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Content Distribution Networks (CDN) are key for providing worldwide services and content to end-users. In this work, we propose three multiobjective evolutionary algorithms for solving the problem of designing and optimizing cloud-based CDNs. We consider the objectives of minimizing the total cost of the infrastructure (including virtual machines, network, and storage) and the maximization of the quality-of-service provided to end-users. The proposed model considers a multi-tenant approach where a single cloud-based CDN is able to host multiple content providers using a resource sharing strategy. The proposed evolutionary algorithms address the offline problem of provisioning infrastructure resources while a greedy heuristic method is proposed for addressing the online problem of routing contents. The experimental evaluation of the proposed methods is performed over a set of realistic problem instances. Results indicate that the proposed approach is effective for designing and optimizing cloud-based CDNs reducing total costs by up to 10.3% while maintaining an adequate quality of service.

This work addresses the multi-objective resource provisioning problem for building cloud-based CDNs. The optimization objectives are the minimization of VM, network and storage cost, and the maximization of the QoS for the end-user. A brokering model is proposed such that a single cloud-based CDN is able to host multiple content providers applying a resource sharing strategy. Following this model, an offline multiobjective evolutionary approach is applied to optimize resource provisioning while a greedy heuristic is proposed for addressing online routing of content. Experimental results indicate the proposed approach may reduce total costs by up to 10.6% while maintaining high QoS values.

High-Performance Computing (HPC) is a growing necessity of our technological society, HPC demands high loads of parallel computing jobs, an optimal scheduling of the parallel applications tasks is a priority to meet the demands of its users on time. Branch-and-bound (BB) Algorithms and Mathematical Programming (MP) solve complex optimization problems in an optimal manner, some MP or BB even have parallel computing capabilities, making them suitable solutions to solve real-world problems. In this paper, we propose two exact algorithms, a BB and a MP Model for scheduling precedence-constrained applications, on heterogeneous computing systems, as far as we known the first ones on his kind presented in the state of the art. One major contribution of the work is the proposed formulations of the objective function in both methods. Experimental results obtained more than twenty optimal values for synthetic applications from the literature.

A key issue when using distributed computing environments is finding a planning strategy to execute tasks in order to use the computational resources efficiently. This article presents the application of Virtual Savant to solve the heterogeneous computing scheduling problem, a widely-studied problem with several real-world applications. Virtual Savant is a novel method that uses machine learning techniques to automatically generate programs that can be executed in parallel to solve a given problem. Experimental analysis is performed on a set of problem instances generated following methodologies from the related literature. Results show that Virtual Savant is able to outperform MinMin, a well-known heuristic for the studied problem, by up to 15% while showing good scalability properties when increasing the number of computing resources and the dimension of the problem instances.

This article addresses a virtual machine (VM) allocation problem that appears in a novel business model for cloud computing. In this model, a cloud service broker owns a number of cloud reserved instances that outsources to its customers as cheap as on-demand VMs. The objective of the broker is to efficiently manage its reserved resources to maximize its revenue. We enhance the previous definition of the problem by considering more realistic parameters: geographical localization of resources and users, different types of applications, and data transfer costs. We propose a set of heuristics to solve the optimization problem of maximizing the cloud provider profit while offering appropriate Quality-of-Service to the users. The experimental analysis is performed over different scenarios using real data from cloud providers.

The energy consumption of large data centers has been increasing for the last decades and currently is a major concern for economic and environmental reasons. Accurate scheduling of the data center operation and use of renewable energy sources present themselves as promising solutions for this problem. In this paper we study the problem of scheduling workflows of tasks in distributed heterogeneous data centers which are partially powered by renewable energy sources. This problem takes into account quality of service, infrastructure usage, and power consumption of machines and cooling devices. We propose a mathematical model for accurate scheduling solutions.

Evolutionary algorithms are metaheuristic algorithms that provide quasioptimal solutions in a reasonable time. They have been applied to many optimization problems in a high number of scientific areas. In this survey paper, we focus on the application of evolutionary algorithms to solve optimization problems related to a type of complex network likemobilemultihop ad hoc networks. Since its origin, mobile multihop ad hoc network has evolved causing new types of multihop networks to appear such as vehicular ad hoc networks and delay tolerant networks, leading to the solution of new issues and optimization problems. In this survey, we review the main work presented for each type of mobile multihop ad hoc network and we also present some innovative ideas and open challenges to guide further research in this topic.

