing.

As illustrated in Fig. 1, there are three layers in the system model for the workflow scheduling problem in cloud environments [START_REF] Wu | End-to-end delay minimization for scientific workflows in clouds under budget constraint[END_REF]: the task graph layer which is comprised of tasks with precedence constraints, the resource graph layer which represents a network of virtual machines, and the cloud infrastructure layer consisting of a set of data centers connected by network links. According to [START_REF] Abdelkader | Dynamic task scheduling algorithm with load balancing for heterogeneous computing system[END_REF] the problem of task scheduling in heterogeneous systems is finding proper assignment of tasks to machines in order to optimize some performance metrics such as resources utilization, load balancing and execution time. A popular representation of a scientific workflow application is the directed acyclic graph (DAG): G(T, E), where T = {t 1 , t 2 , • • • , t n } is the set of tasks and E is the set of directed edges between tasks. An edge e j i of the form (t i , t j ) of graph G represents the precedence constraint between these tasks, case in which t i is said to be the parent task of t j and t j is said to be the child task of t i . Based on this constraint, a child task can not be executed until all of its parent tasks are completed. If there is data transmission from t i to t j , the t j can start only after all the data from t i has been received. A task without parent is called an entry task, denoted t entry , whereas a task without child is called an exit task, denoted t exit . A sample workflow is shown in Fig 2.

The data exchanged between the virtual machines are represented by a n * n matrix which is denoted by D out , where D out [i, j] represents the amount of transmitted data from the virtual machine V M (t i ) that executes the task t i to the virtual machine V M (t j ) that executes the task t j . We assume that the size of the output data D out ti produced by task t i is known in advance. Let β be a matrix of dimension m * m representing the bandwidth between the different virtual machines that execute the workflow tasks. β[i, j] is the bandwidth between the virtual machine V M i and the virtual machine V M j . Note that this matrix is not necessarily symmetric. Indeed, the bandwidth between V M i and V M j is not necessarily the same as that between V M j and V M i . Fig 3 shows an example of resources graph with transfer speeds (bandwidth) between different virtual machines of a cloud environment. The transfer time between two virtual machines executing the tasks t i and t j , that is V M (t i ) and V M (t j ), is determined by the amount of transferred data and bandwidth between these virtual machines. Note that the transfer time between two tasks running on the same VM equals 0. The transfer time T T is calculated as:

T T (t i , t j ) = D out [i, j] β[i, j] (1)
Let ET be a n * m matrix, where ET (t i , V M j ) is the estimated execution time of the task t i on the virtual machine V M j . The real execution time of a task on a given virtual machine also depends on the amount of its input and output data.

B. Background

the scheduling algorithms for heterogeneous system, the Heterogeneous Earliest Finish Time (HEFT) algorithm has been one of the most frequently cited and used because of both its simplicity and good performance [START_REF] Topcuoglu | Performance effective and low-complexity task scheduling for heterogeneous computing[END_REF]. HEFT is a natural extension of the classical list scheduling algorithm for homogeneous systems to cope with heterogeneity, outperforms other comparable algorithms in terms of minimization of the execution time.

There are two phases for the algorithm. The prioritizing phase for giving a priority to each task and a machine selection phase for selecting a suitable machine that minimizes the execution time. If two or more tasks have equal priority, then the tie is resolved by selecting a task randomly. The last phase is repeated until all tasks are scheduled on suitable machines. This algorithm has relatively low complexity and is very efficient when compared to other algorithms. Thus, HEFT assigns first a weight to each node and edge of the DAG based on the average computation and communication costs, respectively [START_REF] Zhao | An experimental investigation into the rank function of the heterogeneous earliest finish time scheduling algorithm[END_REF]. Rank u represents the length of the longest path from task t i to the exit node, including its computational. It is recursively defined as:

rank u (t i ) = W i + max tj ∈succ(ti) (c i,j + rank u (t j )) (2)
Where succ(t i ) is the set of immediate successors of the task t i , c i,j is the average communication cost of edge e j i , and W i is the average execution cost for task t i . Then, the graph is traversed upwards and rank u value is assigned to all nodes. For the exit task, rank u (t exit ) = W exit . Tasks are then scheduled, in descending order of their rank value, on the machine which gives the smallest estimated finish time.

C. Objective function

Before presenting the objective function, it is necessary to define the ST and F T attributes, which are derived from a given partial schedule. ST (t i ) and F T (t i ) are the earliest starting time and the earliest finishing time of task t i , respectively. For the input task t entry :

ST (t entry ) = 0 (3) F T (t entry ) = ET (t entry , V M j )) (4)
For the other graph tasks, the values of ST and F T are calculated recursively, starting from the entry task. In order to compute the F T of a task t i , all immediate predecessor tasks of t i must have been assigned and scheduled with the consideration of the transfer time as shown in equations 5 and 6:

F T (t i ) = ST (t i ) + min V Mj {ET (t i , V M j )} (5)
ST (t i ) = max tj ∈pred(ti) {F T (t j ) + T T (t j , t i )} (6)
Where pred(t i ) is the set of predecessors of the task t i . We propose a scheduler S defined by a set of resources, a set of tasks to schedule, the total execution time, and the total execution cost. S = (T, R, ET

(t i , V M j ), P C(t i , V M j )) • T = {t 1 • • • t n } is the set of tasks compose the Workflow W. • R = {V M 1 • • • V M m } is the set of virtual machines. • ET (t i , V M j )
is the total execution time of the task t i on V M j . • P C(t i , V M j ):(Processing cost) is the total execution cost of the task t i on V M j , which equals:

P C(t i , V M j ) = Cost(V M j ) * ET (t i , V M j ) (7)
Where Cost(V M j ) is the cost of data processing per hour in the virtual machine V M j , and ET (t i , V M j ) is the execution time of the task t i on V M j .

The objective function of the overall execution time can be defined as follows:

M akespan = min{F T (t exit )} (8)
Therefore, the objective function is to efficiently assign tasks of a given workflow application to virtual machines such that its makespan is minimized.

Subject to:

ti∈T P C ti ≤ Budget(W ) (9) ∀t ∈ T | ∃! V M j ∈ R , µ(t i ) = V M j ( 10
) ti∈rj RAM (t i ) ≤ RAM (V M j ) (11)
ti∈rj

IS(t i ) + OS(t i ) ≤ SC(V M j ) (12)
The associated constraints are given in Eq. 9 to 12. Constraint 9 ensures that the workflow processing cost must be less or equal to the budget dedicated to this workflow.

Constraint 10 ensures that each task is scheduled on a single virtual machine. Where µ is a matching function between the set of tasks and the set of virtual machines.

Constraint 11 ensures that the total RAM required by all tasks running on V M j should be less than the available RAM on this virtual machine.

Constraint 12 ensures that the total disk space (input size and output size) required by all tasks assigned to V M j should be less than the storage capacity available on V M j .

TASK SCHEDULING ALGORITHM

In this section, an algorithm to resolve the above mentioned problems is proposed. The algorithm is consisted of four phases. In the first phase, we specify the load threshold of each machine based on both processing speed and storage capacity. In the second phase, we define the datasets dependencies in order to cluster the datasets into different datacenters based on these dependencies. In the third phase, we group the set of tasks by level, then we assign a value (Rank) for each task based on the algorithm HEFT. Finally, we schedule these tasks on its best machine bases on Matching Game theory.

A. Attribution of VMs threshold phase

The threshold represents the utilization storage percentage for each machine that should not be exceeded. We will attribute a threshold for each machine on the cluster based on its storage capacity and data processing speed. For quantifying and evaluating the performance of each machine for the scientific workflow execution, we implement the HPC Challenge (HPCC), which is benchmark designed to give a picture of overall supercomputer performance including floating point computer power, memory subsystem performance and global network issues. It consists of seven separate workloads building on the success of the TOP500 list Linpack HPL based workload [23].

In this paper, we first use the HPL (High-Performance Linpack Benchmark) [START_REF] Chao | Implementation of a performance-based loop scheduling on heterogeneous clusters[END_REF] measurement as a performance value to know the real datacenter performance on the HPCC benchmark. Then, we record the execution time of each machine based on the output of HPL benchmark. After that, the shortest execution time is used as a reference to normalize the execution time measurements, in such a way we attribute randomly the highest threshold to the machine that has the shortest execution time. Then, the normalized values are used to distribute an appropriate threshold to each machine on the cluster. Thus that a high-speed machine will handle tasks more than low-speed machine.

