A Multi-start Local Search Scheduler for an Energy-aware Cloud Manager

International audienceThe field of cloud computing uses different management techniques for data center virtualization such as OpenNebula. However, computers composing the cloud infrastructure use a significant and growing portion of energy in the world specifically when dealing with virtualization for high performance computing (HPC). Therefore, energy-aware computing is crucial for large-scale systems that consume considerable amount of energy. In this paper, we present a new work that aims to deal with the energy consumption within a realistic cloud infrastructure using OpenNebula as a software management solution. Our scheduler is based on a multi-start local search heuristic that helps to find the best scheduling by dispatching the arriving of virtual machines (VM) according to the minimum energy consumption

A Pareto-based Genetic Algorithm for Optimized Assignment of VM Requests on a Cloud Brokering Environment

International audienceIn this paper, we deal with cloud brokering for the assignment optimization of VM requests in three-tier cloud infrastructures. We investigate the Pareto-based meta-heuristic approach to take into account multiple client and brokercentric optimization criteria. We propose a new multi-objective Genetic Algorithm ( MOGA-CB ) that can be integrated in a cloud broker. Two objectives are considered in the optimization process: minimizing both the response time and the cost of the selected VM instances to satisfy the clients and to maximize the profit of the broker. The approach has been experimented using realistic data of different types of Amazon EC2 instances and their pricing history. The reported results show that MOGA-CB provides efficiently effective Pareto sets of solutions

An Energy-aware Multi-start Local Search Heuristic for Scheduling VMs on the OpenNebula Cloud Distribution

International audienceReducing energy consumption is an increasingly important issue in cloud computing, more specifically when dealing with a cloud distribution dispatched over a huge number of machines. Minimizing energy consumption can significantly reduce the amount of energy bills, and the greenhouse gas emissions. Therefore, many researches are carried out to develop new methods in order to consume less energy. In this paper, we present an Energy-aware Multi-start Local Search algorithm for an OpenNebula based Cloud (EMLS-ONC) that optimizes the energy consumption of an OpenNebula managed geographically distributed cloud computing infrastructure. The results of our EMLS-ONC scheduler are compared to the results obtained by the default scheduler of OpenNebula. The two approaches have been experimented using different (VMs) arrival scenarios and different hardware infrastructures. The results show that EMLS-ONC outperforms the previous OpenNebula's scheduler by a significant margin in terms of energy consumption. In addition, EMLS-ONC is also proved to schedule more applications

A Pareto-based GA for Scheduling HPC Applications on Distributed Cloud Infrastructures

International audienceReducing energy consumption is an increasingly important issue in cloud computing, more specifically when dealing with High Performance Computing (HPC). Minimizing energy consumption can significantly reduce the amount of energy bills and then increases the provider's profit. In addition, the reduction of energy decreases greenhouse gas emissions. Therefore, many researches are carried out to develop new methods in order to consume less energy. In this paper, we present a multi-objective genetic algorithm (MO-GA) that optimizes the energy consumption, CO2 emissions and the generated profit of a geographically distributed cloud computing infrastructure. We also propose a greedy heuristic that aims to maximize the number of scheduled applications in order to compare it with the MO-GA. The two approaches have been experimented using realistic workload traces from Feitelson's PWA Parallel Workload Archive. The results show that MO-GA outperforms the greedy heuristic by a significant margin in terms of energy consumption and CO2 emissions. In addition, MO-GA is also proved to be slightly better in terms of profit while scheduling more applications

Optimisation multi-critère pour l'allocation de ressources sur clouds distribués avec prise en compte de l'énergie

International audienceReducing energy consumption is an increasingly important issue in cloud computing, more specifically when dealing with High Performance Computing (HPC). Minimizing energy consumption can significantly reduce the amount of energy bills and then increases the provider's profit. In addition, the reduction of energy decreases greenhouse gas emissions. Therefore, many researches are carried out to develop new methods in order to consume less energy. In this paper, we present a multi-objective genetic algorithm (MO-GA) that optimizes the energy consumption, CO2 emissions and the generated profit of a geographically distributed cloud computing infrastructure. We also propose a heuristic that aims to maximize the number of scheduled applications in order to compare it with the MO-GA. The two approaches have been experimented using realistic workload traces from Feitelson's Parallel Workload Archive (PWA). The results show that MO-GA outperforms the heuristic by a significant margin in terms of energy consumption and CO2 emissions. In addition, MO-GA is also proved to be slightly better in terms of profit while scheduling more applications. We also propose in the perspectives how to integrate our approach in the project StratusLab for the exploitation of the geographical dispersion offered by EGI

