Multi-Objective Hybrid Flower Pollination Resource Consolidation Scheme for Large Cloud Data Centres

Abstract

Cloud Computing has rapidly emerged as a successful paradigm for providing Information and Communication Technology (ICT) infrastructure. Resource allocation is used to execute user applications in the form of requests for consolidated resources in order to minimize energy consumption and violation of the Service Level Agreement (SLA) for large-scale data centers resource utilization. The energy consumption is usually caused due to local entrapment and violation of SLA during resource assigning and execution. Several researchers have proposed solutions to reduce local entrapments and violations of SLA, to minimize the energy consumption of the entire data center. However, strategies employed in their solutions face entrapment in either local searches or at the global search level with a certain level of SLA violation. In this light, a Multi-Objective Hybrid Flower Pollination Resource Consolidation (MOH-FPRC) scheme for efficient and optimal resource consolidation of data center resources is put forward. The Local Neighborhood Search (LNS) algorithm has been employed for addressing entrapment at the local search level, while the prominent flower pollination algorithm is used to solve the problem of entrapment at the global search level. This, in turn, reduces the energy consumption of the data centers. In addition, clustering strategies have been introduced with a robust migration mechanism to minimize the violation of SLA while also satisfying minimum energy consumption. The simulation results using the MultiRecCloudSim simulator have shown that our proposed MOH-FPRC demonstrates an improved performance on the data center energy consumption, resource utilization, and SLA violation with a 20.5% decrease, 23.9% increase, and 13.5% reduction, respectively, as compared with the benchmarked algorithms. The proposed scheme has proven its efficiency in minimizing energy consumption while at the same time improving the data center resource allocation with minimum SLA violations.

Energy Optimized Workflow Scheduling in IaaS Cloud: A Flower Pollination based Approach

“…Service quality and lifespan are prioritised to reduce DC energy usage and resource under utilization. Mohammed et al 18 32 to lessen SLAV and performance deterioration on the IaaS platform.…”

“…Similarly, hybrid algorithms use heuristic and metaheuristic methods, among others, to improve performance of task scheduling. Although each algorithm has its own strengths and weaknesses, metaheuristic approaches are generally more effective than heuristic-based techniques 18 . Recent studies in cloud computing have demonstrated that combining metaheuristic approaches can produce better results by addressing multiple objectives simultaneously 19 .…”

A Coverage Optimization Algorithm for the Wireless Sensor Network with Random Deployment by Using an Improved Flower Pollination Algorithm

“…The flower pollination algorithm (FPA) is a metaheuristic algorithm inspired by the pollination process of flowers [18], which has good convergence speed and efficiency. It is often used to solve complex and nonlinear optimization problems in real-life situations [19], including wind forecasting [20], resource management [21], and the WSN. Authors introduced a difference algorithm in the FPA, and the improved algorithm has significant competitiveness but does not balance well between local and global pollination methods [22].…”