Resource allocation strategies and task scheduling algorithms for cloud computing: A systematic literature review

The concept of cloud computing has completely changed how computational resources are delivered and used. By enabling on-demand access to collective computing resources through the internet. While this technological shift offers unparalleled flexibility, it also brings considerable challenges, espec...

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Veröffentlicht in:Journal of intelligent systems Jg. 34; H. 1; S. 2309 - 31
Hauptverfasser: Kareem Awad, Waleed, Zainol Ariffin, Khairul Akram, Nazri, Mohd Zakree Ahmad, Yassen, Esam Taha
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin De Gruyter 01.01.2025
Walter de Gruyter GmbH
Schlagworte:
Algorithms
Cloud computing
Completion time
Energy consumption
heuristic approach
Heuristic methods
hyper heuristic approach and mathematical approach
Literature reviews
Optimization
Performance degradation
Resource allocation
Resource management
Resource scheduling
Resource utilization
Scheduling
Systematic review
Task scheduling
ISSN:2191-026X, 0334-1860, 2191-026X
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Abstract The concept of cloud computing has completely changed how computational resources are delivered and used. By enabling on-demand access to collective computing resources through the internet. While this technological shift offers unparalleled flexibility, it also brings considerable challenges, especially in scheduling and resource allocation, particularly when optimizing multiple objectives in a dynamic environment. Efficient allocation and scheduling of resources are critical in cloud computing, as they directly impact system performance, resource utilization, and cost efficiency in dynamic and heterogeneous conditions. Existing approaches often face difficulties in balancing conflicting objectives, such as reducing task completion time while staying within budget constraints or minimizing energy consumption while maximizing resource utilization. As a result, many solutions fall short of optimal performance, leading to increased costs and degraded performance. This systematic literature review (SLR) focuses on research conducted between 2019 and 2023 on scheduling and resource allocation in cloud environment. Following preferred reporting items for systematic reviews and meta-analyses guidelines, the review ensures a transparent and replicable process by employing systematic inclusion criteria and minimizing bias. The review explores key concepts in resource management and classifies existing strategies into mathematical, heuristic, and hyper-heuristic approaches. It evaluates popular algorithms designed to optimize key metrics such as energy consumption, resource utilization, cost reduction, makespan minimization, and performance satisfaction. Through a comparative analysis, the SLR discusses the strengths and limitations of various resource management schemes and identifies emerging trends. It underscores a steady growth in research within this field, emphasizing the importance of developing efficient allocation strategies to address the complexities of modern cloud systems. The findings provide a comprehensive overview of current methodologies and pave the way for future research aimed at tackling unresolved challenges in cloud computing resource management. This work serves as a valuable resource for practitioners and academics seeking to optimize scheduling and allocation in dynamic cloud environments, contributing to advancements in resource management strategies of cloud computing.
AbstractList The concept of cloud computing has completely changed how computational resources are delivered and used. By enabling on-demand access to collective computing resources through the internet. While this technological shift offers unparalleled flexibility, it also brings considerable challenges, especially in scheduling and resource allocation, particularly when optimizing multiple objectives in a dynamic environment. Efficient allocation and scheduling of resources are critical in cloud computing, as they directly impact system performance, resource utilization, and cost efficiency in dynamic and heterogeneous conditions. Existing approaches often face difficulties in balancing conflicting objectives, such as reducing task completion time while staying within budget constraints or minimizing energy consumption while maximizing resource utilization. As a result, many solutions fall short of optimal performance, leading to increased costs and degraded performance. This systematic literature review (SLR) focuses on research conducted between 2019 and 2023 on scheduling and resource allocation in cloud environment. Following preferred reporting items for systematic reviews and meta-analyses guidelines, the review ensures a transparent and replicable process by employing systematic inclusion criteria and minimizing bias. The review explores key concepts in resource management and classifies existing strategies into mathematical, heuristic, and hyper-heuristic approaches. It evaluates popular algorithms designed to optimize key metrics such as energy consumption, resource utilization, cost reduction, makespan minimization, and performance satisfaction. Through a comparative analysis, the SLR discusses the strengths and limitations of various resource management schemes and identifies emerging trends. It underscores a steady growth in research within this field, emphasizing the importance of developing efficient allocation strategies to address the complexities of modern cloud systems. The findings provide a comprehensive overview of current methodologies and pave the way for future research aimed at tackling unresolved challenges in cloud computing resource management. This work serves as a valuable resource for practitioners and academics seeking to optimize scheduling and allocation in dynamic cloud environments, contributing to advancements in resource management strategies of cloud computing.
Author Kareem Awad, Waleed
Nazri, Mohd Zakree Ahmad
Yassen, Esam Taha
Zainol Ariffin, Khairul Akram
Author_xml – sequence: 1
  givenname: Waleed
  surname: Kareem Awad
  fullname: Kareem Awad, Waleed
  email: p131629@siswa.ukm.edu.my
  organization: College of Computer Science and Information Technology, University of Anbar, Al Anbar, 31001, Iraq
– sequence: 2
  givenname: Khairul Akram
  surname: Zainol Ariffin
  fullname: Zainol Ariffin, Khairul Akram
  email: k.akram@ukm.edu.my
  organization: Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600, Bandar Baru Bangi, Malaysia
– sequence: 3
  givenname: Mohd Zakree Ahmad
  surname: Nazri
  fullname: Nazri, Mohd Zakree Ahmad
  email: zakree@ukm.edu.my
  organization: Data Mining and Optimization Research Group (DMO), Centre for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600, Bandar Baru Bangi, Malaysia
– sequence: 4
  givenname: Esam Taha
  surname: Yassen
  fullname: Yassen, Esam Taha
  email: co.esamtaha@uoanbar.edu.iq
  organization: College of Computer Science and Information Technology, University of Anbar, Al Anbar, 31001, Iraq
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Snippet The concept of cloud computing has completely changed how computational resources are delivered and used. By enabling on-demand access to collective computing...
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SubjectTerms Algorithms
Cloud computing
Completion time
Energy consumption
heuristic approach
Heuristic methods
hyper heuristic approach and mathematical approach
Literature reviews
Optimization
Performance degradation
Resource allocation
Resource management
Resource scheduling
Resource utilization
Scheduling
Systematic review
Task scheduling
Title Resource allocation strategies and task scheduling algorithms for cloud computing: A systematic literature review
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Volume 34
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