Parallel Evolutionary Algorithms for Energy Aware Scheduling

International audienceReducing energy consumption is an increasingly important issue in computing and embedded systems. In computing systems, minimizing energy consumption can significantly reduces the amount of energy bills. The demand for computing systems steadily increases and the cost of energy continues to rise. In embedded systems, reducing the use of energy allows to extend the autonomy of these systems. In addition, the reduction of energy decreases greenhouse gas emissions. Therefore, many researches are carried out to develop new methods in order to consume less energy. This chapter gives an overview of the main methods used to reduce the energy consumption in computing and embedded systems. As a use case and to give an example of a method, the chapter describes our new parallel bi-objective hybrid genetic algorithm that takes into account the completion time and the energy consumption. In terms of energy consumption, the obtained results show that our approach outperforms previous scheduling methods by a significant margin. In terms of completion time, the obtained schedules are also shorter than those of other algorithms

An Energy-aware Multi-start Local Search Heuristic for Scheduling VMs on the OpenNebula Cloud Distribution

International audienceReducing energy consumption is an increasingly important issue in cloud computing, more specifically when dealing with a cloud distribution dispatched over a huge number of machines. Minimizing energy consumption can significantly reduce the amount of energy bills, and the greenhouse gas emissions. Therefore, many researches are carried out to develop new methods in order to consume less energy. In this paper, we present an Energy-aware Multi-start Local Search algorithm for an OpenNebula based Cloud (EMLS-ONC) that optimizes the energy consumption of an OpenNebula managed geographically distributed cloud computing infrastructure. The results of our EMLS-ONC scheduler are compared to the results obtained by the default scheduler of OpenNebula. The two approaches have been experimented using different (VMs) arrival scenarios and different hardware infrastructures. The results show that EMLS-ONC outperforms the previous OpenNebula's scheduler by a significant margin in terms of energy consumption. In addition, EMLS-ONC is also proved to schedule more applications

A Pareto-based Metaheuristic for Scheduling HPC Applications on a Geographically Distributed Cloud Federation

International audienceReducing energy consumption is an increasingly important issue in cloud computing, more specif- ically when dealing with High Performance Comput- ing (HPC). Minimizing energy consumption can signif- icantly reduce the amount of energy bills and then in- crease the provider's profit. In addition, the reduction of energy decreases greenhouse gas emissions. Therefore, many researches are carried out to develop new methods in order to make HPC applications consuming less energy. In this paper, we present a multi-objective genetic algorithm (MO-GA) that optimizes the energy consumption, CO2 emissions and the generated profit of a geographically distributed cloud computing infrastructure. We also propose a greedy heuristic that aims to maximize the number of scheduled applications in order to compare it with the MO-GA. The two approaches have been experimented using realistic workload traces from Feitelson's PWA Parallel Workload Archive. The results show that MO-GA outperforms the greedy heuristic by a significant margin in terms of energy consumption and CO2 emissions. In addition, MO-GA is also proved to be slightly better in terms of profit while scheduling more applications

Target to Source Coordinate-wise Adaptation of Pre-trained Models

International audienceDomain adaptation aims to alleviate the gap between source and target data drawn from different distributions. Most of the related works seek either for a latent space where source and target data share the same distribution, or for a transformation of the source distribution to match the target one. In this paper, we introduce an original scenario where the former trained source model is directly reused on target data, requiring only finding a transformation from the target domain to the source domain. As a first approach to tackle this problem, we propose a greedy coordinate-wise transformation leveraging on optimal transport. Beyond being fully independent of the model initially learned on the source data, the achieved transformation has the following three assets: scalability, interpretability and feature-type free (continuous and/or categorical). Our procedure is numerically evaluated on various real datasets, including domain adaptation benchmarks and also a challenging fraud detection dataset with very imbalanced classes. Interestingly, we observe that transforming a small subset of the target features leads to accuracies competitive with "classical" domain adaptation methods