Resource provisioning in edge/fog computing: A Comprehensive and Systematic Review

Shakarami, Ali; Shakarami, Hamid; Ghobaei‐Arani, Mostafa; Nikougoftar, Elaheh; Faraji-Mehmandar, Mohammad

doi:10.1016/j.sysarc.2021.102362

Cited by 75 publications

(31 citation statements)

References 54 publications

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“…Fog computing uses edge devices, e.g., routers and gateways, by users for storage and computing purpose. Finally, MEC places servers in the BSs [29], [30], [31] and is a common concept discussed in 3GPP documents.…”

Section: Cloud Computing and Mobile Edge Computingmentioning

confidence: 99%

Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey

et al. 2022

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One of the key foundations of 5 th Generation (5G) and beyond 5G (B5G) networks is network slicing, in which the network is partitioned into several separated logical networks, taking into account the requirements of diverse applications. In this context, resource management is of great importance to instantiate and operate network slices and meet their performance and functional requirements. Resource management in Radio Access Networks (RANs) is associated with a range of challenges due to network dynamics and the specific requirements of each application while ensuring performance isolation. In this paper, we present a survey on state-of-the-art works that employ Machine Learning (ML) techniques in RAN slicing. We begin by reviewing the challenges, then we review the existing papers on resource management in a comprehensive manner, and classify the papers based on the used ML algorithm, the addressed challenges, and the type of allocated resources. We evaluate the maturity of current methods and state a number of open challenges and some solutions to address these challenges in RAN resource management.

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Section: Cloud Computing and Mobile Edge Computingmentioning

confidence: 99%

Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey

et al. 2022

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“…In the simulated edge-cloud system, there are 1000 tasks randomly requesting one of 100 services. For every task, its required computing size is randomly set between 0.5-1.2GHz, and the amount of its input data is in the range of [5,6]MB, referring to [18]. The deadline of each task is set between 1 and 5 seconds.…”

Section: A Experiments Designmentioning

confidence: 99%

“…The mobile and IoT users' requirements cannot be guaranteed only by cloud computing for service delivery, due to the abundant variety and quantity of Internet services and the high network latency of the cloud [5]. Therefore, more and more service providers use edge computing to improve the service quality, by placing some computing resources close to user devices [6].…”

Section: Introductionmentioning

confidence: 99%

A Particle Swarm Optimization With Lévy Flight for Service Caching and Task Offloading in Edge-Cloud Computing

Gao

Tang

et al. 2022

IEEE Access

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Edge-cloud computing is an efficient approach to address the high latency issue in mobile cloud computing for service provisioning, by placing several computing resources close to end devices. To improve the user satisfaction and the resource efficiency, this paper focuses on the task offloading and service caching problem for providing services by edge-cloud computing. This paper formulates the problem as a constrained discrete optimization problem, and proposes a hybrid heuristic method based on Particle Swarm Optimization (PSO) to solve the problem in polynomial time. The proposed method, LMPSO, exploit PSO to solve the service caching problem. To avoid PSO trapping into local optimization, LMPSO adds lévy flight movement for particle updating to improve the diversity of particle. Given the service caching solution, LMPSO uses a heuristic method with three stages for task offloading, where the first stage tries to make full use of cloud resources, the second stage uses edge resources for satisfying requirements of latency-sensitive tasks, and the last stage improves the overall performance of task executions by re-offloaded some tasks from the cloud to edges. Simulated experiment results show that LMPSO has upto 156% better user satisfaction, upto 57.9% higher resource efficiency, and upto 155% greater processing efficiency, in overall, compared with other seven heuristic and meta-heuristic methods.

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“…Resource autoscaling is a crucial way to provide elastic resource provisioning in cloud/edge computing [18]. Some reactive autoscaling methods perform resource scaling when the runtime consumptions of resources, such as CPU utilization, memory utilization, or network bandwidth, exceed thresholds.…”

Section: Related Workmentioning

confidence: 99%

Cushion : A proactive resource provisioning method to mitigate SLO violations for containerized microservices

et al. 2022

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Deploying microservices in container-based cloud environments increases the agility of resource scaling. However, the delay in autoscaling for microservices caused by container cold start results in response time service-level objectives (SLO) violations under burst workloads. This paper proposes Cushion, a proactive resource provisioning method for containerized microservices to mitigate SLO violations caused by burst workloads, which promptly schedules workloads to reserved container instances when workloads suddenly increase and meanwhile steadily scales the container instances according to the SLOs of the microservices. Cushion was evaluated through prototype-based experiments in a containerized testbed using a benchmark microservice and four web workload traces by comparing it against existing methods. The experimental results show 9.12x times lower SLO violations, 23.75% higher throughput with 24.35% more CPU usage, and 4.85% more memory usage compared to the baseline methods.

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Resource provisioning in edge/fog computing: A Comprehensive and Systematic Review

Cited by 75 publications

References 54 publications

Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey

Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey

A Particle Swarm Optimization With Lévy Flight for Service Caching and Task Offloading in Edge-Cloud Computing

Cushion : A proactive resource provisioning method to mitigate SLO violations for containerized microservices

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