BQProfit: Budget and QoS aware task offloading and resource allocation for Profit maximization under MEC platform

Abstract

In the era of Multi-access Edge Computing (MEC), efficient partitioning of applications and, thereafter, allocation of edge resources to the tasks of the applications is crucial for maximizing the profit of the service provider. Although a few studies have been in recent times, they overlooked many essential parameters such as the dependency on tasks, deadline and demand-based dynamic charging plan, cooperation among edge servers, etc. This paper proposes a novel strategy named BQProfit that has two essential components: a Modified Kernighan–Lin (MKL) task offloading approach and a metaheuristic-based genetic algorithm known as Cost Optimized Resource Allocation (CORA). To evaluate the effectiveness of the proposed strategy, we perform an extensive simulation using synthetic and scientific workflow data sets and compare the result with a baseline policy and two state-of-the-art policies. On average, BQProfit increases the service provider’s profit by 4.1x compared to the benchmark policies and by 1.5x compared to the best-performing benchmark policy. Our strategy also outperforms these policies by reducing task failure rates by 34.06% on average, and 25% compared to the best-performing benchmark policy. Additionally, BQProfit shows an average improvement of 70.83% over the state-of-the-art and 44% over the top-performing benchmark policy.