Performance evaluation of metaheuristics algorithms for workload prediction in cloud environment

Kumar, Jitendra; Singh, Anurag

doi:10.1016/j.asoc.2021.107895

Cited by 13 publications

(2 citation statements)

References 36 publications

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“…Self-service ensures that data are provisioned without user interaction, sales calls, new service bookings, and long and complex contractual relationships, empowering customer service and helping utilization. It indicates that the procurement of a cloud service is entirely automated and essential for creating cloud services at a reasonable price [9]. As per NIST, cloud computing can be defined as: "Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing

Zhou,

Lilhore,

et al. 2023

J Cloud Comp

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Load balancing is a serious problem in cloud computing that makes it challenging to ensure the proper functioning of services contiguous to the Quality of Service, performance assessment, and compliance to the service contract as demanded from cloud service providers (CSP) to organizations. The primary objective of load balancing is to map workloads to use computing resources that significantly improve performance. Load balancing in cloud computing falls under the class of concerns defined as "NP-hard" issues due to vast solution space. Therefore it requires more time to predict the best possible solution. Few techniques can perhaps generate an ideal solution under a polynomial period to fix these issues. In previous research, Metaheuristic based strategies have been confirmed to accomplish accurate solutions under a decent period for those kinds of issues. This paper provides a comparative analysis of various metaheuristic load balancing algorithms for cloud computing based on performance factors i.e., Makespan time, degree of imbalance, response time, data center processing time, flow time, and resource utilization. The simulation results show the performance of various Meta-heuristic Load balancing methods, based on performance factors. The Particle swarm optimization method performs better in improving makespan, flow time, throughput time, response time, and degree of imbalance.

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Section: Introductionmentioning

confidence: 99%

Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing

Zhou,

Lilhore,

et al. 2023

J Cloud Comp

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“…The majority of these models concentrate on workloads on small‐scale grid or high performance computing schemes, which reveals less variance when analyzed to the huge‐scale Sustainable Cloud Data Centers (CDC) 15 . These models do not adjust to the real‐world CC with high variable workloads, resulting severe dilapidation in the accuracy of workload prediction 16 …”

Section: Introductionmentioning

confidence: 99%

Workload prediction for enhancing power efficiency of cloud data centers using optimized self‐attention‐based progressive generative adversarial network

Saravanan,

Santhosh Babu

2023

Int J Communication

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SummaryNowadays, future workload prediction is an important requirement in cloud data centers to maintain flexibility and scalability of resources. However, due to unexpected peaks, drops in workload, noise, and redundancy in user requests, there is a considerable variance in resource demands, making it difficult to accurately predict workloads. Therefore, a self‐attention‐based progressive generative adversarial network (SAPGAN) optimized with Giza Pyramids Construction Algorithm (GPCA)‐based workload prediction is proposed for Sustainable Cloud Data Centers (CDC). At first, redundant data in historical data obtained through CDC are filtered utilizing Markov chain random field (MCRF) co‐simulation method. These pre‐processed historical data are supplied to the SAPGAN. The SAPGAN weight parameters are optimized by GPCA. The proposed method is analyzed using 2 benchmark datasets: HTTP traces from Saskatchewan and NASA. The simulation is implemented in JAVA. The performance metrics is examined to verify the efficacy of the proposed technique. The performance of the proposed approach provides 28.70%, 11.87%, and 14.79% higher accuracy; 30.15%, 11.72%, and 18.34% lesser energy consume for the dataset of NASA; and 5.32%, 2.45%, and 5.67% higher accuracy; 12.36%, 24.24%, and 34.16% lesser energy consume for the dataset of Saskatchewan HTTP traces compared with existing methods, such as auto‐adaptive learning in a dynamic cloud environment (AADEA‐WLP‐CDC), a neural network model depending on biphase adaptive learning for anticipating cloud data center workload (BALNN‐WLP‐CDC) and multiple scale ensemble of deep learning framework for multistep‐ahead cloud workload prediction (EMD‐LSTM‐GAN‐WLP‐CDC).

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Effect of partial loading on a three‐area hydro‐thermal system integrated with realistic dish‐Stirling solar thermal system, accurate model of high‐voltage direct link considering virtual inertia and energy storage systems

Bhagat

Saikia

Babu

2021

Int Trans Electr Energ Syst

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This article presents the studies of automatic generation control (AGC) on a three-area hydro-thermal system under various loading conditions. The study at various loading conditions is investigated because the model parameter of steam and hydro depend on their generation schedule. A maiden attempt has been made to design a three degree of freedom tilt integral derivative (3DOF-TID) controller as a secondary controller. Its gains are optimized using a novel bird swarm algorithm (BSA). Comparison of dynamic responses in a hydrothermal system with the proposed 3DOF-TID reveals better performance over 2DOF-TID, 2DOF-PID controllers. It is also explored that the dynamic system responses are improved in thermal power plants and deteriorate in the hydropower plants when loading increases from its nominal values. Moreover, the dynamic system responses are analysed with the realistic dish-Stirling solar thermal system (RDSTS) considering fixed and random solar insolations. The comparative analysis suggests that RDSTS with fixed insolation enhances system dynamics by 5.72% over random insolation. Further, the comparison of dynamic responses of the system with AC tie-line and parallel AC/accurate high-voltage direct (AHVDC) tie-lines reveals that the system dynamics are improved with the parallel AC/AHVDC tie-lines. Furthermore, the inertia is emulated in the parallel AC/AHVDC tie-line using capacitive energy storage (CES), super magnetic energy storage (SMES) systems, and redox flow battery (RFB). Simulations show that the inertia emulated hydro-thermal-RDSTS List of Symbols and Abbreviations: ESS, energy storage systems; B i , frequency bias factor of ith area; FSDS, fixed speed DSTS system; ΔF i , frequency deviation of ith area; GRC, generation rate constraint; G, gate; GHA, grasshopper algorithm; HHO, Harris Hawks optimization; T ghi , mechanical governor time constant of ith area; K DSTs , T DSTS , gain and time constant of DSTS; K CESi , T CESi , gain and time constant of CES; K SMESi , T SMESi , gain and time constant of SMES; K RFBi , T RFBi , gain and time constant of RFB; K Psi , power system inertia gain of ith area; NS, not settled; US, under shoot; TF, transfer function; RDSTS, realistic DSTS; ΔP tiei-j , tie-power deviation of from ith to jth area; R i , speed regulation of ith area; T di1, T di2 , time constant of FSDS; i, area number (i = 1,2, and 3); T pi , power system time constant of ith area; T wi , hydro turbine time constant of ith area; T SCi , steam turbine time constant of ith area; T RH , reheater time constant; T ij , synchronizing coefficient from ith to jth area; T pi , power system time constant of ith area; T co , cross-over time constant; T gi , speed governor time constant of ith area; VSC, voltage source converter; AHVDC, accurate model of high-voltage direct link; OS, overshoot; ST, settling time; DSTS, dish-Stirling solar thermal system; BSA, bird swarm algorithm.

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Performance evaluation of metaheuristics algorithms for workload prediction in cloud environment

Cited by 13 publications

References 36 publications

Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing

Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing

Workload prediction for enhancing power efficiency of cloud data centers using optimized self‐attention‐based progressive generative adversarial network

Effect of partial loading on a three‐area hydro‐thermal system integrated with realistic dish‐Stirling solar thermal system, accurate model of high‐voltage direct link considering virtual inertia and energy storage systems

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