This article presents a multiobjective approach for scheduling large workflows in distributed datacenters. We consider a realistic scheduling scenario of distributed cluster systems composed of multi-core computers, and a multi-objective formulation of the scheduling problem to minimize makespan, energy consumption and deadline violations. The studied schedulers follow a two-level schema: in the higher-level, we apply a multiobjective heuristic and a multiobjective metaheuristic, to distribute jobs between clusters; in the lower-level, specific backfilling-oriented scheduling methods are used for task scheduling locally within each cluster, considering precedence constraints. A new model for energy consumption in multi-core computers is applied. The experimental evaluation performed on a benchmark set of large workloads that model different realistic high performance computing applications demonstrates that the proposed multiobjective schedulers are able to improve both the makespan and energy consumption of the schedules when compared with a standard Optimistic Load Balancing Round Robin approach.

Energy consumption is one of the main concerns in mobile ad hoc networks (or MANETs). The lifetime of its devices highly depends on the energy consumption as they rely on batteries. The adaptive enhanced distance based broadcasting algorithm, AEDB, is a message dissemination protocol for MANETs that uses cross-layer technology to highly reduce the energy consumption of devices in the process, while still providing competitive performance in terms of coverage and time. We use two different multi-objective evolutionary algorithms to optimize the protocol on three network densities, and we evaluate the scalability of the best found AEDB configurations on larger networks and different densities.

Mobile ad hoc networks (or MANETs) are wireless networks that are spontaneously created by the neighbouring devices, without the use of any kind of infrastructure. These devices usually rely on batteries, so the lifetime of this type of network highly depends on the energy consumption of the devices composing it. Therefore, the optimisation of the energy consumption is a must in a realistic MANET. We deal in this paper with the optimisation of enhanced distance based (or EDB) broadcasting protocol for MANETs. EDB is an improved version of DB (distance based protocol), a state-of-the-art broadcasting protocol for MANETs, that is focussed on saving the energy used by the devices in the dissemination process (an essential component in MANETs), without degrading the network connectivity or the performance in the coverage of the broadcasting process. A set of parameters were identified in EDB and optimised using CellDE, a hybrid multi-objective optimisation algorithm, to maximise the coverage of the broadcast while minimising at the same time both the energy consumption and the broadcast time. The ns3 simulator was used to evaluate the different configurations of EDB. As a result, 100 different solutions are provided for every studied network, covering a wide range of design options; all of them have quality better than or similar to that provided by EDB with previously recommended values for its parameters.

Computational grids are an important emerging paradigm for large-scale distributed computing. As grid systems become more wide-spread, techniques for efficiently exploiting the large amount of grid computing resources become increasingly indispensable. A key aspect in order to benefit from these resources is the scheduling of jobs to grid resources. Due to the complex nature of grid systems, the design of efficient grid schedulers becomes challenging since such schedulers have to be able to optimize many conflicting criteria in very short periods of time. This problem has been tackled in the literature by several different metaheuristics, and our main focus in this work is to develop a new highly competitive technique with respect to the existing ones. For that, we exploit the capabilities of cellular memetic algorithms (cMAs), a kind of memetic algorithm with structured population, for obtaining efficient batch schedulers for grid systems, and the obtained results will be compared versus the state of the art. A careful design of the cMA methods and operators for the problem yielded to an efficient and robust implementation. Our experimental study, based on a known static benchmark for the problem, shows that this heuristic approach is able to deliver very high quality planning of jobs to grid nodes and thus it can be used to design efficient dynamic schedulers for real grid systems. Such dynamic schedulers can be obtained by running the cMA-based scheduler in batch mode for a very short time to schedule jobs arriving in the system since the last activation of the cMA scheduler.

