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To address the problems of high energy consumption and time delay of the offloading strategies in traditional edge computing, a computation offloading strategy for the Internet of Things (IoT) using the improved Particle Swarm Optimization (PSO) in edge computing is proposed. First, a system model and an optimization objective function are construc...
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Citations
... Context-aware task allocation [52] DFD DAG [69] GT IoT, 5G [78] LPA MEC Energy-efficient task allocation [68] MAPE-K IoT [11] Lyapunov MEC [19] Bi-Level MEC [25] DRL IoV [30] QT 5G [43] PSO IoT [47] ILP MEC [71] JTORA MEC [73] hybrid RF-FSO Industrial IoT Dynamic task allocation [13] MPSO VVECNs, VANETs [79] MH IoV [20] AA Fog [23] DP MEC [41] DRL IoT [44] ECTA EC [46] AA Fog [49] ILP [56] GA, GEN IoT [60] PSO EC [64] BPSO Table 1. Cont. ...
... Previous studies like [43] suggested a creative approach to offload computations in IoT. The goal of this study was to reduce both the energy consumption and time delays commonly associated with standard edge computing offloading strategies. ...
Task allocation in edge computing refers to the process of distributing tasks among the various nodes in an edge computing network. The main challenges in task allocation include determining the optimal location for each task based on the requirements such as processing power, storage, and network bandwidth, and adapting to the dynamic nature of the network. Different approaches for task allocation include centralized, decentralized, hybrid, and machine learning algorithms. Each approach has its strengths and weaknesses and the choice of approach will depend on the specific requirements of the application. In more detail, the selection of the most optimal task allocation methods depends on the edge computing architecture and configuration type, like mobile edge computing (MEC), cloud-edge, fog computing, peer-to-peer edge computing, etc. Thus, task allocation in edge computing is a complex, diverse, and challenging problem that requires a balance of trade-offs between multiple conflicting objectives such as energy efficiency, data privacy, security, latency, and quality of service (QoS). Recently, an increased number of research studies have emerged regarding the performance evaluation and optimization of task allocation on edge devices. While several survey articles have described the current state-of-the-art task allocation methods, this work focuses on comparing and contrasting different task allocation methods, optimization algorithms, as well as the network types that are most frequently used in edge computing systems.
... To solve this issue, the work in [26] proposed a distributed asynchronous PSO algorithm based on Message Passing Interface (MPI), but did not take into account fault tolerance and scalability. PSO has been investigated for edge optimization concerns [11], [29]. ...
The wide adoption of edge computing has introduced several issues such as load balancing, resource provision-ing, and workload placement as optimization problems. Particle swarm optimization (PSO) is a nature-inspired stochastic optimization algorithm, whose objective is to iteratively improve the solution of a problem over a given objective. The distribution of PSO to the edge would result in the transfer of resource-intensive computational tasks from the cloud to the edge, leading to more efficient use of existing resources. However, it introduces challenges related to performance and fault tolerance , due to the resource-constrained edge environment with a high probability of faults. We introduce multiple distributed synchronous variants of the PSO algorithm built on top of the Apache Spark distributed computing framework and Kubernetes container orchestration platform. These variants of the algorithm aim at addressing the performance and fault tolerance problems introduced by the execution in an edge network. A PSO algorithm that distributes the load across multiple executor nodes can effectively realize coarse-grained parallelism, thus can obtain a significant increase in performance, but also more fault tolerance and scalability.
... PSO has been investigated for edge optimization concerns, but largely in synchronous forms [9], [25]. In edge computing, a PSO-based solution is presented in [16], which uses a Binary Multi-Objective PSO (BMOPSO) algorithm with a matrix-based encoding to solve the workload placement problem. ...
Edge computing poses a range of optimization problems such as load balancing, resource provisioning, and workload placement. Particle swarm optimization (PSO) is a bio-inspired stochastic optimization algorithm, with the objective to iteratively improve the solution of a problem for a given objective. The distribution of PSO workloads to the edge would transfer resource-intensive computational tasks from central large cloud data centers to the edge, resulting in more efficient use of existing resources there. However, this edge architecture introduces performance and fault tolerance challenges, due to the resource-constrained edge environment with a high probability of faults. We present here an asynchronous variant of an edge-distributed PSO algorithm built on top of the Apache Spark distributed computing framework [2]. This PSO variant aims at solving performance problems introduced by the execution in an edge setting. Our asynchronous PSO algorithm that distributes the load across multiple executor nodes can effectively realize both coarse-and fine-grained parallelism, allowing us to obtain a substantial performance increase.
