Data-intensive application scheduling on Mobile Edge Cloud Computing

• Published in: Journal of network and computer applications Vol. 167; p. 102735
• Main Authors: Alkhalaileh, Mohammad, Calheiros, Rodrigo N., Nguyen, Quang Vinh, Javadi, Bahman
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-10-2020
• Subjects: Application scheduling; Data-intensive offloading; Mobile Edge Cloud; Mobile energy; Resource allocation; Application scheduling; Mobile energy; Mobile Edge Cloud; Data-intensive offloading; Resource allocation
• ISSN: 1084-8045; 1095-8592
• DOI: 10.1016/j.jnca.2020.102735

Mobile cloud computing helps to overcome the challenges of mobile computing by allowing mobile devices to migrate computation-intensive and data-intensive tasks to high-performance and scalable computation resources. However, emerging data-intensive applications pose challenges for mobile cloud computing platforms because of high latency, cost and data location issues. To address the challenges of data-intensive applications on mobile cloud platforms, we propose an application offloading optimisation model that schedules application tasks on an integrated computation environment named Mobile Edge Cloud Computing. The optimisation model is formulated as a mixed integer linear programming model, which considers both monetary cost and device energy as optimisation objectives. Moreover, the allocation process considers parameters related to data size and location, data communication costs, context information and network status. To evaluate the performance of the proposed offloading algorithm, we conducted real experiments on the implemented system with a variety of scenarios, such as different deadline and multi-user parameters. Our results demonstrate the ability of the proposed algorithm to generate an optimised resource allocation plan in response to dramatic fluctuations in application data size and network bandwidth. The proposed technique reduced the execution cost of data-intensive applications by an average of 46% and 76% in comparison with particle swarm optimisation (PSO) and full execution on a mobile device only, respectively. In addition, our new technique reduced mobile energy consumption by 35% and 84%, compared to PSO and full execution on a mobile device only, respectively.