B. Specifying datasets dependencies phase

In our strategy, we initially adapted a dependency matrix to represent the affinity between the datasets. Cloud workflows can be complex, the execution of one task might require many datasets. Furthermore, one dataset might also be required by many tasks. So, we say that the datasets d i and d j have a dependency if there are tasks that will use d i and d j together. The dependency degree is the total number of tasks that use both d i and d j . We use dependency ij to denote the dependency between the datasets d i and d j and the quantity of this dependency is the number of tasks that use both d i and d j . It can be calculated by counting the tasks in common between the task sets of d i and d j , which are denoted as T i and T j .

dependency ij = card(T i ∩ T j ) (13)
The dependency matrix is defined by: DM = {C i,j , (1 i, j m)}. For the elements in the diagonal of DM, each value means the number of tasks that will use this dataset. DM is a symmetric matrix of dimension m * m, where m is the total number of existing datasets. We store the datasets that have the highest dependency degree in the same datacenter based on the total number of tasks that use these datasets as shown in formula 14.

M ax(dependency ij )

As an example in Fig 4 (left side), we assume that we have four datasets (d 1 , d 2 , d 3 and d 4 ), and we have also five tasks to be processed (t 1 to t 5 ), the dependency matrix DM is shown in Fig. 4 (right side).

C. Task sorting phase

The objective of this phase is to define the different levels of a given workflow (DAG). Therefore, tasks belonging to the same level can be execute concurrently. This is because two tasks at the same level do not exchange data (or they are not linked by precedence constraints). Then, a weight is assigned to each node and edge of the graph, depending on processing length of each task in all clusters machines and communication length between tasks. Then, the graph is traversed upwards and a value (rank) is assigned to each node at each level. The level 1 and the last level contains respectively the exit task t exit and the entry task t entry . Tasks are sorting in descending order in the list based on their rank value.

For calculating rank value to give priority to sort tasks to be executed, we use the rank function of the HEFT algorithm. Algorithm 1 shows the pseudo code of the ranking method of HEFT algorithm.

(t i ) = W i + max tj ∈succ(ti) (c i,j + rank u (t j )) 8 end 9 end

D. Virtual machine selection phase

The final phase of the proposed approach is to choose an "optimal" virtual machine to run each of the workflow tasks. We use game theory [START_REF] Roth | Deferred acceptance algorithms: history, theory, practice, and open questions[END_REF], which is a theoretical approach of the game that provides appropriate solid mathematical tools to study the considered issue and to allocate resources to the workflow tasks on the basis of low cost, load balancing and improved tasks execution time. So far, we have modeled the problem of virtual machines selection as a many-to-one matching game where the players are the tasks and the VMs. Each task has the right to choose one VM. By contrast, the Fig. 4: An example of dependecy matrix.

VMs can host one or more task respecting the maximum number of quota q vm . This choice based on the preferences list for all tasks and virtual machines. The preference lists of each task on the workflow P L(t i ) = {V M j * , • • • , } contains the set of VMs, which are selected like potential sites. The elements in preference list P L(t i ) are put in an ascending order according to the processing power of datacenters, their cost processor, memory and storage capacity. So, that the one having the best values of these parameters depending on the requirement of the client has the highest rank. On the other hand, The preference list of the j th virtual machine V M j is P L(V M j ) = {t i * , • • • , }. These tasks are sorted in ascending orders according to different parameters. The tasks are initially prioritized according to the impact of these tasks on the use of the virtual machine in terms of CP U , RAM , etc, such that one having least impact has highest rank. Thus, the key factor for prioritizing tasks is their CPU utilization ratio. As a solution of virtual machines selection phase we used the matching algorithm as shown in algorithm 2 based on the deferred acceptance algorithm introduced in [START_REF] Gale | College admissions and the stability of marriage[END_REF], which is a well-known approach to solving the standard matching games.

An overview of our approach is given by algorithm 3 which includes all the aforementioned phases.

V. PERFORMANCE EVALUATION

In this section, we present the experiments conducted in order to evaluate the performance of the proposed E-HEFT algorithm. The performance metrics, real world workflows used in our experiments, experimental setup and experimental results are shown in the following subsections.

A. Experimental setup

To evaluate a workflow scheduling algorithm, we should measure its performance on some sample workflows. So, there is a need for a good simulator for experimental purposes. One such a simulator is CloudSim [START_REF] Buyya | Modeling and simulation of scalable cloud computing environments and the CloudSim toolkit: Challenges and opportunities[END_REF], which has been widely adopted for the modeling and the evaluation of cloud-based solutions. Particularly, CloudSim provides a generic broker modeled as a class named DataCenterBroker, we extended Algorithm 2: Task-VM matching algorithm Input: Tasks' preference lists, VMs' preference lists . 1 while (∃ a free task t i that still has a V M j to propose to) do 2 Task t i proposes to all VMs on its preference list.

3 if (q vmj ≥ 0) then 4 t i is assigned to the waiting list of V M j 5 else 6
Compare t i with the current tasks on the waiting list for the V M j and reject the task that has least preference. The rejected tasks re-apply to their next best choice.

10

Each VM update its waiting list based on its preference list, and rejects the rest.

end

Output: Every t i is on a waiting list of one of the VMs, and,thus, we have convergence to a stable matching.

this class to support DAG-structured workflows and to model the behavior of this component and its particular placement policies. On the other hands, CloudSim is an extensible simulation toolkit that enables modeling and simulation of cloud computing systems and application provisioning environments. The Cloudsim could implement generic application provisioning techniques that can be extended easily with limited efforts. According to the implementation using Cloudsim, the VMs are considered as the cloud resources and Cloudlets as tasks/jobs. We ran our experiment on one datacenter that contains 20 VMs, and the configuration of virtual machine is shown in Table I. All experiments are performed on a Pentium(R) Dual-Core processor with a speed of 2.8GHz and memory of 8GB.

B. Experimental Workflows

In order to make the results more realistic, it is important to conduct experiments using workload traces from a real system. Bharathi et al. [START_REF] Bharathi | Characterization of scientific workflows[END_REF] investigate the characterizations of six realistic workflows from diverse scientific applications, two of which are used in our experiments, which are Montage for astronomy and CyberShake for earthquake science. Montage [START_REF] Berriman | Montage: a grid-enabled engine for delivering custom science-grade mosaics on demand[END_REF] is an astronomy application that was created by the NASA/IPAC Infrared Science Archive as an open source toolkit that can be used to construct large image mosaics of the sky using input images in the Flexible Image Transport System (FITS) format. The CyberShake [START_REF] Graves | CyberShake: A Physics-Based Seismic Hazard Model for Southern California[END_REF] workflow is a seismology application that calculates Probabilistic Seismic Hazard curves for geographic sites in the Southern California region. Four different sizes of these workflows are chosen, small (around 30 tasks), medium (around 100 tasks), large (1000 tasks) and xlarge (10000 tasks). Fig. 5 shows the approximate structure of a small instance of each workflow used in our experiments. For each workflow, tasks with the same color belong to the same type. It can be seen that these two workflows have different structures, data and computational requirements.

C. Performance metrics

The scheduling problem aims to minimize the total execution time of tasks as well as to achieve a well-balanced load across all VMs in cloud. That is, there are two factors considered here. The first one is the minimization of the tasks completion time. The second one is the evenly distribution of the workload among virtual machines. So, the two metrics used to evaluate the scheduling approaches are makespane and degree of imbalance (DI).

• Makespan:is the maximum needed time to complete the execution of all tasks.

makespan = max i∈{1,•••k} {F T (t i exit )} (15)
Where t i exit , i ∈ {1, • • • k} are the independent tasks that should to run in parallel to complete the execution of the workflow.

• Degree of imbalance (DI): to measure degree of imbalance (DI) of all virtual machines on the cluster, we use the following formula:

DI = L max + L min L avg ( 16
)
Where L is a load of a VM on the cluster, and equals:

L = tasklength(V M j ) C(V M j ) (17)
Where tasklength(V M j ) is the total length of tasks submitted to the V M j and C(V M j ) = pe numj * pe mipsj is the capacity of a V M j , with pe numj is the number of processors in V M j and pe mipsj is the million instructions per second of all processors in V M j .

