Optimal Mobile Computation Offloading with Hard Deadline Constraints

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

doi:10.1109/tmc.2019.2920819

Cited by 34 publications

(17 citation statements)

References 42 publications

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“…In [19], the authors proposed a multi-decision mobile computation offloading scheme that can minimize energy consumption while guaranteeing deadline satisfaction. The authors of [20] investigated a resource provisioning problem that can optimize the cost of a system while ensuring the deadline satisfaction of IoT service.…”

Section: Discussionmentioning

confidence: 99%

Latency-Classification-Based Deadline-Aware Task Offloading Algorithm in Mobile Edge Computing Environments

Choi

Lee

2019

Applied Sciences

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In this study, we consider an edge cloud server in which a lightweight server is placed near a user device for the rapid processing and storage of large amounts of data. For the edge cloud server, we propose a latency classification algorithm based on deadlines and urgency levels (i.e., latency-sensitive and latency-tolerant). Furthermore, we design a task offloading algorithm to reduce the execution time of latency-sensitive tasks without violating deadlines. Unlike prior studies on task offloading or scheduling that have applied no deadlines or task-based deadlines, we focus on a comprehensive deadline-aware task scheduling scheme that performs task offloading by considering the real-time properties of latency-sensitive tasks. Specifically, when a task is offloaded to the edge cloud server due to a lack of resources on the user device, services could be provided without delay by offloading latency-tolerant tasks first, which are presumed to perform relatively important functions. When offloading a task, the type of the task, weight of the task, task size, estimated execution time, and offloading time are considered. By distributing and offloading latency-sensitive tasks as much as possible, the performance degradation of the system can be minimized. Based on experimental performance evaluations, we prove that our latency-based task offloading algorithm achieves a significant execution time reduction compared to previous solutions without incurring deadline violations. Unlike existing research, we applied delays with various network types in the MEC (mobile edge computing) environment for verification, and the experimental result was measured not only by the total response time but also by the cause of the task failure rate.

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Section: Discussionmentioning

confidence: 99%

Latency-Classification-Based Deadline-Aware Task Offloading Algorithm in Mobile Edge Computing Environments

Choi

Lee

2019

Applied Sciences

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“…Some recent works study the joint communication and computation resource allocation to improve the performance of task offloading from a system perspective [5], [10]- [14], [16], [25]- [28]. Specifically, Ren et al [25] propose a channel allocation and resource management approach to make optimal offloading decisions and maximize the longterm network utility.…”

Section: Related Workmentioning

confidence: 99%

“…However, all these works ignore the latency constraint in task offloading, which is significantly important for delay-sensitive applications and attracts increasing research attention [27]. Hekmati et al [5] develops an energy-optimal task offloading algorithm, named OnOpt, which considers the stochastic wireless channels and exploits the Markov chain to obtain the optimal offloading decisions. Wang et al [16] focus on the task offloading problem in non-orthogonal multiple access (NOMA) based edge computing systems, and propose an online-learning algorithm to determine the optimal task and subcarrier allocation decisions for minimize the task execution delay.…”

Section: Related Workmentioning

confidence: 99%

“…The inherently limited and stochastic radio resources calls for effective radio resource allocation strategy, otherwise, the wireless network capacity may be quickly strained, causing low transmission efficiency and dissatisfactions on edge computing services [4]. Moreover, the delay guarantees of offloading tasks are essential for lots of applications, such as interactive gaming and the object recognition and rendering in smart driving [5]. However, the stochasticity of communication channels and computing power of edge servers make the delay control extremely difficult, especially for heterogeneous tasks with different delay requirements.…”

Section: Introductionmentioning

confidence: 99%

“…Recently we have witnessed significantly progress in developing novel approaches to address the challenges in task offloading. In particular, there have been several works on various aspects, including designing energy-efficient offloading strategies [6]- [9], jointly allocating communication and computation resources for performance improvement [10]- [14], lowering the response latency [5], [15], [16], and developing decentralized offloading methods [3], [17]- [22]. However, most of these existing works mainly aim at tackling part of aforementioned issues by weakening other restrictions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

TODG: Distributed Task Offloading with Delay Guarantees for Edge Computing

Sheng¹,

Ren²,

Qiao³

et al. 2021

Preprint

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Edge computing has been an efficient way to provide prompt and near-data computing services for resource-and-delay sensitive IoT applications via computation offloading. Effective computation offloading strategies need to comprehensively cope with several major issues, including the allocation of dynamic communication and computational resources, the deadline constraints of heterogeneous tasks, and the requirements for computationally inexpensive and distributed algorithms. However, most of the existing works mainly focus on part of these issues, which would not suffice to achieve expected performance in complex and practical scenarios. To tackle this challenge, in this paper, we systematically study a distributed computation offloading problem with hard delay constraints, where heterogeneous computational tasks require continually offloading to a set of edge servers via a limiting number of stochastic communication channels. The task offloading problem is then cast as a delay-constrained long-term stochastic optimization problem under unknown priori statistical knowledge. To resolve this problem, we first provide a technical path to transform and decompose it into several slot-level subproblems, then we develop a distributed online algorithm, namely TODG, to efficiently allocate the resources and schedule the offloading tasks with delay guarantees. Further, we present a comprehensive analysis for TODG, in terms of the optimality gap, the delay guarantees, and the impact of system parameters. Extensive simulation results demonstrate the effectiveness and efficiency of TODG.

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RETRACTED: Improving mobile cloud computing service efficiency using energy competent and federated learning process

Thirunavukkarasu

Shanmugapriya

2022

Trans Emerging Tel Tech

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Mobile cloud computing (MCC) helps to make effective communication that is used to transmit various information like voice, data, and video. The MCC environments support different real‐time applications, including gaming and virtual reality implications. During the data transmission, energy conservation is one of the major issues for retaining the application span incorporating distributed and personalized computing and resource sharing. Ability‐sustained energy competent method (ASECM) is introduced to overcome these issues and manage energy efficiency. The ASECM approach analyzes the session span and energy requirements for managing the applications' energy factor. In addition, the federated learning and computation offloading process is incorporated to retain the energy factor for ensuring reliable services. The effective utilization of the offloading process is used to meet the application deadline and energy‐sufficient operations, preventing failures. In the recommendation process, cloud service availability with energy management constraints is considered for reducing energy expenditures. This process is nonrecurrent as the user/application requirements rely on diverse resources over different energy management techniques. The performance is measured using energy efficacy, utilization, application span, and failures.

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Optimal Mobile Computation Offloading with Hard Deadline Constraints

Cited by 34 publications

References 42 publications

Latency-Classification-Based Deadline-Aware Task Offloading Algorithm in Mobile Edge Computing Environments

Latency-Classification-Based Deadline-Aware Task Offloading Algorithm in Mobile Edge Computing Environments

TODG: Distributed Task Offloading with Delay Guarantees for Edge Computing

RETRACTED: Improving mobile cloud computing service efficiency using energy competent and federated learning process

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