Particle Swarm Optimization-Based Energy-Aware Task Scheduling Algorithm in Heterogeneous Cloud

Pradhan, Roshni; Satapathy, Suresh Chandra

doi:10.1007/978-981-19-4990-6_40

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…Starting population of Archimedes optimization approach to begin the optimization procedure. Initializing the position of all the objects is given in equation (7).…”

Section: 81mentioning

confidence: 99%

“…4,5 According to Natural Resources Defense Council report, the data centers in the United States utilized 91 billion kWh of energy at 2013 that is parallel to 34 sizable coal-fired plants. 6,7 The energy consuming data centers is anticipated to increase by 53% by 2020 to reach 140 billion kWh, which is equivalent to 50 power plants' annual output and will cost American business $13 billion in electricity costs and almost 100 million metric tons carbon pollution annually. 8 As a result of the cloud's increased operational costs brought on by excessive energy use and the knowledge of global warming, numerous efforts have been made to reduce the energy consumption of data centers.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, cloud computing infrastructure is required to run massive data centers with thousands of processing nodes that consumes more electricity 4,5 . According to Natural Resources Defense Council report, the data centers in the United States utilized 91 billion kWh of energy at 2013 that is parallel to 34 sizable coal‐fired plants 6,7 . The energy consuming data centers is anticipated to increase by 53% by 2020 to reach 140 billion kWh, which is equivalent to 50 power plants' annual output and will cost American business $13 billion in electricity costs and almost 100 million metric tons carbon pollution annually 8 .…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Pelican and Archimedes optimization algorithm fostered energy aware task scheduling in heterogeneous virtualized cloud computing

S.,

Shanmugam,

et al. 2023

Trans Emerging Tel Tech

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Energy reduction is a key issue of virtualized cloud computing (CC) schemes, because it provide significant benefits, like lower operating costs, improving system effectiveness, securing the environment. Energy‐efficient task scheduling is a feasible mode to attain these objectives. Nevertheless, it is difficult to match cloud resources to user requests in a way that improves performance while meeting a user‐defined deadline for energy consumption reduction. Therefore, hybrid Pelican and Archimedes optimization algorithm fostered energy‐aware task scheduling in heterogeneous virtualized cloud computing (TS‐HVCC‐Hyb‐POA‐AOA) is proposed in this article. A hybrid Pelican and Archimedes optimization algorithm (Hyb‐POA‐AOA) is used to lessen the task duration and the consumption of power in the cloud. For evaluation, the cloud environment uses the provided environment hybrid Pelican and Archimedes optimization algorithm for execute a few common workloads at the simulated data center. The performance metrics, like makespan right skewed analysis, makespan left skewed analysis, energy consumption right skewed analysis, energy consumption left skewed analysis, availability and resource utilization is considered. The performance of the proposed TS‐HVCC‐Hyb‐POA‐AOA method provides 23.69%, 29.50%, and 36.78% higher resource utilization; 38.23%, 31.35%, and 26.19% lower consumption of energy left skewed analysis and 34.52%, 30.28%, and 23.54% lower makespan for right skewed analysis compared with existing methods such as; task scheduling in heterogeneous cloud environment with gray wolf optimization algorithm (TS‐HVCC‐GWOA), hybrid whale optimization approach and differential evolution optimization for multiple objective virtual machine programming at cloud services (TS‐HVCC‐WOA), energy and performance planning in TasterkHeterek virtualized cloud computing using particle swarm optimization algorithm (TS‐HVCC‐PSOA).

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“…Starting population of Archimedes optimization approach to begin the optimization procedure. Initializing the position of all the objects is given in equation (7).…”

Section: 81mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Pelican and Archimedes optimization algorithm fostered energy aware task scheduling in heterogeneous virtualized cloud computing

S.,

Shanmugam,

et al. 2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…It fundamentally revolves around merging artificial intelligence with optimization techniques for enhancing outcomes. This will optimize the consumption of resources, minimize latency, and improve the overall performance of the system [6,7]. This research holds great importance not only in terms of operating efficiency, but also in meeting user expectations, assuring cost-effectiveness, and inspiring sustainability by increasing energy usage.…”

Section: Introductionmentioning

confidence: 99%

Improving Task Scheduling In Cloud Datacenters By Implementation Of An Intelligent Scheduling Algorithm

Mohammad

2024

IJCAI

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LATA: learning automata-based task assignment on heterogeneous cloud computing platform

Gheisari,

ShokrZadeh

2024

J Supercomput

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A cloud computing environment is a distributed system where idle resources are accessible across a wide area network, such as the Internet. Due to the diverse specifications of these resources, computational clouds exhibit high heterogeneity. Task scheduling, the process of dispatching cloud applications onto processing nodes, becomes a critical challenge in such environments. Ensuring high utilization in this heterogeneous environment entails identifying suitable machines or virtual machines capable of efficiently executing jobs, constituting a multi-objective optimization problem. This paper proposes a dynamic Learning Automata-based Task Assignment algorithm, named LATA, to address this challenge. In the algorithm, each application is represented as a Directed Acyclic Graph, with tasks as nodes and data dependencies as edges. Initially, tasks are grouped based on their data dependencies to consolidate independent tasks into one group. Subsequently, a variable-structure learning automaton is assigned to each group of tasks to identify appropriate task-machine combinations. The primary objectives of LATA include minimizing makespan and energy consumption by facilitating efficient task placement to achieve load balance and maximize resource utilization. Additionally, an enhancement is proposed, involving the use of a different grouping policy prior to task assignment to further improve performance. Computer simulation results demonstrate the superior performance of the proposed algorithms in highly heterogeneous environments compared to state-of-the-art algorithms. Notably, total execution time and energy consumption decrease by up to 50% and 37%, respectively.

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Particle Swarm Optimization-Based Energy-Aware Task Scheduling Algorithm in Heterogeneous Cloud

Cited by 4 publications

References 18 publications

Hybrid Pelican and Archimedes optimization algorithm fostered energy aware task scheduling in heterogeneous virtualized cloud computing

Hybrid Pelican and Archimedes optimization algorithm fostered energy aware task scheduling in heterogeneous virtualized cloud computing

Improving Task Scheduling In Cloud Datacenters By Implementation Of An Intelligent Scheduling Algorithm

LATA: learning automata-based task assignment on heterogeneous cloud computing platform

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