A Clustering-Based Approach to Efficient Resource Allocation in Fog Computing

Shooshtarian, L.; Lan, Dapeng; Taherkordi, Amir

doi:10.1007/978-3-030-30143-9_17

Cited by 18 publications

(14 citation statements)

References 15 publications

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“…Various network types have utilized clustering for topology management, such as MANET [24], VANET [25], WSN [15], and IoT [26]. From the application layer viewpoint, clustering techniques have been introduced for different types of needs such as resource allocation [27], applying reputation models [28], service discovery [29], intrusion detection [30], fault monitoring [31], and anomaly detection [32]. A cluster is composed of a number of nodes (i.e., members) and has one or more Cluster Heads (CH) to manage the members and shared resources.…”

Section: Clustering: Basic Concepts and Taxonomymentioning

confidence: 99%

“…To allocate resources efficiently, optimization algorithms should be leveraged, taking into account the pre-defined optimization parameters in resource allocation. Clustering can be the basis for optimization such that the nodes in the cluster are identified based on the optimization goals [316]. In this way, the management of resources and execution of optimization algorithms will become more efficient.…”

Section: Fog and Edgementioning

confidence: 99%

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A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

Shahraki

Taherkordi

Haugen

et al. 2021

IEEE Trans. Netw. Serv. Manage.

Self Cite

109

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Many Internet of Things (IoT) networks are created as an overlay over traditional ad-hoc networks such as Zigbee. Moreover, IoT networks can resemble ad-hoc networks over networks that support device-to-device (D2D) communication, e.g., D2D-enabled cellular networks and WiFi-Direct. In these ad-hoc types of IoT networks, efficient topology management is a crucial requirement, and in particular in massive scale deployments. Traditionally, clustering has been recognized as a common approach for topology management in ad-hoc networks, e.g., in Wireless Sensor Networks (WSNs). Topology management in WSNs and ad-hoc IoT networks has many design commonalities as both need to transfer data to the destination hop by hop. Thus, WSN clustering techniques can presumably be applied for topology management in ad-hoc IoT networks. This requires a comprehensive study on WSN clustering techniques and investigating their applicability to ad-hoc IoT networks. In this paper, we conduct a survey of this field based on the objectives for clustering, such as reducing energy consumption and load balancing, as well as the network properties relevant for efficient clustering in IoT, such as network heterogeneity and mobility. Beyond that, we investigate the advantages and challenges of clustering when IoT is integrated with modern computing and communication technologies such as Blockchain, Fog/Edge computing, and 5G. This survey provides useful insights into research on IoT clustering, allows broader understanding of its design challenges for IoT networks, and sheds light on its future applications in modern technologies integrated with IoT.

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Section: Clustering: Basic Concepts and Taxonomymentioning

confidence: 99%

Section: Fog and Edgementioning

confidence: 99%

A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

Shahraki

Taherkordi

Haugen

et al. 2021

IEEE Trans. Netw. Serv. Manage.

Self Cite

109

View full text Add to dashboard Cite

show abstract

“…c) Resource-aware placement: Shooshtarian et al [9] proposed a two-phase allocation method that hierarchically represents Fog resources and performs local clustering in each layer to optimize resource utilization and network delay. Taneja et al [1] proposed a resource-aware application mapping approach to optimize resource utilization in Fog.…”

Section: Related Workmentioning

confidence: 99%

Multilayer Resource-aware Partitioning for Fog Application Placement

Samani,

Saurabh,

Prodan

2021

Preprint

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Fog computing emerged as a crucial platform for the deployment of IoT applications. The complexity of such applications require methods that handle the resource diversity and network structure of Fog devices, while maximizing the service placement and reducing the resource wastage. Prior studies in this domain primarily focused on optimizing application-specific requirements and fail to address the network topology combined with the different types of resources encountered in Fog devices.To overcome these problems, we propose a multilayer resourceaware partitioning method to minimize the resource wastage and maximize the service placement and deadline satisfaction rates in a Fog infrastructure with high multi-user application placement requests. Our method represents the heterogeneous Fog resources as a multilayered network graph and partitions them based on network topology and resource features. Afterwards, it identifies the appropriate device partitions for placing an application according to its requirements, which need to overlap in the same network topology partition. Simulation results show that our multilayer resource-aware partitioning method is able to place twice as many services, satisfy deadlines for three times as many application requests, and reduce the resource wastage by up to 15 − 32 times compared to two availability-aware and resourceaware state-of-the-art methods.

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“…Load balancing outperforms traditional approaches in terms of cost and performance. In virtual machines, several high-work-load functions are avoided or added to low-work-load machines [2].Load balancing techniques that are employed toward improve the system performance, preserve system constancy, and develop fault tolerance systems via baking at a low cost. A variety of load balancing techniques are described, each with its individual set of benefits and drawbacks based on the system's capacity to distribute workload.ABSTRACT -Fog computing has become a new trend in the Internet of things domain and cloud computing applications.…”

mentioning

confidence: 99%

Hybrid Load Balancing Algorithm for Fog Computing Environment

Abuhamdah¹,

Al-Shabi²

2022

IJSECS

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Fog computing has become a new trend in the Internet of things domain and cloud computing applications. It is a novel model to achieve the availability, flexibility and better responding time. In spite of that, there is so many challenges facing computing environments such as the misuse of the resources and load-balancing between them, which has a major effect on performance. The requirement of effective and robust load-balancing algorithms is one of the most significant interest in this field. Many researchers suggested various load-balancing algorithms in fog computing, but there is still inefficiency in the system performance and misalignment in load -balancing. This paper will provide a description of numerous concepts such as computing fog, fog nodes, load balancing and then we recommend a load-balancing algorithm to enhance the fog-computing environment performance, which is a hybrid algorithm benefits from the optimizing processing time (OPT) algorithms. In order to explore the proposed algorithm performance, a comparison made with other algorithms. Results indicates that using the proposed optimizing processing time algorithm in load-balancing algorithm has superior response and processing time than the compared algorithms to user requests, and the data total cost centre’s as well.

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A Clustering-Based Approach to Efficient Resource Allocation in Fog Computing

Cited by 18 publications

References 15 publications

A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

Multilayer Resource-aware Partitioning for Fog Application Placement

Hybrid Load Balancing Algorithm for Fog Computing Environment

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