Distributed parallel computing platforms contribute for a large part to some of the most powerful computers. Such architectures are typically based on accelerators (General Purpose computing on Graphics Processing Units, Many Integrated Cores e.g. Xeon Phi co-processors) and/or a large number of interconnected computing nodes. Obviously, they raise new challenges, typically in terms of scalability, robustness, adaptability and security. At the advent of the quest for Ultra scale Computing Systems, this paper addresses the issue of fault tolerance toward Byzantine failures over's such platforms. Indeed, the inherent unpredictable nature of these errors render their detection, not speaking of their correction, hard or even impossible to perform at large-scale. At this level, Algorithm-Based Fault Tolerance (ABFT) techniques where the fault tolerance scheme is tailored to the algorithm performed, seems the most promising approaches to deal with such failures. In this context, Evolutionary Algorithms (EAs), especially panmictic global parallel EAs, exhibit a remarkable resilience against byzantine failures modeled as cheating faults as demonstrated either empirically or theoretically in previous studies. In this paper, we extend this analysis to the case of distributed EAs based on the cellular model leading to distributed Cellular Evolutionary Algorithms (dCEAs). Our empirical study over a set or reference optimization problem confirm the ABFT nature of dCEAs. To our knowledge, this is the first study of dCEAs under the perspective of cheating issues and crash faults in a domain of distributed computations, thus opening new insights and perspectives for the design of competitive ultra-scale system based on evolutionary programming models.

Graphical abstractAEDB is an advanced adaptive protocol for information dissemination in mobile ad hoc networks MANETs. It is based on the Distance Based broadcasting protocol, and it acts differently according to local information to minimize the energy and network use, while maximizing the coverage of the broadcasting process. As most of the existing communication protocols, AEDB relies on different thresholds for adapting its behavior to the environment. We propose in this work to look for configurations that induce a stable performance of the protocol in different networks by automatically fine tuning these thresholds thanks to the use of cooperative coevolutionary multi-objective evolutionary algorithms. Finding robust solutions for this problem is important because MANETs have a highly unpredictable and dynamic topology, features that have a strong influence on the performance of the protocol. Consequently, robust solutions that show a good performance under any circumstances are required. In this work, we define different fitness functions that taken into account robust metrics for better guiding the algorithm towards more robust solutions. They are: median, constrained, worst coverage, and worst hypervolume. Results show, that the two worst-case approaches perform better, not only in case of robustness but also in terms of accuracy of the reported AEDB configurations on a large set of networks. Display Omitted The Adaptive Enhanced Distance Based Broadcasting Protocol, AEDB hereinafter, is an advanced adaptive protocol for information dissemination in mobile ad hoc networks (MANETs). It is based on the Distance Based broadcasting protocol, and it acts differently according to local information to minimize the energy and network use, while maximizing the coverage of the broadcasting process. As most of the existing communication protocols, AEDB relies on different thresholds for adapting its behavior to the environment. We propose in this work to look for configurations that induce a stable performance of the protocol in different networks by automatically fine tuning these thresholds thanks to the use of cooperative coevolutionary multi-objective evolutionary algorithms. Finding robust solutions for this problem is important because MANETs have a highly unpredictable and dynamic topology, features that have a strong influence on the performance of the protocol. Consequently, robust solutions that show a good performance under any circumstances are required. In this work, we define different fitness functions that measure robustness of solutions for better guiding the algorithm towards more robust solutions. They are: median, constrained, worst coverage, and worst hypervolume. Results show, that the two worst-case approaches perform better, not only in case of robustness but also in terms of accuracy of the reported AEDB configurations on a large set of networks.

International audience; This article presents a two-level strategy for scheduling large workloads of parallel applications in multicore distributed systems, taking into account the minimization of both the total computation time and the energy consumption of solutions. Nowadays, energy efficiency is of major concern when using large computing systems such as cluster, grid, and cloud computing facilities. In the approach proposed in this article, a combination of higher-level (i.e., between distributed systems) and lower-level (i.e., within each data-center) schedulers are studied for finding efficient mappings of workflows into the resources in order to maximize the quality of service, while reducing the energy required to compute them. The experimental evaluation demonstrates that accurate schedules are computed by using combined list scheduling heuristics (accounting for both problem objectives) in the higher level, and ad-hoc scheduling techniques to take advantage of multicore infrastructures in the lower level. Solutions are also evaluated with two user- and administrator-oriented metrics. Significant improvements are reported on the two problem objectives when compared with traditional load-balancing and round-robin techniques.

