Joint Resource Allocation and Cache Placement for Location-Aware Multi-User Mobile-Edge Computing

Chen, Jiechen; Xing, Hong; Lin, Xiaohui; Nallanathan, Arumugam; Bi, Suzhi

doi:10.1109/jiot.2022.3196908

Cited by 21 publications

(6 citation statements)

References 33 publications

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“…The authors in [14] propose a long short-term memory (LSTM)based caching algorithm to minimize the weighted delivery cost. In [15], [16], the authors design joint caching and resource allocation strategies to minimize energy consumption. The authors in [17] study proactive edge caching problem for device-to-device (D2D)-assisted wireless networks and present a bidirectional LSTM (BiLSTM)-based content popularity prediction algorithm.…”

Section: A Literature Reviewmentioning

confidence: 99%

Content Fetching Delay Optimization-Based Caching and Resource Allocation for UAV-Enabled Networks

Bayessa,

Chai,

Liang

et al. 2024

IEEE Access

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In this work, we investigate content caching problem in unmanned aerial vehicle (UAV)enabled networks where UAVs are allowed to store user requested content so as to enhance the content transmission performance of users. To predict user content request, we propose a bidirectional long shortterm memory-based algorithm. Then, the content transmission delay of users is examined and the UAV deployment, content caching and resource allocation problem is formulated as an overall content fetching delay minimization problem. As the formulated problem is difficult to solve, we transform it into two subproblems which are solved iteratively. To tackle the UAV deployment and content caching subproblem, we first design a modified K-means-based clustering scheme and then propose a UAV deployment strategy by using quadratic transformation. To solve subcarrier and power allocation subproblem, we apply Lagrangian dual method to determine power allocation strategy and propose a Kuhn-Munkres algorithm-based subcarrier allocation strategy. Simulation results show that our approach achieves approximately 11.1% lower overall content fetching delay compared to the existing algorithm.INDEX TERMS Unmanned aerial vehicles, user clustering, UAV deployment, content caching, resource allocation.

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Section: A Literature Reviewmentioning

confidence: 99%

Content Fetching Delay Optimization-Based Caching and Resource Allocation for UAV-Enabled Networks

Bayessa,

Chai,

Liang

et al. 2024

IEEE Access

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“…The work in Chen et al 32 studied the problem to minimize the expected energy consumption formulated as a mixed integer nonconvex problem, which was solved by a deep learning‐based scheme. The work in Li et al 33 addressed the problem to minimize delay while satisfying the long‐term budget constraints on service placement by using only current system information.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, the work in Liu et al 30 studied the storage and bandwidth capacities without computation capacity to maximize the number of users assigned to edge nodes. The works in earlier studies 3,[31][32][33] studied joint service placement and user assignment with storage, computation, and bandwidth constraints in edge clouds. The work in He et al 3 studied the problem of service placement and request scheduling in MEC systems to maximize the number of users.…”

mentioning

confidence: 99%

Joint service placement and user assignment model in multi‐access edge computing networks against base‐station failure

Taka

Oki

2023

Int J Network Mgmt

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Multi‐access edge computing (MEC) enables users to exploit the resources of cloud computing at a base station (BS) in proximity to the users where an MEC server is hosted. While we have advantage of being able to communicate with low latency and small network load in MEC networks, the resources in BSs are limited. One challenge is where to provide users with services from to make efficient use of resources. Furthermore, to enhance the reliability of MEC system, the case that a BS fails needs to be considered. This paper proposes a service placement and user assignment model with preventive start‐time optimization against a single BS failure in MEC networks. The proposed model preventively determines the service placement and user assignment in each BS failure pattern to minimize the worst‐case penalty which is the largest penalty among all failure patterns. We formulate the proposed model as an integer linear programming problem. We prove that the considered problem is NP‐hard. When the problem size becomes large, it may not be solved in a practical computation time. To solve larger size problems, we introduce two algorithms: one is the greedy algorithm with allocation upgrade and the other is with allocation upgrade and preemption. The results show that the introduced algorithms obtain solutions with smaller worst‐case penalty than the benchmark in a practical time.

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“…They optimized transmission power, computation resource allocation, and offloading ratio and reached the insightful conclusion that if a task has a stringent delay requirement (less than a threshold), it cannot be processed in a partitioned way. In [17], requests from representative locations are grouped, and computation results for requests with duplicated inputs selectively cached. They formulated the cache decision, bandwidth allocation, and computing resource allocation problem as an MINLP problem, aiming to minimize the energy consumption of the base station (BS) and all users.…”

Section: Related Workmentioning

confidence: 99%

“…We summarize part of the studies mentioned above in Table 1. Furthermore, recent research has explored combining deep reinforcement learning with computation offloading to enhance performance [13,17,[30][31][32][33]. However, these approaches do not address the problem of maximizing the number of accepted requests among a flood of requests with limited resources in an edge-enabled asymmetrical network.…”

Section: Related Workmentioning

confidence: 99%

Water Pumping and Refilling (WPR): A Resource Allocation Algorithm for Maximizing Acceptance Ratio in Asymmetrical Edge Computing Networks

Liu

2023

Symmetry

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Computation offloading has received a significant amount of attention in recent years, with many researchers proposing joint offloading decision and resource allocation schemes. However, although existing delay minimization schemes achieve minimum delay costs, they do so at the cost of losing possible further maximization of the number of serviced requests. Furthermore, the asymmetry between uplink and downlink poses challenges to resource allocation in edge computing. This paper addresses this issue by formulating the joint computation offloading and edge resource allocation problem as a mixed-integer nonlinear programming (MINLP) problem in an edge-enabled asymmetrical network. Leveraging the margin between a delay-minimum scheme and a near-deadline scheme, a water pumping and refilling (WPR) algorithm is proposed to maximize the number of accepted requests. The WPR algorithm can function both as a supplementary algorithm to a given offloading scheme and as a standalone algorithm to obtain a resource allocation scheme following a customizable refilling policy. The simulation results demonstrated that the proposed algorithm outperforms delay-minimum schemes in achieving a high acceptance ratio.

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Joint Resource Allocation and Cache Placement for Location-Aware Multi-User Mobile-Edge Computing

Cited by 21 publications

References 33 publications

Content Fetching Delay Optimization-Based Caching and Resource Allocation for UAV-Enabled Networks

Content Fetching Delay Optimization-Based Caching and Resource Allocation for UAV-Enabled Networks

Joint service placement and user assignment model in multi‐access edge computing networks against base‐station failure

Water Pumping and Refilling (WPR): A Resource Allocation Algorithm for Maximizing Acceptance Ratio in Asymmetrical Edge Computing Networks

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