D. Result analysis

We compared our E-HEFT algorithm with the Heterogeneous Earliest Finish Time (HEFT) [START_REF] Topcuoglu | Performance effective and low-complexity task scheduling for heterogeneous computing[END_REF] and MinMin Task Scheduling Heuristic (MinMin-TSH) [START_REF] Blythe | Task scheduling strategies for workflow-based applications in grids[END_REF]. The MinMin-TSH algorithm is a widely-used scheduling algorithm. This is a task-based greedy algorithm that allocates each ready-to-run task to a machine based only on the local information of that task. There are two phases in MinMin-TSH algorithm. In the first phase it finds the minimum execution time of all tasks. Then in the second phase it select the tasks with the minimum execution time among all the tasks and assign that task on that resource. The same procedure is repeated by MinMin-TSH algorithm until all tasks are scheduled. We have chosen MinMin-TSH [START_REF] Blythe | Task scheduling strategies for workflow-based applications in grids[END_REF] algorithm and HEFT [START_REF] Topcuoglu | Performance effective and low-complexity task scheduling for heterogeneous computing[END_REF] because these heuristics run in polynomial time, produce efficient schedules and have been used as baselines in many related works.

The makespan results (the average of 5 executions) for the Montage and Cybershake DAGs are shown in incurs less data movement during scientific workflow execution due to the datasets dependencies specified in the second phase of our algorithm.

From Fig 7, it can be seen that our algorithm (E-HEFT) can effectively balance the load among all virtual machines and has a lesser degree of imbalance when compared with other two algorithms. This is due to the attribution of VMs utilization threshold specified in the first phase of our algorithm.

VI. CONCLUSION AND FUTURE WORK

Task scheduling is one of the main issues in cloud computing. Efficient task scheduling is essential for achieving good system performance. In this paper, we have proposed an enhancement of Heterogeneous Earliest Finish Time algorithm (E-HEFT) for achieving tasks scheduling with load balancing among virtual machines. The main idea of the enhancement algorithm is first attribute a utilization threshold for each VM that should not to be exceeded. Then, it specifies the datasets dependencies to store the dependent datasets on the same datacenter. After that, the set of tasks are sorted based on the rank value of the HEFT algorithm. Finally, we have used matching game theory for selecting virtual machines to execute the submitted tasks. To evaluate the performance of our algorithm, we have compared our proposed algorithm with some existing techniques based on the execution time (i.e., makespan) and degree of imbalance (DI). The experimental results show that our algorithm not only balances the load, but also takes less time to execute tasks compared to the two other algorithms. In the future, we plan to extend the proposed work to take into account others criteria like execution cost. We also aim to improve our algorithms to run in real cloud environments, such as Amazon so that it can be utilized for deploying applications in real life environments.

Fig. 1 :

 1 Fig. 1: System model of workflow application scheduling on cloud Computing.

Fig. 2 :

 2 Fig.2: An example of workflow application.

Fig. 3 :

 3 Fig. 3: Architectural model.

Algorithm 1 :

 1 HEFT ranking algorithm 1 for each task t i in the graph (DAG) do 2 calculate average execution time on all VMs 3 if task t i is the last task then 4 rank value of t i = its average execution time

Fig. 5 :

 5 Fig. 5: The structure of two realistic scientific workflows.

  Fig 6, while Fig. 7 shows the comparison of degree of imbalance between E-HEFT, HEFT and MinMin-TSH Algorithms. According to the experimental results in Fig 6, it can be seen that the total running time of scientific workflows lengthens with increase of the number of submitted tasks. It is clearly evident from the graph that E-HEFT is more efficient when compared with other two algorithms based on both workflows. According to Montage workflow, our proposed algorithm (E-HEFT) outperforms HEFT and MinMin-TSH algorithms in terms of the average makespan of the submitted tasks by 21.37%, and 28.98% respectively as shown in Fig 6a. On the other hand, The E-HEFT showed an average improvement of 26.07% in relation of HEFT and 21.93% in relation of MinMin-TSH based on Cybershake workflow as shown in Fig 6b. It is worth mentioning that makespan is closely pertinent to the volume of data movement for data intensive workflow. So, we can state that those improvements variations are mainly due to the volume of data that is transferred among activities in each workflow. Compared with two other algorithms, our algorithm

Fig. 6 :

 6 Fig. 6: Average makespane of the three algorithms based on Montage and CyberShake workflows.

Fig. 7 :

 7 Fig. 7: Comparison between algorithms (E-HEFT, HEFT and MinMin-TSH) based on degree of imbalance.

TABLE I :

 I Initial parameters of virtual machine

	parameters of virtual machine	Value
	Number of VM cores	4
	Mips of each core	2000MI
	RAM	1GB


Lafficher Robin

Digne Mathieu

Salvatori Fabien
email: fabien.salvatori@ifpen.fr

Boualleg Malika

Colson Didier

Puel François

Development of new alumina precipitation routes for catalysis applications

Keywords: A1, Precipitation A1, Micromixing B1, Alumina B1, Dawsonite

-Alumina is a widely used porous material for catalytic application. Possible routes for alumina improvement can be the use of alternative precursors as well as innovative precipitation technologies. In this study, we compare the influence of both precursor chemistry and mixing efficiency on γ-alumina properties. The conventionally used boehmite and the NH4-dawsonite precursors were precipitated using three mixing technologies: a conventional stirred-tank reactor, a rotor-stator mixer and a sliding surface mixing device. It was observed that, in the study conditions, -alumina mean pore diameter and porous volume were particularly sensitive to both precursor and mixing technology, while specific surface area was rather precursor dependent. A wide porosity range can thus be covered at isospecific surface area, using several precursor/mixing technology systems.

Introduction

Supported heterogeneous catalysts are widely used in refining and petrochemical processes. One way to better catalyst performances is to develop supports with enhanced properties. The properties of alumina-based catalyst supports are to a large extent fixed by the properties of the initial solid precursor, such as boehmite (AlOOH), generally obtained by precipitation in aqueous solution [START_REF] Euzen | Alumina[END_REF]. Possible routes for alumina improvement can thus be the use of alternative precursors as well as innovative precipitation technologies compared to the commonly used stirred tank reactor. Such a combination could lead to new textural properties.

Ammonium dawsonite (NH4Al(OH)2CO3) can be an interesting alternative choice for preparation of-Al2O3 with high purity level and new textural properties [START_REF] Santiago | In-line dispersion-precipitation method for the synthesis of metal-substituted dawsonites. Genesis of oxide materials with superior properties[END_REF]. Various methods can be used to synthesize this precursor [START_REF] Ali | Dawsonite-Type Precursors for Catalytic Al, Cr, and Fe Oxides: Synthesis and Characterization[END_REF], but the main one remains the precipitation in aqueous phase of aluminium salts (Al(NO3)3, (NH4)Al(SO4)2, or AlCl3) with precipitating agents such as (NH4)2CO3 or NH4HCO3.

As precipitation of sparingly soluble materials, such as boehmite or NH4-dawsonite, is generally associated with very fast reactions, the solid properties can be strongly influenced by mixing quality [START_REF] Bałdyga | Interaction between Mixing[END_REF]. Therefore, the choice of the mixing technology is particularly important. The most commonly used reactor for precipitation is the stirred tank reactor in batch or semi-batch configuration. However, this technology suffers from two important drawbacks: the specific power dissipated by the stirrer is not homogeneously distributed in the whole reactor [START_REF] Franke | The influence of the operational conditions on the precipitation process[END_REF], and, due to discontinuous operation, precipitation conditions change throughout the process [START_REF] Santiago | In-line dispersion-precipitation method for the synthesis of metal-substituted dawsonites. Genesis of oxide materials with superior properties[END_REF]. Using fast contacting mixers in continuous configuration could allow a better supersaturation control thanks to a more efficient mixing, while maintaining constant precipitation conditions throughout the process.

With this work, we present a comparative study of different γ-alumina properties, prepared by precipitation of boehmite or NH4-dawsonite, using three mixing technologies: a stirred tank reactor (STR) operated in semi-batch, and two fast mixers operated continuously, a sliding surface mixing device (SSMD) and a rotor-stator mixer (RSM). This work complements previous studies on boehmite precipitation with SSMD technology [START_REF] Rousseaux | Chemical reactors of special geometry for the precipitation of mineral particles[END_REF] and NH4-dawsonite precipitation with RSM technology [START_REF] Santiago | In-line dispersion-precipitation method for the synthesis of metal-substituted dawsonites. Genesis of oxide materials with superior properties[END_REF].

