Cloud Allocation and Consolidation Based on a Scalability Metric

In this paper, we present a new allocation and resource consolidation system based on a scalability metric. According to cloud computing principles, the end users rent computing and big data analytic services with a pay-as-you-go cost model. However, when users’ data size increases or when the application stresses the memory or requires more computing power, they need to scale their rental to achieve approximately the same performance, such as task completion time and normalized system throughput. In this paper, we propose to delegate the responsibility to scale-up and scale-out the cloud system to a new component of the cloud orchestrator. The decision is taken on a metric that quantifies the scalability of the cloud system consistently under different system expansion configurations. The scalability metric is defined as a ratio between the new and the current situation, over the size of the i-th workload in terms of CPU cores and a performance metric. The considered metrics are the waiting time in the queue and the average resource (cores) usage rate during task execution. To validate our approach, we conduct experiments by emulation, on a real platform. The experimental results demonstrate the validity of the proposed general automatic strategies for cloud allocation and consolidation.