Algorithms for Energy Minimization in Cloud Service Allocation under Reliability Constraints

We consider allocation problems that arise in the context of service allocation in Clouds. More specifically, we assume on the one part that each computing resource is associated to a capacity constraint, that can be chosen using Dynamic Voltage and Frequency Scaling (DVFS) method, and to a probability of failure. On the other hand, we assume that the service runs as a set of independent instances of identical Virtual Machines. Moreover, there exists a Service Level Agreement (SLA) between the Cloud provider and the client that can be expressed as follows: the client comes with a minimal number of service instances which must be alive at the end of the day, and the Cloud provider offers a list of pairs (price, compensation), this compensation being paid by the Cloud provider if it fails to keep alive the required number of services. On the Cloud provider side, each pair corresponds actually to a guaranteed success probability of fulfilling the constraint on the minimal number of instances. In this context, given a minimal number of instances and a probability of success, the question for the Cloud provider is to find the number of necessary resources, their clock frequency and an allocation of the instances (possibly using replication) onto machines. This solution should satisfy all types of constraints during a given time period while minimizing the energy consumption of used resources. We consider two energy consumption models based on DVFS techniques, where the clock frequency of physical resources can be changed. For each allocation problem and each energy model, we prove deterministic approximation ratios on the consumed energy for algorithms that provide guaranteed probability failures, as well as an efficient heuristic, whose energy ratio is not guaranteed. Key-words: Cloud, reliability, approximation, energy savings ∗ Inria, University of Bordeaux ha l-0 07 88 96 4, v er si on 2 23 M ay 2 01 3 Algorithmes d’approximation sur la minimisation d’énergie pour l’allocation de services dans un Cloud sous contraintes de fiabilités Résumé : Nous considérons un problème d’allocation de services dans des Clouds. Les resources de calcul sont caractérisées par une probabilité de panne, et une contrainte de capacité, qui peut être ajustée grâce à la technique dite de Dynamic Voltage and Frequency Scaling (DVFS). Il existe un contrat entre le fournisseur et le client, le fournisseur assurant au client qu’un certain nombre d’instances du service du client sera toujours en train de s’exécuter à la fin de la journée, avec une certaine probabilité. La question est donc de savoir à quelle vitesse devront tourner les processeurs, et à quel point les services devront être répliqués sur les machines. Nous exhibons des algorithmes d’approximation, prouvons leurs facteurs d’approximation sur l’énergie consommée, et décrivons des heuristiques performantes. Mots-clés : Cloud, fiabilité, approximation, économie d’énergie ha l-0 07 88 96 4, v er si on 2 23 M ay 2 01 3 Energy Minimization in Cloud Service Allocation 3