A Multi-object Optimization Cloud Workflow Scheduling Algorithm Based on Reinforcement Learning

Wu, Jiahao; Peng, Zhiping; Cui, Delong; Li, Qirui; He, Jiping

doi:10.1007/978-3-319-95933-7_64

Cited by 12 publications

(6 citation statements)

References 6 publications

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“…In [16], an improved fuzzy logic rule with GT to balance and control the load between the physical machines has been presented. In [17], a Q-learning model to optimize the deadline and balance load for a given task having a weighted-objective function to schedule the workload has been presented. In [18], a technique, RADAR, to allocate resources to a given task in the cloud environment has been presented.…”

Section: Literature Surveymentioning

confidence: 99%

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Reliable and efficient webserver management for task scheduling in edge-cloud platform

Sangani¹,

Patil²

2023

IJECE

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<span lang="EN-US">The development in the field of cloud webserver management for the execution of the workflow and meeting the quality-of-service (QoS) prerequisites in a distributed cloud environment has been a challenging task. Though, internet of things (IoT) of work presented for the scheduling of the workflow in a heterogeneous cloud environment. Moreover, the rapid development in the field of cloud computing like edge-cloud computing creates new methods to schedule the workflow in a heterogenous cloud environment to process different tasks like IoT, event-driven applications, and different network applications. The current methods used for workflow scheduling have failed to provide better trade-offs to meet reliable performance with minimal delay. In this paper, a novel web server resource management framework is presented namely the reliable and efficient webserver management (REWM) framework for the edge-cloud environment. The experiment is conducted on complex bioinformatic workflows; the result shows the significant reduction of cost and energy by the proposed REWM in comparison with standard webserver management methodology.</span>

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Section: Literature Surveymentioning

confidence: 99%

“…As a result, the web server needs to decide the order in which it executes the workload so that the parallel efficiency can be maximized and delay can be reduced. In this work the task's ascendent ordering outcome 𝑆 𝑣 is used to measure tasks selectivity using the (17).…”

Section: Task Ordering Webserver Managementmentioning

confidence: 99%

Reliable and efficient webserver management for task scheduling in edge-cloud platform

Sangani¹,

Patil²

2023

IJECE

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“…Iranpour et al [17] proposed a distributed load-balancing and admission-control algorithm based on a fuzzy game-theoretic model for large-scale SaaS clouds. Wu et al [20] proposed an improved Q-learning algorithm with weighted fitness value function for optimization of completion time and load balancing in cloud environment.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, as novel machine learning algorithms are becoming increasingly versatile and powerful, considerable research efforts are paid to using reinforcement learning (RL) and Q-learning-based algorithms [20]- [23] in finding nearoptimal workflow scheduling solutions. Nevertheless, most existing contributions focused on single-objective workflow scheduling with service-of-level (SLA) agreement constraints.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Workflow Scheduling With Deep-Q-Network-Based Multi-Agent Reinforcement Learning

et al. 2019

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Cloud Computing provides an effective platform for executing large-scale and complex workflow applications with a pay-as-you-go model. Nevertheless, various challenges, especially its optimal scheduling for multiple conflicting objectives, are yet to be addressed properly. The existing multi-objective workflow scheduling approaches are still limited in many ways, e.g., encoding is restricted by prior experts' knowledge when handling a dynamic real-time problem, which strongly influences the performance of scheduling. In this paper, we apply a deep-Q-network model in a multi-agent reinforcement learning setting to guide the scheduling of multi-workflows over infrastructure-as-a-service clouds. To optimize multi-workflow completion time and user's cost, we consider a Markov game model, which takes the number of workflow applications and heterogeneous virtual machines as state input and the maximum completion time and cost as rewards. The game model is capable of seeking for correlated equilibrium between make-span and cost criteria without prior experts' knowledge and converges to the correlated equilibrium policy in a dynamic real-time environment. To validate our proposed approach, we conduct extensive case studies based on multiple well-known scientific workflow templates and Amazon EC2 cloud. The experimental results clearly suggest that our proposed approach outperforms traditional ones, e.g., non-dominated sorting genetic algorithm-II, multi-objective particle swarm optimization, and game-theoretic-based greedy algorithms, in terms of optimality of scheduling plans generated. INDEX TERMS Multi-objective workflow scheduling, deep-Q-network (DQN), multi-agent reinforcement learning (MARL), infrastructure-as-a-service (IaaS) cloud, quality-of-service (QoS).

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“…In (25) integrated fuzzy with GT for admission control and balancing load among physical machines. In (26) , for optimizing the task completion time and balance load presented Qhttps://www.indjst.org/ Learning model with weighted objective function for workload scheduling on a cloud platform. However, this model is not evaluated considering modern scientific workflows.…”

Section: Introductionmentioning

confidence: 99%

Delay Aware and Performance Efficient Workflow Scheduling of Web Servers in Hybrid Cloud Computing Environment

Sangani¹,

Rodd²

2022

IJST

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A Multi-object Optimization Cloud Workflow Scheduling Algorithm Based on Reinforcement Learning

Cited by 12 publications

References 6 publications

Reliable and efficient webserver management for task scheduling in edge-cloud platform

Reliable and efficient webserver management for task scheduling in edge-cloud platform

Multi-Objective Workflow Scheduling With Deep-Q-Network-Based Multi-Agent Reinforcement Learning

Delay Aware and Performance Efficient Workflow Scheduling of Web Servers in Hybrid Cloud Computing Environment

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