Adaptive Fault Tolerant Resource Allocation Scheme for Cloud Computing Environments

Sathiyamoorthi, V; Keerthika, P.; Suresh, P.; Zhang, Zuopeng; Rao, Adiraju Prasanth; Logeswaran, K.

doi:10.4018/joeuc.20210901.oa7

Cited by 33 publications

(24 citation statements)

References 31 publications

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“…The proposed technique encompasses four stages such as local phase, individual phase, fault tolerance phase, and global phase. Sathiyamoorthi et al [13] present an adaptive and effective fault tolerant scheduling method to help error-free scheduling. The presented technique considered the highly effective variables namely present workload and failure rate of the resource for optimum Quality of Service (QoS).…”

Section: Related Workmentioning

confidence: 99%

Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme for Cloud Environment

Muralidharan¹,

Latha²

2023

Intelligent Automation &Amp; Soft Computing

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In cloud computing (CC), resources are allocated and offered to the clients transparently in an on-demand way. Failures can happen in CC environment and the cloud resources are adaptable to fluctuations in the performance delivery. Task execution failure becomes common in the CC environment. Therefore, fault-tolerant scheduling techniques in CC environment are essential for handling performance differences, resource fluxes, and failures. Recently, several intelligent scheduling approaches have been developed for scheduling tasks in CC with no consideration of fault tolerant characteristics. With this motivation, this study focuses on the design of Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme (GTO-FTASS) in CC environment. The proposed GTO-FTASS model aims to schedule the tasks and allocate resources by considering fault tolerance into account. The GTO-FTASS algorithm is based on the social intelligence nature of gorilla troops. Besides, the GTO-FTASS model derives a fitness function involving two parameters such as expected time of completion (ETC) and failure probability of executing a task. In addition, the presented fault detector can trace the failed tasks or VMs and then schedule heal submodule in sequence with a remedial or retrieval scheduling model. The experimental validation of the GTO-FTASS model has been performed and the results are inspected under several aspects. Extensive comparative analysis reported the better outcomes of the GTO-FTASS model over the recent approaches.

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Section: Related Workmentioning

confidence: 99%

Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme for Cloud Environment

Muralidharan¹,

Latha²

2023

Intelligent Automation &Amp; Soft Computing

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“…Authors of [7] proposed an Adaptive Fault Tolerant Resource Allocation (AFTRA) approach, as a proactive fault tolerance measure. The proposed approach was implemented based on an application that was self-adaptive with respect to its current position or state in its state space and the applications or tasks that were submitted.…”

Section: Fault Tolerance Approaches In the Cloud Computingmentioning

confidence: 99%

“…Thus, for the Cloud, failures will lead to the loss of resources and money [6]. Therefore, fault tolerance measures are vital for cloud computing due to its high dynamicity, huge number of the resources and possibilities of failures in cloud resources [7]. Thus, the outcome of fault control is a big challenge because of its direct influence on the reliability, safety, productivity and operational efficiency of a composite Cloud service.…”

Section: Introductionmentioning

confidence: 99%

Fault Tolerance for Composite Cloud Services: A MAS-based Novel Approach

Hioual

Hemam

et al. 2022

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Several Cloud services may be composed in order to respond quickly to the needs of users. Unfortunately, when running such a service some faults may occur. The outcome of fault control is a big challenge. In this paper, the authors propose a new approach based back recovery and multi-agent planning methods. The proposed architecture based MAS (Multi-Agent System) is composed of two main types of Agents : a Composition Manager Agent (CMA) and a Supervisor Agent (SA). The role of the CMA is to create a set of plans as an oriented graph where the nodes are the Cloud services and the valued arcs represent the composition order of these services. This agent saves checkpoints (nodes) in a stable memory so that there are at least one possible path. However, the SA ensures that the running plan is working properly; otherwise, it informs the CMA to select another sub-plan. Experimental results show the performance and effectiveness of the proposed approach.

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“…During the testing process, it is necessary to pay attention to safety factors and environmental factors, to ensure the safety of the subjects during the testing process, and to ensure the reliability and validity of the testing results. At present, the physical fitness test of body function mainly refers to the methods and means in the field of rehabilitation, but it cannot meet the needs of functional training well, and the means that can accurately evaluate are still relatively scarce [25,26].…”

Section: Construction Of Functional Physical Fitness Trainingmentioning

confidence: 99%

Data Mining Method Based on Mobile Network Communication in Volleyball Training

Han

2022

Mobile Information Systems

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In recent years, volleyball has developed rapidly, and the requirements of athletes’ physical fitness have been continuously improved. The way to improve physical fitness is physical training. Therefore, how to improve sports performance and physical fitness level has become a problem that needs to be solved in current volleyball training. This paper adopts the methods of literature materials, expert interviews, testing methods, experimental methods, mathematical statistics, and logical analysis methods and conducts experiments on the actual situation of college volleyball students by using mobile network communication data mining. Comparing the test and experimental data, it was found that the test groups had significant differences in the test scores of agility, jumping ability, lower arm explosive power, upper body power, abdominal strength, back strength, and coordination. The sample T test was performed on the improvement of the experimental group and the control group (boys) in the 30 m running and 9 m × 10 movement test scores, and P3 = 0.442 and P9 = 0.338 ( P > 0.05 ) were obtained, respectively. The posttest score was 0.064 higher than the pretest score.

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Adaptive Fault Tolerant Resource Allocation Scheme for Cloud Computing Environments

Cited by 33 publications

References 31 publications

Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme for Cloud Environment

Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme for Cloud Environment

Fault Tolerance for Composite Cloud Services: A MAS-based Novel Approach

Data Mining Method Based on Mobile Network Communication in Volleyball Training

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