HighlightsHeterogeneous, energy-aware precedence constrained (DAG) scheduling problem.Three multi-objective algorithms schemas are adapted: MOCell, NSGAII and IBEA.A representation with corresponding mutations and grouping crossover operators.Experimentation on a diversified and large set of real and synthetic applications.MOCell schema is the most versatile and the best performing one. The ongoing increase of energy consumption by IT infrastructures forces data center managers to find innovative ways to improve energy efficiency. The latter is also a focal point for different branches of computer science due to its financial, ecological, political, and technical consequences. One of the answers is given by scheduling combined with dynamic voltage scaling technique to optimize the energy consumption. The way of reasoning is based on the link between current semiconductor technologies and energy state management of processors, where sacrificing the performance can save energy.This paper is devoted to investigate and solve the multi-objective precedence constrained application scheduling problem on a distributed computing system, and it has two main aims: the creation of general algorithms to solve the problem and the examination of the problem by means of the thorough analysis of the results returned by the algorithms.The first aim was achieved in two steps: adaptation of state-of-the-art multi-objective evolutionary algorithms by designing new operators and their validation in terms of performance and energy. The second aim was accomplished by performing an extensive number of algorithms executions on a large and diverse benchmark and the further analysis of performance among the proposed algorithms. Finally, the study proves the validity of the proposed method, points out the best-compared multi-objective algorithm schema, and the most important factors for the algorithms performance.

International audience; Advantages of bringing small-world properties in mobile ad hoc networks (MANETs) in terms of quality of service has been studied and outlined in the past years. In this work, we focus on the specific class of vehicular ad hoc networks (VANETs) and propose to un-partition such networks and improve their small-world properties. To this end, a subset of nodes, called injection points, is chosen to provide backend connectivity and compose a fully-connected overlay network. The optimisation problem we consider is to find the minimal set of injection points to constitute the overlay that will optimise the small-world properties of the resulting network, i.e., (1) maximising the clustering coefficient (CC) so that it approaches the CC of a corresponding regular graph and (2) minimising the difference between the average path length (APL) of the considered graph and the APL of corresponding random graphs. Two accurate evolutionary algorithms (namely, NSGAII and MOCHC) are used to find an upper-bound of high quality solutions to this new multi-objective optimisation problem, on realistic instances in the city-centre of Luxembourg. The obtained sets of solutions are then used to assess the performance of five novel heuristics proposed to solve the problem, i.e., two centralised and three decentralised. The results provided by these heuristics turned out to be extremely accurate with respect to the solutions found by the evolutionary algorithms.

Describes how evolutionary algorithms (EAs) can be used to identify, model, and minimize day-to-day problems that arise for researchers in optimization and mobile networking Mobile ad hoc networks (MANETs), vehicular networks (VANETs), sensor networks (SNs), and hybrid networks—each of these require a designer’s keen sense and knowledge of evolutionary algorithms in order to help with the common issues that plague professionals involved in optimization and mobile networking. This book introduces readers to both mobile ad hoc networks and evolutionary algorithms, presenting basic concepts as well as detailed descriptions of each. It demonstrates how metaheuristics and evolutionary algorithms (EAs) can be used to help provide low-cost operations in the optimization process—allowing designers to put some “intelligence” or sophistication into the design. It also offers efficient and accurate information on dissemination algorithms topology management, and mobility models to address challenges in the field. Evolutionary Algorithms for Mobile Ad Hoc Networks : Instructs on how to identify, model, and optimize solutions to problems that arise in daily research Presents complete and up-to-date surveys on topics like network and mobility simulators Provides sample problems along with solutions/descriptions used to solve each, with performance comparisons Covers current, relevant issues in mobile networks, like energy use, broadcasting performance, device mobility, and more Evolutionary Algorithms for Mobile Ad Hoc Networks is an ideal book for researchers and students involved in mobile networks, optimization, advanced search techniques, and multi-objective optimization.

