Fog computing dynamic load balancing mechanism based on graph repartitioning

Song, Ningning; Gong, Chao; An, Xueli; Zhan, Qiang

doi:10.1109/cc.2016.7445510

Cited by 162 publications

(66 citation statements)

References 1 publication

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“…In this context, Hassan et al [91] and Ye et al [92] have focused on offloading devices in the IoT/end-users stratum to nodes in the fog stratum. Instead, Fricker et al [93], Ningning et al [94] have only focused on the fog stratum and addressed offloading and load redistribution. In turn, Li et al [95] also focus on the fog stratum only and propose coding schemes that lead to redistributing tasks and/or injecting redundant ones in the system.…”

Section: ) Offloading and Load Redistributionmentioning

confidence: 99%

“…From a similar perspective, Ningning et al [94] propose a dynamic load balancing algorithm in the fog stratum. It allows coping with the dynamic arrival and exit of nodes inside a fog domain.…”

Section: ) Offloading and Load Redistributionmentioning

confidence: 99%

“…Instead, as a fog node joins/creates a fog domain, additional/novel resources would be available for IoT/end-user devices that can be connected to it. So far, only one work studies this problem (i.e., [94]) with the objective of enabling dynamic load balancing among fog nodes in a fog domain upon the arrival and exit of a fog node. However, the authors do not account for  No agreement on the degree of heterogeneity among nodes and devices in terms of computing and storage capabilities.…”

Section: ) Mobilitymentioning

confidence: 99%

See 2 more Smart Citations

A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges

Mouradian

Naboulsi

Yangui

et al. 2018

IEEE Commun. Surv. Tutorials

851

405

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Cloud computing with its three key facets (i.e., IaaS, PaaS, and SaaS) and its inherent advantages (e.g., elasticity and scalability) still faces several challenges. The distance between the cloud and the end devices might be an issue for latencysensitive applications such as disaster management and content delivery applications. Service Level Agreements (SLAs) may also impose processing at locations where the cloud provider does not have data centers. Fog computing is a novel paradigm to address such issues. It enables provisioning resources and services outside the cloud, at the edge of the network, closer to end devices or eventually, at locations stipulated by SLAs. Fog computing is not a substitute for cloud computing but a powerful complement. It enables processing at the edge while still offering the possibility to interact with the cloud. This article presents a comprehensive survey on fog computing. It critically reviews the state of the art in the light of a concise set of evaluation criteria. We cover both the architectures and the algorithms that make fog systems. Challenges and research directions are also introduced. In addition, the lessons learned are reviewed and the prospects are discussed in terms of the key role fog is likely to play in emerging technologies such as Tactile Internet.

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Section: ) Offloading and Load Redistributionmentioning

confidence: 99%

Section: ) Offloading and Load Redistributionmentioning

confidence: 99%

Section: ) Mobilitymentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges

Mouradian

Naboulsi

Yangui

et al. 2018

IEEE Commun. Surv. Tutorials

851

405

View full text Add to dashboard Cite

show abstract

“…Song et al [174] pointed out that existing load-balancing algorithms for cloud platforms that operate in a single cluster cannot be directly applied to a dynamic and peer-to-peer fog computing architecture. To realize efficient load-balancing, they abstracted the fog architecture as a graph model where each vertex indicates a node, and the graph edge denotes data dependency between tasks.…”

Section: Load-balancingmentioning

confidence: 99%

Resource Management in Fog/Edge Computing

2019

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Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets, is an upcoming computing paradigm, referred to as fog/edge computing. Fog/edge resources are typically resource-constrained, heterogeneous, and dynamic compared to the cloud, thereby making resource management an important challenge that needs to be addressed. This article reviews publications as early as 1991, with 85% of the publications between 2013-2018, to identify and classify the architectures, infrastructure, and underlying algorithms for managing resources in fog/edge computing.

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“…Due to this uneven distribution, the overload and underload problems exist, which paved the way for load balancing [3,4]. Load balancing [5,6] is a term that contributes to the proper distribution of the resources among all the virtual machines available in the cloud [7][8][9]. Also, it does not place any single virtual machine loaded, but it distributes the workload among the multiple virtual machines [10,11].…”

Section: Introductionmentioning

confidence: 99%

Multiple Objective–Based Modified Exponential Gravitational Search Algorithm to VMM Strategy for Load Balancing in Cloud

Polepally¹,

Chatrapati²

2017

IJIES

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Cloud computing is a trending topic in the field of science and technology since the internet dependent services have been growing rapidly. In this environment, there are a lot of immense infrastructures and resources to satisfy the internet users. When a large number of service requests reach at a particular time, load balancing becomes a necessity. Load balancing involves the effective migration of the resources from the loaded physical machine to the other physical machine. For the effective migration, a method named Modified Exponential Gravitational Search Algorithm based on Virtual Machine Migration strategy (MEGSA-VMM) has been proposed that uses the gravitational concepts for performing the frequency-based velocity computations. MEGSA algorithm is the integration of the gravitational search algorithm and exponential weighted moving average theory. Also, the qualityof-Service (QoS) constraints considered for VM migration are migration cost, migration time, resource usage and energy. Simulation of the proposed method and the comparison of the results obtained, with the traditional methods like Ant-Colony Optimization (ACO), Gravitational Search Algorithm (GSA) and Exponential Gravitational Search Algorithm (EGSA) is performed. The proposed method is found to achieve an optimum migration with a minimum energy at a rate of 0.26 and minimum migration cost at a rate of 0.015.

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Fog computing dynamic load balancing mechanism based on graph repartitioning

Cited by 162 publications

References 1 publication

A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges

A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges

Resource Management in Fog/Edge Computing

Multiple Objective–Based Modified Exponential Gravitational Search Algorithm to VMM Strategy for Load Balancing in Cloud

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