Analysis of Load Balancing Algorithms Used in the Cloud Computing Environment: Advantages and Limitations

Bouflous, Zakariyae; Ouzzif, Mohammed; Bouragba, Khalid

doi:10.1007/978-3-031-18344-7_13

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…The suggested algorithm creates five groups to split tasks belonging to each group and share characteristics such as user type, task type, job size, and work delay. In order to run task-based efficient programs on distributed operating systems and save energy, a real-time dynamic scheduling system was developed [21]. There is currently no optimal multiprocessor solution for the NP-hard job scheduling problem.…”

Section: Previous Workmentioning

confidence: 99%

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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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Section: Previous Workmentioning

confidence: 99%

“…Almost all of these studies have not covered the idea of lowering energy usage and scheduling. The references [20,21,24,25] have used edge computing methodologies. They also employ cloud computing in many aspects of their work.…”

Section: Comparative Analysismentioning

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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“…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 13 . Moreover, the majority of existing TS works fail to capture the potential features of CC including dynamism, elasticity, and heterogeneity in the utilization of computing resources which results in ignoring the needs of user QoS 14 .…”

Section: Introductionmentioning

confidence: 99%

“…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. 13 Moreover, the majority of existing TS works fail to capture the potential features of CC including dynamism, elasticity, and heterogeneity in the utilization of computing resources which results in ignoring the needs of user QoS. 14 Thus, meta-heuristic-based optimization algorithms that can potentially handle huge search space are necessary for scheduling tasks in large-scale applications.…”

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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Increasing the Speed and Performance of the Drupal CMS Server for Industrial IoT Technologies

Satsyk,

Cagáňová,

Reshetylo

et al. 2023

EAI/Springer Innovations in Communication and Computing

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Analysis of Load Balancing Algorithms Used in the Cloud Computing Environment: Advantages and Limitations

Cited by 3 publications

References 13 publications

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

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

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

Increasing the Speed and Performance of the Drupal CMS Server for Industrial IoT Technologies

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