Workload Prediction of Cloud Workflow Based on Graph Neural Network

Gao, Ming; Li, Yuchan; Yu, Jixiang

doi:10.1007/978-3-030-87571-8_15

Cited by 8 publications

(1 citation statement)

References 30 publications

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“…The performance of the cloud is directly proportional to how the resources are handled and allocated [15]. If the resources are scheduled and managed efficiently, the cloud can execute a more significant number of tasks, thereby providing satisfactory results to the end users [4]. Hence, to provide the best results [19], resource scheduling is an important aspect of the cloud [18].…”

Section: Introductionmentioning

confidence: 99%

Implementing FCFS and SJF for finding the need of Reinforcement Learning in Cloud Environment

Lahande

Kaveri

2022

ITM Web Conf.

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Cloud has grown significantly and has become a popular serviceoriented paradigm offering users a variety of services. The end-user submits requests to the cloud in the form of tasks with the expectation that they will be executed at the best possible lowest time, cost and without any errors. On the other hand, the cloud executes these tasks on the Virtual Machines (VM) by using resource scheduling algorithms. The cloud performance is directly dependent on how the resources are managed and allocated for executing the tasks. The main aim of this research paper is to compare the behaviour of cloud resource scheduling algorithms: First Come First Serve (FCFS) and Shortest Job First (SJF) by processing high-sized tasks. This research paper is broadly divided into four phases: the first phase includes an experiment conducted by processing approximately 80 thousand tasks from the Alibaba task event dataset using the resource scheduling algorithms: FCFS and SJF on the cloud VMs under different circumstances; the second phase includes the experimental results; the third phase includes a empirical analysis of the behaviour of resource scheduling algorithms; the last phase includes the proposed need of Reinforcement Learning (RL) to improve cloud resource scheduling and its overall performance.

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