Mayfly Taylor Optimisation-Based Scheduling Algorithm with Deep Reinforcement Learning for Dynamic Scheduling in Fog-Cloud Computing

被引:8
|
作者
Shruthi, G. [1 ,2 ]
Mundada, Monica R. [3 ]
Sowmya, B. J. [3 ]
Supreeth, S. [2 ]
机构
[1] VTU, MS Ramaiah Inst Technol, Bengaluru 560054, India
[2] REVA Univ, Sch CSE, Bengaluru 560064, India
[3] MS Ramaiah Inst Technol, Dept Comp Sci & Engn, Bengaluru 560054, India
关键词
RESOURCE-ALLOCATION; TASKS; SYSTEMS; ENERGY;
D O I
10.1155/2022/2131699
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Fog computing domain plays a prominent role in supporting time-delicate applications, which are associated with smart Internet of Things (IoT) services, like smart healthcare and smart city. However, cloud computing is a capable standard for IoT in data processing owing to the high latency restriction of the cloud, and it is incapable of satisfying needs for time-sensitive applications. The resource provisioning and allocation process in fog-cloud structure considers dynamic alternations in user necessities, and also restricted access resources in fog devices are more challenging. The global adoption of IoT-driven applications has led to the rise of fog computing structure, which permits perfect connection for mobile edge and cloud resources. The effectual scheduling of application tasks in fog environments is a challenging task because of resource heterogeneity, stochastic behaviours, network hierarchy, controlled resource abilities, and mobility elements in IoT. The deadline is the most significant challenge in the fog computing structure due to the dynamic variations in user requirement parameters. In this paper, Mayfly Taylor Optimisation Algorithm (MTOA) is developed for dynamic scheduling in the fog-cloud computing model. The developed MTOA-based Deep Q-Network (DQN) showed better performance with energy consumption, service level agreement (SLA), and computation cost of 0.0162, 0.0114, and 0.0855, respectively.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] Fuzzy Reinforcement Learning Algorithm for Efficient Task Scheduling in Fog-Cloud IoT-Based Systems
    Ghafari, Reyhane
    Mansouri, Najme
    [J]. JOURNAL OF GRID COMPUTING, 2024, 22 (04)
  • [2] SQGA: Quantum Genetic Algorithm-based Workflow Scheduling in Fog-Cloud Computing
    Belmahdi, Raouf
    Mechta, Djamila
    Harous, Saad
    Bentaleb, Abdelhark
    [J]. 2022 INTERNATIONAL WIRELESS COMMUNICATIONS AND MOBILE COMPUTING, IWCMC, 2022, : 131 - 136
  • [3] DRLBTSA: Deep reinforcement learning based task-scheduling algorithm in cloud computing
    Mangalampalli, Sudheer
    Karri, Ganesh Reddy
    Kumar, Mohit
    Khalaf, Osama Ibrahim
    Romero, Carlos Andres Tavera
    Sahib, GhaidaMuttashar Abdul
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2024, 83 (03) : 8359 - 8387
  • [4] DRLBTSA: Deep reinforcement learning based task-scheduling algorithm in cloud computing
    Sudheer Mangalampalli
    Ganesh Reddy Karri
    Mohit Kumar
    Osama Ibrahim Khalaf
    Carlos Andres Tavera Romero
    GhaidaMuttashar Abdul Sahib
    [J]. Multimedia Tools and Applications, 2024, 83 : 8359 - 8387
  • [5] Genetic-Based Algorithm for Task Scheduling in Fog-Cloud Environment
    Khiat, Abdelhamid
    Haddadi, Mohamed
    Bahnes, Nacera
    [J]. JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT, 2024, 32 (01)
  • [6] DSPVR: dynamic SFC placement with VNF reuse in Fog-Cloud Computing using Deep Reinforcement Learning
    Zahedi F.
    Mollahoseini Ardakani M.
    Heidary-Sharifabad A.
    [J]. Journal of Ambient Intelligence and Humanized Computing, 2023, 14 (04) : 3981 - 3994
  • [7] Cooperative agents-based approach for workflow scheduling on fog-cloud computing
    Marwa Mokni
    Sonia Yassa
    Jalel Eddine Hajlaoui
    Rachid Chelouah
    Mohamed Nazih Omri
    [J]. Journal of Ambient Intelligence and Humanized Computing, 2022, 13 : 4719 - 4738
  • [8] Cooperative agents-based approach for workflow scheduling on fog-cloud computing
    Mokni, Marwa
    Yassa, Sonia
    Hajlaoui, Jalel Eddine
    Chelouah, Rachid
    Omri, Mohamed Nazih
    [J]. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2021, 13 (10) : 4719 - 4738
  • [9] An Intelligent Scheduling Strategy in Fog Computing System Based on Multi-Objective Deep Reinforcement Learning Algorithm
    Ibrahim, Media Ali
    Askar, Shavan
    [J]. IEEE ACCESS, 2023, 11 : 133607 - 133622
  • [10] Energy-makespan optimization of workflow scheduling in fog-cloud computing
    Ijaz, Samia
    Munir, Ehsan Ullah
    Ahmad, Saima Gulzar
    Rafique, M. Mustafa
    Rana, Omer F.
    [J]. COMPUTING, 2021, 103 (09) : 2033 - 2059