Mobile-Edge Computation Offloading for Ultradense IoT Networks

Guo, Hongzhi; Li, Jiajia; Zhang, Jie; Sun, Wen; Kato, Nei

doi:10.1109/jiot.2018.2838584

Cited by 280 publications

(111 citation statements)

References 44 publications

Supporting

Mentioning

104

Contrasting

Order By: Relevance

“…In order to serve as many requests as possible from the BSs, the operator has to jointly optimize these decisions while simultaneously satisfying storage, computation, and communication constraints. Clearly, this is an important problem that differs substantially from previous related studies (e.g., see [3], [4] and the survey in [1]) that did not consider storage-constrained BSs and asymmetric communication requirements. While a few recent works [8], [9], [10] studied the impact of storage in MEC, they neither considered all the features of these systems discussed above nor provided optimal or approximate solutions for the joint service placement and request routing problem.…”

Section: A Motivationmentioning

confidence: 78%

“…While there have been several interesting approaches to determine the execution (or offloading) of services in MEC, e.g., [3] and [4], to cite two of the most recent, an important aspect has been hitherto overlooked. Specifically, many services today require not only the allocation of computation resources, but also a non-trivial amount of data that needs to be pre-stored (or pre-placed) at the BS.…”

Section: A Motivationmentioning

confidence: 99%

See 1 more Smart Citation

Joint Service Placement and Request Routing in Multi-cell Mobile Edge Computing Networks

Poularakis

Llorca

Tulino

et al. 2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

249

View full text Add to dashboard Cite

The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low-latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low-latency services by enabling the execution of computing tasks at the network-periphery, in proximity to end-users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non-trivial amounts of data need to be stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in MEC-enabled multi-cell networks with multidimensional (storage-computation-communication) constraints. We show that this problem generalizes several problems in literature and propose an algorithm that achieves close-to-optimal performance using randomized rounding. Evaluation results demonstrate that our approach can effectively utilize the available resources to maximize the number of requests served by low-latency edge cloud servers.

show abstract

Section: A Motivationmentioning

confidence: 78%

Section: A Motivationmentioning

confidence: 99%

Joint Service Placement and Request Routing in Multi-cell Mobile Edge Computing Networks

Poularakis

Llorca

Tulino

et al. 2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

249

View full text Add to dashboard Cite

show abstract

“…Also, constraint (d) ensures the assignment of all the components to available slots in the related VC. In this study, it is assumed that the system parameters such as t T rans j m , λ jj and |κ j | involved in (4) are average statistics and will stay unchanged during graph job allocation [23] [24].…”

Section: Problem Formulation Of Computation-intensive Graph Job Amentioning

confidence: 99%

Allocation of Computation-Intensive Graph Jobs Over Vehicular Clouds in IoV

Liwang

Hosseinalipour

Gao

et al. 2020

IEEE Internet Things J.

View full text Add to dashboard Cite

“…In addition to the computationally-intensive applications, billions of IoT devices are expected to be deployed for various applications such as health monitoring, environmental monitoring and smart cities, to name a few. These applications require a large number of low-power and resource-constrained wireless nodes to collect, pre-process, and analyze huge amounts of sensory data [6], which may not be feasible due to the limited on-board computing resources.…”

Section: Introductionmentioning

confidence: 99%

Energy-Aware Multi-Server Mobile Edge Computing: A Deep Reinforcement Learning Approach

Naderializadeh¹,

Hashemi

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

View full text Add to dashboard Cite

We investigate the problem of computation offloading in a mobile edge computing architecture, where multiple energy-constrained users compete to offload their computational tasks to multiple servers through a shared wireless medium. We propose a multi-agent deep reinforcement learning algorithm, where each server is equipped with an agent, observing the status of its associated users and selecting the best user for offloading at each step. We consider computation time (i.e., task completion time) and system lifetime as two key performance indicators, and we numerically demonstrate that our approach outperforms baseline algorithms in terms of the trade-off between computation time and system lifetime.

show abstract

Mobile-Edge Computation Offloading for Ultradense IoT Networks

Cited by 280 publications

References 44 publications

Joint Service Placement and Request Routing in Multi-cell Mobile Edge Computing Networks

Joint Service Placement and Request Routing in Multi-cell Mobile Edge Computing Networks

Allocation of Computation-Intensive Graph Jobs Over Vehicular Clouds in IoV

Energy-Aware Multi-Server Mobile Edge Computing: A Deep Reinforcement Learning Approach

Contact Info

Product

Resources

About