Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling

Dinh, Thinh Quang; Tang, Jianhua; Lã, Quang Duy; Quek, Tony Q. S.

doi:10.1109/tcomm.2017.2699660

Cited by 585 publications

(90 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Specifically, in [21], an efficient one-dimensional search algorithm is designed to minimize the computation task scheduling delay under the power consumption constraint. In [2], a computation task offloading optimization framework is designed to jointly optimize the computation task execution latency and the mobile device's energy. In [2], an online dynamic task offloading scheme is proposed to achieve a trade-off between the task execution delay and the mobile device's energy consumption in mobile edge computing with energy harvesting devices.…”

Section: Related Workmentioning

confidence: 99%

“…In [2], a computation task offloading optimization framework is designed to jointly optimize the computation task execution latency and the mobile device's energy. In [2], an online dynamic task offloading scheme is proposed to achieve a trade-off between the task execution delay and the mobile device's energy consumption in mobile edge computing with energy harvesting devices. In [22], a suboptimal algorithm is proposed to minimize the maximal weighted cost of the task execution latency and the mobile device's energy consumption while guaranteeing user fairness.…”

Section: Related Workmentioning

confidence: 99%

“…In this section, to evaluate the performance of the proposed SCACO strategy, we implement and simulate our strategy on Python 3.6 using a Dell R530 server configured with one CPU (2.2 GHz 8 cores). We set the experimental parameters referring to the literatures [2,25,42,43]. Major simulation parameters are described in detail as follows.…”

Section: Experiments Parametersmentioning

confidence: 99%

“…With the increasing popularity of mobile devices (e.g., smart phones and tablets), the number of various mobile applications (i.e., virtual reality (VR) and face recognition) is explosively growing [1]. To process such a large number of complex mobile applications e ciently, mobile devices need to be equipped with considerable resources (i.e., high computing capacity and battery power) [2,3]. Unfortunately, due to the limited physical size of mobile devices, they are usually resource-constrained.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Security and Cost-Aware Computation Offloading via Deep Reinforcement Learning in Mobile Edge Computing

Huang

et al. 2019

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

With the explosive growth of mobile applications, mobile devices need to be equipped with abundant resources to process massive and complex mobile applications. However, mobile devices are usually resource-constrained due to their physical size. Fortunately, mobile edge computing, which enables mobile devices to offload computation tasks to edge servers with abundant computing resources, can significantly meet the ever-increasing computation demands from mobile applications. Nevertheless, offloading tasks to the edge servers are liable to suffer from external security threats (e.g., snooping and alteration). Aiming at this problem, we propose a security and cost-aware computation offloading (SCACO) strategy for mobile users in mobile edge computing environment, the goal of which is to minimize the overall cost (including mobile device’s energy consumption, processing delay, and task loss probability) under the risk probability constraints. Specifically, we first formulate the computation offloading problem as a Markov decision process (MDP). Then, based on the popular deep reinforcement learning approach, deep Q-network (DQN), the optimal offloading policy for the proposed problem is derived. Finally, extensive experimental results demonstrate that SCACO can achieve the security and cost efficiency for the mobile user in the mobile edge computing environment.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Experiments Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Security and Cost-Aware Computation Offloading via Deep Reinforcement Learning in Mobile Edge Computing

Huang

et al. 2019

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…In [19], a MECbased architecture for decision making processes of the transport network control, such as handover and traffic offloading, is proposed. A method and device for determining a bearer by a MEC in the handover process is proposed in [20].…”

Section: Related Work and Research Motivationmentioning

confidence: 99%

Mobile Edge Service for Intersystem Handover

Pencheva¹,

Atanasov²,

Kireva-Mihova³

et al. 2018

IJET

View full text Add to dashboard Cite

Multi-access Edge Computing (MEC) appears to be an integrating technology for radio access networks to enhance the access capacity, optimize network performance and improve quality of experience for end users. MEC distributes cloud capabilities for storage and computing in the radio access network, close to the end users. As far as the development of advanced Application Programming Interfaces is a key area, we propose a new mobile edge service that enables 3rd party control on intersystem handover. The proposed service enables authorized applications to initiate intersystem handover following specific policy. The service is described by information flows illustrating the basic functionality, data models that provide mediation functions, and handover state models considering some implementation issues.

show abstract

Profit‐aware coalition formation in fog computing providers: A game‐theoretic approach

Anglano

Canonico

Castagno

et al. 2019

Concurrency and Computation

View full text Add to dashboard Cite

We consider fog computing scenarios where data generated by a set of IoT applications need to be processed locally by a set of fog nodes, belonging to distinct Fog Infrastructure Providers (FIPs) sharing the same co-location facility, with the aim of increasing their profits. This is a challenging goal as it requires reducing costs and meeting QoS targets despite time-varying workloads. We argue that these FIPs may find it profitable to cooperate by mutually sharing their workload and resources, and we show (by using a game-theoretical framework) that this is indeed the case when stable coalitions can be formed. Based on these results, in this paper, we present (1) a mathematical model for maximizing the profit obtained for allocating IoT applications to a group of FIPs, and (2) a coalition formation algorithm that allows each FIP to decide with whom to cooperate so as to increment its profits. The efficacy of the devised algorithm is assessed by means of an experimental evaluation taking into account different workload intensities. The results from these experiments show the capability of the proposed algorithm to form coalitions of FIPs that are profitable and stable in all the scenarios we take into consideration.

show abstract

Offloading in Mobile Edge Computing: Task Allocation and Computational Frequency Scaling

Cited by 585 publications

References 32 publications

Security and Cost-Aware Computation Offloading via Deep Reinforcement Learning in Mobile Edge Computing

Security and Cost-Aware Computation Offloading via Deep Reinforcement Learning in Mobile Edge Computing

Mobile Edge Service for Intersystem Handover

Profit‐aware coalition formation in fog computing providers: A game‐theoretic approach

Contact Info

Product

Resources

About