Let’s Share VMs: Optimal Placement and Pricing across Base Stations in MEC Systems

Siew, Marie; Guo, Kun; Cai, Desmond; Li, Lingxiang; Quek, Tony Q. S.

doi:10.1109/infocom42981.2021.9488854

Cited by 16 publications

(4 citation statements)

References 26 publications

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“…It provides a service environment closer to both users and IoT devices by deploying several mobile edge servers. Service placement is a well-investigated problem in mobile edge computing that advocates for providing service offering at the users' side [2,3]. Various works have studied different aspects of this problem.…”

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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“…Another line of work is collaborative edge computing to maintain the quality of service for users in light of user mobility. This involves strategies like VM or service migration across base stations [14], [27]- [29] and user association optimization [30].…”

Section: Related Workmentioning

confidence: 99%

“…[1], [4]- [7]. Besides dealing with the technical challenges of computation offloading, along the road towards implementation of MEC, resource pricing and procurement solutions to optimize revenue [8]- [14] can serve as complementary mechanisms to control user demands for MEC resources and ensure good quality-ofservice despite limited available resources. In cloud computing platforms (e.g.…”

Section: Introductionmentioning

confidence: 99%

Towards Effective Resource Procurement in MEC: a Resource Re-selling Framework

Siew¹,

Guo²,

Cai³

et al. 2022

Preprint

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On-demand and resource reservation pricing models have been widely used in cloud computing, catering to different user requirements. Nevertheless, in Multi-Access Edge Computing (MEC), as the edge has limited resources compared to the cloud, on-demand users may not get their jobs served on time, or at all, if too many resources were reserved by reservation plan users. Concurrently, reservation plan users may possess excess un-utilized quota. To optimize this resource mismatch scenario, we propose a Sharing Quota Model (SQM) where reservation plan users can re-sell unused resource quota to on-demand users, with the mobile network operator (MNO) taking a commission. To analyze the user's aggregate behavior at equilibrium and investigate the MNO's incentive of allowing re-selling, we formulate a 3-stage non-cooperative Stackelberg Game. Solving this game, we characterize the optimal strategies of buyers and re-sellers. We show that on aggregate, users' optimal strategies give rise to 4 disjoint regions, dependent on the MNO's prices and supply levels. Based on this, we characterise the MNO's optimal prices for on-demand users. Numerical results show that having both the sharing and on-demand pool gives the MNO an optimal revenue when the on-demand pool's supply is low, and when the MNO's commission is low.

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“…A set of works considers the data caching problem in the edge computing environment, proposing schemes that maxi-mize the data caching revenue of the operator [181], or that improve content delivery speeds, network traffic congestion, cache resource utilization efficiency, and users' quality of experience in highly populated cities [182]. Referring to the VNF architecture, the SFC and VNF placement can be performed reducing the execution time and the resource utilization [183], taking into account both service requirements and the resource capacity in the edge [184], maximizing revenue at the network level while matching demand [185], minimizing both energy consumption and resource utilization [186], or maximizing the number of user request admissions while minimizing their admission cost (i.e., computing cost on instantiations of requested VNF instances and the data packet traffic processing of requests in their VNF instances, and the communication cost of routing data packet traffic of requests between users and the MEH hosting their requested VNF instances) [187].…”

Section: Yesmentioning

confidence: 99%

5G Multi-access Edge Computing: Security, Dependability, and Performance

Nencioni,

Garroppo,

Olimid

2021

Preprint

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The main innovation of the Fifth Generation (5G) of mobile networks is the ability to provide novel services with new and stricter requirements. One of the technologies that enable the new 5G services is the Multi-access Edge Computing (MEC). MEC is a system composed of multiple devices with computing and storage capabilities that are deployed at the edge of the network, i.e., close to the end users. MEC reduces latency and enables contextual information and real-time awareness of the local environment. MEC also allows cloud offloading and the reduction of traffic congestion. Performance is not the only requirement that the new 5G services have. New missioncritical applications also require high security and dependability. These three aspects (security, dependability, and performance) are rarely addressed together. This survey fills this gap and presents 5G MEC by addressing all these three aspects. First, we overview the background knowledge on MEC by referring to the current standardization efforts. Second, we individually present each aspect by introducing the related taxonomy (important for the not expert on the aspect), the state of the art, and the challenges on 5G MEC. Finally, we discuss the challenges of jointly addressing the three aspects.

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Let’s Share VMs: Optimal Placement and Pricing across Base Stations in MEC Systems

Cited by 16 publications

References 26 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

Towards Effective Resource Procurement in MEC: a Resource Re-selling Framework

5G Multi-access Edge Computing: Security, Dependability, and Performance

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