Computation Peer Offloading for Energy-Constrained Mobile Edge Computing in Small-Cell Networks

Chen, Lixing; Zhou, Sheng; Xu, Jie

doi:10.1109/tnet.2018.2841758

Cited by 331 publications

(139 citation statements)

References 33 publications

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“…Given the tunnel, the optimal offloading was shown to be achieved by the well-known "string-pulling" strategy, graphically referring to pulling a string across the tunnel. Last, Chen et al proposed an online peer offloading framework based on Lyapunov optimization and game theoretic approaches in [129], which enables small BSs cooperation to handle the spatially uneven computation workloads in the network.…”

Section: Centralizedmentioning

confidence: 99%

A Survey on Mobile Edge Computing: The Communication Perspective

Mao

You

Zhang

et al. 2017

IEEE Commun. Surv. Tutorials

4,358

1,998

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Abstract-Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discusse a set of issues, challenges and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

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Section: Centralizedmentioning

confidence: 99%

A Survey on Mobile Edge Computing: The Communication Perspective

Mao

You

Zhang

et al. 2017

IEEE Commun. Surv. Tutorials

4,358

1,998

View full text Add to dashboard Cite

show abstract

“…In particular, we consider the processor capacity (i.e. CPU frequency) as the key component of computing resource since it decides the processing delay of tasks at edge servers as considered in most existing works [24], [25].…”

Section: A Network Structure and Resource Rentalmentioning

confidence: 99%

Budget-Constrained Edge Service Provisioning With Demand Estimation via Bandit Learning

Chen

2019

IEEE J. Select. Areas Commun.

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Shared edge computing platforms, which enable Application Service Providers (ASPs) to deploy applications in close proximity to mobile users are providing ultra-low latency and location-awareness to a rich portfolio of services. Though ubiquitous edge service provisioning, i.e., deploying the application at all possible edge sites, is always preferable, it is impractical due to often limited operational budget of ASPs. In this case, an ASP has to cautiously decide where to deploy the edge service and how much budget it is willing to use. A central issue here is that the service demand received by each edge site, which is the key factor of deploying benefit, is unknown to ASPs a priori. What's more complicated is that this demand pattern varies temporally and spatially across geographically distributed edge sites. In this paper, we investigate an edge resource rental problem where the ASP learns service demand patterns for individual edge sites while renting computation resource at these sites to host

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“…Jia et al concentrated on cost minimization caused by dynamic changes of offloading request, and proposed a prediction method to decide whether releasing or creating instances of network functions inside various cloudlets. Chen et al considered an energy constrained scenario, and defined a peer offloading game among multiple BSs. Through analyzing the equilibrium and corresponding efficiency loss, they designed a strategy to guide every BS's task offloading decision, so as to enable the distributed and autonomous offloading by edge servers themselves.…”

Section: Related Workmentioning

confidence: 99%

Dynamic service deployment for budget‐constrained mobile edge computing

Zhou

Fan

Wang

et al. 2019

Concurrency and Computation

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Summary Currently, Mobile edge computing (MEC) is facing a great challenge that is how to make full use of edge resources to provide a seamless support for compute‐intensive latency‐sensitive applications. Prior studies often make a simple assumption that tasks can be executed upon every edge server, but the assumption does not hold in practical scenarios. Because a specific application task often corresponds to a certain service that provides the corresponding running environment, whereas an edge server only has limited resources and cannot offer too many services. How to decide service deployment of so many types of services among multiple edge servers is also a big challenge. To address the challenge, we study dynamic service deployment for latency‐sensitive applications. We first model the long‐term budget‐constrained latency minimization problem as a multi‐slot latency minimization problem based on the Lyapunov framework. By doing this, the hardness of a problem is significantly reduced, since we never require future information to solve the long‐term optimization. Furthermore, we extend our study by joining the task scheduling optimization, where every edge server is fully utilized in an even more efficient collaborative manner. Our extensive experiments show that the proposed algorithms can bring short latency with low cost.

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Computation Peer Offloading for Energy-Constrained Mobile Edge Computing in Small-Cell Networks

Cited by 331 publications

References 33 publications

A Survey on Mobile Edge Computing: The Communication Perspective

A Survey on Mobile Edge Computing: The Communication Perspective

Budget-Constrained Edge Service Provisioning With Demand Estimation via Bandit Learning

Dynamic service deployment for budget‐constrained mobile edge computing

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