Experimental set-up

Precipitated boehmite was synthesized at 60 °C from an aluminium sulphate solution (A) and a sodium aluminate solution (B) by simultaneous dosing of the reactants, with molar ratio RB/A = 4. Precipitated NH4-dawsonite was synthesized at 60 °C from an aluminium nitrate solution (A') and a carbonate ammonium solution (B') by simultaneous dosing of the reactants, with molar ratio RB'/A' = 8. The 3.5-L glass baffled STR used in this study is equipped with a three blade propeller TTP Mixel. The stirring speed was fixed so that the mean specific dissipated power ε̅ = 0,2 W.kg -1 . The feed points were located in the discharge stream of the impeller, where the specific energy dissipation is the highest. Boehmite synthesis were set up at a constant pH of 9.7 and at final alumina concentration [Al2O3] = 36 g.L -1 . Dawsonite synthesis were set up at pH between 7.5 and 8.5, and at final alumina concentration [Al2O3] = 8 g.L -1 .

The SSMD (Fig. 1a) was designed based on the device presented by Rousseau et al [START_REF] Rousseaux | Micromixing efficiency of a novel slidingsurface mixing device[END_REF]. It consists of a stainless steel cylindrical chamber in which is located a 120 mm large rotating disc. The gap h between the disc and the chamber flat bottom can be adjusted between 1 and 3 mm. Reactants were injected in the confined mixing zone under the disc through a central feed tube (A or A') and four lateral symmetrical feed tubes (B or B'), situated at equal distance r = 40 mm from the center. Boehmite synthesis were set up at pH between 9.1 and 9.9, and at final alumina concentration [Al2O3] = 36 g.L -1 . Dawsonite synthesis were set up at pH 7.5 and 8.5, and at final alumina concentration [Al2O3] = 10 g.L - 1 .

The RSM (Fig. 1b) used in this study is a DK40 from CAT, equipped with a double feed system. The rotor radius is rR = 16,5 mm and the gap between rotor and stator is e = 1 mm. Boehmite synthesis were set up at pH between 8.5 and 9.5, and at final alumina concentration [Al2O3] = 12 g.L -1 . Dawsonite synthesis were set up at pH 7.5 and 8.0, and at final alumina concentration [Al2O3] = 10 g.L -1 .

As flow rates in continuous mixers (SSMD, RSM) were much higher than that in STR, precipitation duration was shorter for a same final suspension volume. A precise pH control was therefore more complicated, which explains the slight differences observed depending on the mixer used. However, the repeatability was tested for some operating conditions with the three mixers, and was satisfying according the precipitated solids properties.

Following the synthesis, suspensions were filtered, washed with water and dried at 120 °C for 14 h. The dried precursors (noted B/xxx and D/xxx, for boehmite and NH4-dawsonite respectively, xxx corresponding to the mixer used: STR, SSMD or RSM) were then calcined at 500 °C for 4 h to obtain γ-Al2O3 final support (noted Al-B/xxx and Al-D/xxx, for boehmite derived and NH4-dawsonite derived alumina, respectively, xxx corresponding to the mixer used) Solids were characterized by X-ray diffraction (XRD) and thermogravimetric analysis (TGA) to determine their crystallinity and purity. Their textural properties (specific surface area SBET, pore volume Vp and mean pore diameter Dp) were determined by using nitrogen adsorption analysis.

Results and discussion

Precipitation in stirred tank reactor

First, boehmite and NH4-dawsonite have been precipitated in the STR in semi-batch mode. Both precursors and derived aluminas were characterized in order to be used as references for the rest of the study.

XRD analysis of precipitated products confirmed the presence of a unique crystalline phase, corresponding in each case to the expected precursor (boehmite or NH4-dawsonite), as well as the absence of major impurity.

Physical properties of both precursors and their derived alumina are summarized in Table 1. It is interesting to note that the precursors displayed quite different textural properties. Despite a similar pore volume, NH4-dawsonite exhibited a lower specific surface area but a mean pore diameter four times higher. After calcination at 500 °C, a strong increase of porosity and specific surface area was noted in the case of dawsonite derived alumina : its pore volume reached 1.0 cm 3 .g -1 , and its specific surface area outreached 420 m².g -1 , leading to a solid with superior textural properties compared to boehmite derived alumina. Interestingly, we noted that decreasing the NH4-dawsonite reactants molar ratio from 8 to 3 led to an increase of the derived alumina porosity, with pore volume near 1.2 cm 3 .g -1 , mean pore diameter in the range of 30 nm and specific surface area still higher than 400 m².g -1 .

However, it turns out that an important part of the dawsonite derived alumina specific surface area is due to microporosity. This can be explained by the thermal decomposition process of NH4-dawsonite, in which H2O, NH3 and CO2 are released in important quantities, leading to the formation of intracrystalline porosity [START_REF] Hu | Fabrication of high-surface-area γ-alumina by thermal decomposition of AACH precursor using low-temperature solid-state reaction[END_REF]. Indeed, NH4-dawsonite mass loss measured by TGA is in the range of 60%, which is close to the theoretical value of 63%. On the contrary, boehmite only lost 23% mass, corresponding to both dehydration of hydroxyl groups and physisorbed water.

Fast-mixer influence

In SSMD case, the mixing influence on the solid properties was investigated with the following operating parameters: the disc rotational speed (N = 150-2000 rpm), the total reactants flow rate (Q = 150 -2500 ml.min -1 ) and the confined mixing zone height under the disc (h = 1-3 mm). Micromixing times were directly determined using Eq. (1), proposed by Rousseaux et al. [START_REF] Rousseaux | Micromixing efficiency of a novel slidingsurface mixing device[END_REF] in case of a laminar flow in the confined mixing zone.

In RSM case, the mixing influence on the solids properties were investigated with the following operating parameter: the rotor rotational speed (N = 2500-7000 rpm). Micromixing times were determined using Eq. (2) [START_REF] Baldyga | Comparison of the Engulfment and the Interaction-by-Exchange-withthe-Mean Micromixing Models[END_REF], the specific power dissipated being estimated using Eq. (3) [START_REF] Renouf | Etude d'un procédé d'émulsification : suivi rhéologique in situ dans un rhéoémulseur et formation de gouttes primaires dans un champ d'écoulement[END_REF]. This correlation was adapted from rotor-stator in batch configuration and did not account for the potential flow rate influence. However, since studies on specific power dissipated by rotor-stator mixers are still very empirical, and so limited to the studied tool, there is no general correlation for in-line rotor-stator mixers [START_REF] Kowalski | An expression for the power consumption of in-line rotor-stator devices[END_REF].

The studied micromixing times range from 1 to 4 ms on the RSM, and from 10 to 180 ms on the SSMD. In comparison, STR micromixing time in the studied conditions, estimated with Eq. ( 2) and ( 4), is in the range of 15 ms. However, for a given micromixing time, continuous configuration still allows a better control of the supersaturation as the environment in which supersaturation occurs remains the same during the whole precipitation process.

The mixing influence on boehmite and NH4-dawsonite crystallographic and textural properties was thus studied. As in the precipitation in STR, the precursor purity was first verified by XRD and TG analysis. The mass losses measured during a thermal treatment of the precursors precipitated with fast contacting mixers were in the same range as that of the precursors precipitated in the STR.

It can be clearly noted on Fig. 2 that precipitation in a fast contacting mixer led to a decrease in the precursor respective crystallite size, from 15 to 40%, compared to precipitation in STR. Moreover, we can observe that the crystallite size is sensitive to the micromixing time, and increased with it, particularly in NH4-dawsonite case. This is consistent with a better supersaturation control at smaller micromixing times: a higher supersaturation level is thus reached, leading to a more intense primary nucleation kinetic, and so to a crystallite size decrease. Fig. 3 presents the alumina textural properties evolution with micromixing time for both fast contacting mixers. In boehmite derived alumina case (products Al-B/SSMD and Al-B/RSM), micromixing time does not seem to have a major influence on the textural properties of the solids: both the specific surface area and the pore volume remained approximately constant, ranging from 250 to 300 m².g -1 and from 0.3 to 0.4 cm 3 .g -1 , respectively. Only the solid precipitated at micromixing time of 1 ms exhibited a higher specific surface area, near 350 m².g -1 . Furthermore, any effect of the fast contacting mixer type can be observed, as the textural properties obtained with the RSM are in the SSMD's continuity. On the contrary, dawsonite derived alumina (products Al-D/SSMD and Al-D/RSM) showed a biggest sensitivity to micromixing. If the specific surface variations between the precursors precipitated at various micromixing time (data not shown) finally disappeared after the calcination step, the pore volume remains particularly sensitive to this parameter. This lead to alumina exhibiting specific surface area higher than 400 m².g -1 and tunable pore volume between 0.5 and 0.9 cm 3 .g -1 . However, it is more difficult in that case to determine a potential influence of the fast contacting mixer in the same range of micromixing time, as the pore volume can vary from 0.5 to 0.7 cm 3 .g -1 for micromixing times between 1 and 10 ms.

