MDP-Based Scheduling Design for Mobile-Edge Computing Systems with Random User Arrival

Huang, Shanfeng; Lv, Bojie; Wang, Rui

doi:10.1109/globecom38437.2019.9013178

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…It is assumed that the system model in the MEC consists of three parts: the user terminal, the wireless channel, and the MEC server, as shown in Fig. 1 and this process is considered as a MDP [22]. The channel status admits a second-order Markov process…”

Section: System Modelmentioning

confidence: 99%

“…In the above equation, n E denotes the expected value of the costs incurred by the corresponding offloading policy. Therefore, the system state is a finite-state Markov process under the initial state 0 S of the offloading strategy π , and the optimal solution can be found through the MDP framework [22]. The main symbols and descriptions involved in the article are shown in Table 1.…”

Section: System Modelmentioning

confidence: 99%

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A Privacy-preserving and Energy-efficient Offloading Algorithm based on Lyapunov Optimization

2022

KSII TIIS

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In Mobile Edge Computing (MEC), attackers can speculate and mine sensitive user information by eavesdropping wireless channel status and offloading usage pattern, leading to user privacy leakage. To solve this problem, this paper proposes a Privacy-preserving and Energy-efficient Offloading Algorithm (PEOA) based on Lyapunov optimization. In this method, a continuous Markov process offloading model with a buffer queue strategy is built first. Then the amount of privacy of offloading usage pattern in wireless channel is defined. Finally, by introducing the Lyapunov optimization, the problem of minimum average energy consumption in continuous state transition process with privacy constraints in the infinite time domain is transformed into the minimum value problem of each timeslot, which reduces the complexity of algorithms and helps obtain the optimal solution while maintaining low energy consumption. The experimental results show that, compared with other methods, PEOA can maintain the amount of privacy accumulation in the system near zero, while sustaining low average energy consumption costs. This makes it difficult for attackers to infer sensitive user information through offloading usage patterns, thus effectively protecting user privacy and safety.

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Section: System Modelmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

A Privacy-preserving and Energy-efficient Offloading Algorithm based on Lyapunov Optimization

2022

KSII TIIS

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“…Dynamic programming via MDP has been considered in resource allocation of wireless systems [17]- [24] or information systems [25]- [27]. For example, infinite-horizon MDP was used to optimize the cellular uplink transmissions [17], [18], downlink transmissions [19], and relay networks [20], [21], where the average transmission delay is either minimized or constrained.…”

Section: A Related Workmentioning

confidence: 99%

Joint Optimization of File Placement and Delivery in Cache-Assisted Wireless Networks With Limited Lifetime and Cache Space

Wang

Cui

et al. 2020

IEEE Trans. Commun.

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In this paper, the scheduling of downlink file transmission in one cell with the assistance of cache nodes with finite cache space is studied. Specifically, requesting users arrive randomly and the base station (BS) reactively multicasts files to the requesting users and selected cache nodes. The latter can offload the traffic in their coverage areas from the BS. We consider the joint optimization of the abovementioned file placement and delivery within a finite lifetime subject to the cache space constraint.Within the lifetime, the allocation of multicast power and symbol number for each file transmission at the BS is formulated as a dynamic programming problem with a random stage number. Note that there are no existing solutions to this problem. We develop an asymptotically optimal solution framework by transforming the original problem to an equivalent finite-horizon Markov decision process (MDP) with a fixed stage number. A novel approximation approach is then proposed to address the curse of dimensionality, where the analytical expressions of approximate value functions are provided. We also derive analytical bounds on the exact value function and approximation error. The approximate value functions depend on some system statistics, e.g., requesting users' distribution. One reinforcement learning algorithm is proposed for the scenario where these statistics are unknown.

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“…In the scenario that APs and edge servers are connected via software defined network (SDN), the authors in [5] proposed a heuristic algorithm to dispatch the jobs to the closest edge servers according to their locations. Considering random jobs arrival and job offloading to a single edge server, the authors in [11], [12] formulate the offloading problem as an infinite-horizon Markov decision process (MDP). In the above works, a centralized dispatcher with complete and updated knowledge of the system states was assumed in the edge computing systems, which might be impractical.…”

Section: Related Workmentioning

confidence: 99%

Online Distributed Job Dispatching with Outdated and Partially-Observable Information

Hong

Wang

et al. 2020

Preprint

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In this paper, we investigate online distributed job dispatching in an edge computing system residing in a Metropolitan Area Network (MAN). Specifically, job dispatchers are implemented on access points (APs) which collect jobs from mobile users and distribute each job to a server at the edge or the cloud. A signaling mechanism with periodic broadcast is introduced to facilitate cooperation among APs. The transmission latency is non-negligible in MAN, which leads to outdated information sharing among APs. Moreover, the fully-observed system state is discouraged as reception of all broadcast is time consuming. Therefore, we formulate the distributed optimization of job dispatching strategies among the APs as a Markov decision process with partial and outdated system state, i.e., partially observable Markov Decision Process (POMDP). The conventional solution for POMDP is impractical due to huge time complexity. We propose a novel low-complexity solution framework for distributed job dispatching, based on which the optimization of job dispatching policy can be decoupled via an alternative policy iteration algorithm, so that the distributed policy iteration of each AP can be made according to partial and outdated observation. A theoretical performance lower bound is proved for our approximate MDP solution. Furthermore, we conduct extensive simulations based on the Google Cluster trace. The evaluation results show that our policy can achieve as high as 20.67% reduction in average job response time compared with heuristic baselines, and our algorithm consistently performs well under various parameter settings.

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MDP-Based Scheduling Design for Mobile-Edge Computing Systems with Random User Arrival

Cited by 10 publications

References 16 publications

A Privacy-preserving and Energy-efficient Offloading Algorithm based on Lyapunov Optimization

A Privacy-preserving and Energy-efficient Offloading Algorithm based on Lyapunov Optimization

Joint Optimization of File Placement and Delivery in Cache-Assisted Wireless Networks With Limited Lifetime and Cache Space

Online Distributed Job Dispatching with Outdated and Partially-Observable Information

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