One of the main challenges in cloud computing is to increase the availability of computational resources, while minimizing system power consumption and operational expenses. This article introduces a power efficient resource allocation algo- rithm for tasks in cloud computing data centers. The developed approach is based on genetic algorithms which ensure perfor- mance and scalability to millions of tasks. Resource allocation is performed taking into account computational and networking requirements of tasks and optimizes task completion time and data center power consumption. The evaluation results, obtained using a dedicated open source genetic multi-objective framework called jMetal show that the developed approach is able to perform the static allocation of a large number of independent tasks on homogeneous single-core servers within the same data center with a quadratic time complexity. Index Terms—Data center, Cloud Computing, Genetic Algo- rithm, Resource Allocation, Power Efficiency.

Due to the highly unpredictable topology of ad hoc networks, most of the existing communication protocols rely on different thresholds for adapting their behavior to the environment. Good performance is required under any circumstances. Therefore, finding the optimal configuration for those protocols and algorithms implemented in these networks is a complex task. We propose in this work to automatically fine tune the AEDB broadcasting protocol for MANETs thanks to the use of cooperative coevolutionary multi-objective evolutionary algorithms. AEDB is an advanced adaptive protocol based on the Distance Based broadcasting algorithm that acts differently according to local information to minimize the energy and network use, while maximizing the coverage of the broadcasting process. In this work, it will be fine tuned using multi-objective techniques in terms of the conflicting objectives: coverage, energy and network resources, subject to a broadcast time constraint. Because of the few parameters of AEDB, we defined new versions of the problem in which variables are discretized into bit-strings, making it more suitable for cooperative coevolutionary algorithms. Two versions of the proposed method are evaluated and compared versus the original NSGA-II, providing highly accurate tradeoff configurations in shorter execution times.

This paper studies a self-organized criticality model called sandpile for dynamically load-balancing tasks arriving in the form of Bag-of-Tasks in large-scale decentralized system. The sandpile is designed as a decentralized agent system characterizing a cellular automaton, which works in a critical state at the edge of chaos. Depending on the state of the cellular automaton, different responses may occur when a new task is assigned to a resource: it may change nothing or generate avalanches that reconfigure the state of the system. The abundance of such avalanches is in power-law relation with their sizes, a scale-invariant behavior that emerges without requiring tuning or control parameters. That means that large--catastrophic--avalanches are very rare but small ones occur very often. Such emergent pattern can be efficiently adapted for non-clairvoyant scheduling, where tasks are load balanced in computing resources trying to maximize the performance but without assuming any knowledge on the tasks features. The algorithm design is experimentally validated showing that the sandpile is able to find near-optimal schedules by reacting differently to different conditions of workloads and architectures.

International audience; Mobile ad hoc networks are infrastructure less communication networks that are spontaneously created by a number of mobile devices. Due to the highly fluctuating topology of such networks, finding the optimal configuration of communication protocols is a complex and crucial task. Additionally, different objectives must be usually considered. Small changes in the values of the parameters directly affects the performance of the protocol, promoting one objective while reducing another. Therefore, multi-objective optimisation is needed for fine tuning the protocol. In this work, we propose a novel parallel multi-objective local search that optimises an energy efficient broadcasting algorithm in terms of coverage, energy used, broadcasting time, and network resources. The proposed method looks for appropriate values for a set of 5 variables that markedly influence the behavior of the protocol to provide accurate tradeoff configurations in a reasonable short execution time. The new proposed algorithm is validated versus two efficient multi-objective evolutionary algorithms from the state of the art, offering comparable quality results in much shorter times.

