edgecomputing is a new computing method, and task scheduling is challenging work. Using edgecomputing in intelligent buildings for managing smart home de-vices has gained popularity because it can reduce the delay a...
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edgecomputing is a new computing method, and task scheduling is challenging work. Using edgecomputing in intelligent buildings for managing smart home de-vices has gained popularity because it can reduce the delay and network congestion brought by cloudcomputing. edgecomputing has the advantage of fast response speeds, but its computing capacity is limited. To solve this practical problem, a system framework of collaborative cloud and edge computing is constructed for intelligent buildings. First, the communication time, task completion time, and CPU energy consumption are considered comprehensively, and a mathematical model of the system is developed. Con-sidering the compute-intensity task, the splitting ra-tio is determined for tasks to achieve the collaboration of cloudcomputing and edgecomputing. Then, the search mechanism of a single gene mutation in the ge-netic algorithm (GA) is introduced to compensate for the defects of the salp swarm algorithm (SSA), while focusing on the search ability and optimization effi-ciency. Finally, the proposed strategy is theoretically analyzed and experimentally evaluated. The simula-tion results show that the hybrid algorithm of SSA-GA has better performance than other algorithms, and the proposed collaborative cloud and edge computing task scheduling strategy demonstrated a lower delay and makespan.
By performing data processing at the network edge, mobile edgecomputing can effectively overcome the deficiencies of network congestion and long latency in cloudcomputing systems. To improve edgecloud efficiency wi...
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By performing data processing at the network edge, mobile edgecomputing can effectively overcome the deficiencies of network congestion and long latency in cloudcomputing systems. To improve edgecloud efficiency with limited communication and computation capacities, we investigate the collaboration between cloudcomputing and edgecomputing, where the tasks of mobile devices can be partially processed at the edge node and at the cloud server. First, a joint communication and computation resource allocation problem is formulated to minimize the weighted-sum latency of all mobile devices. Then, the closed-form optimal task splitting strategy is derived as a function of the normalized backhaul communication capacity and the normalized cloud computation capacity. Some interesting and useful insights for the optimal task splitting strategy are also highlighted by analyzing four special scenarios. Based on this, we further transform the original joint communication and computation resource allocation problem into an equivalent convex optimization problem and obtain the closed-form computation resource allocation strategy by leveraging the convex optimization theory. Moreover, a necessary condition is also developed to judge whether a task should be processed at the corresponding edge node only, without offloading to the cloud server. Finally, simulation results confirm our theoretical analysis and demonstrate that the proposed collaborative cloud and edge computing scheme can evidently achieve a better delay performance than the conventional schemes.
This article establishes a three-tier mobile edgecomputing(MEC) network, which takes into account the cooperation between unmanned aerial vehicles(UAVs). In this MEC network, we aim to minimize the processing delay o...
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This article establishes a three-tier mobile edgecomputing(MEC) network, which takes into account the cooperation between unmanned aerial vehicles(UAVs). In this MEC network, we aim to minimize the processing delay of tasks by jointly optimizing the deployment of UAVs and offloading decisions,while meeting the computing capacity constraint of UAVs. However, the resulting optimization problem is nonconvex, which cannot be solved by general optimization tools in an effective and efficient way. To this end, we propose a two-layer optimization algorithm to tackle the non-convexity of the problem by capitalizing on alternating optimization. In the upper level algorithm, we rely on differential evolution(DE) learning algorithm to solve the deployment of the UAVs. In the lower level algorithm, we exploit distributed deep neural network(DDNN) to generate offloading decisions. Numerical results demonstrate that the two-layer optimization algorithm can effectively obtain the near-optimal deployment of UAVs and offloading strategy with low complexity.
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