Application Loading and Computing Allocation for Collaborative Edge Computing

Abstract

The emergence of Mobile Edge Computing (MEC) provides a near-field tasking platform to meet the latency requirements of the growing number of compute-intensive mobile applications. However, physical memory constraints limit the number of application services that can be loaded on the edge server (ES) simultaneously, and the non-uniform distribution of traffic in the mobile network makes it difficult to fully utilize the resources in the edge network. In addition, for MEC platform operators, how to achieve the expected profit of application service providers (ASP) to attract more ASPs is also important. To address these issues, in this paper we propose an ASP profit-aware solution for jointly optimizing application loading, task allocation, and compute resource allocation across multi-ES, minimizing system latency while maintaining ASP profitability. We first formulate the problem as a long-term stochastic optimization problem with ASP profit constraints, transform it into a single time slot optimization problem using the Lyapunov optimization framework, and then, using the power of genetic algorithms (GA), we propose an online heuristic algorithm to obtain a near-optimal strategy for each time slot. Simulation results show that our algorithm is effective in reducing system latency in the long term, while demonstrating performance that ensures more ASPs with desired profits.

Keywords

• Edge computing
• application loading
• computing allocation
• service provider profit-aware
• load balance