International audience; Advantages of bringing small-world properties in mobile ad hoc networks (MANETs) in terms of quality of service has been studied and outlined in the past years. In this work, we focus on the specific class of vehicular ad hoc networks (VANETs) and propose to un-partition such networks and improve their small-world properties. To this end, a subset of nodes, called injection points, is chosen to provide backend connectivity and compose a fully-connected overlay network. The optimisation problem we consider is to find the minimal set of injection points to constitute the overlay that will optimise the small-world properties of the resulting network, i.e., (1) maximising the clustering coefficient (CC) so that it approaches the CC of a corresponding regular graph and (2) minimising the difference between the average path length (APL) of the considered graph and the APL of corresponding random graphs. In order to face this new multi-objective optimisation problem, the NSGAII algorithm was used on realistic instances in the city-centre of Luxembourg. The accurate tradeoff solutions found by NSGAII (assuming global knowledge of the network) will permit to better know and understand the problem. This will later ease the design of decentralised solutions to be used in real environments, as well as their future validation.

International audience; This article presents a new parallel hybrid evolutionary algorithm to solve the problem of virtual machines subletting in cloud systems. The problem deals with the efficient allocation of a set of virtual machine requests from customers into available pre-booked resources from a cloud broker, in order to maximize the broker profit. The proposed parallel algorithm uses a distributed subpopulations model, and a Simulated Annealing operator. The experimental evaluation analyzes the profit and make span results of the proposed methods over a set of problem instances that account for realistic workloads and scenarios using real data from cloud providers. A comparison with greedy heuristics indicates that the proposed method is able to compute solutions with up to 133.8% improvement in the profit values, while accounting for accurate make span results.

This paper proposes a decentralized and self-organized agent system for dynamically load-balancing tasks arriving in the form of Bags-of-Tasks (BoTs) in large-scale decentralized systems. the approach is inspired by the emergent behavior of the sand pile model, a cellular automaton behaving at the edge of chaos. Depending on the state of the cellular automaton, rather different responses may occur when a new task is assigned to a resource. It may change nothing or generate avalanches that reconfigure the state of the system. the proportion between the abundance of avalanches and their sizes shows a power-law relation, a scale-invariant behavior that does not need to be tuned. That means that large - catastrophic - avalanches are very rare but small ones occur very often. Such a smart and emergent behavior fits well with the idea of non-clairvoyant scheduling, where tasks are load balanced into computing resources trying to maximize the performance but without assuming any knowledge on the tasks features. in order to study the viability of the approach, we have conducted an empirical experimentation which shows that the sand pile is able to find near-optimal schedules by reacting differently to different conditions of workloads and architectures.

In this work, we focus on vehicular ad hoc networks, also called VANETs, which are communication networks that devices (in this case vehicles) use to exchange messages in a decentralized fashion, i.e., using no preexisting infrastructure. We first assess the feasibility of relying on a tree-based topology management structure for mobile ad hoc networks and in particular for VANETs. Next, we enhance DAGRS, an existing decentralized model for enabling distributed tree management and build BODYF on it, an efficient broadcast algorithm. Several broadcasting algorithms of the state of the art are implemented in order to compare the performance of BODYF. The approach is validated by simulation through three realistic scenarios located in Luxembourg city: the city center for both pedestrian and vehicles, and a highway environment. The comparison is made in terms of the coverage achieved by the broadcasting process as well as the complexity of the messages. DAGRS/BODYF approach outperforms other existing protocols in terms of both the number of devices reached and the network use.

In mobile ad hoc networks (or MANETs) devices usually rely on batteries, so the lifetime of these networks highly depends on the energy consumption of the devices composing them. In this paper, we improve the performance of the adaptive enhanced distance based broadcasting algorithm, AEDB, as well as we optimize it using a hybrid multi-objective algorithm, CellDE. The optimization is done by maximizing the coverage achieved by the broadcast process while minimizing at the same time both, the energy consumption and the broadcast time. CellDE provides a wide range of different solutions for the configuration of AEDB, outperforming all of them the performance of the protocol using the original configuration.

This chapter proposes a novel Grid-based scheduling algorithm that optimizes both computation and communications costs of workflow applications. Based on a hierarchical two-steps optimization process, a super scheduler first applies a Recursive Convex Clustering Algorithm (RCCA) that efficiently clusters tasks while minimizing communication costs. In the second step, a resource-broker assigns the generated convex sets to resources clusters. Local schedulers then optimize the makespan for the group of tasks assigned to their cluster, using a graphic processing unit (GPU)-based parallel cellular Genetic Algorithm(cGA). The performance improvement brought by this novel two-step scheduling algorithm compared to a hierarchical list-scheduling approach is empirically demonstrated on different real-world workflow applications.

