Nesryne Mejri

Briag Dupont

Georges Da Costa

Energy-aware scheduling of malleable HPC applications using a Particle Swarm optimised greedy algorithm

Keywords: Energy Efficiency, Malleable jobs, Particle Swarm Optimization, High Performance Computing, Scheduling

The scheduling of parallel tasks is a topic that has received a lot of attention in recent years, in particular, due to the development of larger HPC clusters. It is regarded as an interesting problem because when combined with performant hardware, it ensures fast and efficient computing. However, it comes with a cost. The growing number of HPC clusters entails a greater global energy consumption which has a clear negative environmental impact. A green solution is thus required to find a compromise between energy-saving and high-performance computing within those clusters. In this paper, we evaluate the use of malleable jobs and idle servers powering off as a way to reduce both jobs mean stretch time and servers average power consumption. Malleable jobs have the particularity that the number of allocated servers can be changed during runtime. We present an energy-aware greedy algorithm with Particle Swarm Optimized parameters as a possible solution to schedule malleable jobs. An in-depth evaluation of the approach is then outlined using results from a simulator that was developed to handle malleable jobs. The results show that the use of malleable tasks can lead to an improved performance in terms of power consumption. We believe that our results open the door for further investigations on using malleable jobs models coupled with the energy-saving aspect.

Introduction

The current world is massively abundant with valuable data. This by itself represents a reason -among othersfor the increasing number of High-Performance Computing (HPC) clusters, data centers, and cloud computing services seen in the past few years. This need for constantly processing enormous loads of data requires not only physical means but also software actors capable of using the hardware capabilities to its fullest. These actors should act in an objective-oriented way when handling data. This is a scheduling problem that is not considered new, neither in applied fields nor in research. Indeed it has been a hot topic for years since it touches various domains such as industrial processes, manufacturing, computing and transportation planning. In most of these applications, it is mainly concerned with how to dispatch a set of processable resources over a finite set of processing resources, in a way that optimizes one or several objectives. Unsurprisingly it is the topic of this paper as there is a growing interest in parallel and distributed computing where it is critical to ensure a smooth execution for the distributed applications. Another related matter that rises is energy efficiency, as more HPC clusters and data centers increase the CO 2 emission rate. As a matter of fact [START_REF] Ferreboeuf | Lean ict: Towards digital sobriety[END_REF] states that digital technologies account for between 2.5% to 3.7% of the global greenhouse gas emissions. Data centres represent 19% of the total energy required for the production and use of digital devices. The energy consumed by data centres is expected to increase by about 4% per year. Clearly reducing the energy consumption should be a goal and this issue should be approached from different angles. Moreover it has been shown that servers account for up to 75% of a data center energy consumption [START_REF] Pinel | Energy-efficient scheduling on milliclusters with performance constraints[END_REF], and within this value, 50% can be due to idle servers [START_REF] Villebonnet | Energy aware dynamic provisioning for heterogeneous data centers[END_REF]. The total energy consumed is not the only issue, the rate at which energy is consumed (power) must also be considered as it is bounded. For example the US Department of Energy EISC (Exascale Initiative Steering Committee) has set 20 MW as the upper limit for a buildable exascale system [START_REF] Shalf | Exascale computing technology challenges[END_REF].

The energy saving problem can be solved from different perspectives, such as the hardware one or the software one. From the software part, a scheduler implementation can play a key role in the energy consumption of a data centre.

Usually, there are two main requirements to consider when building a scheduler; fairness and energy consumption. A scheduler is fair when it guarantees that every submitted job has a chance to be executed within a reasonable time. This is known as fair-share mechanisms [START_REF] Sedighi | Fairness of task scheduling in high performance computing environments[END_REF].

Hence, the combined cost of a fair, performant and energy-efficient scheduler should be optimised to achieve an efficient use of servers and a lower energy and power consumption within data centers and HPC clusters. In the following section, a brief review of the literature is made. Next, the optimization problem is formally presented by outlining the objectives and the performance criteria. Afterward, the used approach is thoroughly explained along with the adopted assumptions and the optimisation process. Following, the experimental setup is described. Lastly, the experimental results are presented and discussed.

Literature Review

Since scheduling parallel jobs is an NP-Hard problem [START_REF] Rauchecker | High-performance computing for scheduling decision support: A parallel depth-first search heuristic[END_REF] no known algorithm can solve it in polynomial time, and only approximations can be used to find near-optimal solutions. Consequently, the matter has been approached from different perspectives and setups depending on the used scheduling algorithm and the nature of the tasks to perform. Trystram-Mounié et al. [START_REF] Trystram | Scheduling parallel tasks: Approximation algorithms[END_REF] described approximations for algorithms taking into account diverse paradigms of jobs; rigid ones, moldable ones, and malleable ones, analyzing and comparing the theoretical performance of each paradigm. Rigid jobs have a fixed number of required resources that cannot be changed once a job has been scheduled. Furthermore, if this number is greater than the number of available resources, the job may not be scheduled. Similarly, for moldable jobs, the number of required resources cannot be changed during runtime. However, the number of resources to allocate is defined by the scheduler before the job starts its execution. Lastly, malleable jobs are the most flexible ones since they behave like moldable jobs with the exception that the scheduler can adapt the number of their required resources even during execution [START_REF] Hungershofer | On the combined scheduling of malleable and rigid jobs[END_REF]. For malleable jobs, the process of changing the number of required resources at runtime is called a reconfiguration. An example depicting malleable jobs and rigid jobs can be seen in Figure 1. An approach from literature [START_REF] Casanova | Minimizing stretch and makespan of multiple parallel task graphs via malleable allocations[END_REF] uses the malleable jobs paradigm because of its flexibility and efficiency. However, assumptions are made about the homogeneity of the cluster, and PTGs (Parallel Task Graphs or Directed Acyclic Graphs) are considered as perfect malleable tasks. These assumptions do not reflect real-life setups. In reality, malleable jobs cannot be perfect, and their reconfiguration necessarily induces overheads such as communication between nodes and time for data to be transferred. Another work [START_REF] Guérouta | Mixed integer linear programming for quality of service optimizationin clouds[END_REF] addressing cloud computing services focuses more on the optimisation aspect of providing high QoS and the improvement of energy consumption. It also provides a comparison of the Mixed Integer Linear Programming (MILP) approach against the Genetic Algorithm approach for the same objectives.

Finally, cutting-edge simulators such as Batsim [START_REF] Dutot | Batsim: A realistic language-independent resources and jobs management systems simulator[END_REF] or SimGrid [START_REF] Casanova | Simgrid: A generic framework for large-scale distributed experiments[END_REF] do not support the scheduling of malleable jobs. Consequently, researchers are restricted from experimentally investigating potential improvements of performance offered by this paradigm.

Formulation of the problem

This section formally introduces the optimization problem, that is given a set of malleable jobs, the goal is to find a scheduling strategy that optimizes the performance and the power consumption by respectively using job reconfigurations and turning off idle servers. We define the following useful notions:

• A job is a set of identical parallel tasks submitted at the same time and that can be run on n distinct servers.

• The submission time is the time at which a job enters the queue of the scheduler.

• The speed-up factor α, is a real number between 0.5 and 1 indicating how parallelisable the computations within a job are.

• The mass corresponds to execution time of the job if it were to be run on a single server. In a rigid job context, the mass is directly related to the required time to complete the job, as it would be run over a fixed number of servers.

• The makespan is the time needed to complete a job once it starts executing. In a malleable job context, the completion time of a job also depends on factors such as the number of used servers n, the speed-up factor α and whether the job has been reconfigured. For a constant number of servers n it is defined similarly as in [START_REF] Prasanna | Generalized multiprocessor scheduling and applications to matrix computations[END_REF]:

makespan = mass n α
• The data D is the amount of data to transfer from n to m servers in case a reconfiguration takes place. • The reconfiguration time T n→m is the time needed to transfer data from n to m servers. It is given by:

T n→m = D n ( n m -1) if n ≥ m D m ( m n -1) if n ≤ m 3.1. Criteria
The goal of this study is to reduce the average rate of energy consumption or power without compromising the fairness of the scheduler.

