QoS and load balancing aware task scheduling framework for mobile cloud computing environment

Abstract

Mobile Cloud Computing MCC is consolidation of cloud computing and mobile computing that resolves the problem of battery power, storage, mobility, context-awareness and resource scarcity in mobile devices. Remote execution of resource-intensive applications saves energy and enhances performance significantly. Thus, utilising resource-rich cloud infrastructure is inevitable for remote execution. The proposed QoS and Load Balancing Aware QALBA approach formulates task scheduling using Enriched-Look ahead HEFT algorithm E-LHEFT. It utilises MAUI Mobile Assistance Using Infrastructure architecture to execute the compute-intensive tasks. E-LHEFT algorithm modifies the processor selection phase of LHEFT algorithm using task grouping and uses the Pareto principle for effective load balancing of Physical Machine PM. The proposed scheduling strategy exploits mobile gaming application for experimental validations. The cloudsim results revealed that the proposed strategy saves the battery level of the mobile device and reduces makespan with less latency and achieves load balancing between cloud resources.