A Reinforcement Learning Based Workflow Application Scheduling Approach in Dynamic Cloud Environment

Wei, Yi; Kudenko, Daniel; Liu, Shijun; Pan, Li; Wu, Lei; Meng, Xiangxu

doi:10.1007/978-3-030-00916-8_12

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Methodology Parameters [17] RL to make a scheduling model for cloud computing Makespan, total power cost in datacenters, energy consumption, migration time [18] RL task scheduling with Q-learning Makespan, total power cost, energy consumption [19] Task scheduling optimization algorithm called MPSO Makespan, energy consumption, packet delivery ratio, trust value [20] FELARE Ontime task completion rate, energy saving [21] RLFTWS Makespan, resource usage [22] WBCNF Computation time of tasks [23] BRCH-GWO Makespan [24] RELIEF Communication delay, reliability [25] PSO-based multipurpose algorithm Turnaround time, makespan [26] Regressive WO algorithm Processing cost, load balancing tasks [27] GO Makespan, resource utilization [28] Dynamic task scheduling algorithm based on an improved GA Total execution time and resource utilization ratio [29] PSO based on an AC algorithm Task completion time, makespan [30] DRL-based task scheduling Makespan, computation time [31] MRLCC is an approach for organizing tasks that are based on Meta RL Energy consumption, total cost, makespan [32] A novel DRL-based framework Cost and throughput, makespan [33] RLFTWS Execution time, degree of imbalance [15] DRL model Response time, makespan, CPU utilization [34] Deep reinforcement learning with PPSO SLA violation, makespan [35] DRL Cloud is an NDR-learning-based RP and TS system Estimated completion time, resource utilization [36] Deep Q-network model Degree of imbalance, cost, makespan [37] SDM reinforcement learning Energy consumption, resource utilization [38] DRLHCE Response time, degree of imbalance [39] DQN Makespan, total cost [40] Reinforcement learning Makespan [41] DDDQN-TS Task response time [31] Q-learning Makespan…”

Section: Authormentioning

confidence: 99%

A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing

Shiva Rama Krishna,

Mangalampalli

2023

Applied Sciences

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Task scheduling poses a wide variety of challenges in the cloud computing paradigm, as heterogeneous tasks from a variety of resources come onto cloud platforms. The most important challenge in this paradigm is to avoid single points of failure, as tasks of various users are running at the cloud provider, and it is very important to improve fault tolerance and maintain negligible downtime in order to render services to a wide range of customers around the world. In this paper, to tackle this challenge, we precisely calculated priorities of tasks for virtual machines (VMs) based on unit electricity cost and these priorities are fed to the scheduler. This scheduler is modeled using a deep reinforcement learning technique which is known as the DQN model to make decisions and generate schedules optimally for VMs based on priorities fed to the scheduler. This research is extensively conducted on Cloudsim. In this research, a real-time dataset known as Google Cloud Jobs is used and is given as input to the algorithm. This research is carried out in two phases by categorizing the dataset as a regular or large dataset with real-time tasks with fixed and varied VMs in both datasets. Our proposed DRFTSA is compared to existing state-of-the-art approaches, i.e., PSO, ACO, and GA algorithms, and results reveal that the proposed DRFTSA minimizes makespan compared to PSO, GA, and ACO by 30.97%, 35.1%, and 37.12%, rates of failure by 39.4%, 44.13%, and 46.19%, and energy consumption by 18.81%, 23.07%, and 28.8%, respectively, for both regular and large datasets for both fixed and varied VMs.

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Section: Authormentioning

confidence: 99%

A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing

Shiva Rama Krishna,

Mangalampalli

2023

Applied Sciences

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“…Moreover, their reward is defined only from the runtime of the current task while we also take into account the waiting times. The method presented in Reference 66 also leverages reinforcement learning to generate a workflow scheduling plan to be executed in the cloud but there it is first necessary to classify the application tasks into different execution levels. Such execution levels are considered as part of the state to the MDP formulation.…”

Section: Related Workmentioning

confidence: 99%

An incremental reinforcement learning scheduling strategy for data‐intensive scientific workflows in the cloud

Nascimento

Silva

Paes

et al. 2021

Concurrency and Computation

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Summary Most scientific experiments can be modeled as workflows. These workflows are usually computing‐ and data‐intensive, demanding the use of high‐performance computing environments such as clusters, grids, and clouds. This latter offers the advantage of the elasticity, which allows for changing the number of virtual machines (VMs) on demand. Workflows are typically managed using scientific workflow management systems (SWfMS). Many existing SWfMSs offer support for cloud‐based execution. Each SWfMS has its scheduler that follows a well‐defined cost function. However, such cost functions should consider the characteristics of a dynamic environment, such as live migrations or performance fluctuations, which are far from trivial to model. This article proposes a novel scheduling strategy, named ReASSIgN, based on reinforcement learning (RL). By relying on an RL technique, one may assume that there is an optimal (or suboptimal) solution for the scheduling problem, and aims at learning the best scheduling based on previous executions in the absence of a mathematical model of the environment. For this, an extension of a well‐known workflow simulator WorkflowSim is proposed to implement an RL strategy for scheduling workflows. Once the scheduling plan is generated via simulation, the workflow is executed in the cloud using SciCumulus SWfMS. We conducted a throughout evaluation of the proposed scheduling strategy using a real astronomy workflow named Montage.

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Weighted double deep Q-network based reinforcement learning for bi-objective multi-workflow scheduling in the cloud

Huang

Wang

et al. 2021

Cluster Comput

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A Reinforcement Learning Based Workflow Application Scheduling Approach in Dynamic Cloud Environment

Cited by 5 publications

References 10 publications

A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing

A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing

An incremental reinforcement learning scheduling strategy for data‐intensive scientific workflows in the cloud

Weighted double deep Q-network based reinforcement learning for bi-objective multi-workflow scheduling in the cloud

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