Overall, it can be noted that alumina prepared by precipitation in fast mixers exhibit a specific surface area relatively close to that of alumina prepared in STR (or a bit lower for some boehmite derived alumina), but a lower porosity.

In the case of boehmite derived alumina, this trend can be observed on the whole micromixing time range studied. Fig. 4 shows the N2 adsorption-desorption isotherms and pore size distributions trend obtained with the three mixers. Alumina prepared with both fast mixers exhibited very close properties, but differed from the one prepared with the STR. Since the hysteresis loops are representative of the pore network shape, we can assume that crystallite arrangement depends on the use of semi-batch or continuous mixers, but not on the micromixing time.

In the case of dawsonite derived alumina, we can observe that at high micromixing times, the textural properties of alumina prepared with SSMD tend towards that of alumina prepared with STR. Comparison of N2 adsorption-desorption isotherms and pore size distributions (Fig. 5) confirms the micromixing influence on these alumina porosity. Indeed, for micromixing times shorter than 15 ms, pore network shape and pore size distribution obtained with both fast contacting mixers were similar. However, alumina prepared in the SSMD at micromixing times higher than 15 ms exhibited intermediate properties between those obtained with the RSM and the STR. Thus, NH4-dawsonite appears to be more sensitive than boehmite to micromixing time in the studied range. Those two trends could be explained by precipitation kinetics with very different intensities, depending on the precursor.

In this study, two alumina precursors, boehmite and NH4-dawsonite, were precipitated in three different mixing technologies. The influence of micromixing time on the precursors and their respective alumina properties were evaluated.

It was observed that precipitation in a continuous fast mixer leads to a decrease of the solid porosity compared to the precipitation in a classical semi-batch STR.

However, in continuous mode, only NH4-dawsonite turns out to be sensitive to micromixing time. It is thus possible to tune the pore volume between 0.5 and 0.9 cm 3 .g -1 on the derived alumina, but not the specific surface area, which always range between 400 and 450 m².g -1 after the calcination step.

It is also interesting to note that for given micromixing conditions, boehmite and NH4-dawsonite lead to alumina exhibiting very different textural properties. Overall, it can be observed that dawsonite derived alumina are more porous than boehmite derived alumina, with both larger mean pore diameter and larger pore size distribution. Dawsonite derived alumina also exhibit particularly high specific surface area, but part of it is due to microporosity created during the calcination step, because of their particular thermal decomposition process.

It is thus possible to get varied -alumina with specific porosity range using the appropriate precursor/mixing technology system (Fig. 6).

Figure captions

Fig. 1 .Fig. 2 .

 12 Fig. 1. Schematic representation of fast contacting mixers : (a) SSMD and (b) RSM Single column fitting image

Fig. 3 .

 3 Fig. 3. (a) Specific surface area and (b) pore volume evolution with micromixing time of boehmite and NH4-dawsonite derived alumina prepared with different fast contacting mixers Twocolumn fitting image

Fig. 4 .

 4 Fig. 4. (a) N2 adsorption-desorption isotherm and (b) pore size distributions of boehmite derived alumina prepared with different mixing technologies Two-column fitting image

Fig. 5 .

 5 Fig. 5. (a) N2 adsorption-desorption isotherm and (b) pore size distributions of NH4-dawsonite derived alumina prepared with different mixing technologies Two-column fitting image

Table 1 .

 1 Physical properties of precursors and derived alumina precipitated in the STR

	Tables
	Sample	L a (Å)	SBET (m².g -1 )	Smicro b (m².g -1 )	Vp (cm 3 .g -1 )	Dp (nm)
	B/STR	47	286	0	0,70	3,9
	D/STR	114	198	0	0,72	16,3
	Al-B/STR	-	299	7	0,83	5,6
	Al-D/STR	-	428	198	1,01	2,7 -16,4

a Crystallite size determined using Scherrer equation. b Determined using tplot method.

Acknowledgements

The authors would like to thank the Physics and Analysis Division and the Process Experimentation Division (IFPEN) for technical assistance. This research did not receive any specific grant from funding agencies in the public, commercial, or notfor-profit sectors.


Aurélie Galfré
email: aurelie.galfre@univ-lyon1.fr

Emilie Gagniere
email: emilie.gagniere@univ-lyon1.fr

Isabelle Pitault
email: isabelle.pitault@univ-lyon1.fr

François Puel
email: francois.puel@centralesupelec.fr

Mélaz Tayakout-Fayolle
email: melaz.tayakout-fayolle@univ-lyon1.fr

Lignin

A Galfre

Lignin Solvolysis

Emilie Gagnière

Lignin solvolyse study

Keywords: lignin, dissolution, solvolyse, ATR-IR

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Lignocellulosic biomass is the only available and most abundant renewable carbon resource on earth. Due to the large depletion on fossil resources, converting biomass into fuels and platform molecules is essential to reduce the consumption of non-renewable resources and increase the energetic independence of our country.

Lignin, which contributes around 19-35 % of lignocellulosic weight [1], is a by-product and is currently used as a low-value-fuel to generate electrical energy in the pulp and paper industry. Nevertheless, lignin is the only renewable resource of aromatic compounds in the nature [2]. Hence, it is highly leading to develop successful processes for converting it [3], [4].

Owing to its complex structure, lignin conversion into valuable chemical products is a difficult task. The challenge consists in selectively breaking down lignin main bonds between building blocks (mainly C-O bonds of aryl ethers and carbon-carbon bonds) [2] and defunctionnalised it to the target products. Lignin depolymerisation can be undergone in several ways such as the hydroconversion that is the main target of the ANR Lignarocat project. In this study, we focused on the solvolyse way which should allow understanding the first step of lignin hydroconversion. During solvolyse, thermal depolymerisation of lignin is carried out at high temperature in presence of solvent and without addition of catalyst or gaseous hydrogen. In these conditions, radicals may largely react to form residues by condensation which complicates the understanding of the chemical mechanisms of lignin depolymerisation. The solvent nature (H-donor) has also a great impact on reducing condensation reactions and residues formation. However, its role is not fully understood, especially its capability to solubilise lignin before its depolymerisation. Hence, the lignin behaviour, i.e. its physical or reactive solubilisation in the solvent medium is not yet elucidated. Thus, the study objectives are as follows [2]:

 Understand the lignin behaviour in the solvent, i.e. its physical or reactive solubilisation.  Understand the impact of solvent nature on lignin thermolysis and solvolyse reactions. This work summarizes the first experimental results after the successful development of our experimental set up, with a focus on the IR (InfraRed) spectroscopy results obtained with in situ and on-line ATR (Attenuated Total Reflectance) /IR probe. The experimental work focuses on the system lignin/tetralin, but several other systems will be later performed.

Experimental Materials

A wheat straw soda lignin (Protobind 1000) was supplied by Green Value (Switzerland). Further information was not provided by the supplier. Lignin was preliminary dried at 60°C during one night to remove the presence of water. ATR (Attenuated Total Reflectance) /IR (InfraRed) spectrum of the lignin is presented in Figure 2 whereas its weight molecular distribution (measured by Gel Permeation Chromatography (GPC) without acetylation according to procedure described in [7]) is in Figure 5.

1,2,3,4-tetrahydronaphthalene (tetralin) was supplied by Sigma-Aldrich. Its molar purity grade is 99%.

Experimental set up

The apparatus was built up with the support of ANR Lignarocat project [START_REF] Laurenti | CE05 LIGNAROCAT Document Scientifique final 2014[END_REF]. A schematic representation of the experimental set up is depicted in Figure 1.

