Efficient HPC and Energy-Aware Proactive Dynamic VM Consolidation in Cloud Computing

Abstract

The adoption of High-Performance Computing (HPC) applications has gained an extensive interest in the Cloud computing. Current cloud vendors utilize separate management tools for HPC and non-HPC applications, missing out on the consolidation benefits of virtualization. Non-HPC applications executed in the cloud may interfere with resource-hungry HPC applications, which is a key performance challenge. Furthermore, correlations between application major performance indicators, such as response time and throughput, with resource capacities reveal that conventional placement strategies are impacting virtual machine efficiency, resulting in poor resource optimization, increased operating expenses, and longer wait times. Since applications often underutilized the hardware, smart execution of HPC and Non-HPC applications on the same node can boost system and energy efficiency. This research incorporates proactive dynamic VM consolidation to enhance the resource usage and performance while maintaining energy efficiency. The proposed algorithm generates a workload-aware fine-grained classification by employing machine learning techniques to generate complimentary profiles that alleviate cross-application interference by intelligently co-locating non-HPC and HPC applications. The research used CloudSim to simulate real HPC workloads. The results verified that the proposed algorithm outperforms all heuristic methods with respect to the metrics in key areas.

Equally Spread Current Execution Load Modelling with Optimize Response Time Brokerage Policy for Cloud Computing

“…Real HPC workloads were simulated for the study using CloudSim. The outcomes proved that, in terms of the metrics in important areas, the suggested algorithm beats all heuristic methods [26][27][28].…”

“…Proactive dynamic VM consolidation is used in this research to improve resource utilisation and performance without sacrificing the energy economy [26]. The suggested algorithm creates a fine-grained categorization that takes workload considerations into account by utilising machine learning techniques to create complementary profiles that reduce cross-application interference by strategically colocating HPC and non-HPC workloads.…”