Classically, the objective of the scheduler is to minimise either the total makespan (the time to complete all the given jobs) or the mean or maximum stretch time. Due to the distributed aspect of the problem, the stretch time is potentially a more beneficial metric than the makespan, as it takes into account the time a job spends waiting in the queue. In a rigid job context, the stretch time is defined as the ratio of the difference between the termination time and the submission time to the actual job execution time. As outlined earlier, the execution time of malleable jobs can vary as it depends on the number of used servers as well as whether the job has been reconfigured. So the definition of the stretch time is adapted to: stretch time = termination time -submission time mass

The other criterion to take into account is the power consumption of all the servers. Realistic energy consumption approximations of the different server states were used in this study and are shown in Table 1. These values correspond to measurements made on the Tarus Grid'5000 cluster which was composed of 16 Dell PowerEdge R720 nodes each with two Intel Xeon E5-2630 [START_REF] Poquet | Simulation approach for resource management, Data structures and algorithms[END_REF]. It can be easily shown that saving energy by turning off a server is only profitable when the shutdown duration is of at least 260 s. This implies that a cycle of turning off, staying off for the minimum span of 260 s, and restarting a server lasts for about 362 s. In the model, the minimum allowed duration for a power-off is 362 s. As a second metric, the normalized mean power consumption is considered. It is defined as the mean power consumption divided by the power consumption of an idle server.

normalised mean power = mean power idle server power

Objective

Under the following scheduling constraints:

• A job must eventually be scheduled and completed.

• No two jobs can be scheduled at the same time on the same servers.

• A job can only be scheduled or reconfigured on servers that are powered on and idle.

The goal is to minimise the combined cost of the mean stretch time and the normalized mean power consumption. Therefore the objective function can be formalized as an energy-delay-product (EDP), which is a metric that had been used in [START_REF] Azad | Advances in GPU Research and Practice, 1st Edition[END_REF], and that expresses the trade-off between the performance and the power consumption of our model. Thus the objective function is expressed as:

Minimise: mean stretch time × normalized mean power

Optimisation strategy

This section presents the adopted approach for minimising the objective function under the previously mentioned constraints.

Assumptions

The following assumptions were made when implementing the simulation environment supporting malleable jobs.

• The user submits a job request and the scheduler handles it.

• The attributes of a job request are the submission time, the speed-up factor α, the mass, the minimum and the maximum number of servers required to run the job. All these parameters are considered known to the scheduler.

• The data amount D of a job can be queried by the scheduler at any time. This is a reasonable assumption as in reality, it should be possible to know how much data a program is using at a given time.

• There is an upper limit D max to the amount of data a job can have, and this limit is known to the scheduler.

• The scheduler has no knowledge about any future states, it only knows about the current and past states.

• The mass submitted by the user is only an estimate. Hence, the scheduler cannot use it to make exact predictions about the termination time.

Greedy Algorithm

Adding job reconfigurations and server power-offs to the capabilities of the scheduler increases the complexity of the scheduling problem. Therefore the selected approach to tackling the problem is to implement a greedy algorithm and use a meta-heuristic to tune its parameters.

Greedy algorithms are heuristic-based algorithms that always take the best immediate, or local, solution while finding an answer. Greedy algorithms find the overall or globally optimal solution for some optimization problems but may find less-than-optimal solutions for some instances of other problems [START_REF] Black | greedy algorithm[END_REF]. The implementation steps are described in Algorithm 1.

Algorithm 1: Greedy algorithm

Input The scheduler's implementation is based on three main parts. The first one prioritises the scheduling of the jobs in the queue per order of time submission. Once the queue is empty and there are still some idle servers, the scheduler will try to reconfigure the running jobs by prioritising the ones with the highest remaining mass. The third part takes place when the queue is empty, and there is no possibility of reconfiguring any of the running jobs, at that moment, the scheduler will shut down the unused servers. Again the scheduler's main challenge is that it has no knowledge about the submission time of the upcoming jobs nor their parameters. Consequently, it cannot estimate in advance whether a reconfiguration would actually improve the performance as the servers that will be used for the reconfiguration may be required at any time for a new job. For a similar reason, shutting down idle servers may not always improve power consumption. Hence, in the next two sections, an explanation of the decision processes mentioned as reconfig decision and shutdown decision in Algorithm 1 is presented.

Handling Reconfigurations

Finding a good representation of the decision-making process of the reconfigurations is not a trivial problem. Therefore, three inequalities are introduced as candidates to model the condition at which a reconfiguration should take place. The first condition is considered the most basic one. It takes into account the main factors that are assumed to directly impact the outcome of reconfiguration: that is the number of used servers and the speed-up factor α of the job. It is also considered as the least complex as it introduces only three parameters. It is used later in the report within the setup Swarm1. The common attributes to the three conditions are: n new which is the new number of servers that a job would be running on after a reconfiguration, n max is the maximum number of servers a job can be assigned to (specified in the job request), and the speed-up factor α. Consequently, the condition1 is met if:

( n new n max ) wn × α wα × s reconf ig > 0.5
where w n is a weight between 0 and 1 and that is given to the ratio nnew nmax . The speed-up factor α also carries a weight w α . s reconf ig is a scaling factor between 0 and 1. The second condition is paired with the setup Swarm2. It is slightly more complex as it introduces an extra parameter w D that needs to be tuned. The idea is that since the duration of a reconfiguration is impacted by the amount of data to transfer between the servers, it would be fair to embed such amount in the decision-making process. In that way, a reconfiguration takes place if the following con-dition2 is met if:

( n new n max ) wn × α wα × ( D D max ) w d × s reconf ig > 0.5
where D is the amount of data of the job, and D max is the maximum amount of data any job can have. This ratio carries a weight w D ranging from 0 to 1, and that should be also tuned. Finally condition3 introduces a bias term instead of the scaling factor s reconf ig which is in the range of -0.5 and 0.5. It also relies on the tanh function and the previously mentioned parameters to decide whether a reconfiguration should be performed. It is used in the setup Swarm3, and is met if:

tanh(( n new n max ) wn × α wα × ( D D max ) w d + bias) > 0.5
For condition1 and condition2, the value of 0.5 in each of the conditions is arbitrary, other values were tested and it did not seem to affect significantly the results. For con-dition3, 0.5 was chosen over 0 to push the decision process towards using reconfigurations more often as the tanh function is not symmetrical around 0.5.

Handling Power-offs

For power-offs, it is assumed that the main factor that affects whether it is beneficial to shut down the idle servers is the ratio of available servers to the total number of servers. This decision, referred to as shutdown decision in Algorithm 1, is based on the following condition:

( n av n tot ) w of f × s of f > 0.5
where n av is the number of available servers, n tot the total number of servers, w of f a weight between 0 and 1 that reflects the importance of this ratio when making the shutdown decision and s of f is a scaling factor on the previous ratio. Additionally, instead of always powering off the servers for the same constant amount of time, two different shutdown durations are proposed; t 1 of f and t 2 of f . Indeed, the scheduler does not know in advance whether the shutdown will be beneficial, so a probability p t 1 of f for choosing t 1 of f over t 2 of f is introduced.

Greedy algorithm parameters

The decision process is thus influenced by the combination of the previously mentioned parameters from the shutdown process and the chosen condition of the job reconfiguration. The weights and scaling factors range between 0 and 1. The bias term range is between -0.5 and 0.5. The powering off times are only constrained by the 362 seconds value that is the minimum duration that guarantees energy saving. Thus during the optimization process, either eight or nine parameters need to be tuned. A summary of these parameters can be found in Table 2. Additionally, it is worth mentioning that when tests were run, it was more challenging to find a condition that models the decision-making process of the reconfigurations than finding a condition modelling power-offs. The latter condition seemed to be effective, as it explicitly uses the ratio of idle servers to the total number of servers.

