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Ventresque, Anthony
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Ventresque, Anthony
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Ventresque, Anthony
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Now showing 1 - 6 of 6
- PublicationSOC: Satisfaction-Oriented Virtual Machine Consolidation in Enterprise Data CentersServer sprawl is a problem faced by data centers, which causes unnecessary waste of hardware resources, collateral costs of space, power and cooling systems, and administration. This is usually combated by virtualization based consolidation, and both industry and academia have put many efforts into solving the underlying virtual machine (VM) placement problem. However, IT managers’ preferences are seldom considered when making VM placement decisions. This paper proposes a satisfaction-oriented VM consolidation mechanism (SOC) to plan VM consolidation while taking IT managers’ preferences into consideration. In the mechanism, we propose: (1) an XML-based description language to express managers’ preferences and metrics to evaluate the satisfaction degree; (2) to apply matchmaking to locate entities [i.e., VMs and physical machines (PMs)] that best match each other’s preferences; (3) to employ the VM placement algorithm proposed in our previous work to minimize the number of hosts required and the resource wastage on allocated hosts. SOC is compared with two baselines: placement-only and matchmaking-only. The simulation results show that most of the VM-to-PM mappings output from placement-only violate given preferences, while SOC has a satisfaction degree close to matchmaking-only, without requiring too many PMs as matchmaking-only does, but only an amount close to placement-only. In brief, SOC is effective in minimizing the number of hosts required to support a certain set of VMs, while maximizing the satisfaction degree of both managers from the provider and requester side.
553Scopus© Citations 8 - PublicationVM reassignment in hybrid clouds for large decentralised companies: A multi-objective challengeOptimising the data centres of large IT organisations is complex as (i) they are composed of various hosting departments with their own preferences and (ii) reassignment solutions can be evaluated from various independent dimensions. But in reality, the problem is even more challenging as companies can now choose from a pool of cloud services to host some of their workloads. This hybrid search space seems intractable, as each workload placement decision (seen as running in a virtual machine on a server) is required to answer many questions: can we host it internally? In which hosting department? Are the capital allocators of this hosting department ok with this placement? How much does it save us and is it safe? Is there a better option in the Cloud? Etc. In this paper, we define the multi-objective VM reassignment problem for hybrid and decentralised data centres. We also propose H2¿D2, a solution that uses a multi-layer architecture and a metaheuristic algorithm to suggest reassignment solutions that are evaluated by the various hosting departments (according to their preferences). We compare H2¿D2 against state-of-the-art multi-objective algorithms and find that H2¿D2 outperforms them both in terms of quantity (approx 30% more than the second-best algorithm on average) and quality of solutions (19% better than the second-best on average).
555Scopus© Citations 31 - PublicationIs seeding a good strategy in multi-objective feature selection when feature models evolve?Context: When software architects or engineers are given a list of all the features and their interactions (i.e., a Feature Model or FM) together with stakeholders 'preferences' their task is to find a set of potential products to suggest the decision makers. Software Product Lines Engineering (SPLE) consists in optimising those large and highly constrained search spaces according to multiple objectives reflecting the preference of the different stakeholders. SPLE is known to be extremely skill- and labour-intensive and it has been a popular topic of research in the past years.Objective: This paper presents the first thorough description and evaluation of the related problem of evolving software product lines. While change and evolution of software systems is the common case in the industry, to the best of our knowledge this element has been overlooked in the literature. In particular, we evaluate whether seeding previous solutions to genetic algorithms (that work well on the general problem) would help them to find better/faster solutions.Method: We describe in this paper a benchmark of large scale evolving FMs, consisting of 5 popular FMs and their evolutions – synthetically generated following an experimental study of FM evolution. We then study the performance of a state-of-the-art algorithm for multi-objective FM selection (SATIBEA) when seeded with former solutions.Results: Our experiments show that we can improve both the execution time and the quality of SATIBEA by feeding it with previous configurations. In particular, SATIBEA with seeds proves to converge an order of magnitude faster than SATIBEA alone.Conclusion: We show in this paper that evolution of FMs is not a trivial task and that seeding previous solutions can be used as a first step in the optimisation - unless the difference between former and current FMs is high, where seeding has a limited impact.
