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Abstract. Elasticity is one of the distinguishing characteristics associated with Cloud computing emergence. It enables cloud resources to auto-scale to cope with workload demand. Multi-instances horizontal scaling is the common scalability architecture in Cloud; however, its current implementation is coarse-grained, while it considers Virtual Machine (VM) as a scaling unit, this implies additional scaling-out overhead and limits it to specific applications. To overcome these limitations, we propose Elastic VM as a fine-grained vertical scaling architecture. Our results proved that Elastic VM architecture implies less consumption of resources, mitigates Service Level Objectives (SLOs) violation, and avoids scaling-up overhead. Furthermore, it scales broader range of applications including databases.

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Energy efficient resource management has become a significant concern in virtualized data centers to reduce operational costs and extend systems' lifetime. The opportunity of reducing energy can be achieved by using Dynamic Voltage Frequency Scaling (DVFS) and hosts consolidation.However, energy management of emerging High Performance Computing (HPC) clouds that host CPU-intensive jobs is more challenging. In this work, we present an optimization solution to assuage the trade-offs between energy and acceptance ratio of jobs. To achieve this, we consider the current multicore processor architecture, which supports DVFS scheme. Furthermore, we tailored an energy model for multicore processor based on the number of active cores, the average running frequency, and memory. A power-aware local VM scheduler is also implemented at the host level. Importantly, we show the importance of including static power in the energy model. Finally, we compared our approach with pure DVFS and DVFS with live migration. The results show that our approach outperforms the other approaches in terms of energy, SLA violation percentage, system utilization, and number of finished jobs. As a future work, we will implement this in a heterogeneous cluster and will consider the cost of turning off and on the hosts.