The next step is to find the combination of parameter values that minimises the objective function. Since they are continuous and relatively numerous, the search space is very large. Thus to explore it and find an optimal solution a meta-heuristic algorithm, namely Particle Swarm Optimisation (PSO), is used.

Particle swarm optimization

As mentioned in the previous section, the goal of the particle swarm optimization (PSO) is not to directly find an optimal solution to schedule the different jobs but rather to find an optimal set of parameters that could improve the decision processes of the greedy algorithm.

PSO is a nature-inspired algorithm that imitates the behaviour of animal flocks. A Swarm is a group of particles which are mobile entities characterised by their position, velocity and cost. A swarm converges towards an ideal location as its individuals move with respect to the group best-known position and their respective best-known position. This technique enables for an exploration of the search space while converging towards an optimal solution [START_REF] Bratton | Defining a standard for particle swarm optimization[END_REF]. The used algorithm is similar to the one described in [START_REF] Clerc | The particle swarm -explosion, stability, and convergence in a multidimensional complex space[END_REF]. It uses the reflect method to deal with boundary escape from [START_REF] Helwig | Experimental analysis of bound handling techniques in particle swarm optimization[END_REF]. The used PSO algorithm is outlined in Algorithm 2.

Algorithm 2: PSO algorithm Initialise population position vectors ( x i ) randomly for number of steps do for i=1 to Population Size do

v i = χ( v i + φ 1 ( p i -x i ) + φ 2 ( p g -x i )) x i = x i + v i if f ( x i ) < f ( p i ) then p i = x i end handle boundaries() end for i=1 to Population Size do if f ( x i ) < f ( p g ) then p g = x i end end end
In our experiments, the set of parameters is either represented as an 8-dimensional vector or a 9-dimensional vector. x i depicts the position of the i th particle. Each particle has a velocity vector v i , used to update the vector x i , at every step. v i is calculated using:

v i = χ( v i + φ 1 ( p i -x i ) + φ 2 ( p g -x i ))
where χ is a constant influencing the magnitude of update. It was found that a value of 0.1 works well compared to higher values that tend to push particles out of bounds too quickly. p i is the particle best-known position, p g is the global best position. φ 1 and φ 2 are random numbers between 0 and 2 generated for each particle at each time step. The cost function f for a particular configuration is a function mapping a position vector to a real number. As for boundary escape, if a parameter j of x i goes below or above the x j min/max thresholds then it is handled as follows:

x ij = x jmax -(x ij -x jmax ) if x ij > x jmax x ij = x jmin + (x jmin -x ij ) if x ij < x jmin

Experimental setup

Simulation environment

As there are no known simulators -to our knowledgesupporting the scheduling of malleable jobs, a simulation environment was developed using python to test the performance of the designed scheduling strategy. At the time of writing this paper, it only provided reconfiguration of jobs from a smaller to a larger number of servers as it was thought to be more relevant in decreasing the mean stretch time. An example of the visualisation output of the simulator can be seen in Figure 2.

Jobs Generation

To simulate a realistic environment, Google data centreslike jobs were generated similar to [START_REF] Costa | Modeling and generating large-scale google-like workload[END_REF]. The only difference lies in the way a job is defined. In [START_REF] Costa | Modeling and generating large-scale google-like workload[END_REF] jobs are sets of small sequential tasks schedulable in a particular order. In our approach, a job is a single parallelisable task. A uniform random distribution is used to generate parameters such as the speed-up factors, the data and the required number of servers. All the undertaken experiments generated 50 jobs to be scheduled on ten servers. A summary of the job generation parameters is shown in Table 3 where the dynamism is the average duration between two consecutive jobs and the disparity is the ratio of the average makespan to the median makespan. The values for the mass and disparity correspond to the average values obtained in [START_REF] Costa | Modeling and generating large-scale google-like workload[END_REF].

Particle Swarm search of parameters

Since the number of parameters to tune is based on which condition is used for reconfiguration, three different swarms of 30 particles each were trained. Swarm1 (training), Swarm2 (training) and Swarm3 (training) use respectively condition1, condition2 and condition3. Each particle of each trained swarm was assigned set of valid parameters following the laws described in [START_REF] Costa | Modeling and generating large-scale google-like workload[END_REF] and the values in Table 3. At each epoch k a set G k is generated, where G k = {S 1 , S 2 , .., S 50 } and S i is a set of 50 different jobs. Every particle had to schedule G k independently. This partitioning of the set of jobs G k into smaller subsets S i is used to prevent the parameters within particles from overfitting a particular set of jobs. It also ensures that the job generator is updated with newer seeds guaranteeing diversity among the jobs. The scheduling of one set S i is further referred to as an experiment. The cost function is evaluated for each particle, by taking the average cost of the 50 S i within G k . Also, every set of generated jobs G k at epoch k is different from the one generated at epoch k + 1. The total number of used epochs is 100. This value was chosen as it was noticed that all particles had converged towards the same set of parameters and there was little change from one epoch to the next.

The mean cost of all the particles as a function of the number of epochs for each trained swarm is depicted in Figure 3.

The graph shows that for each of the swarms, the parameter search went through three different phases. This shows that the performance of the scheduler is highly dependent on the set of jobs to be scheduled. The found parameters are shown in Table 4.

The implication of the found results will be discussed in more details in the next section. Nevertheless, it is already worth noting that even though every particle's parameters are initialised randomly, every trained swarm learned two different shutdown time durations a short one for t 1 of f and a long one for t 2 of f , and whenever a power-off takes place at least 60% of the times, it lasts for a shorter amount of time. The difference in values for the shutdown times found by Swarm2 is quite large. In fact t 1 of f is close to the minimum required duration for energy-saving whereas t 2 of f is more than twice as large as the values found by Swarm1 and Swarm3. However the probability for choosing t 1 of f over t 2 of f in Swarm2 is about 96%, meaning that longer shutdown times will be rare. No indication was given in the initial values of the particles that one time should be longer than the other.

Comparison setups

To evaluate the performance of the scheduler, ten different experimental setups are introduced and run under identical conditions. In each of these setups, jobs are generated such there is only one big set of sets of jobs G 1 that is composed of 100 subsets S i , and where each S i contains 50 different jobs to be scheduled on ten identical servers. The main distinction between the first four setups and the last six lies within the decision-making process, which is based on the parameters defined in Table 2. In the first four setups, the reconfigurations and power-offs are performed whenever possible without any involvement of the parameters. Whereas the last six setups rely on those parameters to make a decision.

• FIFO (FIFO): The base case. It is only the first step of the greedy algorithm, a naive FIFO scheduler where the jobs are scheduled according to their submission time.

• FIFO & Reconfigurations (FIFO-Rcfg): A FIFO scheduler that always reconfigures jobs whenever servers are available.

• FIFO & Power off (FIFO-Poff ): A FIFO scheduler that always shuts down idle servers -when possiblefor a constant downtime of 900 seconds.

• FIFO & Reconfigurations & Power off (FIFO-Rcfg-Poff ): A FIFO scheduler combining reconfigurations and power-offs in the same fashion as in FIFO-Poff and FIFO-Rcfg.

• Random parameters (Rand-Param1 ): A FIFO scheduler that uses condition1 to reconfigure jobs and shuts down idle servers based on randomly generated parameters.

• Random parameters (Rand-Param2 ): A FIFO scheduler that uses condition2 to reconfigure jobs and shuts down idle servers based on randomly generated parameters.

• Random parameters (Rand-Param3 ): A FIFO scheduler that uses condition3 to reconfigure jobs and shuts down idle servers based on randomly generated parameters.

• With Swarm1 parameters (Swarm1 ): A FIFO scheduler that uses condition1 and that is capable of reconfiguring jobs and shutting down servers based on the PSO parameters found by Swarm1 (training).

