Delay Optimization Strategy for Service Cache and Task Offloading in Three-Tier Architecture Mobile Edge Computing System

Li, Limiao; Zhang, Heng

doi:10.1109/access.2020.3023771

Cited by 26 publications

(13 citation statements)

References 35 publications

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“…Integer programming is a mathematical optimisation program in which some or all of the variables are restricted to be integers. Various forms of this technique are applied including Integer linear programming [4], [46], [96], [162], [184], [187], [191], mixed-integer linear programming [15], [85], [137], [155], [166], [174], [182], and mixed-integer nonlinear programming [176], [190], [192].…”

Section: H: Heuristicsmentioning

confidence: 99%

Dynamic Service Placement in Multi-Access Edge Computing: A Systematic Literature Review

et al. 2022

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The advent of new cloud-based applications such as mixed reality, online gaming, autonomous driving, and healthcare has introduced infrastructure management challenges to the underlying service network. Multi-access edge computing (MEC) extends the cloud computing paradigm and leverages servers near end-users at the network edge to provide a cloud-like environment. The optimum placement of services on edge servers plays a crucial role in the performance of such service-based applications. Dynamic service placement problem addresses the adaptive configuration of application services at edge servers to facilitate end-users and those devices that need to offload computation tasks. While reported approaches in the literature shed light on this problem from a particular perspective, a panoramic study of this problem reveals the research gaps in the big picture. This paper introduces the dynamic service placement problem and outline its relations with other problems such as task scheduling, resource management, and caching at the edge. We also present a systematic literature review of existing dynamic service placement methods for MEC environments from networking, middleware, applications, and evaluation perspectives. In the first step, we review different MEC architectures and their enabling technologies from a networking point of view. We also introduce different cache deployment solutions in network architectures and discuss their design considerations. The second step investigates dynamic service placement methods from a middleware viewpoint. We review different service packaging technologies and discuss their trade-offs. We also survey the methods and identify eight research directions that researchers follow. Our study categorises the research objectives into six main classes, proposing a taxonomy of design objectives for the dynamic service placement problem. We also investigate the reported methods and devise a solutions taxonomy comprising six criteria. In the third step, we concentrate on the application layer and introduce the applications that can take advantage of dynamic service placement. The fourth step investigates evaluation environments used to validate the solutions, including simulators and testbeds. We introduce real-world datasets such as edge server locations, mobility traces, and service requests used to evaluate the methods. We compile a list of open issues and challenges categorised by various viewpoints in the last step.

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Section: H: Heuristicsmentioning

confidence: 99%

Dynamic Service Placement in Multi-Access Edge Computing: A Systematic Literature Review

et al. 2022

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“…The tasks have different computing workloads following the distribution of the general service rate. Hence, we model the waiting time of requests at the cloudlet as an M/G/1 queuing system [ 17 ].…”

Section: Analysis Of Total Response Time and Problem Formulationmentioning

confidence: 99%

“…Specifically, we formulate the problem of selecting which cloudlet to cache each program data and replica for minimizing the total response time experienced by the IoT user. Since the service time at the cloudlet can be different depending on the computing workload of the task, we analyze the total response time of the task with the waiting time based on the M/G/1 queuing model for the problem formulation [ 17 , 18 ]. Since the formulated problem is known to be NP-complete, we propose a heuristic caching algorithm to solve the total response time minimization problem.…”

Section: Introductionmentioning

confidence: 99%

Delay-aware distributed program caching for IoT-edge networks

Kim

Lee

et al. 2022

PLoS ONE

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Edge computing is a novel network architecture that is in proximity to the end devices in an Internet of Things (IoT). As the IoT becoming a major factor in our daily life, provisioning a low response time of the services to IoT users through edge computing is an important problem. Caching necessary program data for the task in an edge node effectively reduces the response time of the computation task. However, due to the increase of IoT users and devices, it is noteworthy that limited-resource edge nodes would receive a number of tasks, having a heavy burden of processing the requests. Therefore, the limited resource and caching space at cloudlet need the careful design of the caching algorithm to utilize the space of multiple edge nodes and relieve the burden of computations. In this paper, we propose a cooperative program caching system that makes different edge nodes cooperatively store program data and cache the replicas of the data requested frequently to handle a number of requests from IoT users. In particular, we develop a cooperative caching algorithm that caches the appropriate number of data replicas depending on the number of requests on each cloudlet and the popularity of the data to minimize the response time. The simulation results show that the proposed cooperative caching algorithm can effectively reduce the response time for IoT users compared to other existing algorithms.

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“…A three-tier architecture with users, edge servers, and remote cloud servers is proposed in study [66], with the capability to adjust caching decisions in line with popular patterns of time and space services. System delay and lower energy consumption are achieved in simulations.…”

Section: Caching Mechanism Improvementmentioning

confidence: 99%

A Survey on Caching in Mobile Edge Computing

Zhao

Zhang

Zhou

et al. 2021

Wireless Communications and Mobile Computing

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Boosted by the visions of 5G technology, along with the proliferation of IoT devices, we are lucky to bear witness of a tremendous shift within the Internet, mobile computing, and ubiquitous applications pervading people’s daily life. To catch up with the speed of this evolution, new architectures, which enable us to decentralize and focus more on the edge of the network, have to show up. To effectively address the record-breaking growth of data traffic, we also have to come up with new caching mechanisms in coping with users’ demanding QoE along with other aspects, such as data privacy and energy efficiency. In this paper, we intend to begin with reviews on edge caching. First, we make a detailed overview of mobile edge caching. Then, we move on to the QoS and QoE part, discussing the related literature concerning these topics. We then start to address factors concerning edge caching and QoE. Four cutting-edge applications utilizing these technologies with challenges posed by current network architectures are made clear. Our paper closes with some future research directions.

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Delay Optimization Strategy for Service Cache and Task Offloading in Three-Tier Architecture Mobile Edge Computing System

Cited by 26 publications

References 35 publications

Dynamic Service Placement in Multi-Access Edge Computing: A Systematic Literature Review

Dynamic Service Placement in Multi-Access Edge Computing: A Systematic Literature Review

Delay-aware distributed program caching for IoT-edge networks

A Survey on Caching in Mobile Edge Computing

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