An Energy-optimized Embedded load balancing using DVFS computing in Cloud Data centers

Javadpour, Amir; Sangaiah, Arun Kumar; Pinto, Pedro; Ja’fari, Forough; Zhang, Weizhe; Abadi, Ali Majed Hossein; Ahmadi, Sayyed Hamid Reza

doi:10.1016/j.comcom.2022.10.019

Cited by 54 publications

(16 citation statements)

References 18 publications

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“…110 Dynamic load adjustment is a technique for distributing the workload among the current hosts by executing virtual machine migration and analyzing workload performance at various time intervals. 111 Using two strategies, controller clustering, which targets the dynamic controller, and switch migration, which manages the load distribution between controllers to maintain load, Hu et al 49 present a complete review that explains the multi-controller concept and highlights the role of load balancing.…”

Section: Controller Load Balancingmentioning

confidence: 99%

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Controller placement problem during SDN deployment in the ISP/Telco networks: A survey

Sapkota,

Dawadi,

Joshi

2023

Engineering Reports

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With the successful implementation of Software‐Defined Networking (SDN) in data center networking, the way forward for its deployment in the ISP/Telco network is becoming prominent. Small and medium‐sized networks may easily adopt SDN. The research on SDN deployment and implementation for a large‐scale network is continuing. This paper properly presents the current research status of Controller Placement Problem (CPP) and Multi‐CPP (MCPP) over SDN with their specific challenges and provides a comprehensive review of the major performance metrics, that is, latency, and controller load balancing techniques. This survey highlights the use of network partitioning‐based CPP and clustering approaches and their benefits in the context of SDN deployment. Moreover, this paper highlights the importance of implementing SDN and SDN security issues into ISP/Telco networks. Finally, we provide some key areas of ongoing research and discuss the future research direction regarding the various SDN‐based Controller Placement (CP) issues in the next‐generation IP and advanced networking technologies.

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Section: Controller Load Balancingmentioning

confidence: 99%

“…Due to commercial concerns, load balancing is one of the most crucial topics in SDN‐related research 110 . Dynamic load adjustment is a technique for distributing the workload among the current hosts by executing virtual machine migration and analyzing workload performance at various time intervals 111 …”

Section: Performance Metrics In Sdnmentioning

confidence: 99%

Controller placement problem during SDN deployment in the ISP/Telco networks: A survey

Sapkota,

Dawadi,

Joshi

2023

Engineering Reports

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“…Moreover, the utilization of meta‐heuristic algorithms for handling the problems of TS in the Cloud has visualized significant enhancements by minimizing the search space of solutions to attain efficiency. However, the incorporation of meta‐heuristic algorithms increases the computational time and in some specific cases provides local optimal solutions when they handle huge solution space 12 . They are also prone to imbalanced trade‐offs between exploration and exploitation and suffer from premature convergence.…”

Section: Introductionmentioning

confidence: 99%

“…However, the incorporation of meta-heuristic algorithms increases the computational time and in some specific cases provides local optimal solutions when they handle huge solution space. 12 They are also prone to imbalanced trade-offs between exploration and exploitation and suffer from premature convergence. These limitations of meta-heuristic algorithms have maximized the probability of resulting in sub-optimal solutions that directly influence service provisioning performance with respect to the satisfaction of required QoS objectives.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Guided Differential Evolution‐based Slime Mould Algorithm‐based efficient Multi‐objective Task Scheduling for Cloud Computing Environments

Nithiavathy,

Janakiraman,

Deva Priya

2023

Trans Emerging Tel Tech

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In a Cloud Computing (CC) environment, users are charged based on on‐demand resource utilization and expected Quality of Service (QoS). Multi‐Objective‐based Task Scheduling (MOTS) problem formulated for achieving the expected QoS in a CC scenario is always NP‐complete. The majority of existing Task Scheduling (TS) algorithms are not efficient in determining global optimal solutions due to huge space offered by large‐scale problem instances and characteristics inherent in NP‐complete problems. In this article, an Adaptive Guided Differential Evolution‐based Slime Mould Algorithm (AGDESMA) is proposed for dealing with the problem of Multi‐Objective large‐scale TS optimization over Infrastructure‐as‐a‐Service (IaaS) CC environment. The proposed AGDESMA is formulated with the merits of Slime Mould Algorithm (SMA) and Adaptive Guided Differential Evolution (AGDE) for achieving a proper balance amid exploitation and exploration phases and preventing the solutions from getting trapped into minimum local regions. It specifically adopts the method of AGDE for preventing premature convergence with maximized diversity of population and local search of swarm agents. The experimental validation of the proposed AGDESMA is conducted using CloudSim based on the synthesized and standard workload traces with larger instances up to a maximum of 5000. The results confirm the predominance of the proposed AGDESMA scheme over benchmarked schemes based on computational overhead and trade‐off between makespan and financial cost.

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“…So users can access their data anywhere in the world, and they do not need high-performance hardware and storage systems because all computing and storage operations are performed by cloud service providers and well-equipped and advanced servers. Meanwhile, the scheduling and resource allocation problem in cloud computing is important because it directly affects the amount of energy consumption and reduction of latency in service provision [7]. A scheduling system in cloud computing can be any mechanism to ensure the provision of application requirements.…”

Section: Introductionmentioning

confidence: 99%

Energy Consumption Reduction Strategy and a Load Balancing Mechanism for Cloud Computing in IoT Environment

Zhang¹,

Li²

2022

IJACSA

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Modern networks are built to be linked, agile, programmable, and load-efficient in order to overcome the drawbacks of an unbalanced network, such as network congestion, elevated transmission costs, low reliability, and other problems. The many technological devices in our environment have a considerable potential to make the connected world concept a reality. The Internet of Things (IoT) is a research community initiative to bring this idea to life. Cloud computing is crucial to making it happen. The load balancing and scheduling significantly increase the possibility of using resources and provide the grounds for reliability. Even if the intended node is under low or high loading, the load balancing techniques can increase its efficiency. This paper presents a scheduling technique for optimal resource allocation with enhanced particle swarm optimization and virtual machine live migration technique. The proposed technique prevents excessive or low server overloads through optimal allocation and scheduling tasks to physical servers. The proposed strategy was implemented in the cloudsim simulator environment and compared and showed that the proposed method is more effective and is well suited to decreasing execution time and energy consumption. This solution provides grounds to reduce energy consumption in the cloud environment while decreasing execution time. The simulation results showed that the amount of energy consumption compared to particle crowding has decreased by 10% and compared to PSO (Particle Swarm Optimization) scheduling by more than 8%. Also, the execution time has been reduced by 18% compared to particle swarm scheduling and by 8% compared to PSO.

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An Energy-optimized Embedded load balancing using DVFS computing in Cloud Data centers

Cited by 54 publications

References 18 publications

Controller placement problem during SDN deployment in the ISP/Telco networks: A survey

Controller placement problem during SDN deployment in the ISP/Telco networks: A survey

Adaptive Guided Differential Evolution‐based Slime Mould Algorithm‐based efficient Multi‐objective Task Scheduling for Cloud Computing Environments

Energy Consumption Reduction Strategy and a Load Balancing Mechanism for Cloud Computing in IoT Environment

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