LOAD BALANCING, TASK AND RESOURCE SCHEDULING IN CLOUD ENVIRONMENT

LOAD BALANCING, TASK
AND RESOURCE
SCHEDULING IN CLOUD
ENVIRONMENT
Research Paper
OCT 8, 2019
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CRITICAL REVIEW
Critical Review In Cloud Computing, Engineering & Technology
Tags: Tutors India Computer Science Dissertation Topics Topics in Computer Science Computer Science dissertation help

SHORT NOTES
Reviewed the literature article by considering load
balancing, task and resource scheduling method for
efficient cloud service provisioning.
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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appropriate tools, technologies and approaches, thereby proving
their effectiveness in real time applications.

LOAD BALANCING,
TASK AND RESOURCE
SCHEDULING IN
CLOUD ENVIRONMENT
- RESEARCH PAPER

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. 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.
01

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. 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.
02

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 meta-heuristic Bat-algorithm. The suggested method has been
evaluated by considering synthetic datasets, as a resulted shown superiority than the
traditional approach. But, the integrity of VM and overhead remain unaddressed. Also,
needs to validate its performance for the multidimensional resource scheduling process.
03

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. However, the resource management,
integrity of VM remains unaddressed. Also, needs to handle QoS requirements like security
and reliability for very large workload instances.
04

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. 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.
05

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. 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.

COMPARISON TABLE
WORK BY METHOD IMPLEMENTATION TOOL FINDINGS & CONCLUSION LIMITATION / FUTURE SCOPE
Priya et al.(2018)
LB
and RS method
CloudSim
simulator toolkit
High resource scheduling
efficiency.
Reduced the system
response time.
Scheduling of task not addressed.
Needs to validate the performance of
success ration.
Nasr et al. (2018)
OPFT task
scheduling method
CloudSim
simulator toolkit
Task Completion Time has
been minimized
Scheduling performance is poor.
Multidimensional resource scheduling was
not addressed.
Adhikari et al. (2019)
Load balancing
resource clustering
method
CloudSim
simulator toolkit
Attained less execution cost,
makespan within the given
constraints
Multidimensional resource scheduling
was not addressed.
Needs to scrutinize the trade-off among
different nature-inspired algorithms with
task deployment policies.
Adhikari et al. (2019) 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.
Chaudhary and
Kumar (2019)
HG-GS based
load scheduling
technique
CloudSim
simulator toolkit
Minimized the computation cost
Task scheduling and resource management
remain unaddressed
Arunarani et al. (2019)
Reviewed the task
scheduling methods
and related metrics
NA
Suggested to enhance the
system performance by single
and multi-objective-based task
scheduling
To enhance of scheduling performance by
combining VM consolidation and task
scheduling strategies
1)
2)
3)
4)
5)
6)
S.No.

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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LOAD BALANCING, TASK AND RESOURCE SCHEDULING IN CLOUD ENVIRONMENT

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