• With Swarm2 parameters (Swarm2 ): A FIFO scheduler that uses condition2 and that is capable of reconfiguring jobs and shutting down servers based on the PSO parameters found by Swarm2 (training).

• With Swarm3 parameters (Swarm3 ): A FIFO scheduler that uses condition3 and that is capable of reconfiguring jobs and shutting down servers based on the PSO parameters found by Swarm3 (training).

Results

In this section, the method used for ranking the different setups is outlined. The results of the experiments are analysed by first looking at the number of power-offs and reconfigurations performed by each setup. Then, the individual components of the cost are investigated. Following, a statistical analysis of the overall costs is performed. Finally a resilience test on the best performing setup along with a scale-up test are presented.

Ranking methodology

To draw conclusions from the results, a ranking between each of the setups is established. In this section, the statistical method for the ranking is described. The first step of the approach is to rank each setup for each scheduled set of jobs. A rank of 1 denotes the best performing setup (lowest mean cost) and a rank of 10 the worst performing one. As one hundred sets S i of jobs were used for the experiment, one hundred rankings are obtained. Then, the average ranking of each setup is calculated. To evaluate whether those average ranks are significantly different from the mean rank (5.5), the Friedman χ 2 F and its corresponding p -value are used. Thus for a confidence level of 95% corresponding to an α value of 0.05, it is meaningful to perform post-hoc tests as described in [START_REF] García | An extension on "statistical comparisons of classifiers over multiple data sets" for all pairwise compar-isons[END_REF] if the p-value is less than 0.05. The post-hoc tests make it possible to identify significant pairwise differences among the different setups. This is done by first finding the standard error (SE) between two setups using:

SE = k(k + 1) 6N = 10 × 11 6 × 100 = 0.265
where k is the setup count and N the number of experiments. Using the average ranks and the standard error the z -values are computed using:

z = R i -R j SE
where R i and R j are the average ranks of two different setups. From the z -values, the corresponding probabilities (p -values) are found. The null hypothesis is that the difference in ranks is due to chance. However, if a p-value is below α = 0.05, then the null hypothesis can be rejected with level of confidence of 95%.

Reconfigurations and Power-offs results

Table 5 shows the average number of reconfigurations and the average number of power-offs per setup. The most striking result is that the average number of reconfigurations performed by the setup Swarm1 is zero. This implies that the parameters do not allow for any reconfigurations to take place for the generated jobs. The parameters from Swarm2 allow for very few reconfigurations to take place. This is also an unexpected result as one would expect that reconfigurations could improve the average stretch time. This result seems to highlight the difficulty of deciding when to perform reconfigurations and that if they are not carefully undertaken the performance might not improve. The average number of reconfigurations made by the three setups Rand-Param show that the improved cost reached by the Swarm setups is due to the optimization process and not just the choice of the equation describing the decision process. The average numbers of power-offs for the three Swarm setups are of the same order of magnitude which can be explained by the fact that the equations and parameters governing the power-off decision process for those three setups are the same. The different Swarm based set-ups allow for a relatively large number of poweroffs. It may seem that this could cause power spikes and have a negative impact on the energy saved. However modern hardware can slightly shift the time of powering different servers on and off so that they do not all take place at the same time and therefore avoiding power spikes.

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Ranking by the mean stretch time

Figure 4 shows the distributions of the mean stretch time metric obtained from conducting 100 experiments for each of the different setups. Although it cannot be seen on the graphs, the setup FIFO-Rfcg-Poff has a maximum mean stretch time (around 80) that is much higher than in any of the other setups. The distribution from FIFO-Poff also suggests that mean stretch time was higher than for the other setups in some instances. The distributions of the mean stretch times of the setups using the decision equations seem to have similar shapes for random parameters and PSO-obtained parameters. Another setup pair with similar distributions corresponds to FIFO and FIFO-Rcfg. This is not surprising as the logic between these two setups is quite similar. Using the statistical methodology previously described, the Friedman χ 2

F is found to be equal to 442.05 which corresponds to a p -value of 1.43 × 10 -89 which signifies that it is meaningful to perform post-hoc tests to identify significant pairwise differences. The ranking obtained with a confidence level of 95% is shown in Table 7: Setups ranking by the mean normalised power Table 6. Clearly, the setups using the PSO-obtained parameters ranked better than the other setups. The setups with the worst performances are FIFO-Poff and FIFO-Rcfg-Poff. This result can be explained by the fact that if the power-offs are not scheduled properly then the mean stretch time increases as the servers may be off at times when there is high demand.

Ranking by the mean normalized power consumption

Similarly to the previous section, Figure 5 shows the different distributions of the average normalised power metric of each setup. The range of values seems to vary over a smaller range from one setup to another compared to the stretch times shown in the previous section. This is expected as power is a measure of the rate of energy transformation, and there is a narrower range of allowed values (with respect to the states of the servers) at a given time. Unsurprisingly the setups that do not allow performing power-offs have a higher minimum average normalised power. The Friedman χ 2

F is found to be equal to 831.72 which corresponds to a p -value of 3.15 × 10 -173 which signifies that it is meaningful to perform post-hoc tests to identify significant pairwise differences. The ranking obtained with a confidence level of 95% is shown in Table 7. As expected, the FIFO-Poff setup that allows performing power-offs whenever possible ranks best on this metric. The setups that did not allow for any power-offs rank last. Again the three Swarm setups rank among the best ones.

Ranking by the overall cost

In this section the obtained cost values are compared to identify which setup may lead to a better overall performance. As a reminder, the cost function is defined as the product of the mean stretch time and the mean normalised power. Figure 7 shows pairwise comparisons of the number of times a particular setup obtained a lower cost value than an another setup. Clearly the three Swarm setups perform better than all the other setups on the majority of the experiments.

The average ranking over the 100 experiments can be seen in Table 8. It can be noted that no setup has an average rank that is close to 1, meaning that there is probably no setup that outranks all the others on most occasions.

The Friedman χ 2 F is in this case equal to 448.09 which corresponds to a p -value of 7.33 × 10 -91 . Thus for a confidence level of 95% corresponding to an α value of 0.05, it is meaningful to perform post-hoc tests to identify significant pairwise differences among the different setups. weak ranking among the six setups is shown in Table 9.

The worst performing setup is FIFO-Rcfg-Poff. It implies that reconfigurations and power-offs require a careful decision-making process to ensure an increase in performance, and that it is not beneficial to perform them whenever it is possible. The three Swarm setups are all ranked first, which can be explained by the exploration of the parameter space that allowed the algorithms to 'learn' to react to the job generator's distribution. Interestingly, there is no statistical evidence that FIFO performs better than Rand-params1 or Rand-params3 as they are all ranked second. To evaluate the significance of this ranking, a resilience test is described in the next section.

Resilience of PSO parameters

To test the ability of the three Swarm setups to respond to changes in the job generator's distribution, the setups were re-run under identical conditions to the ones in the previous section. The parameters of the job generator were slightly adjusted to simulate an environment where the frequency, the mass and the data of the jobs increased. Thus simulating an increase in the demand. The change of values can be found in Table 10.

Table 11 shows that this time the Swarm1, Swarm2, Rand-Param2 and Rand-Param3 allowed performing reconfigurations. This shows that the decision process of reconfigurations is sensitive to the distribution of the jobs and that a slight change in the job's parameters results in a different scheduling strategy.

Figure 8 displays the different distributions of the costs. It can be seen that the setup FIFO-Poff that allows for power-offs to be performed whenever possible seems to have an overall cost distribution skewed towards lower values. This can be explained by the fact that this setup is the one that performs the highest number of power-offs per set of jobs thus entailing a lower average power consumption. This does not necessarily imply that it is a beneficial setup as the mean stretch time can be very large. The Friedman Chi-square value χ 2 F = 165.67 and its corresponding p -value (4.93 × 10 -31 ) suggest that post-hoc testing can be meaningful. This time the ranking cannot be as clearly established as in the previous section and the weak ranking displayed in Table 12 was produced using a confidence level of 90%. This time again Swarm2 ranks first. However, the other two setups Swarm1 and Swarm3 did not perform as well as they rank third. It is worth mentioning that the simple FIFO scheduler that performs neither reconfigurations nor power-offs ranks second. Unsurprisingly using random parameters for the decision processes leads to a poor overall performance.

