Optimal Multi-Decision Mobile Computation Offloading With Hard Task Deadlines

Hekmati, Arvin; Teymoori, Peyvand; Todd, T.D.; Zhao, Dongmei; Karakostasy, George

doi:10.1109/iscc47284.2019.8969696

Cited by 10 publications

(3 citation statements)

References 8 publications

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“…The significance of the study is that reduces the execution delay for the decision-making process and deals with how these foundations are used to reach the optimal time and server for the mobile node to connect. The work in [4] tackles with the optimal decision for computation off-loading by setting hard task deadlines. The paper uses a Markovian OST model which is computed using dynamic programming.…”

Section: Related Workmentioning

confidence: 99%

Optimal Load-Aware Task Offloading in Mobile Edge Computing

Polycarpou¹,

Anagnostopoulos²,

Kolomvatsos³

2021

International Conference of Control, Dynamic Systems, and Robotics

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Mobile Edge Computing (MEC) has emerged as a new computing paradigm to provide computing resources and storing applications closer to the end-users at the operator network boundary. One of the main challenges of MEC is task offloading, i.e., the transfer of computational tasks to a remote processor or external platforms such as a grid of servers or the Cloud. Task offloading mainly faces when and where is best to offload tasks to mitigate a smart device's energy consumption and workload. This paper tackles this challenge by adopting the principles of Optimal Stopping Theory (OST) with three time-optimised sequential decision-making models. A performance evaluation is provided with upon real data-sets on which our proposed models are applied and compared to the theoretical optimal model. Our results show how close our models can be to the theoretical optimal one based on probabilistic and scaling factors. Moreover, in our performance evaluation section, we conclude that one of the applied sequential models can be extremely close to the optimal one making it suitable in single-user and competitive user scenarios.

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Section: Related Workmentioning

confidence: 99%

Optimal Load-Aware Task Offloading in Mobile Edge Computing

Polycarpou¹,

Anagnostopoulos²,

Kolomvatsos³

2021

International Conference of Control, Dynamic Systems, and Robotics

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“…They formulated the transmission scheduling problem as dynamic programming, and its optimal scheduling and two suboptimal scheduling algorithms have been derived. Hekmati et al considered the multidecision problem when task execution completion times are subject to hard deadline constraints, and when the wireless channel can be modeled as a Markov process [16]. They proposed an online mobile task offloading algorithm named MultiOpt to develop the offloading policy.…”

Section: Offloading Decision-makingmentioning

confidence: 99%

“…The task offloading architecture, the offloading policy, and the offloading granularity are three main branches of the current research on MCC [11]. How to develop offloading policies has been studied in many previous works [12][13][14][15][16]. The offloading policy aims to improve the MD performance and determines whether a task should be offloaded to the cloud.…”

Section: Introductionmentioning

confidence: 99%

Task admission control for application service operators in mobile cloud computing

Jin

Hua

Wang

2020

J Wireless Com Network

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The resource constraint has become an important factor hindering the further development of mobile devices (MDs). Mobile cloud computing (MCC) is a new approach proposed to extend MDs’ capacity and improve their performance by task offloading. In MCC, MDs send task requests to the application service operator (ASO), which provides application services to MDs and needs to determine whether to accept the task request according to the system condition. This paper studies the task admission control problem for ASOs with the consideration of three features (two-dimensional resources, uncertainty, and incomplete information). A task admission control model, which considers radio resource variations, computing, and radio resources, is established based on the semi-Markov decision process with the goal of maximizing the ASO’s profits while guaranteeing the quality of service (QoS). To develop the admission policy, a reinforcement learning-based policy algorithm, which develops the admission policy through system simulations without knowing the complete system information, is proposed. Experimental results show that the established model adaptively adjusts the admission policy to accept or reject different levels and classes of task requests based on the ASO load, available radio resources, and event type. The proposed policy algorithm outperforms the existing policy algorithms and maximizes the ASO’s profits while guaranteeing the QoS.

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An Efficient Computation Offloading Strategy in Wireless Powered Mobile-Edge Computing Networks

Zhou

Liang

et al. 2022

Algorithms and Architectures for Parallel Processing

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Optimal Multi-Decision Mobile Computation Offloading With Hard Task Deadlines

Cited by 10 publications

References 8 publications

Optimal Load-Aware Task Offloading in Mobile Edge Computing

Optimal Load-Aware Task Offloading in Mobile Edge Computing

Task admission control for application service operators in mobile cloud computing

An Efficient Computation Offloading Strategy in Wireless Powered Mobile-Edge Computing Networks

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