Task Caching, Offloading, and Resource Allocation in D2D-Aided Fog Computing Networks

Lan, Yanwen; Wang, Xiaoxiang; Wang, Dongyu; Liu, Zhaolin; Zhang, Yibo

doi:10.1109/access.2019.2929075

Cited by 58 publications

(50 citation statements)

References 39 publications

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“…The utility maximization is solved through a genetic algorithm. The simulation evaluations in [202] indicate that the proposed task caching algorithm achieves higher utilities than various benchmark approaches, particularly for high task computing demands and dense nearby UE populations. A shortcoming of the task caching in [202] is that the caching is conducted in an offline manner, e.g., over night.…”

Section: ) Task Cachingmentioning

confidence: 99%

“…The simulation evaluations in [202] indicate that the proposed task caching algorithm achieves higher utilities than various benchmark approaches, particularly for high task computing demands and dense nearby UE populations. A shortcoming of the task caching in [202] is that the caching is conducted in an offline manner, e.g., over night. A typical motivating example for task caching is the computation of augmented and virtual reality computations for visitors to a specific location, e.g., a museum, lecture hall, or laboratory for handson experiments.…”

Section: ) Task Cachingmentioning

confidence: 99%

“…While device-enhanced MEC caching studies have generally examined the cache placement and delivery of abstract data files or video files, the study [202] has ventured into the new domain of task caching. Task caching was originally introduced in the pure MEC context in [223] and is conceptually similar to the MEC studies that jointly consider computation offloading and caching [32], [224]- [226].…”

Section: ) Task Cachingmentioning

confidence: 99%

“…In particular, task caching refers to the caching of the (potentially large) data set resulting from a complex computing task, e.g., a computation relating to augmented or virtual reality rendering. The study [202] has examined this task caching in the context of a device-enhanced MEC system with UEs, MEC computing nodes (referred to as fog nodes), and a remote cloud. Computed task data sets are cached on the UEs according to a proposed near-optimal task caching algorithm in an offline manner, e.g., over night.…”

Section: ) Task Cachingmentioning

confidence: 99%

See 3 more Smart Citations

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

et al. 2019

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Multi-access edge computing (MEC) has recently been proposed to aid mobile end devices in providing compute-and data-intensive services with low latency. Growing service demands by the end devices may overwhelm MEC installations, while cost constraints limit the increases of the installed MEC computing and data storage capacities. At the same time, the ever increasing computation capabilities and storage capacities of mobile end devices are valuable resources that can be utilized to enhance the MEC. This article comprehensively surveys the topic area of device-enhanced MEC, i.e., mechanisms that jointly utilize the resources of the community of end devices and the installed MEC to provide services to end devices. We classify the device-enhanced MEC mechanisms into mechanisms for computation offloading and mechanisms for caching. We further subclassify the offloading and caching mechanisms according to the targeted performance goals, which include throughput maximization, latency minimization, energy conservation, utility maximization, and enhanced security. We identify the main limitations of the existing device-enhanced MEC mechanisms and outline future research directions. INDEX TERMS Caching, computation offloading, device-to-device (D2D) communication, mobile edge computing (MEC). I. INTRODUCTION A. MOTIVATION

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Section: ) Task Cachingmentioning

confidence: 99%

Section: ) Task Cachingmentioning

confidence: 99%

Section: ) Task Cachingmentioning

confidence: 99%

Section: ) Task Cachingmentioning

confidence: 99%

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Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

et al. 2019

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“…In IoT systems, a number of users often request for the offloading of the same data and similar tasks to the MEC servers [10], [17]. The repeatedly requested tasks are expected to be generated from diverse IoT applications including smart vehicles, e-health services, interactive gaming, smart homes, industrial monitoring, and virtual reality applications [17]- [19]. Caching popular IoT data items at the network edge can play an important role in reducing the duplicate content transmission [3], [20].…”

Section: Introductionmentioning

confidence: 99%

Green and Secure Computation Offloading for Cache-Enabled IoT Networks

et al. 2020

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The ever-increasing number of diverse and computation-intensive Internet of things (IoT) applications is bringing phenomenal growth in global Internet traffic. Mobile devices with limited resource capacity (i.e., computation and storage resources) and battery lifetime are experiencing technical challenges to satisfy the task requirements. Mobile edge computing (MEC) integrated with IoT applications offloads computation-intensive tasks to the MEC servers at the network edge. This technique shows remarkable potential in reducing energy consumption and delay. Furthermore, caching popular task input data at the edge servers reduces duplicate content transmission, which eventually saves associated energy and time. However, the offloaded tasks are exposed to multiple users and vulnerable to malicious attacks and eavesdropping. Therefore, the assignment of security services to the offloaded tasks is a major requirement to ensure confidentiality and privacy. In this article, we propose a green and secure MEC technique combining caching, cooperative task offloading, and security service assignment for IoT networks. The study not only investigates the synergy between energy and security issues, but also offloads IoT tasks to the edge servers without violating delay requirements. A resource-constrained optimization model is formulated, which minimizes the overall cost combining energy consumption and probable security-breach cost. We also develop a two-stage heuristic algorithm and find an acceptable solution in polynomial time. Simulation results prove that the proposed technique achieves notable improvement over other existing strategies. INDEX TERMS Caching, energy, IoT, mobile edge computing, security. DARYOUSH HABIBI (Senior Member, IEEE) received the B.E. degree (Hons.

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A Novel Machine Learning Approach to Delay Efficient Offloading Strategy for Mobile Edge Computing

Bharti¹,

Chaudhary²,

Snigdh³

2022

Lecture Notes in Electrical Engineering

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Task Caching, Offloading, and Resource Allocation in D2D-Aided Fog Computing Networks

Cited by 58 publications

References 39 publications

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

Green and Secure Computation Offloading for Cache-Enabled IoT Networks

A Novel Machine Learning Approach to Delay Efficient Offloading Strategy for Mobile Edge Computing

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