Joint Optimization of Server Placement and Content Caching in Mobile Edge Computing Networks

Liu, Zhen; Zhang, Jiawei; Wu, Jingyan

doi:10.1145/3375998.3376024

Cited by 7 publications

(8 citation statements)

References 6 publications

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“…Furthermore, because user data access patterns in WMANs are challenging to forecast [45], creating an adequate data caching technique with high access hit ratio is challenging. The number of content cached at cloudlet increases with the number of offloaded tasks increasing [46]. Content cache performance significantly impacts delay and bandwidth [47].…”

Section: ) Cache Contentmentioning

confidence: 99%

A Survey on Cloudlet Computation Optimization in the Mobile Edge Computing Environment

Muwafaq¹,

Noordin²,

Othman³

et al. 2023

IJACSA

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Mobile Edge Computing (MEC) uses to perform computation operations at the edge of a network for mobile devices. This allows the deployment of more powerful and efficient computing resources in a cost-effective, lightweight and scalable manner. MEC can optimize mobile device performance, enhance security and privacy, improve battery life, provide increased bandwidth, and reduce latency across wireless networks. Cloudlets are a new concept of computations that can perform at the edge of the networks. The service provider can deploy cloudlets services in a MEC environment with the ability for mobile devices to offload their tasks to cloudlets. In the MEC environment, the offloading problem depends on cloudlets' availability of computation resources. Also, the deployment method of cloudlets in the environment will affect the task offloading. This paper investigates the approach to the cloudlet deployment and task offloading problem in the MEC environment. First demonstrate that the problem has to be considered a Multi-objective optimization problem since it needs more than one objective to be optimized. Then prove that the problem is NP-completeness, give an overview of existing solutions using the meta-heuristic algorithms, and suggest future solutions for this problem. Finally, explain the advantages of using Variable-length of solution space with meta-heuristic algorithms for this problem.

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Section: ) Cache Contentmentioning

confidence: 99%

A Survey on Cloudlet Computation Optimization in the Mobile Edge Computing Environment

Muwafaq¹,

Noordin²,

Othman³

et al. 2023

IJACSA

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“…These metrics encompass robustness, coverage, latency, performance, cost efficiency [22], throughput, load balancing, and energy consumption [11]. Several studies have focused on minimizing costs [23] [24] [25], optimizing edge server energy consumption [11], and reducing latency [26] [27]. Furthermore, there are research endeavors that aim to achieve multi-objective optimization, such as providing low-latency services while ensuring cost-effective deployments [28] [29].…”

Section: Related Workmentioning

confidence: 99%

User Experience Oriented Edge Server Placement

Wang,

Liu,

Yang

et al. 2023

Preprint

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Edge computing holds promising prospects and addresses certain limitations of cloud computing. In the realm of edge computing, service providers deploy edge servers in close proximity to users to minimize network latency. To optimize the utilization of limited edge servers, service providers typically adopt a decentralized approach to their placement. However, in real-world edge computing environments, runtime failures are bound to occur after the placement of edge servers, significantly degrading the user experience. Therefore, edge server placement strategies must consider scenarios involving edge server failures.In general, redundant placement techniques are employed to ensure the reliability of services deployed on edge servers. In practice, edge servers consist of one or more physical machines, which leads us to refer to the physical edge server placement as the PESP problem. From the perspective of mobile users, the PESP solution aims to provide cost-effective and stable services. We measure service stability using a robustness metric and, for the first time, introduce the average utility indicator to gauge service efficiency. Average utility pertains to the performance users obtain from edge servers, such as CPU performance. When placing edge servers, we take into account both average utility and robustness to ensure an optimal user experience.In this paper, we formally model the problem of user experience-oriented physical edge server placement (PESPU) and prove its NP-hardness. To tackle this problem, we propose an optimization method based on integer programming (PESPU-O) to find optimal solutions for small-scale problems. For large-scale problems, we present three heuristics to find solutions. Finally, we conduct experiments using real-world datasets to demonstrate the effectiveness and efficiency of our approaches.

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“…The optimization problem of content placement for cooperative caching was studied in [ 10 ]. In [ 11 ], the authors investigated the popularity-based content caching at network edges. The influence of unknown user density and content popularity on the cache placement at edge servers was studied in [ 12 ].…”

Section: Related Workmentioning

confidence: 99%

Optimal Designs of SVC-Based Content Placement and Delivery in Wireless Caching Networks

Zhang

Zhong

Ren

et al. 2023

Sensors

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To allieviate the heavy traffic burden over backhaul links and improve the user’s quality of service (QoS), edge caching plays an important role in wireless networks. This paper investigated the optimal designs of content placement and transmission in wireless caching networks. The contents to be cached and requested were encoded into individual layers by scalable video coding (SVC), and different sets of layers can provide different viewing qualities to end users. The demanded contents were provided by helpers caching the requested layers, or by the macro-cell base station (MBS) otherwise. In the content placement phase, this work formulated and solved the delay minimization problem. In the content transmission phase, the sum rate optimization problem was established. To effectively solve the nonconvex problem, the methods of semi-definite relaxation (SDR), successive convex approximation (SCA), and arithmetic-geometric mean (AGM) inequality were adopted, after which the original problem was transformed into the convex form. The numerical results show that the transmission delay is reduced by caching contents at helpers. Moreover, the fast convergence of the proposed algorithm for solving the sum rate maximization problem is presented, and the sum rate gain of edge caching is also revealed, as compared to the benchmark scheme without content caching.

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Joint Optimization of Server Placement and Content Caching in Mobile Edge Computing Networks

Cited by 7 publications

References 6 publications

A Survey on Cloudlet Computation Optimization in the Mobile Edge Computing Environment

A Survey on Cloudlet Computation Optimization in the Mobile Edge Computing Environment

User Experience Oriented Edge Server Placement

Optimal Designs of SVC-Based Content Placement and Delivery in Wireless Caching Networks

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