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Research Article Open access CC BY 4.0

Comprehensive Survey for Cloud Computing Based Nature-Inspired Algorithms Optimization Scheduling

Hazha Saeed Yahia, Subhi R. M. Zeebaree, Mohammed A. M. Sadeeq, Nareen O. M. Salim, Shakir Fattah Kak, Adel AL-Zebari, Azar Abid Salih, Helat Ahmed Hussein

Asian Journal of Research in Computer Science · pp. 1–16 · Published 3 May 2021

10.9734/ajrcos/2021/v8i230195

Abstract

Many applications in the real world include optimizing specific targets, such as cost minimization, energy conservation, climate, and maximizing production, efficiency, and sustainability. The optimization problem is strongly non-linear with multifunctional landscapes under several dynamic, non-linear constraints in some instances. It is challenging to address those issues. Also, with the increasing strength of modern computers, simplistic brute force methods are still inefficient and unwanted. Practical algorithms are also vital for these implementations whenever possible. Cloud computing has become an essential and popular emerging computing environment that supports on-demand services and provides internet-based services. Cloud computing allows a range of services and tools to be easily accessed from anywhere in the world. Since cloud computing has global access to its services, there will always be threats and challenges facing its servers and services, such as; task scheduling, security, energy efficiency, network load, and other challenges. In the research area, many algorithms have been addressed to solve these problems. This paper investigates relevant analysis and surveys on the above topics, threats, and outlooks. This paper offers an overview of nature-inspired algorithms, their applications, and valuation, emphasizing cloud computing problems. Many problems in science and engineering can be viewed as optimization problems with complex non-linear constraints. Highly nonlinear solutions typically need advanced optimization algorithms, and conventional algorithms can have difficulty addressing these issues. Because of its simplicity and usefulness, nature-inspired algorithms are currently being used. There are nevertheless some significant concerns with computing and swarming intelligence influenced by evolution.

Cloud computing nature-inspired algorithms optimization scheduling

References (48)

  1. 1 Multimodal Emotion Recognition using Deep Learning [DOI]
  2. 2 Workflow Scheduling Using Hybrid GA‐PSO Algorithm in Cloud Computing [DOI]
  3. 3 eHealth Cloud Security Challenges: A Survey [DOI]
  4. 4 Heuristic initialization of PSO task scheduling algorithm in cloud computing [DOI]
  5. 5 State of Art for Semantic Analysis of Natural Language Processing [DOI]
  6. 6 Cloud Computing Virtualization of Resources Allocation for Distributed Systems [DOI]
  7. 7 Energy Management for Internet of Things via Distributed Systems [DOI]
  8. 8 Task scheduling for cloud computing using multi-objective hybrid bacteria foraging algorithm [DOI]
  9. 9 Task Scheduling Algorithms in Cloud Computing: A Review [DOI]
  10. 10 A Review on Computational Intelligence Techniques in Cloud and Edge Computing [DOI]
  11. 11 A comprehensive survey of Crow Search Algorithm and its applications [DOI]
  12. 12 Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm [DOI]
  13. 13 Comparison Among Cloud Technologies and Cloud Performance [DOI]
  14. 14 An enhancement of task scheduling in cloud computing based on imperialist competitive algorithm and firefly algorithm [DOI]
  15. 15 Sufficient Comparison Among Cloud Computing Services: IaaS, PaaS, and SaaS: A Review [DOI]
  16. 16 Amelioration of task scheduling in cloud computing using crow search algorithm [DOI]
  17. 17 Survey on Scheduling Issues in Cloud Computing [DOI]
  18. 18 A Survey of Optical Fiber Communications: Challenges and Processing Time Influences [DOI]
  19. 19 Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures [DOI]
  20. 20 A State of Art: Survey for Concurrent Computation and Clustering of Parallel Computing for Distributed Systems [DOI]
  21. 21 Unified Ontology Implementation of Cloud Computing for Distributed Systems [DOI]
  22. 22 Advantages to Disadvantages of Cloud Computing for Small-Sized Business [DOI]
  23. 23 HIGA: Harmony-inspired genetic algorithm for rack-aware energy-efficient task scheduling in cloud data centers [DOI]
  24. 24 IoT Provisioning QoS based on Cloud and Fog Computing [DOI]
  25. 25 A State of Art for Smart Gateways Issues and Modification [DOI]
  26. 26 A state-of-the-art survey on semantic similarity for document clustering using GloVe and density-based algorithms [DOI]
  27. 27 Design and Implementation of Electronic Student Affairs System [DOI]
  28. 28 The Impact of Test Case Generation Methods on the Software Performance: A Review [DOI]
  29. 29 Cost-effective workflow scheduling approach on cloud under deadline constraint using firefly algorithm [DOI]
  30. 30 A Hybrid Algorithm for Scheduling Scientific Workflows in Cloud Computing [DOI]
  31. 31 A Review of Person Recognition Based on Face Model [DOI]
  32. 32 Resource Allocation in Fog Computing: A Review [DOI]
  33. 33 Applications and Evaluations of Bio-Inspired Approaches in Cloud Security: A Review [DOI]
  34. 34 Cloud Task Scheduling Based on Swarm Intelligence and Machine Learning [DOI]
  35. 35 Chaotic symbiotic organisms search for task scheduling optimization on cloud computing environment [DOI]
  36. 36 Utility based load balancing using firefly algorithm in cloud [DOI]
  37. 37 Combination of Multi Classification Algorithms for Intrusion Detection System
  38. 38 ELMS–DPU Ontology Visualization with Protégé VOWL and Web VOWL
  39. 39 Improvised Distributions framework of Hadoop: A review [DOI]
  40. 40 Resources Allocation for Distributed Systems: A Review [DOI]
  41. 41 Task Scheduling for cloud computing Based on Firefly Algorithm [DOI]
  42. 42 Cuckoo-inspired Job Scheduling Algorithm for Cloud Computing [DOI]
  43. 43 Nature Inspired Algorithms in Cloud Computing: A Survey [DOI]
  44. 44 Recourses Utilization in a Distributed System: A Review [DOI]
  45. 45 Cloud computing: Approach, Structure and Security [DOI]
  46. 46 custom: Semantic Search Engine Optimisation (SSEO) for Dynamic Websites: A Review
  47. 47 Nature-inspired algorithms for a scheduling problem in operational planning [DOI]
  48. 48 Remote Controlling Distributed Parallel Computing System over the Cloud (RCDPCSC) [DOI]

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