A Critical Review on Load Balancing, Task And Resource Scheduling In A Cloud Environment

OCT 8, 2019 Research Paper

CRITICAL REVIEW

LOAD BALANCING, TASK AND RESOURCE SCHEDULING IN CLOUD ENVIRONMENT

Critical Review In Cloud Computing, Engineering & Technology

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Reviewed the literature article by considering load balancing, task and resource scheduling method for efficient cloud service provisioning.

SHORT NOTES

There is a possibility to minimize the total cost of computation by new scheduling method on the basis of bio-inspired heuristics.

Single objective- and multi-objective-based task scheduling method for making multidimensional task and resource scheduling model.

Tested the suggested system performance using CloudSim simulator toolkit.

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TutorsIndia Lab @ TutorsIndia.Com E n g i n e e r i n g & t e c h n o l o g y (E&T) Lab a t Tu t o r s I n d i a is i nvo lve d in exploring novel r e sea r c h a r e a s by conducting dynamic r e s e a r c h . It p r o m o t e s i n n o v a t i o n i n a l l fields of e n g i n e e r i n g b y advancing t h e technology with structured a n d continuous research. The problems a n d challenges faced by t h e existing technologies a n d tr ends a r e explored by our r esearchers exists in s c h o l a r l y l i t e r a t u r e , i n t h e o r y, o r i n p r a c t i c e s t h a t n e e d s d e l i b e r a t e i n v e s t i g a t i o n . T h e s e p r o b l e m s a r e identified a n d fixed by our researcher s by suggesting better novel alternatives w i t h a p p r o p r i a t e tools, t e c h n o l o g i e s a n d a p p r o a c h e s , t h e r e b y p r o v i n g t h e i r e ff e c t i v e n e s s i n r e a l t i m e a p p l i c a t i o n s .

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LOAD BALANCING, TASK AND RESOURCE SCHEDULING IN CLOUD ENVIRONMENT Dissertation | Assignment | Essay | Statistical Analysis | Coding & Algorithm | Resit Dissertation | Powerpoint | Poster

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01

Priya et al. (2018) presented a load balancing (LB) and resource scheduling (RS) approach for efficient cloud service provisioning. The simulated results shown that attained better performance with respect to high resource scheduling efficiency, average success rate and reduced the response time. Future Research Scope for PhD Scholars based on Priya et al: But the primary criteria called, scheduling of task not addressed. Also, need to consider LB and RS of intermediate data toward enhancing the system performance by considering privacy-preserving as a metric together with other metrics like average response time and success ration.

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02

Nasr et al. (2018) suggested an Online Potential Finish Time (OPFT) task scheduling method for enhancing the scheduling performance and resolve the QoS issues. The performance of OPFT method is assessed via CloudSim, and the simulated results indicate that this method better efficient. Future Research Scope for PhD Scholars based on Nasr et al: However, the scheduling performance is poor; hence there is a need to improving the scheduling efficiency. Also, integrity of virtual machine and overhead, multidimensional resource scheduling was not addressed.

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03

Adhikari et al. (2019) suggested a load balancing resource clustering method approach toward attaining optimal resource clustering and their cluster centers for faster convergence via metaheuristic Bat-algorithm. Future Research Scope for PhD Scholars based on Ashikari et al: The suggested method has been evaluated by considering synthetic datasets, as a resulted shown superiority than the traditional approach. But, the of VM and overhead integrity unaddressed.

Also, needs to validate its remain the multidimensional performance for scheduling process. resource Dissertation | Assignment | Essay | Statistical Analysis | Coding & Algorithm | Resit Dissertation | Powerpoint | Poster

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04

Adhikari et al. (2019) proposed a chaotic symbiotic organisms search (CMSOS) method for resolving multi-objective large scale task scheduling optimization issues on cloud computing environment. The suggested model performance has been evaluated using synthesized workloads, and standard workload traces for huge instances. The simulated results of CMSOS shown better optimal trade-offs. Future Research Scope for PhD Scholars based on Adhikari et al: However, the resource management, integrity of VM remains unaddressed. Also, needs to handle QoS requirements like security and reliability for very large workload instances.

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05

Chaudhary and Kumar (2019) presented a Hybrid Genetic-Gravitational Search Algorithm (HG-GSA) based load scheduling technique for minimizing the computation cost. The simulated results have shown that they minimized the computation cost which significantly improved the system performance than the traditional method. But the suggested method is not suitable for multidimensional resource scheduling strategies. Future Research Scope for PhD Scholars from Chaudhary: The task scheduling and resource management remain unaddressed. Also, there is possibility to minimize the total cost of computation by new scheduling method on the basis of bio-inspired heuristics.

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06

Arunarani et al. (2019) reviewed the available task scheduling methods and related metrics which is appropriate for a cloud platform. At the end of research they have recommended enhancing the effectiveness of scheduling by combining virtual machine consolidation and task scheduling strategies.

Also, will consider the different input parameters. Future Research Scope for PhD Scholars based on Arunarani et al: Then, there should be a need for single objective- and multi-objective-based task scheduling for enhancing the system performance by hybrid combination of earlier algorithms. But the major concern like integrity of virtual machine and overhead, load balancing, resource management remains unaddressed.

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COMPARISON TABLE S.No.

1)

2)

3)

4)

5)

6)

WORK BY

METHOD

IMPLEMENTATION TOOL

FINDINGS & CONCLUSION

High resource scheduling efficiency.

Reduced the system response time.

LIMITATION / FUTURE SCOPE

Scheduling of task not addressed.

Needs to validate the performance of success ration.

Priya et al.(2018)

LB and RS method

CloudSim simulator toolkit

Nasr et al. (2018)

OPFT task scheduling method

CloudSim simulator toolkit

Task Completion Time has been minimized

Adhikari et al. (2019)

Load balancing resource clustering method

CloudSim simulator toolkit

Attained less execution cost, makespan within the given constraints

CMSOS method

CloudSim simulator toolkit

Better optimal trade-offs amongst financial cost (cost) and execution time.

There is no computational overhead.

Resource management, integrity of VM remains unaddressed.

To validate the performance for large workload instances.

HG-GS based load scheduling technique

CloudSim simulator toolkit

Minimized the computation cost

Task scheduling and resource management remain unaddressed

NA

Suggested to enhance the system performance by single and multi-objective-based task scheduling

Adhikari et al. (2019)

Chaudhary and Kumar (2019) Arunarani et al. (2019)

Reviewed the task scheduling methods and related metrics

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Scheduling performance is poor.

Multidimensional resource scheduling was not addressed.

Multidimensional resource scheduling was not addressed.

Needs to scrutinize the trade-off among different nature-inspired algorithms with task deployment policies.

To enhance of scheduling performance by combining VM consolidation and task scheduling strategies Copyright Š 2019 TutorsIndia. All rights reserved

RECOMMENDATIONS To scrutinize the trade-off among different nature-inspired algorithms with task deployment policies for various types of service level agreements (SLA) with their penalties and load balancing policies.

To minimize the total cost of computation by a new scheduling method on the basis of bio-inspired heuristics. Also will enhance the system performance by hybrid combination of single objective- and multi-objective-based task scheduling method.

To enhancing the cloud system performance for the multidimensional dataset by considering integrity of virtual machine, overhead and multidimensional resource scheduling strategies.

To handle various QoS requirements like security and reliability for very large workload instances.

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