Online dynamic multi-user computation offloading and resource allocation for HAP-assisted MEC: an energy efficient approach

• Published in: Journal of cloud computing : advances, systems and applications Vol. 13; no. 1; pp. 92 - 12
• Main Authors: Chen, Sihan, Jiang, Wanchun
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms; Artificial intelligence; Cloud computing; Computation offloading; Computer Communication Networks; Computer Science; Computer System Implementation; Computer Systems Organization and Communication Networks; Edge computing; Energy consumption; Energy efficiency; HAP; Information Systems Applications (incl.Internet); MEC; Mobile computing; Mobile Edge Computing Meets AI; Online computation offloading; Optimization; Optimization techniques; Randomness; Resource allocation; Software Engineering/Programming and Operating Systems; Special Purpose and Application-Based Systems; Wireless communications; Online computation offloading; MEC; Energy efficiency; HAP; Resource allocation
• ISSN: 2192-113X; 2192-113X
• DOI: 10.1186/s13677-024-00645-5

Nowadays, the paradigm of mobile computing is evolving from a centralized cloud model towards Mobile Edge Computing (MEC). In regions without ground communication infrastructure, incorporating aerial edge computing nodes into network emerges as an efficient approach to deliver Artificial Intelligence (AI) services to Ground Devices (GDs). The computation offloading and resource allocation problem within a HAP-assisted MEC system is investigated in this paper. Our goal is to minimize the energy consumption. Considering the randomness and dynamism of the task arrival of GDs and the quality of wireless communication, stochastic optimization techniques are utilized to transform the long-term dynamic optimization problem into a deterministic optimization problem. Subsequently, the problem is further decomposed into three sub-problems which can be solved in parallel. An online Energy Efficient Dynamic Offloading (EEDO) algorithm is proposed to address these problems. Then, we conduct the theoretical performance analysis for EEDO. Finally, we carry out parameter analysis and comparative experiments, demonstrating that the EEDO algorithm can effectively reduce system energy consumption while maintaining the stability of the system.