Scale up test

The scale-up test aims at simulating a more realistic environment by increasing the number of servers from 10 to 100 while keeping the average number of submitted jobs per server per unit time the same as before. Similar to the resilience test, the goal is to test how the different setups would behave in an environment that is different from the one in which the optimization process took place. The change in the parameters made during the scale-up test can be found in Table 13.

As shown in Table 14, the average number of reconfigurations and power-offs performed by the three Swarm setups during the scale-up test are similar to the ones of the initial experiment. This shows if the jobs distribution is kept the same on a larger scale, the three Swarm setups behave consistently. However, this is not the case for the setups using random parameters, as Rand-Param2 that did not initially allow for reconfigurations, now performs a large average number of reconfigurations.

Figure 14 shows the cost distribution of the different setups. The three Swarm setups have a whisker in the The results show that using a decision process for reconfigurations and power-offs can lead to a low overall cost. This decision process is based on the scheduler state and made use of parameters that were tuned using PSO. However, this performance was found to be sensitive to changes in the way jobs are submitted and the total number of servers in the HPC cluster. The difficulty with taking energy consumption as a metric is that minimising it by turning off servers can have a negative impact on the jobs waiting time in the queue. Therefore a setup that ranks well on one of the metrics does not necessarily rank well on the other. It would be interesting during the PSO training to have variable weights over the metrics within the objective function. It would prevent that one metric gets favoured over the other. This was probably the case with the setups that did not allow for reconfigurations to take place. One of the limitations is that the PSO training can take a long time, especially if the number of servers and jobs are large. Our implementation could be improved by using multi-processing and generating jobs in advance so that they do not have to be re-generated every time a swarm has to be trained. This would increase the speed of experimentation with the proposed equations for the decision process. In this investigation, the job generator simulates a realistic distribution for the frequency at which jobs are submitted and their mass. But, both the speedup factors and the data amount are drawn from uniform random distributions. Using a more realistic distribution for those parameters might improve the ability of the PSO to find patterns in the jobs. Finally, another aspect that is not taken into consideration in this investigation is that turning servers on and off frequently may affect their lifetime. This is particularly important when considering the environmental aspect, as energy is not only required when the servers are used but also when they are manufactured and disposed of.

Conclusions & Perspectives

This paper presented a solution that aims at improving the performance of HPC schedulers by lowering both the mean stretch time and the average power consumption by relying on malleable job reconfigurations and server poweroffs. As current simulators do not support the scheduling of malleable jobs, we implemented our own simulator. The job generator's distribution was modelled based on real data. The proposed algorithm consisted of three steps: FIFO scheduling, reconfiguration attempts and powering off of the remaining idle servers. Various parameters and conditions were used to decide whether reconfigurations and power-offs should be performed. It was assumed that the scheduler has no knowledge about any future states and used only local context knowledge to make decisions.

Three different equations were used to decide whether a reconfiguration should take place and the parameters of those equations were tuned using PSO. It was found that the three different setups using the PSO-obtained parameters performed equally well and better than the other setups as long as the distribution of jobs is similar to the one it was trained on. If the frequency at which jobs are submitted as well as their average masses are increased, one of the setups using PSO-obtained parameters ranks first with a level of confidence of 90%, but the other two did not perform so well. If the number of servers is increased to a more realistic number (100) then it was found that the setup that only used the FIFO scheduling as well as reconfigurations whenever possible performed best. However, this performance was at the expense of the average power metric.

We believe that the malleable job paradigm should be investigated further by, for example, allowing jobs to be assigned to a lower number of servers if the queue becomes too long. This would require keeping some statistics about the average length of the queue at a given time. Being able to accurately predict the remaining mass of a job at a given time would facilitate the evaluation of whether a reconfiguration could be beneficial. Probably one of the biggest challenges remains to find a suitable encoding for malleable tasks so that other meta-heuristic algorithms could be used to find more optimal solutions.

Acknowledgment

The work presented in this paper has been funded by the ANR in the context of the project ENERGUMEN, ANR-18-CE25-0008. We would like to thank Prof. Pascal Bouvry and Dr. Grégoire Danoy for their guidance throughout the project. We would also like to thank Luc Sinet for his invaluable advice on software development and Lucas Romao for his support.

Figure 1 :

 1 Figure 1: Scheduling of moldable jobs (left, middle) and of malleable jobs (right) (from [8])

Figure 2 :

 2 Figure 2: Visual output from the simulator

  From epoch one to about 20, there is a clear improvement in minimizing the cost function. Then from epoch 20 to about 70, particles still have quite different sets of parameters. Yet, these parameters tend to get closer to one another. At this point, the swarm is exploring the neighbourhood of the minimum it would later settle in, which justifies the flattening of the cost values from about epoch 40 to 70. From epoch 70 to 100, the mean cost slowly decreases until convergence, and the swarm finds a minimum optimising the cost function. So the three trained swarm converged at around the same range of epochs. Moreover, those similarities in the cost pattern can be explained by the fact that the training took place under the same seed and that the job generator had not been modified, which resulted in having the same job distribution for each of the swarms.

Figure 3 :

 3 Figure 3: Mean cost of all the particles against the number of epochs during training

3 FIFO

 3 and Rand-Param1 and Rand-Param3 4 FIFO-Rcfg and Rand-Param2 5 FIFO-Rcfg-Poff and FIFO-Poff

Table 6 :

 6 Setups ranking by the mean stretch time

Figure 4 :Figure 5 :Figure 6 :Figure 7 :

 4567 Figure 4: Histograms of the mean stretch time of each setup

Figure 8 :

 8 Figure 8: Cost of each used setup in the resilience test

Figure 9 :

 9 Figure 9: Cost of each used setup in the scale up test

Table 1 :

 1 Reference values for servers power consumption

	Measure	Value
	Power idle	95.00 W
	Power while computing 190.74 W
	Power while turning on 125.17 W
	Power while turning off 101.00 W
	Power while off	9.75 W
	Time to turn on	151.52 s
	Time to turn off	6.10 s

Table 2 :

 2 Overall parameters of the greedy algorithm

		Overall reconfiguration parameters
	w n	w α	s reconf ig	w D	bias
	Servers count weight	Speedup factor α weight	Scaling factor	Data weight	Bias term
		Power off parameters
	w of f	s of f	t 1 of f	t 2 of f	p t1 of f
	Available servers count weight	Scaling factor	Duration 1	Duration 2	t 1 of f proba-bility

Table 3 :

 3 Parameters used for the generation of jobs

Table 4 :

 4 Configuration of parameters found by PSO of each of the trained swarms

		Reconfiguration parameters
		Swarm1	Swarm2	Swarm3
	w n	0.175	0.348	0.529
	w α	0.742	0.833	0.645
	s reconf ig	0.331	0.579	N/A
	w D	N/A	0.730	0.289
	bias	N/A	N/A	-0.106
		Power off parameters
		Swarm1	Swarm2	Swarm3
	w of f	0.455	0.516	0.494
	s of f	0.760	0.814	0.813
	t 1 of f	899	528	632
	t 2 of f	1405	2962	1233
	p t 1 of f	0.717	0.959	0.615

Table 5 :

 5 Average number of reconfigurations and power-offs of each setup

Table 8 :

