Integrated Task Caching, Computation Offloading and Resource Allocation for Mobile Edge Computing

Chen, Zhixiong; Jia, Yunjian

doi:10.1109/globecom38437.2019.9013927

Cited by 11 publications

(2 citation statements)

References 10 publications

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“…Specifically, in our previous work [14], [19], [20], we integrated the task program caching mechanism into the COMO technique and designed a model-based task program caching algorithm to minimize the average energy consumption or latency for all time slots. The authors in [21] investigated a single MEC server that assists a mobile user in executing a sequence of computation tasks and used the task program caching technique to reduce the computation delay and energy consumption of the mobile user.…”

Section: A Related Workmentioning

confidence: 99%

Dynamic Task Software Caching-Assisted Computation Offloading for Multi-Access Edge Computing

Chen

Alam

et al. 2022

IEEE Trans. Commun.

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In multi-access edge computing (MEC), most existing task software caching works focus on statically caching data at the network edge, which may hardly preserve high reusability due to the time-varying user requests in practice. To this end, this work considers dynamic task software caching at the MEC server to assist users' task execution. Specifically, we formulate a joint task software caching update (TSCU) and computation offloading (COMO) problem to minimize users' energy consumption while guaranteeing delay constraints, where the limited cache size and computation capability of the MEC server, as well as the time-varying task demand of users are investigated. This problem is proved to be non-deterministic polynomial-time hard, so we transform it into two sub-problems according to their temporal correlations, i.e., the real-time COMO problem and the Markov decision process-based TSCU problem. We first model the COMO problem as a multi-user game and propose a decentralized algorithm to address its Nash equilibrium solution. We then propose a double deep Q-network (DDQN)-based method to solve the TSCU policy. To reduce the computation complexity and convergence time, we provide a new design for the deep neural network (DNN) in DDQN, named state coding and action aggregation (SCAA). In SCAA-DNN, we introduce a dropout mechanism in the input layer to code users' activity states. Additionally, at the output layer, we devise a two-layer architecture to dynamically aggregate caching actions, which is able to solve the huge state-action space problem. Simulation results show that the proposed solution outperforms existing schemes, saving over 12% energy, and converges with fewer training episodes.

show abstract

Section: A Related Workmentioning

confidence: 99%

Dynamic Task Software Caching-Assisted Computation Offloading for Multi-Access Edge Computing

Chen

Alam

et al. 2022

IEEE Trans. Commun.

Self Cite

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show abstract

“…To improve the reusability of cached data, the task software caching technique was proposed to cache the task software at the MEC server to assist the COMO. Specifically, our previous work [14], [19], [20] integrated the task program caching mechanism into the COMO technique and designed a model-based task program caching algorithm to minimize the average energy consumption or latency for all time slots. The authors in [21] investigated a single MEC server that assists a mobile user in executing a sequence of computation tasks and used the task program caching technique to reduce the computation delay and energy consumption of the mobile user.…”

Section: A Related Workmentioning

confidence: 99%

Dynamic Task Software Caching-assisted Computation Offloading for Multi-Access Edge Computing

Chen¹,

Yi²,

Alam³

et al. 2022

Preprint

Self Cite

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In multi-access edge computing (MEC), most existing task software caching works focus on statically caching data at the network edge, which may hardly preserve high reusability due to the time-varying user requests in practice. To this end, this work considers dynamic task software caching at the MEC server to assist users' task execution. Specifically, we formulate a joint task software caching update (TSCU) and computation offloading (COMO) problem to minimize users' energy consumption while guaranteeing delay constraints, where the limited cache size and computation capability of the MEC server, as well as the time-varying task demand of users are investigated. This problem is proved to be non-deterministic polynomial-time hard, so we transform it into two sub-problems according to their temporal correlations, i.e., the real-time COMO problem and the Markov decision process-based TSCU problem. We first model the COMO problem as a multi-user game and propose a decentralized algorithm to address its Nash equilibrium solution. We then propose a double deep Q-network (DDQN)based method to solve the TSCU policy. To reduce the computation complexity and convergence time, we provide a new design for the deep neural network (DNN) in DDQN, named state coding and action aggregation (SCAA). In SCAA-DNN, we introduce a dropout mechanism in the input layer to code users' activity states. Additionally, at the output layer, we devise a two-layer architecture to dynamically aggregate caching actions, which is able to solve the huge state-action space problem. Simulation results show that the proposed solution outperforms existing schemes, saving over 12% energy, and converges with fewer training episodes.

show abstract

An Optimized Greedy-Based Task Offloading Method for Mobile Edge Computing

Zhou

Lin

Duan

et al. 2022

Lecture Notes in Computer Science

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Integrated Task Caching, Computation Offloading and Resource Allocation for Mobile Edge Computing

Cited by 11 publications

References 10 publications

Dynamic Task Software Caching-Assisted Computation Offloading for Multi-Access Edge Computing

Dynamic Task Software Caching-Assisted Computation Offloading for Multi-Access Edge Computing

Dynamic Task Software Caching-assisted Computation Offloading for Multi-Access Edge Computing

An Optimized Greedy-Based Task Offloading Method for Mobile Edge Computing

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