A Novel Deep Reinforcement Learning based service migration model for Mobile Edge Computing

Park, Sung Woon; Boukerche, Azzedine; Guan, Shichao

doi:10.1109/ds-rt50469.2020.9213536

Cited by 22 publications

(12 citation statements)

References 25 publications

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“…Rui et al [15] propose a novel service migration method based on state adaptation and deep reinforcement learning to overcome network failures, and they use the satisfiability modulo theory to solve the candidate space of migration policies. Liu et al [16,17] design a reinforcement learning-based framework by using a deep Q-network for a single user service migration system, which was realized to choose the optimal migration strategy in edge computing. Yuan et al [18] study the service migration and mobility optimization problem by proposing a two-branch convolution-based deep Q-network to maximize the composite utility.…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing

Shi

et al. 2022

Network

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Mobile edge computing is an emerging paradigm that supplies computation, storage, and networking resources between end devices and traditional cloud data centers. With increased investment of resources, users demand a higher quality-of-service (QoS). However, it is nontrivial to maintain service performance under the erratic activities of end-users. In this paper, we focus on the service placement problem under the continuous provisioning scenario in mobile edge computing for multiple mobile users. We propose a novel dynamic placement framework based on deep reinforcement learning (DSP-DRL) to optimize the total delay without overwhelming the constraints on physical resources and operational costs. In the learning framework, we propose a new migration conflicting resolution mechanism to avoid the invalid state in the decision module. We first formulate the service placement under the migration confliction into a mixed-integer linear programming (MILP) problem. Then, we propose a new migration conflict resolution mechanism to avoid the invalid state and approximate the policy in the decision modular according to the introduced migration feasibility factor. Extensive evaluations demonstrate that the proposed dynamic service placement framework outperforms baselines in terms of efficiency and overall latency.

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Section: Related Workmentioning

confidence: 99%

A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing

Shi

et al. 2022

Network

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“…In addition, some researchers have proposed novel service migration algorithm and architecture to support mobility tasks based on reinforcement learning, which can efficiently reduce the extra delay and energy cost of the migration process [35]. The literature [36] modeled the service migration problem as a complex optimization and implemented deep reinforcement learning to approximate the optimal policy.…”

Section: Service Migration In Edge Computing (Ec)mentioning

confidence: 99%

“…To show the superiority of the proposed DCOS algorithm in terms of delay and energy metrics, we compare it with two baseline schemes, i.e., ❼ Deep Deterministic P olicy Gradient (DDPG) [43]: The scheduling of tasks is based on the task offloading strategy only, and the DQN network outputs the optimal target node for offloading. ❼ E xtensive S ervice M igration Model (ESM ) [36]: The task is processed on the local node. If the node has not configured the related service, the system model performs the service migration, according to the optimal policy related to the migration costs.…”

Section: Experimental Settingsmentioning

confidence: 99%

Dynamic Computation Offloading with Deep Reinforcement Learning in Edge Networks

Bai

et al. 2022

Preprint

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With the booming proliferation of user requests in the Internet of Things (IoT) network, Edge Computing (EC) is emerging as a promising paradigm to provide flexible and reliable services. Considering the resource constraints of IoT devices, for some delay-aware user requests, a heavy workload IoT device may not respond on time. EC spurs a popular wave of offloading user requests to edge servers at the edge of the network. The orchestration for user-requested offloading schemes is a remarkable challenge for the execution time and energy consumption for IoT devices in edge networks. To address this challenge, we propose a dynamic computation offloading strategy consisting of the following two parts: (i) we propose the concept of intermediate nodes, which can minimize the time and energy cost to process the current task by dynamically combining task offloading and service migration strategies; and (ii) based in the current network’s workload state, we model such intermediate node selection problem as multiple dimensional Markov Decision Process (MDP) spaces and implement deep reinforcement learning algorithms to reduce the large MDP space and achieve fast decision-making. Experimental results show that this strategy performs better than existing baseline approaches on the reduction of delay and energy consumption.

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“…In order to verify the average completion time, we also implemented Extensive Service Migration(ESM) [19], Always Migration(AM) [20], Counterfactual Multi-Agent (COMA) [21], Never Migration (NM) as a baseline. AWDDPG adopts the experience replay mechanism to reduce the correlation between samples, and designs an adaptive weighted sampling method to increase the sampling efficiency, which greatly increases the convergence speed and stability.…”

Section: Performance Evaluationmentioning

confidence: 99%

Distributed Task Migration Optimization in MEC by Deep Reinforcement Learning Strategy

Cui

Zhang

et al. 2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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─Mobile management is a challenging technology in Mobile Edge Computing (MEC). When the device is moving, computation tasks need to be dynamically migrated between multiple edge servers to maintain service continuity. This paper proposes a migration optimization of distributed task in MEC by deep reinforcement learning solution to optimize delay. In Multi-agent Deep Reinforcement Learning (MADRL), we construct an adaptive weight deep deterministic policy gradient (AWDDPG) algorithm to optimize the migration cost and service delay, and adopt centralized training and distributed execution to solve the high-dimensional problem. Experiments show that our algorithm greatly reduces the service delay compared with the related algorithms.

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A Novel Deep Reinforcement Learning based service migration model for Mobile Edge Computing

Cited by 22 publications

References 25 publications

A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing

A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing

Dynamic Computation Offloading with Deep Reinforcement Learning in Edge Networks

Distributed Task Migration Optimization in MEC by Deep Reinforcement Learning Strategy

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