 8 The Average ranking of each setup over the 100 experiments

	Setup	Average ranking
	FIFO		5.49
	FIFO-Rcfg	7.22
	FIFO-Poff	7.58
	FIFO-Rcfg-Poff	9.02
	Rand-Param1	4.97
	Rand-Param2	6.72
	Rand-Param3	5.07
	Swarm1		3.12
	Swarm2		2.45
	Swarm3		3.39
	Rank	Setup
	1	Swarm1 and Swarm2 and Swarm3
	2	FIFO and Rand-Param1 and Rand-Param3
	3	Rand-Param2 and FIFO-Rcfg and FIFO-Poff
	4	FIFO-Rcfg-Poff

Table 9 :

 9 Setups ranking by the overall cost

Table 11 :

 11 Average number of reconfigurations and power-offs of each setup in the resilience test

Table 12 :

 12 Setups ranking during the resilience test

lower costs that is longer than in Figure 6 and Figure 8. This implies that the lower quartiles spreads over a larger range of costs than in the previous two tests. There is some overlap of the different distributions and further analysis needs to be conducted to establish a ranking. The Friedman Chi-square value χ 2 F = 358.68 and its coresponding p -value (8.80 × 10 -72 ) suggest that post-hoc testing can be meaningful. The weak ranking is shown in Table 15. The result is rather unexpected as the setup FIFO-Rcfg that allowed for reconfigurations to take place whenever possible ranks first. A large number of servers provides a larger number of possible ways to schedule a given job. A reconfiguration is less likely to have a large effect on the fractional number of servers being used if the number of servers is large. This result can also be explained by the fact that the setup FIFO-Rcfg performed really well under the mean stretch time metric, it did however perform very poorly on the average power metric. The different three Swarm setups ranked equally well at rank 4 despite a behavior that is consistent with the initial test. When scaling up there does not seem to be a significant advantage in using one of the decision equation over any of the other ones. The setup that allows for reconfigurations and power-offs to take place whenever possible ranks last similar to all the previous tests.


Aurélien Gay

Sébastien Migeon

Geological fluid flow in sedimentary basins Les fluides géologiques dans les bassins sédimentaires

Keywords: fluid seep, methane hydrates, bottom-simulating reflector (BSR), dynamic seepage, seismic pipes, fluid migration. I

This thematic issue on geological fluid flow in sedimentary basins is part of the Action Marges project. It is a large French coordination program that has been launched in 2008 in order to focus the continental margin research on a cross disciplinary research and develop new concepts that have an important impact for society, for resources and for risk assessment.

Actions Marges (AM) is coordinated by INSU with financial contribution of TOTAL, CNRS, BRGM and IFREMER. One of the themes is dedicated to "Fluids -Organic Matter -Mineral Matter" (FO3M) and the challenge was to define fluid flow as the centerpiece of a cycle starting from the organic matter preservation at seabed, its transformation during burial forming fluids, the upward fluid migration through fine-grained sediments, the temporarily fluid storage into reservoirs such as sedimentary bodies or gas hydrates, the fluid expulsion at seafloor, to its implications on seabed stability or climate change.

This special issue aims at bringing together recent results from the scientific community involved in the FO3M project since 2012, with a special focus on the interplay between mineralogical, geochemical, physical and biological processes occurring during fluid remobilization and migration within the shallow interval (0-1000 m) of sedimentary basins.

II. Introduction (Français):

Ce numéro spécial sur les migrations de fluides dans les bassins sédimentaires fait partie intégrante du projet Action Marges. C'est un programme de recherche coordonnée, lancé en 2008, qui s'intéresse principalement aux marges continentales à travers une approche interdisciplinaire permettant d'apporter des réponses concrètes aux problèmes de société, de développer les ressources ou encore de mieux évaluer et de limiter les risques. Le projet Action Marges (AM) est piloté par l'INSU avec une contribution financière de TOTAL S.A., du CNRS, du BRGM et de l'IFREMER.

Un des thèmes développé concerne les "Fluides-Matière Organique-Matières Minérales" (FO3M) et son objectif majeur était de placer les fluides au centre d'un cycle qui démarre par la préservation de la matière organique sur le fond de la mer dès le dépôt des sédiments, et qui se poursuit par la préservation de la matière organique au cours de l'enfouissement l'amenant à la production de fluides, par la migration de fluides à travers la colonne sédimentaire, par le stockage temporaire de fluides dans des réservoirs comme les corps sédimentaires ou les hydrates de gaz, et jusqu'à l'expulsion des fluides sur le fond de la mer ayant une implication possible sur les changements climatiques due à la nature des fluides expulsés.

Ce numéro spécial réunit ainsi des synthèses des derniers travaux récents conduits par la communauté de chercheurs impliquée dans le projet Action Marges, en particulier depuis 2012. Une attention particulière a été portée sur les processus minéralogiques, géochimiques, physiques et biologiques qui entrent en jeu lors de la remobilisation et de la migration des fluides dans les premiers 1000 m de la colonne sédimentaire.

III. Organic matter preservation and shallow fluid migration:

The initial sediment lithification starts with complex interactions involving minerals, shallow interstitial water, decomposing organic matter and living organisms. This is the eogenesis domain (0-1000 m below seafloor) in which the sediments are subject to physical, chemical and mechanical transformations defining the early fabric of rocks (Figure 1). This interval is intensively prospected for its geological resources (hydrocarbons, metal deposits, geothermal energy). In most basins worldwide, it consists of very fine-grained sediments and it is supposed to play the role of a seal for fluids migration. Local sand-prone deposits of various origin, architecture and thickness can also be found, acting as pathways or reservoirs for fluids.

For the first time, the solid phase BrOrg has been evidenced as a conservative tracer of the debromination of sedimentary organic matter in a shallow core (<50 m) taken on the Nile deep-sea fan (Murat et al., this issue). The rate of debromination illustrates the complex interplay between organic matter, microbes and inorganic compounds in unconsolidated sediments. It obviously depends on the TOC, meaning that it increases with TOC up to 3% due to the availability of the bacterial population. However, the debromination process decreases for TOC higher than 4%. This is of high interest for organic-matter transformation as the BrOrg tracer determines how fine-grained organic-rich intervals become potential source rocks at depth and how bacterial populations modify fluids and organic matter characteristics during early stages of burial (Figure 1). In addition, the BrOrg can be used as a tool for defining intervals that were "fluid engineers" in the eogenesis domain once the organic matter has been transformed at depths where diagenesis is the most active process for lithification (>1000 m below seafloor).

However, the eogenesis domain is commonly affected by polygonal faulting due to a volume loss by contraction of smectite-prone clay sediments during burial [START_REF] Andresen | Bulls-eye" pockmarks and polygonal faulting in the Lower Congo Basin: relative timing and implications for fluid expulsion during shallow burial[END_REF][START_REF] Cartwright | Diagenetically induced shear failure of fine-grained sediments and the development of polygonal fault systems[END_REF][START_REF] Gay | Geological controls on focused fluid flow associated with seafloor seeps in the Lower Congo Basin[END_REF]. This process is of high interest to constrain fluids circulation within fractured reservoirs and/or preservation of the sediment-cover integrity but it is still not well constrained as this interval can either promote the migration of fluids from underlying reservoirs [START_REF] Gay | Polygonal fault-furrows system related to early stages of compaction -upper Miocene to recent sediments of the Lower Congo Basin[END_REF][START_REF] Lonergan | The geometry of polygonal fault in Tertiary mudrocks of the North Sea[END_REF]. In addition, mineralized fluids intensify diagenesis in the fracture planes, rendering this interval all the more impermeable [START_REF] Laurent | High-resolution architecture of a polygonal fault interval inferred from Geomodel applied to 3D seismic data from the Gjallar Ridge, Vøring Basin, Offshore Norway[END_REF]. The detailed analysis of the geometric relationships between high-impedance seismic anomalies and polygonal fault pairs reveals that they can act as a seal during early stages of burial (Ho et al., 2016). In the Congo Basin, The Pliocene-present interval contains widespread fluid escape features, at seabed and in the subsurface, indicating past and ongoing bypass of the regional seal which is intensely affected by a major polygonal fault system [START_REF] Gay | Polygonal fault-furrows system related to early stages of compaction -upper Miocene to recent sediments of the Lower Congo Basin[END_REF]. Casenave et al. (this issue) have shown that this polygonal fault interval might also have sealing properties. In this case, fluids are mainly driven along major faults to reach the seabed, leading to more focused and localized fluid seeps (Figure 1). The next challenge will be then to define where, when and how does this polygonal fault interval occur. This can only be done by understanding the behavior of clay grains and fluids during early burial stages.

