Journal Article10.1109/TVT.2022.3222596
Collaborative Content Caching and Task Offloading in Multi-Access Edge Computing
9
TL;DR: In this article , a task offloading and cache placement algorithm based on multi-objective artificial bee colony is proposed to maximize the hit ratio and minimize the service latency under the constraints of MEC server's computing resources and cache space.
read more
Abstract: Mobile augmented reality (MAR) applications are gaining popularity. There are significant challenges in handling the latency-sensitive and computation-intensive tasks generated by MAR applications on mobile devices. The emergence of mobile edge computing (MEC) provides a new idea to improve the computing capability of resource-constrained mobile terminals. In this paper, we study the task offloading and cache placement of MAR tasks in multi-MEC server cooperation system. The problem of the task offloading and cache placement is formulated to maximize the hit ratio and minimize the service latency under the constraints of MEC server's computing resources and cache space. To solve this problem, we propose a task offloading and cache placement (MOTOCP) algorithm based on multi-objective artificial bee colony. Pareto optimal relation is introduced in the optimization process to find the optimal solution. Extensive evaluation verifies that our proposed algorithm has better performance.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Multi-objective Deployment Optimization of UAVs for Energy-Efficient Wireless Coverage
Xiuming Zhu,Linbo Zhai,Nianxin Li,Yumei Li,Feng Yang +4 more
TL;DR: Multi-objective deployment optimization of UAVs for energy-efficient wireless coverage aims to maximize coverage utility and minimize energy consumption. The problem is NP-hard and a novel Improved Multi-objective Grey Wolf Optimizer (ImMOGWO) algorithm is proposed to find the optimal solution.
5
Lyapunov-guided Deep Reinforcement Learning for service caching and task offloading in Mobile Edge Computing
Nianxin Li,Linbo Zhai,Zeyuan Ma,Xiumin Zhu,Yumei Li +4 more
TL;DR: Lyapunov-guided Deep Reinforcement Learning for service caching and task offloading in Mobile Edge Computing reduces service delay by optimizing edge service caching and task offloading policies.
3
Mobility-Aware Cooperative Service Caching for Mobile Augmented Reality Services in Mobile Edge Computing
Qingyang Fan,Weizhe Zhang,Chen Ling,Rahul Yadav,Desheng Wang,Hui He +5 more
TL;DR: This paper proposes SCRMA, a mobility-aware cooperative service caching strategy for Mobile Augmented Reality services in Mobile Edge Computing, reducing network delay, fairness factor, and total cost by 11.49%, 33.24%, and 17.86% respectively.
2
A Survey on IoT Task Offloading Decisions in Multi-access Edge Computing: A Decision Content Perspective
Dayong Wang,Kamalrulnizam Bin Abu Bakar,Babangida Isyaku +2 more
TL;DR: The development and existing challenges of task offloading decision-making methods are comprehensively demonstrated, and future research directions are proposed for IoT task offloading decision-making in MEC.
References
Analyzing the video popularity characteristics of large-scale user generated content systems
TL;DR: This paper empirically shows how UGC services are fundamentally different from traditional VoD services, and analyzes the intrinsic statistical properties of UGC popularity distributions, which discuss opportunities to leverage the latent demand for niche videos (or the so-called "the Long Tail" potential).
A Survey on Mobile Augmented Reality With 5G Mobile Edge Computing: Architectures, Applications, and Technical Aspects
TL;DR: The landscape of MAR through the past and its future prospects with respect to the 5G systems and complementary technology MEC are discussed and an informative analysis of the network formation of current and future MAR systems in terms of cloud, edge, localized, and hybrid architectural options is provided.
NOMA-Assisted Multi-Access Mobile Edge Computing: A Joint Optimization of Computation Offloading and Time Allocation
TL;DR: By exploiting non-orthogonal multiple access (NOMA) for improving the efficiency of multi-access radio transmission, this paper studies the NOMA-enabled multi- access MEC and proposes efficient algorithms to find the optimal offloading solution.
324
Caching and operator cooperation policies for layered video content delivery
Konstantinos Poularakis,George Iosifidis,Antonios Argyriou,Iordanis Koutsopoulos,Leandros Tassiulas +4 more
- 10 Apr 2016
TL;DR: It is shown that the problem of finding the caching configuration of video encoding layers that minimizes average delay for a network operator is NP-Hard, and a pseudopolynomial-time optimal solution is established using a connection with the multiple-choice knapsack problem.
142
Task Execution Cost Minimization-Based Joint Computation Offloading and Resource Allocation for Cellular D2D MEC Systems
TL;DR: A heuristic algorithm is proposed that successively solves computation offloading subproblem and resource allocation subproblem by Kuhn–Munkres algorithm and Lagrangian dual method, respectively to achieve efficient information interaction and task management.
70