Figure 1. Experimental set up

The experimental set up comprises an autoclave (0.5L) equipped with heating collar which can work at elevated pressure (<130 bar) and high temperature (<400 °C). Temperature and pressure transmitters allow monitoring the system change. A network of manual valves allows gas inlet (nitrogen, supplied by Air products, purity grade: 99%) into the autoclave and gas outlet outside the autoclave. Liquid phase sampling can be performed by means of three-way valve under middle pressure. A porous metal frit (1µm) avoids solid particles into the liquid sampling.

ATR/IR analysis and probe description

InfraRed spectroscopy (IR) allows characterizing functional groups in a sample. Indeed, chemical bonds vibrate at a very specific frequency when irradiated which depends on the atoms of the bond, but also the environment of the bonds (solvent and neighbouring molecules).

The reactor comprises in situ on-line ATR-IR probe connected to a FT-IR Nicolet™ iS™ 50 spectrometer. The objective is to provide direct insights into the system and follow physical and/or reactive solubilisation of lignin during the run without sampling. The probe can work under high pressure (20 MPa) and high temperature (250°C). It comprises a diamond conical reflection internal element and is swept with a slight flow of dried air. However, the data acquisition window is limited from 600 to 1900 cm -1 .

The use of a high pressure autoclave with ATR-IR probe for on-line in situ spectroscopic monitoring of biomass conversion processes was introduced by Zakzeski and Weckhuysen (2011) [START_REF] Zakzeski | [END_REF]. Their work focused on solubilisation and aqueous phase reforming reaction of lignin.

Spectrum of lignin (32 scans were collected from 400-4000 cm-1 by means of ATR accessory of the Nicolet spectrometer) is presented in Figure 2. Some main functional groups are out of detection range of the probe (Figure 2) but we can see that a large number of lignin functional groups are still present.

Figure 2. Bands assignment in the mid-infrared region for lignin Protobind 1000 [7] Experimental procedure Around 25 g of dried lignin and 275 g of tetralin were poured into the reactor. Then, the autoclave was closed. Nitrogen was bubbled through the liquid along 15 minutes at a pressure near 2 MPa. The reactor was flushed 3 times with nitrogen and finally pressurized to around 0.5 MPa. The system was stirred at 400 rpm. Next the reactor was heated up to the desired temperature. Several ATR/IR spectra of the system lignin/tetralin were collected during the test. After a residence time of several hours, a liquid sample was collected and the run was stopped.

Once ambient temperature was achieved (several hours were required), the reaction mixture was filtered to separate liquid and solid phases. Solid (residual lignin) was dried at atmospheric temperature in presence of extractor hood. "Liquid" phases include the residual solvent (tetralin), a certain amount of solubilised lignin which precipitates over the time (precipitated lignin) and reactive products (which could be in aqueous or in organic liquid phases). Hence, the "liquid" phases were centrifuged to recover precipitated lignin and eventually separate aqueous and organic phases.

Analytical tool off-line

The solid and organic phases were analysed by Gel Permeation Chromatography (GPC) [7]. It allows determining molecular weight distribution of the sample taking the polystyrene as reference.

Results and Discussion

Parameters monitoring and mass balance

Several trials were carried out at different end temperatures (150°C, 240°C) in presence of the same lignin/solvent ratio, at the same initial nitrogen pressure. Figure 3 presents pressure and temperature monitoring during the run "240°C". Experimental procedure steps (from 1 to 5) are fully described in previous section. Several IR spectra were also recorded during the trial. The timing of spectra sampling is also depicted in Figure 3. In Figure 3, we can see that the total pressure of the system has never achieved a constant value. Hence, lignin degradation was suspected at this temperature. During the run "150°C" which is not depicted in Figure 3, the total pressure was constant all along the temperature plateau.

ATR/IR spectra discussion

Several ATR-IR spectra were collected for each run. For the run "150°C", specific bands of lignin did not clearly appear in the collected spectra. Indeed, several bands of lignin and tetralin are in the same range of measure and some bands are shared. Hence, it was quite difficult to conclude if the bands intensity variation had to be attributed to lignin solubilisation or to a change of the intensity of bands of tetralin because of the temperature variation. Consequently, we conclude that only tetralin spectrum was noticeable in the medium.

For the run "240°C", ATR-IR spectra of the medium are depicted in Figure 4a. At ambient temperature and in the first run hours, only tetralin band vibrations were observed and vibrations attributed to the insoluble lignin were not noticed. The change of the ATR/IR spectrum appeared after one hour at 240°C. Some ATR-IR several bands could be attributed without ambiguity to lignin. Indeed, the bands above 1650 cm -1 , namely the C=O in stretching mode, and the bands near 1200 cm -1 (C-O-R aromatics in stretching mode and OH aromatics in deformation mode) belong to lignin spectrum. Nevertheless, be aware that the variation of intensity of the bands at 1600 cm -1 and above 1400 cm -1 could also be attributed to aromatic skeleton of tetralin. Hence, the appearance of new vibrations in the medium spectra was attributed to the formation of dissolved organic species from the lignin. The ATR/IR spectra of initial lignin, precipitated lignin and lignin residual (see experimental procedure) were also collected and are presented in Figure 4.b. The bonds intensity varies but is small between initial lignin and reactive lignins. The appearance of large bands from 2900-3100 cm -1 and from 690 to 780 cm -1 may be attributed to the presence of residual tetralin into the sample. The lignin depolymerisation and hence chemical structure variation could not be validated by the IR spectra study in these two runs. For the run "150°C", the residual lignin distribution is close to the initial lignin one (Figure 5). Lignin depolymerisation did not occur.

GPC analysis

For the run "240°C", the distribution of the precipitated lignin exhibits a smaller width than the initial lignin one (Figure 5). We can conclude that some solid species solubilise into the organic phase with a different molecular weight distribution. Nevertheless, we cannot conclude clearly on the lignin chemical structure variation. The chemical structure of residual lignin was certainly modified during the process operating conditions: the residual lignin was not soluble into the solvent used for the GPC analysis (tetrahydrofuran).

Conclusions

To conclude, the formation of dissolved solid species from the lignin was evidenced by the in situ on-line ATR/IR probe in tetralin at 240°C. Several tools (the total pressure variation all along the temperature plateau at 240°C, GPC analysis) allow validating the chemical modification of lignin during the run. In the future, other solvents will be studied and their effect will be compared.

Figure 3 .

 3 Figure 3. Parameters (pressure, temperature, spectral sampling) monitoring during the run "240°C".

Figure 4 .

 4 Figure 4. a) In situ ATR-IR spectra of the dissolution of Protobind lignin in tetralin for several times and temperature for the run "240°C" 4.b) ATR/IR spectra of initial, precipitated and residual lignin for each run (measured off line in the ATR/IR accessory of the Nicolet spectrometer)

Figure 5

 5 Figure 5 presents the weight molecular distribution of initial, precipitated and residual lignin for each run.

Figure 5 .

 5 Figure 5. Weight molecular distribution of initial, precipitated and residual lignin for each run

Acknowledgements

This work has been supported by the "Agence Nationale de la Recherche" (ANR). The authors are particularly grateful to the members of technical staff (Jean Pierre Valour, Sébastien Urbaniak, Géraldine Agusti, Chantal Lorentz (IRCELyon)) for their contribution to the quality of experimental set-up and analyses.


Aurélie Galfré
email: aurelie.galfre@univ-lyon1.fr

Isabelle Pitault

Melaz Tayakout-Fayolle

François Puel

Emilie Gagnière

Lignin solvolyse study

Highlights

 The lignin physical or reactive solubilization in tetralin is followed by ATR-IR probe.

 The formation of dissolved solid species from lignin was detected in tetralin from 170°C  The chemical modification of lignin was evidenced by CHONS and GPC.

Introduction

Lignin, which contributes around 19-35 % of lignocellulosic weight [1], is the only renewable resource of aromatic compounds in the nature [START_REF] Laurenti | CE05 LIGNAROCAT Document Scientifique final 2014-05-05[END_REF]. Due to the large depletion on fossil resources, converting lignin into platform molecules is highly leading [START_REF] Laurenti | CE05 LIGNAROCAT Document Scientifique final 2014-05-05[END_REF]. Its structure is a polymer network with cross linking branches. The polymer structure is based on three repetitive units (p-courmaryl, guaiacyl and syringyl units) which differ by the number and the position of the substituted methoxyl groups on aromatic rings [1].

