Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing

Son, A-Young; Huh, Eui‐Nam

doi:10.3390/app9173550

Cited by 9 publications

(3 citation statements)

References 48 publications

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“…PlanetLab allows multiple services to run concurrently and continuously, each in its slice of PlanetLab. With hundreds of research projects hosted on PlanetLab, some studies in service placement have also utilised this testbed [263]- [265]. PlanetLab was officially shut down in May 2020 [266].…”

Section: ) Testbedsmentioning

confidence: 99%

Dynamic Service Placement in Multi-Access Edge Computing: A Systematic Literature Review

et al. 2022

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The advent of new cloud-based applications such as mixed reality, online gaming, autonomous driving, and healthcare has introduced infrastructure management challenges to the underlying service network. Multi-access edge computing (MEC) extends the cloud computing paradigm and leverages servers near end-users at the network edge to provide a cloud-like environment. The optimum placement of services on edge servers plays a crucial role in the performance of such service-based applications. Dynamic service placement problem addresses the adaptive configuration of application services at edge servers to facilitate end-users and those devices that need to offload computation tasks. While reported approaches in the literature shed light on this problem from a particular perspective, a panoramic study of this problem reveals the research gaps in the big picture. This paper introduces the dynamic service placement problem and outline its relations with other problems such as task scheduling, resource management, and caching at the edge. We also present a systematic literature review of existing dynamic service placement methods for MEC environments from networking, middleware, applications, and evaluation perspectives. In the first step, we review different MEC architectures and their enabling technologies from a networking point of view. We also introduce different cache deployment solutions in network architectures and discuss their design considerations. The second step investigates dynamic service placement methods from a middleware viewpoint. We review different service packaging technologies and discuss their trade-offs. We also survey the methods and identify eight research directions that researchers follow. Our study categorises the research objectives into six main classes, proposing a taxonomy of design objectives for the dynamic service placement problem. We also investigate the reported methods and devise a solutions taxonomy comprising six criteria. In the third step, we concentrate on the application layer and introduce the applications that can take advantage of dynamic service placement. The fourth step investigates evaluation environments used to validate the solutions, including simulators and testbeds. We introduce real-world datasets such as edge server locations, mobility traces, and service requests used to evaluate the methods. We compile a list of open issues and challenges categorised by various viewpoints in the last step.

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Section: ) Testbedsmentioning

confidence: 99%

Dynamic Service Placement in Multi-Access Edge Computing: A Systematic Literature Review

et al. 2022

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“…In the application placement literature, various challenges limit the user‐centric metrics such as QoS and QoE. To address these challenges, reviewed papers attempt to utilize their proposed approaches that are mainly categorized in six classes: exact solutions 21‐26 that most of them are considered as a form of mixed integer programming (MIP) problem, framework‐based, 27‐29 heuristic‐based, 30‐33 machine learning‐based, 34‐37 metaheuristic‐based, 38‐40 and model‐based 41‐43 approaches. These approaches will be reviewed in the following.…”

Section: Related Workmentioning

confidence: 99%

“…Qian et al 35 presented their service placement strategy based on the hybrid form of greedy algorithm and federated learning to preserve user privacy and QoS in the edge‐cloud system. Son and Huh 36 proposed a service placement strategy based on fuzzy analytical hierarchical process in a distributed cloud system to enhance consumed energy, performance, and costs of such a system. Lin et al 37 studied deep neural networks and constrained completion time and its extension, adaptive completion time, as a solution for service deployment problems to enhance response time and throughput of the edge‐cloud system, respectively.…”

Section: Related Workmentioning

confidence: 99%

Toward an autonomic approach for Internet of Things service placement using gray wolf optimization in the fog computing environment

2021

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Divers and the huge amount of data produced by the Internet of Things (IoT) applications on the one hand, and inherent limitations of local equipment to handle these data, on the other hand, leads to present emerging closer technologies to the end‐users such as fog computing environment. Nevertheless, despite the numerous advantages of such an environment, it still needs state‐of‐the‐art approaches to cope with some inherent limitations. In the literature, resource placement strategies are generally proposed to address such problems, in which the IoT applications are mapped to fog nodes. However, despite its importance, different approaches attempt to enhance the overall system's performance and users' expectations: none of such approaches is satisfactory. In this article, to deploy IoT applications on fog nodes, an autonomic IoT service placement approach based on the gray wolf optimization scheme is proposed, enhancing the system's performance while considering execution costs. Besides, the autonomic concepts help make an appropriate automanagement system that fits better the fog environment's dynamic behavior. Simulation results demonstrate that the proposed approach outperforms the other approaches and converges to the solution in near‐optimal application deployment on fog nodes in respect of the performance of performing services that are 93.7%, the performance of the average waiting time for performed services that are 100%, the remaining services sent to an extra provisioned period that is zero.

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Machine learning methods for service placement: a systematic review

Keshavarz Haddadha,

Rezvani,

MollaMotalebi

et al. 2024

Artif Intell Rev

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With the growth of real-time and latency-sensitive applications in the Internet of Everything (IoE), service placement cannot rely on cloud computing alone. In response to this need, several computing paradigms, such as Mobile Edge Computing (MEC), Ultra-dense Edge Computing (UDEC), and Fog Computing (FC), have emerged. These paradigms aim to bring computing resources closer to the end user, reducing delay and wasted backhaul bandwidth. One of the major challenges of these new paradigms is the limitation of edge resources and the dependencies between different service parts. Some solutions, such as microservice architecture, allow different parts of an application to be processed simultaneously. However, due to the ever-increasing number of devices and incoming tasks, the problem of service placement cannot be solved today by relying on rule-based deterministic solutions. In such a dynamic and complex environment, many factors can influence the solution. Optimization and Machine Learning (ML) are two well-known tools that have been used most for service placement. Both methods typically use a cost function. Optimization is usually a way to define the difference between the predicted and actual value, while ML aims to minimize the cost function. In simpler terms, ML aims to minimize the gap between prediction and reality based on historical data. Instead of relying on explicit rules, ML uses prediction based on historical data. Due to the NP-hard nature of the service placement problem, classical optimization methods are not sufficient. Instead, metaheuristic and heuristic methods are widely used. In addition, the ever-changing big data in IoE environments requires the use of specific ML methods. In this systematic review, we present a taxonomy of ML methods for the service placement problem. Our findings show that 96% of applications use a distributed microservice architecture. Also, 51% of the studies are based on on-demand resource estimation methods and 81% are multi-objective. This article also outlines open questions and future research trends. Our literature review shows that one of the most important trends in ML is reinforcement learning, with a 56% share of research.

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Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing

Cited by 9 publications

References 48 publications

Dynamic Service Placement in Multi-Access Edge Computing: A Systematic Literature Review

Dynamic Service Placement in Multi-Access Edge Computing: A Systematic Literature Review

Toward an autonomic approach for Internet of Things service placement using gray wolf optimization in the fog computing environment

Machine learning methods for service placement: a systematic review

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