Enhancing Mobile Edge Computing with Efficient Load Balancing Using Load Estimation in Ultra-Dense Network

Abstract

With the exponential growth of mobile devices and the emergence of computationally intensive and delay-sensitive tasks, the enormous demand for data and computing resources has become a big challenge. Fortunately, the combination of mobile edge computing (MEC) and ultra-dense network (UDN) is considered to be an effective way to solve these challenges. Due to the highly dynamic mobility of mobile devices and the randomness of the work requests, the load imbalance between MEC servers will affect the performance of the entire network. In this paper, the software defined network (SDN) is applied to the task allocation in the MEC scenario of UDN, which is based on routing of corresponding information between MEC servers. Secondly, a new load balancing algorithm based on load estimation by user load prediction is proposed to solve the NP-hard problem in task offloading. Furthermore, a genetic algorithm (GA) is used to prove the effectiveness and rapidity of the algorithm. At present, if the load balancing algorithm only depends on the actual load of each MEC, it usually leads to ping-pong effect. It is worth mentioning that our method can effectively reduce the impact of ping-pong effect. In addition, this paper also discusses the subtask offloading problem of divisible tasks and the corresponding solutions. At last, simulation results demonstrate the efficiency of our method in balancing load among MEC servers and its ability to optimize systematic stability.

Are Turn-by-Turn Navigation Systems of Regular Vehicles Ready for Edge-Assisted Autonomous Vehicles?

“…Other schedulers could easily be employed for testing. • Similar to related work [27] and [28], due to the data size of the result of the computation being very small, we assume that the time to send back the results to the AV is negligible. • As we do not have the speed limit of the roads in the dataset, we assume that the regular vehicles travel at the maximum speed allowed when the roads are free and at the traffic speed when there is traffic.…”

A hybrid GA-PSO strategy for computing task offloading towards MES scenarios

“…In mobile edge computing environment network , a task offloading problem whose optimization goal is MES load balancing has been proved to be an NP-hard problem ( Chen et al, 2021 ). B stands for small base station (SBSs).…”

“…These solutions can effectively reduce computing latency through cloud computing, thereby alleviating network congestion. This solution has become a hot topic due to its excellent delay performance and security characteristics ( Chen et al, 2021 ). Mobile devices with limited resources can obtain excellent performance ( Mahmud, Ramamohanarao & Buyya, 2018 ; Mao et al, 2017 ) and perform tasks efficiently by offloading computing tasks to nearby mobile edge servers.…”

Maximizing Heterogeneous Server Utilization with Limited Availability Times for Divisible Loads Scheduling on Networked Systems

“…To better adapt to different network topologies of networked systems, various task-scheduling models based on the DLT have been studied. For example, divisible-load scheduling models have been applied on the bus network [ 11 ], multi-level tree network [ 12 ], Gaussian, mesh, torus network [ 13 ], complete b-Ary tree network [ 14 ], Cloud platform [ 15 ], time-sensitive network [ 16 ], wireless sensor networks [ 17 ], Edge platform [ 18 ], Fog platform [ 19 ], and so on.…”