Abstract
Edge computing has become a promising solution to overcome the user equipment (UE) constraints such as low computing capacity and limited energy. A key edge computing challenge is providing computing services with low service congestion and low latency, but the computing resources of edge servers were limited. User task randomness and network inhomogeneity brought considerable challenges to limited-resource MEC systems. To solve these problems, the presented paper proposed a blocking- and delay-aware schedule strategy for MEC environment service workflow offloading. First, the workflow was modeled in mobile applications and the buffer queue in servers. Then, the server collaboration area was divided through a collaboration area division method based on clustering. Finally, an improved particle swarm optimization scheduling method was utilized to solve this NP-hard problem. Many simulation results verified the effectiveness of the proposed scheme. This method was superior to existing methods, which effectively reduces the blocking probability and execution delay and ensures the quality of the experience of the user.
Highlights
In recent years, the rapid growth of mobile Internet and the proliferation of user devices [1, 2] has led to more new mobile applications such as face recognition, augmented reality [3], and mobile online games emerging. ese services require more computation resources due to intensive computation
Particle swarm optimization (PSO) is a reliable algorithm to obtain feasible solutions from a large search space by using the principle of evolution. e problem of minimizing blocking probability and execution delay is NP hard. e goal of the problem proposed in this paper is to find an optimal approximate solution
Efficient offloading of service workflow in mobile applications was an essential content in edge computing research
Summary
The rapid growth of mobile Internet and the proliferation of user devices [1, 2] has led to more new mobile applications such as face recognition, augmented reality [3], and mobile online games emerging. ese services require more computation resources due to intensive computation. The 5 G era with ultra-high bandwidth and ultra-low latency [4] means that cloud computing often fails to meet the strict delay-sensitive application requirements due to unpredictable network delays and expensive bandwidth [5] To remedy these limitations, the utilization of computing resources at the network edge has been proposed as a solution, and mobile edge computing has recently been utilized as a new computing paradigm. For the task offloading problem of multiuser shared resources in the mobile edge environment, a corresponding joint optimization problem optimization algorithm was developed to minimize the user task completion time [11]. E goal to find an optimal approximate solution must be obtained in a very short time To meet these challenges, a blocking- and delay-aware strategy for service workflow offloading in the MEC environment was proposed.
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