Multi-Objective Virtual Machine Placement in Virtualized Data Center Environments
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
PMAX: tenant placement in multitenant databases for profit maximization
Proceedings of the 16th International Conference on Extending Database Technology
Defragmenting the cloud using demand-based resource allocation
Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems
IBM zEnterprise unified resource manager platform performance management
IBM Journal of Research and Development
Self-Adaptive Resource Allocation in Cloud Applications
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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We consider a self-managing, self-organizing pool of virtualized computer servers that provides infrastructure as a service (IaaS) for enterprise computing workloads. A global controller automatically manages the pool in a top down manner by periodically varying the number of servers used and re-assigning workloads to different servers. It aims to use as few servers as possible to minimize power usage while satisfying per-workload service level requirements. Each server is self-organizing. It has a local workload manager that dynamically varies the capacity allocated to each workload to satisfy per-workload service level objectives. This paper evaluates the impact of four alternative workload manager policies on the quality of service provided by the resource pool. The policies include: i) a non-work-conserving feedback controller, ii) a work-conserving feedback controller, iii) a work-conserving feedback controller with fixed per-workload scheduling weights to support differentiated service, and iv) a work-conserving feedback controller with dynamic per-workload weight to provide differentiated service while minimizing penalties. A case study involving three months of data for 138 SAP applications shows that the work-conserving policy significantly outperforms the non-work-conserving policy. The dynamic weight policy is better able to minimize penalties than the other policies while treating workloads fairly. Our study offers insights into the trade-offs between performance isolation, efficient resource sharing, and quality of service.