This paper considers computation offloading and service caching in a three-tier mobile cloud-edge computing structure, in which mobile Users (MUs) have subscribed to the cloud Service Center (CSC) for computation offl...
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This paper considers computation offloading and service caching in a three-tier mobile cloud-edge computing structure, in which mobile Users (MUs) have subscribed to the cloud Service Center (CSC) for computation offloading services and paid related fees monthly or yearly, and the CSC provides computation services to subscribed MUs and charges service fees. Long transmission distance and communication resource shortage caused by the increasing number of offloaded MUs may make the CSC unable to satisfy the delay requirements of MUs. Hence, the CSC can purchase some computation and communication resources from edge Servers (ESs) with limited caching capacities and computation resources to assist MUs in computation offloading. However, from the perspective of the CSC, it remains open to jointly optimize the strategies of computation offloading, service caching, and resource allocation to meet the delay requirements of MUs while reducing the cost of the CSC. Therefore, a novel Deep Reinforcement Learning-based Computation Offloading and Service Caching Mechanism, named DRLCOSCM is proposed to jointly optimize the offloading decision, service caching, and resource allocation strategies, so as to minimize the cost of the CSC while ensuring the delay requirements of MUs. In DRLCOSCM, the optimization problem is formulated as a Mixed Integer Non-Linear Programming (MINLP) problem, and an Asynchronous Advantage Actor-Critic-based (A3C-based) algorithm is proposed to solve the problem. The simulation results show that DRLCOSCM significantly outperforms the other baseline methods in different scenarios.
This article proposes a novel Reverse Auction-based Computation Offloading and Resource Allocation Mechanism, named RACORAM for the mobile cloud-edge computing. The basic idea is that the cloud Service Center (CSC) re...
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This article proposes a novel Reverse Auction-based Computation Offloading and Resource Allocation Mechanism, named RACORAM for the mobile cloud-edge computing. The basic idea is that the cloud Service Center (CSC) recruits edge server owners to replace it to accommodate offloaded computation from nearby resource-constraint mobile Devices (MDs). In RACORAM, the reverse auction is used to stimulate edge server owners to participate in the offloading process, and the reverse auction-based computation offloading and resource allocation problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) problem, aiming to minimize the cost of the CSC. The original problem is decomposed into an equivalent master problem and subproblem, and low-complexity algorithms are proposed to solve the related optimization problems. Specifically, a Constrained Gradient Descent Allocation Method (CGDAM) is first proposed to determine the computation resource allocation strategy, and then a Greedy Randomized Adaptive Search Procedure based Winning Bid Scheduling Method (GWBSM) is proposed to determine the computation offloading strategy. Meanwhile, the CSC's payment determination for the winning edge server owners is also presented. Simulations are conducted to evaluate the performance of RACORAM, and the results show that RACORAM is very close to the optimal method with significantly reduced computational complexity, and greatly outperforms the other baseline methods in terms of the CSC's cost under different scenarios.
This paper proposes a novel Reverse Auction-based Computation Offloading and Resource Allocation Mechanism, named RACORAM for the mobile cloud-edge computing. The basic idea is that the cloud Service Center (CSC) recr...
详细信息
ISBN:
(数字)9781665488792
ISBN:
(纸本)9781665488792
This paper proposes a novel Reverse Auction-based Computation Offloading and Resource Allocation Mechanism, named RACORAM for the mobile cloud-edge computing. The basic idea is that the cloud Service Center (CSC) recruits edge server owners to replace it to accommodate offloaded computation from nearby resource-constraint mobile Devices (MDs). In RACORAM. the reverse auction is used to stimulate edge server owners to participate in the offloading process, and the reverse auction-based computation offloading and resource allocation problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) problem, aiming to minimize the cost of the CSC. Specifically, a Greedy Randomized Adaptive Search Procedure based Winning Bid Scheduling Method (GWBSM) is proposed to determine the computation offloading strategy. Simulations are conducted to evaluate the performance of RACORAM, and the results show that RACORAM is very close to the optimal method with significantly reduced computational complexity, and greatly outperforms the other baseline methods in terms of the CSC's cost under different scenarios.
This paper jointly considers computation offloading, service caching and resource allocation optimization in a three-tier mobile cloud-edge computing structure, in which mobile Users (MUs) have subscribed to cloud Ser...
详细信息
ISBN:
(纸本)9781665409261
This paper jointly considers computation offloading, service caching and resource allocation optimization in a three-tier mobile cloud-edge computing structure, in which mobile Users (MUs) have subscribed to cloud Service Center (CSC) for computation offloading services and paid related fees monthly or yearly, and the CSC provides computation services to subscribed MUs and charges service fees. The problem is formulated as Mixed Integer Non-Linear Programming (MINLP), aiming to meet the delay requirements of MUs while reducing the cost of the CSC. Then, an Asynchronous Advantage Actor-Critic-based (A3C-based) method is proposed to solve the optimization problem. The simulation results show that the proposed A3C-based method significantly outperforms the other baseline methods in various scenarios.
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