440Scopus© Citations 12 - PublicationSelf-Balancing Decentralized Distributed Platform for Urban Traffic SimulationMicroscopic traffic simulation is the most accurate tool for predictive analytics in urban environments. However, the amount of workload (i.e., cars simulated simultaneously) can be challenging for classical systems, particularly for scenarios requiring faster than real-time processing (e.g., for emergency units having to make quick decisions on traffic management). This challenge can be tackled with distributed simulations by sharing the load between simulation engines running on different computing nodes, hence balancing the processing power required. This paper studies the performance of dSUMO, i.e., a distributed microscopic traffic simulator. dSUMO is fully decentralized and can dynamically balance the workload between its computing nodes, hence showing important improvements against classical, centralized and not dynamic, solutions.
704Scopus© Citations 14 - PublicationA comparative study of multi-objective machine reassignment algorithms for data centres(Springer, 2019-09-20)
; ; ; ; At a high level, data centres are large IT facilities hosting physical machines (servers) that often run a large number of virtual machines (VMs)— but at a lower level, data centres are an intricate collection of interconnected and virtualised computers, connected services, complex service-level agreements. While data centre managers know that reassigning VMs to the servers that would best serve them and also minimise some cost for the company can potentially save a lot of money—the search space is large and constrained, and the decision complicated as they involve different dimensions. This paper consists of a comparative study of heuristics and exact algorithms for the Multi-objective Machine Reassignment problem. Given the common intuition that the problem is too complicated for exact resolutions, all previous works have focused on various (meta)heuristics such as First-Fit, GRASP, NSGA-II or PLS. In this paper, we show that the state-of-art solution to the single objective formulation of the problem (CBLNS) and the classical multi-objective solutions fail to bridge the gap between the number, quality and variety of solutions. Hybrid metaheuristics, on the other hand, have proven to be more effective and efficient to address the problem – but as there has never been any study of an exact resolution, it was difficult to qualify their results. In this paper, we present the most relevant techniques used to address the problem, and we compare them to an exact resolution ( -Constraints). We show that the problem is indeed large and constrained (we ran our algorithm for 30 days on a powerful node of a supercomputer and did not get the final solution for most instances of our problem) but that a metaheuristic (GeNePi) obtains acceptable results: more (+188%) solutions than the exact resolution and a little more than half (52%) the hypervolume (measure of quality of the solution set).488Scopus© Citations 6 - PublicationExact and Hybrid Solutions for the Multi-objective VM Reassignment Problem(World Scientific Publishing, 2017-02-23)
; ; ; Machine Reassignment is a challenging problem for constraint programming (CP) and mixed integer linear programming (MILP) approaches, especially given the size of data centres. Hybrid solutions mixing CP and heuristic algorithms, such as, large neighbourhood search (CBLNS), also struggle to address the problem given its size and number of constraints. The multi-objective version of the Machine Reassignment Problem is even more challenging and it seems unlikely for CP, MILP or hybrid solutions to obtain good results in this context. As a result, the first approaches to address this problem have been based on other optimisation methods, including metaheuristics. In this paper we study three things: (i) under which conditions a mixed integer optimisation solver, such as IBM ILOG CPLEX, can be used for the Multi-objective Machine Reassignment Problem; (ii) how much of the search space can a well-known hybrid method such as CBLNS explore; and (iii) can we find a better hybrid approach combining MILP or CBLNS and another recent metaheuristic proposed for the problem (GeNePi). We show that MILP can handle only small or medium scale data centres, and with some relaxations, such as, an optimality tolerance gap and a limited number of directions explored in the search space. CBLNS on the other hand struggles with the problem in general but achieves reasonable performance for large instances of the problem. However, we show that our hybridisation improves both the quality of the set of solutions (CPLEX+GeNePi and CBLNS+GeNePi improve the solutions by +17.8% against CPLEX alone and +615% against CBLNS alone) and number of solutions (8.9 times more solutions than CPLEX alone and 56.76 times more solutions than CBLNS alone), while the processing time of CPLEX+GeNePi and CBLNS+GeNePi increases only by 6% and 16.4% respectively. Overall, the study shows that CPLEX+GeNePi is the best algorithm for small instances (CBLNS+GeNePi only gets 45.2% of CPLEX+GeNePi’s hypervolume) while CBLNS+GeNePi is better than the others on large instances (that CPLEX+GeNePi cannot address).583Scopus© Citations 13