The resolution of complex problems requires the use of advanced approximate algorithms and huge computational resources to find accurate solutions in reasonable time. Computing clusters, as well as GPGPUs, grids, and cloud computing infrastructures arise as appropriate innovative computing platforms to implement and execute such algorithms and find accurate solutions to complex problems arising in many application areas. This special issue contains extended versions of the best papers presented in HPCLATAM-2012 and SCCG-2012 conferences, as well as other novel contributions. In total, 27 papers were submitted to the special issue, from which only 10 were finally accepted for publication. These are outstanding contributions proposing novel designs of highly efficient parallel algorithms to solve complex problems, or dealing with salient problems in the fields of grid and cloud computing. In the first paper of this compilation, “Approximating Static List Schedules in Dynamic Multithreaded Applications”, Camargo et al. propose in their contribution the use of list algorithms to optimally schedule algorithms in a multi-thread environment. The difficulty of the approach deals with the fact that a dynamic environment is considered, and no assumptions are assumed to be known about the programs. A novel algorithm is presented to build a directed cyclic graph representing a multithread program from a data flow description, in form of a directed acyclic graph. Several

In modern parallel and distributed systems, inter-processor communications are a crucial factor of performance. The time for exchanging data is usually larger than that for computing elementary operations. Consequently, these communications slow down the execution of the application scheduled on the computing platform. Accounting for these communications is essential for attaining efficient hardware and software utilization. Moreover, energy dissipation due to the transfer of data between processing elements has become a major concern. Therefore, in this paper we develop an energy-aware static algorithm, which intrinsically optimizes the energy consumption due to the transfer of data in a distributed system. This is achieved by properly allocating and scheduling the tasks that constitute the applications on the processing elements, minimizing inter-processor communications. The proposed algorithm is a new Cellular Genetic Algorithm based on task clustering techniques. That is, the genetic operators work considering groups of tasks instead of applying them directly on the tasks. Simulation results showed that this algorithm is very compelling in terms of application completion time, inter-processor communication and energy communication dissipation.

We deal in this paper with the important problem of partitioning in ad hoc networks. In our approach, we assume that some devices might have other communication interfaces rather than Wi-Fi and/or Bluetooth allowing to connect remote devices (e.g., technologies such as GPRS or HSDPA). This would allow us to build hybrid networks for overcoming the network partitioning. Hence, the problem considered in this work is to establish remote links between devices (called bypass links) in order to maximize the QoS of the network by optimizing its properties to make it small world. Additionally, the number of this kind of links in the network should be minimized as well, since we consider that not all the devices have these communication capabilities, or it could be a requirement to minimize the use of the long range network (for example, in the case its use supposes some cost). We face the problem with four different GAs (both parallel and sequential) and compare their behaviors on six different network instances. All the algorithms were tested with a new encoding of the problem, which is demonstrated to provide more accurate results than the previously existing one.

Cellular genetic algoritms (cGAs) are characterized by its grid structure population, in which individuals can only interact with their neighbors. This kind of algorithms has demonstrated to have a high numerical performance thanks to the good exploration/exploitation balance they perform in the search space. Although cGAs seem very appropriate for parallelism, there is a low number of works proposing or studing parallel models for clusters of computers. This is probably because the model requires a high communication level between sub-populations due to the tight interactions among individuals. These parallel versions are however needed to cope with the high computational requirements of the current real-world problems. This article proposes a new parallel cellular genetic algorithm which maintains (or even improves because its asynchronicity) the numerical behaviour of a serial cGA, while at the same time it provokes an important reduction on the execution time for finding the optimal solution.