In the Sobrarbe Delta (Eocene Ainsa Basin), diagenetic conditions were limited to low temperatures (up to 75°C), meaning that sediments have been buried to a maximum of 2 km (Odonne et al., this issue). Both some sliding surfaces and their surrounding deposits were investigated. Results reveal higher smectite ratio close to the sliding surfaces and smectites were found at the wall of dissolution voids of K-feldspars (Figure 1). This indicates that fluids preferentially circulated following the sliding surfaces that acted as major heterogeneity within the sediment pile. In these conditions, clay minerals such as smectite appear as a robust marker of shallow fluid circulation as the kaolinite requires a temperature of 130-140°C to change into illite.

IV. Seafloor fluid seepage

Once fluids (water, gas, oil or a mix) reach the seafloor, they are expelled in the water column forming sub-circular depressions called pockmarks [START_REF] Hovland | Seabed pockmarks and seepages[END_REF]. Pockmarks have been reported in various depositional systems at water depths ranging from 30 m to over 3000 m (for a detailed review see [START_REF] Rollet | Characterisation and correlation of active hydrocarbon seepage using geophysical data sets: an example from the tropical, carbonate Yampi Shelf, Northwest Australia[END_REF][START_REF] Gay | Geological controls on focused fluid flow associated with seafloor seeps in the Lower Congo Basin[END_REF]. They generally form in unconsolidated, fine-grained deposits. They are cone-shaped circular or elliptical depressions, ranging from a few meters to 800 m or more in diameter and from 1 m to 80 m in depth. They concentrate in fields extending over several square kilometers. For the last decade, discoveries of new fluid seep structures were not reported although both academic and industrial surveys intensified.

However, the use of high-resolution geophysical imagery systems leads to the identification of unknown high-density field of pockmarks (about 3500 sub-circular pockmarks per square km) in unsuspected shallow marine environment (about 35 m water depth) like the Bay of Concarneau (France) (Baltzer et al., this issue). The pockmarks appears to be active as the fields limits coincide with the settlements of Haploops tube mats covering muddy deposits. To date, this is the highest density of pockmarks ever found in marine environment (Figure 1). The acquisition of bathymetry and other geophysical tools in the same area in 2011 and 2014 was essential in order to determine lasting of Haploops communities related to fluid seeps within pockmarks.

In the Lower Congo Basin (LCB), the combined use of various datasets acquired during the last 15 years and including multibeam bathymetry, seismic data, seafloor videos, seafloor samples and chemical analyses of both carbonate samples and of the water column allowed the identification of two distinctive associations of pockmark-like seabed venting structures, located in water depths of 600-700 m (Casenave et al., this issue). These two features are called "spiders structures" because of their morphology consisting of a large and flat sub-circular depression (the body) surrounded by elongated depressions (the legs). The body consists of muddy lowreflective hemipelagic sediments, covered by patches of bacterial mats and empty shells, and seems dormant while the legs appear to be the present-day active seep location (Figure 1). This Pockmarks and gas seeps can also be detected on acoustic records as long as they are active [START_REF] Dupré | Tectonic and sedimentary controls on widespread gas emissions in the Sea of Marmara: Results from systematic, shipborne multibeam echo sounder water column imaging[END_REF]. This is the case in the Bay of Concarneau where acoustic flares were identified on Chirp profiles (Baltzer et al., this issue). This is also the case on the Romanian sector of the Black Sea where widespread gas flares were evidenced in the water column related to high fluid fluxes escaping from the seabed (Riboulot et al., this issue). Gas flares are a useful tool to easily identify active pockmarks during multibeam and 2D high-resolution seismic surveys (Figure 1). Should we add a sentence explaining that active pockmarks might be related to presence of a gas-hydrate stability zone, so that identification of gas flares could also be a good tool to recognize hydrate areas?

V.

From the source to the seafloor

On the geophysical record , either 2D or 3D seismic data, vertical fluid pathways called pipes (or chimneys) are usually imaged as systematic disruptions and/or offset of the reflections within sub-vertical zones, 50-1000 m wide and up to 1000 m high [START_REF] Løseth | 1000 m long gas blow-out pipes[END_REF]. This interpretation is also supported by observation of amplitude enhancement or dimming. On seismic profiles, the internal structure of pipes is characterized by bent reflections with offset upward (pull-up effect) or downward (pull-down effect) relative to the host stratigraphy by 20 to 150 ms TWT. Pipes are interpreted to represent a high-permeable vertical zone called a seal bypass system [START_REF] Cartwright | Seal bypass system[END_REF]. They are thought to be caused by high fluid overpressure hydro-fracturing sediments of low permeability [START_REF] Arntsen | Seismic modelling of gas chimneys[END_REF][START_REF] Rodrigues | Physical modelling of sand injectites[END_REF]. This geophysical characterization seems actually well constrained in space and time.

However, using a set of derived seismic attributes, it is now possible to identify the root of a pipe (i.e. the depth at which fluids are originating from) and the paleo-pockmark surface (i.e. the level representing the paleo-seafloor at the time the fluid expulsion started? Was active?). In the Gulf of Lion, the fluid pipe induces a deformation of surrounding unconsolidated sediments during upward migration, leading to the formation of a cone in conical or V-shaped structures (Gay et al., this issue). A multi-attribute calculation made on 3D seismic data allows the precise 3D mapping of the points of fluid injection while the top of the cone structure marks the top of the focused migration (Figure 1).

It is classically invoked that most of the fluids reaching the seabed are originating from shallow buried levels (Figure 1) although when carbonate crusts build at the seafloor there is undoubtedly a contribution of deeper fluids allowing oil or thermogenic gas to be expelled (Leon et al., 2010;Leon et al., 2014). This is the case in the Gulf of Lion (Gay et al., this issue), on the Romanian shelf (Riboulot et al., this issue) or in the Bay of Concarneau (Baltzer et al., this issue) where fluid releasing is attested. These vertical chimneys or pipes are among other acoustic anomalies usually present in the shallow sub-surface due to gas accumulations or carbonate precipitation (Figure 1). In the Lower Congo Basin, such anomalies were identified on either 3D seismic data, HR 3D seismic data or 2D-AUV data (Casenave et al., this issue) corresponding to bright spots, elongated acoustic anomalies or black patches.

However, even with 3D seismic imagery, the full architectural pattern of the fluid network remains very complex to determine. Only a few examples of modern fluid-related systems have been studied in details at sea, probably because of the limited number of areas where VHR investigations of the seafloor/sub-surface together with 3D seismic data exists. It is then possible to better understand these features on field analogues, once the stratigraphic intervals hosting them have been uplifted due to tectonic activity and brought above the sea-level (Figure 1). For instance, in the uplifted accretionary prism of Hikurangi Margin (New-Zealand), tubular concretions are outcropping in several Miocene mudrocks intervals (Malié et al., this issue).

Most of the pipes are subvertical with their length-axis perpendicular or sub-perpendicular to the bedding plane. They are 10 to 40 cm in diameter and 50 cm to 10 m in length. In some cases, they are inter-connected, forming a real plumbing system. A direct comparison with seismicscale features is still not realistic to manage because their dimensions are far below the seismic resolution. Anyway, this suggests that a fluid pipe few hundred-meters wide identified on the seismic record is most probably a set of several small-scale pipes together propagating in a large area of sediment impregnated with fluids (Figure 1). This should be the target for the development of future research fields.

