Tips for Handling Cloud Resources, Task Scheduling and load balancing amongst data and user Over the Cloud Environment

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SEP 27, 2019
Research Paper
TIPS FOR HANDLING CLOUD RESOURCES AND LOAD
BALANCING TASK IN CLOUD (PRIVATE AND PUBLIC
CLOUD)
Trending Research Methodologies In Cloud
Computing, Engineering Technology Tags
TutorsIndia Topics Cloud Computing Security
Privacy Issues Load Balancing Copyright 2019
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Load Balancing
Cloud Resource Allocation
Overview
Background
Future Work
KEY POINTS
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Due to rapid growth of cloud technology, number
of user and size of data becomes complex issues.
Resource scheduling and Load balancing are also
the main challenges and concerns in cloud
environments.
BACKGROUND
Scheduling method is used to increase profit of
service providers via minimizing time and power
consumption.
To make a decision for scheduling the task
becomes difficult role.
For addressing the issue, most of researcher
suggested optimization method which minimize
energy consumption schedule task in easy
manner.
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RESOURCE ALLOCATION AND TASK SCHEDULING -
OVERVIEW
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LOAD BALANCING MODEL, METRICS, AND POLICIES
Load balancing model mainly used to balance the
load of machines then distribute the workload
equally to all clouds.
Load distribution based on user requests has
been done on the basis of software deployment in
the cloud data centers.
LBM mainly developed toward avoid overutilization
of resources and underutilization of resources,
refining the latency time for each class of
request.
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CLOUD RESOURCE ALLOCATION STRATEGIES
It will reduce the lack of resources, resource
struggle, fragmentation, resource
under-provisioning and over- provisioning and
minimizing the operational cost of the cloud .
Cloud resource allocation (CRA) strategies
primary used to assurance that physical and/or
virtual resources are allocated correctly to
cloud users.
The primary significance of CRA is illustrating
how the resources are modelled and utilized by
the clients/user.
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IAn effective load balancing and resource
allocation scheme to enhance the performance of
flexible management cloud data and resources for
both multiple and single users cloud system. To
handle various QoS requirements such as
scalability, reliability and security for huge
workload instances for validating the
performance of cloud system.
FUTURE WORK
The optimization approach should consider some
significant objectives like cost, bandwidth,
load balancing, etc with robust machine learning
algorithm.
To design and develop a unique framework which
should comprise deeper knowledge for allocating
and managing resources in cloud, because it
mainly depends upon the cloud service provider
(CSP).
It will consider multi-objective based approach
to optimize the task scheduling model. Copyright
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