Tractable Combinations of Global Constraints

We study the complexity of constraint satisfaction problems involving global constraints, i.e., special-purpose constraints provided by a solver and represented implicitly by a parametrised algorithm. Such constraints are widely used; indeed, they are one of the key reasons for the success of constraint programming in solving real-world problems. Previous work has focused on the development of efficient propagators for individual constraints. In this paper, we identify a new tractable class of constraint problems involving global constraints of unbounded arity. To do so, we combine structural restrictions with the observation that some important types of global constraint do not distinguish between large classes of equivalent solutions.Comment: To appear in proceedings of CP'13, LNCS 8124. arXiv admin note: text overlap with arXiv:1307.179

Virtual Machine Consolidation in Cloud Data Centers Using ACO Metaheuristic

In this paper, we propose the AVVMC VM consolidation scheme that focuses on balanced resource utilization of servers across different computing resources (CPU, memory, and network I/O) with the goal of minimizing power consumption and resource wastage. Since the VM consolidation problem is strictly NP-hard and computationally infeasible for large data centers, we propose adaptation and integration of the Ant Colony Optimization (ACO) metaheuristic with balanced usage of computing resources based on vector algebra. Our simulation results show that AVVMC outperforms existing methods and achieves improvement in both energy consumption and resource wastage reduction

An energy aware application controller for optimizing renewable energy consumption in Cloud computing data centres

Sustainable energy sources such as renewable energies are replacing dirty sources of energy in order to address the environmental challenges of the century. In order to operate data centres with renewable energies we have to mitigate their volatile and variable nature. In this paper, we present the Energy Adaptive Software Controller (EASC), a generic software controller and interface that developers can use to make their application adaptive to renewable energy availability. Adaptivity is realized through the concept of working modes which allow to run an application under various performance levels. We advocate for a collaborative approach involving the developers of the applications in order to use the renewable energies more efficiently. The notion of EASC allows to abstract away the details of application scheduling, execution, and monitoring. We demonstrate the applicability and genericity of the EASC concept through four different instantiations. These instantiations cover two types of applications: task-oriented and service-oriented, and two kind of computing environments: Infrastructure-as-a-Service, and Platform-as-a-Service. The EASC has been trialled in the data centre of the healthcare agency of Trento, Italy and in the laboratory of HP Milan, Italy, with a mix of energy sources: national grid and local solar panels. The experimental results show how the EASC allowed to increase the renewable energies usage of 14% and 4.73% for Trento and HP Labs trials, respectively

GeNePi: a multi-objective machine reassignment algorithm for data centres

Data centres are facilities with large amount of machines (i.e., servers) and hosted processes (e.g., virtual machines). Managers of data centres (e.g., operators, capital allocators, CRM) constantly try to optimise them, reassigning `better' machines to processes. These man- agers usually see better/good placements as a combination of distinct objectives, hence why in this paper we de ne the data centre optimisa- tion problem as a multi-objective machine reassignment problem. While classical solutions to address this either do not nd many solutions (e.g., GRASP), do not cover well the search space (e.g., PLS), or even can- not operate properly (e.g., NSGA-II lacks a good initial population), we propose GeNePi, a novel hybrid algorithm. We show that GeNePi out- performs all the other algorithms in terms of quantity of solutions (nearly 6 times more solutions on average than the second best algorithm) and quality (hypervolume of the Pareto frontier is 106% better on average)

Locality-aware Cooperation for VM Scheduling in Distributed Clouds

International audienceThe promotion of distributed cloud computing infrastructures as the next platform to deliver the Utility Computing paradigm, leads to new virtual machines (VMs) scheduling algorithms leveraging peer to peer approaches. Although these proposals considerably improve the scalability, leading to the management of hundreds of thousands of VM over thousands of physical machines (PMs), they do not consider the network overhead introduced by multi-site infrastructures. This overhead can have a dramatic impact on performance if there is no mechanism for favoring intra-site vs. inter-site manipulations. This paper introduces a new building block designed over a Vivaldi over- lay which maximizes efficient collaborations between PMs. We combined this mechanism with DVMS, a large scale virtual machine scheduler and showed its benefit by discussing several experiments performed on four distinct sites of the Grid'5000 testbed. Thanks to our proposal and with- out changing the scheduling decision algorithm, the number of inter-site operations has been reduced by 72%. This result provides a glimpse of the promising future of locality properties to improve performance of massive distributed cloud platforms