VI. The dual role of hydrates in cover integrity

The sub-vertical fluid pipes (or chimneys) are often disrupted at depth by a high-amplitude reflection parallel to the seafloor. This reflection is interpreted as a bottom-simulating reflector (BSR) corresponding to the lower thermodynamic limit of the gas-hydrate stability zone [START_REF] Shipley | Seismic evidence for widespread possible occurrence of gas-hydrate horizons or continental slopes and rises[END_REF][START_REF] Kvenvolden | Methane hydrate -a major reservoir of carbon in the shallow geosphere?[END_REF][START_REF] Hyndman | A seismic study of methane hydrate marine bottom simulating reflectors[END_REF]. This can be also considered as the top of the free-gas zone beneath the hydrates [START_REF] Mackay | Origin of bottom-simulating reflectors: Geophysical evidence from the Cascadia accretionary prism[END_REF]. BSRs are characterized by a reverse polarity reflection compared to the seafloor. This is due to a decrease in seismic impedance and, therefore, of seismic velocity. The main restriction for the occurrence of hydrates is the presence/supply of sufficient amounts of stabilizing gas molecules (Rempel and Buffet, 1997). As outlined by [START_REF] Egorov | Gas hydrates that outcrop on the sea floor: stability models[END_REF], the steady-state model for gas hydrates requires a methane flux from below to compensate for its diffusive dissipation into the pore water [START_REF] Sultan | Pockmark formation and evolution in deep water Nigeria: Rapid hydrate growth versus slow hydrate dissolution[END_REF]. This statement is confirmed on the Romanian shelf where BSRs are deflected upward (Riboulot et al., this issue), suggesting a localized positive heat flow anomaly probably due to an ascending movement of fluids through the sedimentary column [START_REF] De Batist | Active Hydrate Destabilization in Lake Baikal[END_REF][START_REF] Gay | Isolated seafloor pockmarks linked to BSRs, fluid chimneys, polygonal faults and stacked Oligocene-Miocene turbiditic palaeochannels in the Lower Congo Basin[END_REF]. It means that gas hydrates are locally not impermeable, allowing fluids to migrate upward along any major discontinuity such as faults, and then to form pockmarks at the seabed (Figure 1). So, there is a close association between BSRs at depth, indicating an active fluid flux, and seafloor pockmarks. This is also the case in the Lower Congo

Basin where the new "spider structures" are located above patches of BSR (Casenave et al. this issue).

As gas hydrates are usually considered impermeable, additional free gas may migrate laterally right beneath the hydrates. Once they find a way up, they can escape to the seabed. On the seismic record, this kind of seepage is often evidenced by hyperbolae located at the landward termination of the BSR and overlaid with seafloor gas escape structures (Figure 1) (Riboulot et al. this issue; Casenave et al., this issue).

Gas hydrates have a dual role in sedimentary basins as 1) they temporarily store fluids migrating from deeper geological horizons to their stability zone or as free gas beneath them and 2) they avoid fluids to reach the seabed with properties similar to a barrier. In consequence, they can be considered as both a reservoir and a cover in the shallow subsurface (Figure 1).

VII. Triggering and control mechanisms:

In the uplifted accretionary prism of Hikurangi Margin (New-Zealand), trending of fossil tubular carbonate concretions coincides with tectonic lineament suggesting that the main factor controlling the fluid expulsion and location is the tectonic activity (Malié et al., this issue). It is suggested that the tectonic uplift together with fault activity can shift the depth of the Hydrate Stability Zone (HSZ), leading to dissociation of pre-existing hydrates and releasing of methane.

Gas hydrates are also "climate sensitive" as they form or dissolve in response to sea-level variations controlling bottom seawater temperatures. In the Lower Congo Basin, the various stratigraphic levels hosting fluid escape structures have been correlated to sea-level rises following the latest Holocene lowstands (Casenave et al., this issue). This is due to free gas naturally migrating landward due to the pronounced tilting of the base of the gas hydrate stability zone at 600-700 m water depths (close to theoretical pinch-out of the BSR).

This relation with sealevel variations has also been evidenced in the Gulf of Lion where most of the modern pockmarks were formed during the last sea-level rise (Gay et al., this issue). Leon et al. (2014) have shown that in the western Mediterranean Sea, the propagation of internal waves alongshore may act together with the general sea-level rise at the beginning of the transgressive period as a hydraulic pump for fluids trapped at shallow depths, resulting in the formation of pockmarks (Figure 1). It suggests that, at the beginning of each transgression, large amount of methane-rich fluids might be released into the ocean and atmosphere, possibly increasing the greenhouse effect (Dunkley [START_REF] Jones | A Palaeogene perspective on climate sensitivity and methane hydrate 684 instability[END_REF].

The effect of hydraulic pumping can be responsible for the formation of the large fields of pockmarks in the Bay of Concarneau (Baltzer et al., this issue). At a shorter time-scale, the highfrequency expulsion activity may be related to the action of spring tides (Figure 1). Although the initial pockmark craters is thought to be formed during seismic events (Baltzer et al., this issue), pockmarks could be activated when the tidal pressure would be high enough during spring tides, leading to the formation of plumes in the water column.

As previously proposed by [START_REF] Hasiotis | A pockmark field in the Patras Gulf (Greece) and its activation during the 14/7/93 seismic event[END_REF], these observations imply that gas supply towards pockmarks is a continuous process during the life-span of the seep structure (Figure 1).

It also means that the sediments within the migration pathways are loaded with gas, which is of high interest for seafloor hazards.

VIII. Conclusion

Recent studies of fluid migrations in sedimentary basins have shown that these phenomena are ubiquitous and interact with the surrounding sedimentary rocks over the basin's history, from its early opening stages, to the mature infilling, or even the ultimate exhumation stages. Finally, these fluids act as major markers for the geodynamic events that structure the basins.

In order to understand the role of fluids in sedimentary basins, several challenges must be met.

We must, in particular, determine the source of the fluids, their pathways and the timing of these migrations and releasing at the seafloor. Thus, depending on the geodynamic context of a given basin and on its evolution over time, several fluid migration histories may overlap, though these fluid events are all preserved in the sedimentary record. The goal is thus to identify a number of geophysical, mineralogical and geochemical markers of fluid migration and expulsion. They are closely controlled 1) by early sediment deformations and fracturing and 2) by the location of bodies and sedimentary sequences likely to temporarily store and/or conduct fluids laterally all along the margin and 3) by allocyclic phenomena such as sealevel variations.

In this special issue, the methods have combined complementary approaches to analyse fluid migrations in sedimentary basins: 1) geophysical characterisation based on 2D, 3D and 2D-AUV seismic data, core and exploratory well data, 2) sedimentological and mineralogical characterisation based on petrophysical data and crystallographic and texture analyses, 3) identification of geochemical markers on samples collected at various depths of the sedimentary column up to the surface and 4) definition of the mechanical parameters triggering and controlling fluid re-mobilisation.

The final goal of this research field will be to generate a concept of "fluid sequences" that, in a similar fashion to sequence stratigraphy, will be used as a direct approach to determine the migration pathways and potential traps, and to quantify the volume and nature of expelled fluids. This is of major importance as it will help determining the timing and activity of seafloor fluid expulsion leading to geohazard assessment (catastrophic fluid blow-out, landslide triggering etc...). As a reverse approach, it will make possible to define the evolutionary status of a sedimentary basis and to infer its geodynamic context at any given period of its history. The aim is thus to significantly improve fluid expulsion models in sedimentary basins, which is a challenge for basin exploration (open window over the petroleum system, seafloor stability and fluid accumulation).

IX. Acknowledgements

X.

  observation is similar to what was already described for more conventional pockmark structures on the Nile margin(Bayon et al., ;[START_REF] Dupré | Tectonic and sedimentary controls on widespread gas emissions in the Sea of Marmara: Results from systematic, shipborne multibeam echo sounder water column imaging[END_REF] Migeon et al., 2014) but it is the first time that such a specific sea-bed planform morphology associated with such a fluid-circulation activity is reported.