VANETs are ad hoc networks in which devices are vehicles moving at high speeds. This kind of network is getting more and more importance since it has many practical and important applications, like multimedia file sharing (e.g., maps, music, news, weather), or dissemination of alarm messages (e.g., accidents, traffic jams, bad road conditions). One important problem faced in ad hoc networks is network partitioning, causing the formation of isolated clusters, and preventing devices in different clusters from communicating. Usually, devices composing the ad hoc network are provided with other communication interfaces rather than Wi-Fi and/or Bluetooth that allow them to connect to remote devices, such as GPRS/HSDPA. Additionally, there exists some network infrastructure in cities or roads that could be used by VANETs (e.g. hotspots). By taking advantage of these technologies and infrastructures, devices could be able to form a hybrid network, establishing remote links between them (called bypass links) in order to improve the network connectivity by joining, for example, separate clusters. In this work, we face the problem of optimizing the number and location of these remote connections for maximizing the QoS of the network. We use an efficient genetic algorithm with structured population, called cellular genetic algorithm (cGA), to optimize this hard problem. The evaluation of the quality of the network connectivity is made using small world properties. Our goal is to find highly accurate solutions (that could be used as reference values for future works) and then analyze the influence of the quality of the solutions in the real behavior of the network. This is achieved by using the JANE simulator to disseminate a message in the network using two broadcasting protocols having different features.

Voice over IP VoIP is very fast growing technology for the delivery of voice communications and multimedia data over internet with lower cost. Early technical solutions mirrored the architecture of the legacy telephone network. Now, they have adopted the concept of distributed cloud VoIP. These solutions typically allow dynamic interconnection between users on any domains. However, providers face challenges to use infrastructure in the best efficient and cost-effective ways. Hence, efficient scheduling and load balancing algorithms are a fundamental part of this approach, especially in presence of the uncertainty of a very dynamic and unpredictable environment. In this paper, we formulate the problem of dynamic scheduling of VoIP services in distributed cloud environments and propose a model for bi-objective optimisation. We consider it as the special case of the bin packing problem, and discuss solutions for provider cost optimisation while ensuring quality of service.

In This work we study cellular evolutionary algorithms, a kind of decentralized heuristics, and the importance of the induced exploration/exploitation balance on different problems. It is shown that, by choosing synchronous or asynchronous update policies, the selection pressure, and thus the exploration/exploitation tradeoff, can be influenced directly, without using additional ad hoc parameters. Synchronous algorithms of different neighborhood-to-topology ratio, and asynchronous update policies are applied to a set of benchmark problems. Our conclusions show that the update methods of the asynchronous versions, as well as the ratio of the decentralized algorithm, have a marked influence on its convergence and on its accuracy.

Computational grids are an important emerging paradigm for large-scale distributed computing. As grid systems become more wide-spread, techniques for efficiently exploiting the large amount of grid computing resources become increasingly indispensable. A key aspect in order to benefit from these resources is the scheduling of jobs to grid resources. Due to the complex nature of grid systems, the design of efficient grid schedulers becomes challenging since such schedulers have to be able to optimize many conflicting criteria in very short periods of time. In this work we exploit the capabilities of cellular memetic algorithms (cMAs) for obtaining efficient batch schedulers for grid systems. A careful design of the cMA methods and operators for the problem yielded to an efficient and robust implementation. Our experimental study, based on a known static benchmark for the problem, shows that this heuristic approach is able to deliver very high quality planning of jobs to grid nodes and thus it can be used to design efficient dynamic schedulers for real grid systems. Such dynamic schedulers can be obtained by running the cMA-based scheduler in batch mode for a very short time to schedule jobs arriving to the system since the last activation of the cMA scheduler.

International audience; Mobile ad hoc networks (MANETs) are infrastructure-less communication networks spontaneously created by a number of mobile devices. Due to its highly fluctuating topology, finding the optimal configuration of communication protocols is a complex and crucial task. Additionally, different objectives must be usually considered. In our previous work, we introduced a novel parallel multi-objective local search to optimise the AEDB broadcasting protocol in terms of coverage, energy used, broadcasting time, and network resources. In this paper, we extend our previous work by studying three different versions of the problem, that are restricting the minimum coverage value for valid solutions, in order to accelerate the search and make it focus on the desired area of the search space. Additionally, the convergence speed of the algorithm was analysed, and different population initialisation techniques were compared. The new algorithm is validated vs. efficient state-of-the art multi-objective evolutionary techniques, offering high quality results in much shorter times, especially for high coverage solutions.