Owing to its complex structure, lignin conversion into valuable chemical products is a difficult task. The challenge consists in selectively breaking down lignin bonding motifs between building blocks (mainly C-O bonds of aryl ethers and carbon-carbon bonds) [START_REF] Laurenti | CE05 LIGNAROCAT Document Scientifique final 2014-05-05[END_REF] and defunctionnalised it to the target products. Severe conditions (high temperature, high hydrogen pressure and catalyst) are required to carry out lignin depolymerisation. In our project, H-donor solvent was added to the system. The results [START_REF] Joffres | [END_REF]4] show that the solvent nature (H-donor) had a great impact on reducing condensation reactions and residues formation. However, several points have to be clarified to complete the reaction scheme [START_REF] Laurenti | CE05 LIGNAROCAT Document Scientifique final 2014-05-05[END_REF]. Hence, the lignin behavior, i.e. its physical or reactive solubilization in the solvent medium was not yet elucidated. Moreover the impact of solvent nature on the first steps of lignin depolymerisation was not fully understood. To clarify theses important points, we focus our experimental work on studying lignin depolymerisation in solvolyse conditions (medium temperature, without catalyst or gas hydrogen addition). The experimental set-up comprises in situ and on-line ATR (Attenuated Total Reflectance) /IR (Infrared) probe which allows following the first steps of lignin depolymerisation and solubilization in the medium. Several systems (lignin/solvent) would be studied.

Methods

The experimental set up consists in an autoclave (0.5L) equipped with heating collar which can work at elevated pressure (<130 bar) and high temperature (<400 °C). The reactor comprises in situ and on-line ATR-IR probe connected to a FT-IR Nicolet™ iS™ 50 spectrometer. The objective is to provide direct insights into the system and follow physical and/or reactive solubilization of lignin during the run without sampling. The probe can work under high pressure (20 MPa) and high temperature (250°C). It comprises a diamond conical reflection internal element and is swept with a slight flow of dried air. However, the data acquisition window is limited from 600 cm -1 to 1900 cm -1 . Spectrum of lignin is presented in Figure 1.a. Some main functional groups are out of detection range of the probe but we can see that a large number of lignin functional groups are still presents.

The ATR/IR results are completed by several analytical tools. Indeed, after the run, the reaction mixture is filtered to separate liquid and solid phases. The chemical structure and composition of the solid residue are analyzed by Nuclear Magnetic Resonance ( 13 C and 31 P, 2 D NMR) and elemental analyses (C, H, O, N, S) [4]. Its molecular weight distribution taking the polystyrene as reference is determined by Gel Permeation Chromatography (GPC) [4]. The organic liquid phase is analyzed by gas chromatography (GC2D): it allows identifying the main products from lignin degradation [4].

Results and discussion

The experiments were performed in presence of wheat straw lignin and tetralin. Several trials were carried out from 150 to 240°C in presence of the same lignin/solvent ratio. To avoid unwanted oxidation reactions, the reaction took place in presence of nitrogen. During the temperature rise and close to 170 °C, the ATR/IR probe had successfully detected the appearance of new vibrations in the medium spectra. The new vibrations corresponded to the lignin ones (Figures 1.b). However, the lignin degradation was not evidenced with the ATR/IR spectra of the medium. The residual solids were characterized with analytical tools off line. The results showed that residual solids had a different weight distribution and a different elemental analysis from initial lignin. Hence, the lignin degradation along the temperature rise was evidenced. Consequently, the appearance of new vibrations in the ATR/IR spectra was attributed to the formation of dissolved organic species from the lignin. RMN analyses would confirm the lignin degradation, would allow evidencing the bonds modification and complete the reaction scheme.

Conclusions

To conclude, the formation of dissolved solid species from lignin was evidenced by the in situ on-line ATR/IR probe in tetralin from 170°C. The chemical modification of lignin was evidenced by analytical tools off line (CHONS and GPC) which allow confirming the reactive solubilization of the lignin with the temperature rise in the medium. RMN analyses would confirm the lignin degradation. In the future, other solvents will be studied and their effect will be compared.

Figure 1 .a

 1 Figure 1.a Bands assignment in the mid-infrared region for lignin Protobind 1000 [3] Figure 1.b In situ ATR-IR spectra of the dissolution of lignin in tetralin for several times temperature for the run "200 °C""


Mohamad Buheji
email: buhejim@gmail.com

Understanding Necessity Entrepreneurship

Keywords: necessity entrepreneurship, innovation, inspiration economy, youth economy, resilience economy

Necessity Entrepreneurship (NE) haven"t been closely covered in literature, especially with the speed of development in the last half century and the influence on different socio-economic perspectives. In this book review paper NE is synthesised as a means for self-employment solution on demand. Services of necessity entrepreneurs" survival and their type of mindset are reviewed, along with their best practices. Brazil success as a model NE country is covered as an example of what can any country do to move people out of poverty through NE schemes. Other NE schemes as: fall-back system, close mentorship and coaching are explored in this review to show their importance compared to financial support. The paper recommends clear transition programs of self-employment and self-reliance that goes beyond the government responsibility by calling for more involvement of private sector, NGOs and even religious organisations. Further, book future improvements are suggested to give this book more shining role towards this highly important subject.

Introduction

The urge for this book review is part of its title that focuses on the most neglected entrepreneurs in the world, i.e. those that come to entrepreneurship out of necessity. However, the uniqueness of this work come mainly due to its ability to address the unemployment issue in a more positive way, with suggestions that ease the transition of many populations of the society to self-employment. This work was followed by many thinking tanks such the Stanford University team [START_REF] Fairlie | Opportunity versus Necessity Entrepreneurship: Two Components of Business Creation[END_REF] study.

The Brewer and Gibson (2016) work shows how the self-employment helps to relieve the welfare system and promote the more efficient market. As a researcher, one could see the influence of this work cover many socio-economic perspectives that consider self-employment a solution for on-demand parenting, the ageing population, early retirement, youth economy, migration economy, etc. Thus this book is part of a roadmap that could be a cause for economic stability and quality of life development.

The work of Brewer and Gibson (2016) show that the culture and spirit of s triving towards entrepreneurship lead always to positive results in a country socio-economic development, even from those who fail in becoming self-employed. As the book shows that even those who fail while attempting entrepreneurship out of necessity beco me usually more competent if they decided to be re-employed.

Defining Necessity Entrepreneurship

Most of the known definitions of "entrepreneurship" are based on the observations of practices of opportunity entrepreneurs (OEs), i.e. those who started their business when they spot an opportunity in the market. Necessity Entrepreneurship (NE) is about the different type of people. It is about entrepreneurs who started their businesses when they could no t find another means of living or employment. Such type of entrepreneurship is very important to developing and emerging economies. With the increase of instability in socio-economic condition, NE initiatives are growing faster than the mature OEs. It is estimated that one billion individuals in both developed and developing nations have no stable income or any viable options but to start a small NE activity.

Services of Necessity Entrepreneurs Survival

The editors of the book review presents variety of approaches for NE"s. There are the start-up support programs as matching the unemployment allowance, or special mentoring or companionship programs or social insurance program. Also, there are the transformation programs from loan to grants in certain countries till the entrepreneurs reach the safe zone. Another NE"s service that came from Harvard University is Entrepreneurship Finance Lab (EFL"s) that was developed to ease the disbursement of loans for developing countries. However, one can"t see how EFL is dedicated for the NEs, specially that it uses psychometrics for loans risk assessment which actually is opposite of the idea of easing the loans of the start-ups and microstate where risk is very high yet even for low amount of loan. EFL"s would be a best practice if it ensures the high accessibility for those in need and not only the speed of loan approval. Therefore, one could argue that an EFL"s service should address the variety of necessity entrepreneurial cases as migrants, house wives, elderly, etc.

Entrepreneurial Mindset

Even though one could agree for importance of the financial support that both Brewer and Gibson (2016) for any entrepreneurship start-up, the book on the other hand fail to study or review or explore the importance of entrepreneurial mindset, spirit and idea, despite its importance to the theme of the book.

Best Practices in Necessity Entrepreneurship Support Services

"Institutional Case Studies On Necessity Entrepreneurship" carries lots of scattered best practices and success stories that could meet the requirement of inspiration economy for an independent business model with high autonomy for governance and survival. Buheji (2017a). This can be seen in the Greek experience in diversifying their entrepreneurship programs by customising them based on: the gender "Unemployed woman from 22-64", or "Young people", Educated "Young Scientists", or "Multi-member Families", or based on Geographic location that is designed to "Decentralise the Entrepreneurship Program" from the main city. Some of these ideas complement the recent work on the "Handbook of Youth Economy" in Buheji (2018a) where different employment solutions or initiatives were discussed to overcome the gap in the coming generation necessity entrepreneurship programs.

