Distributed edge computation offloading makes use of distributed wireless edge devices to perform offloaded computation in parallel, which can substantially reduce the computation time. In this article, we explore dis...
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Distributed edge computation offloading makes use of distributed wireless edge devices to perform offloaded computation in parallel, which can substantially reduce the computation time. In this article, we explore distributed edge computation offloading where the computation workloads of edge devices are correlated with their communication workloads. In particular, we study the fundamental problem of computation workload allocation and communication scheduling for minimizing the total completion time of the computationoffloading. To solve this problem, we need to tackle several challenges due to the precedence constraints of computations and communications, the interference constraints of wireless edge devices, and the correlation between computation and communication workloads. We consider preemptive, half-preemptive, and non-preemptive networks for the formulated problem, respectively. For each setting, we first develop a simplified problem of computation allocation, based on which we then devise an efficient and feasible policy that can arbitrarily approach the optimal policy. For half-preemptive and non-preemptive networks, we also characterize the optimal communication orders. Our results provides useful insights for the computation-communication co-design of distributed edge computation offloading. We evaluate the proposed algorithms using simulation results, which corroborate the advantages of the algorithms.
edge computation offloading has made some progress in the fifth generation mobile network(5G).However,load balancing in edge computation offloading is still a challenging ***,with the continuous pursuit of low executi...
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edge computation offloading has made some progress in the fifth generation mobile network(5G).However,load balancing in edge computation offloading is still a challenging ***,with the continuous pursuit of low execution latency in 5G multi-scenario,the functional requirements of edge computation offloading are further *** the above challenges,we raise a unique edge computation offloading method in 5G multi-scenario,and consider user *** method consists of three functional parts:offloading strategy generation,offloading strategy update,and offloading strategy ***,the offloading strategy is generated by means of a deep neural network(DNN),then update the offloading strategy by updating the DNN ***,we optimize the offloading strategy based on changes in user *** summary,compared to existing optimization methods,our proposal can achieve performance close to the *** simulation results indicate the latency of the execution of our method on the CPU is under 0.1 seconds while improving the average computation rate by about 10%.
Nowadays, vehicle edgecomputation supports a novel computing resource provisioning roadmap for green smart cities, which benefits the distributed intelligent applications, such as unmanned vehicle. Despite the fact t...
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ISBN:
(纸本)9781538674628
Nowadays, vehicle edgecomputation supports a novel computing resource provisioning roadmap for green smart cities, which benefits the distributed intelligent applications, such as unmanned vehicle. Despite the fact that vehicle edgecomputation can better offload computing resource, there are still certain problems in implementing vehicle edgecomputation in green smart cities. To begin, the geographical imbalance in computing resource results in a time latency when computationoffloading. Second, the security of computing resource is an issue that cannot be disregarded. This is because the loss of some sensitive data in computing resource may result in repercussions that cannot be undone. To address aforementioned challenges, we present a collaborative-filtering privacy-preserving vehicular edge computation offloading approach (CVECO). By utilizing collaborative filtering, the CVECO algorithm is able to reduce the latency of the computationoffloading. Meanwhile, the CVECO algorithm is able to efficiently provide high security and protect computing resource privacy by applying multiple privacy mechanisms. Finally, the results of the simulation indicate that the CVECO algorithm is capable of lowering the latency associated with the computationoffloading while simultaneously preserving a high degree of safety regarding the computing resource. To the best of our knowledge, our proposed approach is capable of performing vehicle edge computation offloading well, which permits a rational use of electricity in green smart cities, further lowering greenhouse gas emissions.
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