PhD Dissertation Writing Help - DISSERTATION ALGORITHM DEVELOPMENT TIPS FOR DEVELOPING HILL CLIMBING ALGORITHM FOR CLOUD-BASED TASK SCHEDULING

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Research paper
Dissertation Algorithm Development Tips for
Developing HILL CLIMBING ALGORITHM FOR
CLOUD-BASED TASK SCHEDULING Dissertation
Algorithm Development Ideas from Existing Recent
Research Gaps in Computer Science Engineering
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SHORT NOTES
Cloud Computing included various tasks executed
by the available resources to provide high
performance and reduce the task completion time,
etc. Cntd....
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The task scheduling is an NP-hard
(Nondeterministic Polynomial) problem in cloud
computing.
Task scheduling is a process of allocating tasks
to available resources so that the performance
metrics are improved. A dynamic scheduling
algorithm that uses a hill-climbing algorithm to
reduce the completion time of tasks while
maximization of throughput and utilization of
resources. Hill Climbing Algorithm for
cloud-based task scheduling allocation of
independent tasks to available resources in
order to achieve the load balance.
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Background
Cloud computing is a platform of resource
provisions and allowing consumers to utilize
computing resources based on multiple service
providers. Cloud has basically involved a wide
variety of resources and services that subsumed
memory, storage, processing, and virtual
machines. Cloud Computing, task scheduling is an
NP-hard problem and the metaheuristic technique
was used to solve the issue of task
scheduling. Evolutionary algorithms have been
proposed such as hill climbing to optimize the
solutions based on PSO and local search operators.
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Hill-Climbing Algorithm
Hill-Climbing Algorithm come's under the category
of meta- heuristic techniques and is a perfect
approach to solve the NP-hard problems faced in
the cloud-based task scheduling. Hill-Climbing
Algorithm is an optimization technique to obtain
a better solution and the advantage of using a
technique is to utilize the resources based on a
favourable feedback mechanism.
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Hill-Climbing Algorithm Based Load Balancing and
Reliability
If a user submits the tasks to the broker, it
collects the tasks into a set and queries into
the Cloud Information Services (CIS). CIS
produces all of the features regarding available
resources like processing elements, VMs number,
and rating of each PE. Broker makes use of the
information in mapping operation based on a
proposed hill-climbing algorithm.
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Hill-Climbing Algorithm
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Based on the hill-climbing algorithm, the broker
saves each VMs capability for mapping the
operation of tasks. The capability weight sets
on the creation of virtual machine VMj. ??_??
(0)???_?_(????????_?? )???_?_(??????????_??
) Capability weight of VM is updated based on
the allocation of any task or it is returned from
VMj. ??_?? (??)??_?? (??)???_?? (??) In the
case of decrement, the capability weight is
determined as ?Cj(t) -Load when the task is
mapped to VMj, where Load is the task
computation workload. When the task is
accomplished and VMj is freed, the capability is
incremented i.e. ?Cj(t) Load. We define the
move on the side of the increasing value of
proposed hill climbing for the next task
mapping. ??_?? (??)??arg ??????????_(?????????)
? (???_?? (??) )?? s Available and suitable
VMs for the current task. Journal support
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Load Balancing Factor
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The rule of the uphill move is operating
completely based on the performance of the VM,
lets say, if there is a high capability power
for VM then it will be chosen with high
desirability. It means its very important to
balance the load for all running VMs. To balance
the load, the factor of the VMj load (LBs) is
appended to uphill move rule during the selection
of VM in order to compute the tasks to finish
the rate. The modified equation is ??_??
(??)??arg ??????????_(???????????????????????) ?
(???_?? (??) )(?? ) ????_(??(1-??) )
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Reliability Factor
If any task is finished or failed or returned
from VM, no need to make changes to steps of
capability weight that means ignoring the faults
and reliability of the VM. In case of successful
completion of the task on VM and VMj is free
from the tasks, the updated capability weight is
as follows ??_?? (??)??_?? (??)???????? If a
task goes wrong on VM, the capability weight is
affected. ??_?? (??)??_?? (??)????????
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Implementation and Experimental Results
Table Cloud Sim Parameters
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Graphical representation
Figure 1- Standard Deviation with Fault Insertion
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Figure 2- Standard Deviation with fault insertion
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Conclusion

• Hill-climbing algorithm with load balancing and
  adaptive virtual machine reliability has been
  proposed to solve the NP-hard problem of task
  scheduling in cloud computing resources.
• It determines the available resources for
  upcoming tasks that included reliability and
  load.
• Proposed hill-climbing algorithm prefers the
  virtual machines because they have a good
  computing capability, high reliability, and a
  light load in order to execute the tasks for
  getting more balanced and have better
  performances.
• Parameter values are determined and simulation
  results stated that the proposed hill-climbing
  algorithm improves resource utilization and
  provides a high level of performance.

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Future Scopes
Hill-climbing Algorithm can be used on fog
computing Can be used in optimal deployment of
components of cloud-hosted application Can be
used for large scale data in cloud computing Can
be used in Workflow scheduling in cloud
computing Can be implemented in adaptive fault
tolerance for online tasks scheduling in cloud
computing
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