Another best practice model can be seen also in the USA minority focus on entrepreneurship programs that help to develop and accelerate the different minorities. It is also similar to the Indian program for disadvantaged youth such as the Bharatiya Yuva Shakti Trust (BYST) which address exactly the book theme. The success of BYST can be referred to the measures taken to see identify and monitor the amount of people employed by those youths who got the loan & mentorship, which today at a record rate of 1:10. i.e. each one young entrepreneur is employing ten others.

The book also brings in the story of Lakshimi Venkatesan (resembling prince Charles program in UK) comes also from India. The story shows how NGO"s can help necessity entrepreneurs succeed. It is similar to work carried and published under the International Inspirational Economy Project (IIEP) which works with school dropouts, or low performing students program. However, Lakshimi and her team teach us more innovative techniques for sustainability through the mentorship clinics both (mobile one or in case of emergency), Buheji (2018a).

Women as part of Necessity Entrepreneurs

One the strengths of the book is the focus on the minorities or where equalities need to be empowered. The Prospera is a Mexican case for microenterprise among women necessity entrepreneurs that deliver support to empower female-led micro-businesses through promoting more their civic engagement, with very low budgets. Such programs support local economies and inspire Mexican to support their local community economy. The uniqueness of the Prospera"s program is that it is have many simple yet important learning steps for many

Issues in Social Science

ISSN 2329-521X 2017, Vol. 5, No. 2 decision makers, strategic planner, economists, etc. The case focusses and measures on social change that can be achieved due to NEs selective efforts for specific gender or demographics. The Prospera have many schemes as "Learn and Prosper", "Be responsible and Prosper" and "Share and Prosper" through using the following inspiration formula: (Empowerment with Tools + Market Linkages+ Civic Engagement).

Necessity Entrepreneurs Fall-back System

The NE fall-back system is only available in limited countries in the world and still it is under debate by many researchers and practitioners. However, this book show how leading countries as Switzerland and Austria have managed to establish fall-back system for NEs that give them the assurance of their ability to fall back safely and automatically to the social-welfare programs, if they couldn"t continue their entrepreneurial startup. This case could really be a special line for future studies as a way of building entrepreneurial spirit for the unemployed. This program is highly needed in developing countries where there is high unemployment of young generations who finish higher education. Thus if youth are promised to come back in the social welfare system if they fail in their businesses this would crea te a highly innovative youth economy environment where they would be willing to take risks to try and explore new possibilities which would revive the economy.

Mentorship for Necessity Entrepreneurs

Non-financial business support in many studies found to be of high importance and even surpass financial support in a different stages as many evidences have shown and specially in turbulent economies. Many start-ups and even anchor entrepreneurs need a type of mentorship to clear or codify the small gaps or the undeveloped business concept areas.

However, Brewer and Gibson (2016) don"t clearly differentiate between mentorship of the classical entrepreneurial programs where most of the sponsors or business/start-ups incubators and accelerators would provide the professional training along with both general education and coaching or consulting and that mentorship of NEs. It could"ve been more interesting if the authors discussed the success stories of the NEs whom used the integrated three services in differentiated and delivered better results.

NE Coaching Programs

Many NEs need specific focused coaching besides mentorship programs. The Academy for Creating Enterprise (the Stephen W. Gibson project) in the Philippines is certainly a unique project where an isolated home is dedicated for support NEs youth in poverty areas where necessity entrepreneurial supervision is given to each entrepreneur till they become independent and create a turn in their life.

Another NEs coaching project is in Mexico. An academy that is blend mentorship and coaching with a variety of programs that target the residents and the night workers through a unique curriculum. The curriculum used by the academy is very useful for both the academics and practitioners as it gives a profound knowledge for NEs with each program target to tackle challenges for "Where there are no jobs?"

Banks for Necessity Entrepreneurs

It could have been great if the editors explained in a chapter the type of necessities conditions that pull towards entrepreneurship. i.e. poverty, skills, family issues, unemployment, job dissatisfaction, etc. All these are reflected in stories inside the books, however, could"ve been great if abstracted at the beginning of the chapter.

Avoiding the NE Free-Riders

One would agree that any country"s NE policies should avoid "free-rider" phenomena where loans are taken during the period of unemployment and the business plans are neglected once jobs are found. Therefore, it highly recommended that the feasibility studies s hould not be considered the only main pre-requisite for start-ups support.

Supporting Transition from Unemployment to Self-Employment

European and OECD countries emphasised in the last three decades on creating diversified paths that would ensure the best transformation from unemployment to self-employment. The results we have today a culture in these countries that believe in "small bus iness" as a source of personal economic growth and employment prosperity.

OECD is working more on doing many projects that would enhance the probability of those transform to be self-employed once they have stayed in a long time as unemployed.

Role of NGOs and Charity Organisation in establishing NEs Self-Reliance

Using the bible "One heart one mind" with "no poor among them" the church of Latter-Day Saint established a self-reliance scheme that can be considered as a reference of excellent for many religious communities that should work on establishing resilience economy in their societies [START_REF] Buheji | Understanding the Power of Resilience Economy: An Inter-Disciplinary Perspective to Change the World Attitude to Socio-Economic Crisis[END_REF]. The foresight of future in the church led to NEs dedicate employment workshops that target to build economically resilient follower and self-reliant community members. The idea of "Self-resilience centres" and its capacity assessment and income gap tools is similar the research latest work on "Breaking the Shield" that focused on the role of Humanitarian organisations in Bosnia to minimise those in poverty. [START_REF] Buheji | Breaking the Shield-Introduction to Inspiration Engineering: Philosophy, Practices and Success Stories[END_REF].

Brazil as a Model NE Country

Despite different allegation of corruption claims on Brazil, this country deserves a praise for the amount of change it managed to bring to many poor communities with a record achievement and in short time. Brazil brought about 11% of the population out poverty due to focused programs as the NE"s self-employment schemes. The Brazil program management team represented by SEBRAE deserve to be studied deeper. The design of their training program is comprehensive, but the most important for NE"s is the rural empowerment and quality programs.

Areas for Future Improvement

This book is full of best practices and surely it fits many readers with different backgrounds. However, the book gives too much emphasis on financial support rather than focusing on the utilisation and optimisation of the necessity entrepreneurship. The book goes through different details again as showing the different partnership programs for getting the funding which at sometimes found too boring and cause a loss of appreciation of the idea.

Discussing financial institutes as the Chinese "Hanhua Guarantee" is not appreciated due it is being out the scope of "necessity entrepreneurship". Yet one has to admit that there would be some learning about how should the banks in other developing countries deliver it services relevant to SME"s.

In certain areas of the book loses the dedicated reader with the details and empty debate about the different countries bureaucratic policies in relevance to entrepreneurship. It is advised that such policies, the ones that are most relevant to the scope of the book, be summaries in a table, or in one or two pages. The length of the policies presented makes the book as a ledger or a listing of programs at a specific time than a thinking book and country management culture philosophy that is valid for many years. Therefore, this review would avoid the discussion about the programs structures, but would focus on the processes and the successful practices that managed to enhance the self-employment transformation.

Despite that the editors of the book were successful in putting important section titles, as for example, "single payment vs. recurring payment", or "provision of a fall-back solution"; when the paragraph is assessed it couldn"t address the importance of the titles in both quality and size. These big and unique ideas could have opened lots of insights for the readers, if it was effectively explored and discsussed.

Conclusion

Despite the scattered information in this edited book, one can consider it as a ground-breaking book for NE focused studies. The book didn"t cover lots of trends that would affect NE in the future, as for example "Sharing and Collaborative Economy", [START_REF] Buheji | Review Paper -Creative Destruction and the Sharing Economy[END_REF].

Clearly, Brewer and Gibson (2016) show transition programs of self-employment and self-reliance that goes beyond the government responsibility and which closely involve other stakeholders of the community. The different stories in the book support the essence of "necessity entrepreneurship" which found to be very touching and even inspiring.

Further studies on "Necessity Entrepreneurship" and how it has been or it can be associated with the creation and development, not only growth, of the communities are highly recommended. Countries strategic future foresight studies should include such important sustainability and resilient economy based concept. It is with these concepts we can only have hope for the future of our coming generations.