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HILL CLIMBING ALGORITHM FOR CLOUD-BASED TASK SCHEDULING Dissertation Algorithm Development Ideas from Existing Recent Research Gaps in Computer Science Engineering Domain
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You will find the best dissertation research areas / topics for future researchers enrolled in Engineering.
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In order to identify the future research topics, we have reviewed the Engineering literature (recent peerreviewed studies) on the AI in optimization problems Cloud Computing included various tasks executed by the available resources to provide high performance and reduce the task completion time, etc.
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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. Copyright Š 2019 PhdAssistance. All rights reserved
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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. Copyright Š 2019 PhdAssistance. All rights reserved
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Hill-Climbing Algorithm Hill-Climbing Algorithm come's under the category of metaheuristic 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. Hire PhD Assistance experts to develop your algorithm and coding implementation for your Computer Science dissertation Services. Copyright © 2019 PhdAssistance. All rights reserved
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Hill-Climbing Algorithm Based on the hill-climbing algorithm, the broker saves each VM’s capability for mapping the operation of tasks. The capability weight sets on the creation of virtual machine VMj. đ??ś_đ?‘— (0)= đ?‘ƒ_ _( đ?‘›đ?‘˘đ?‘š _đ?‘— )∗ đ?‘ƒ_ _( đ?‘šđ?‘–đ?‘?đ?‘ _đ?‘— )
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Capability weight of VM is updated based on the allocation of any task or it is returned from VMj. đ??ś_đ?‘— (đ?‘Ą)=đ??ś_đ?‘— (đ?‘Ą)+∆đ??ś_đ?‘— (đ?‘Ą) In the case of decrement, the capability weight is determined as ΔCj(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. ΔCj(t) = Load. We define the move on the side of the increasing value of proposed hill climbing for the next task mapping. đ?‘€_đ?‘ (đ?‘Ą)= arg đ?‘šđ?‘Žđ?‘Ľ _(đ?‘ đ?‘‰đ?‘€đ?‘†) {(đ??ś _đ?‘ (đ?‘Ą) )^đ?‘&#x;} s â‚Ź Available and suitable VMs for the current task.
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Load Balancing Factor The rule of the uphill move is operating completely based on the performance of the VM, let’s say, if there is a high capability power for VM then it will be chosen with high desirability. It means it’s 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: đ?‘€_đ?‘ (đ?‘Ą)= arg đ?‘šđ?‘Žđ?‘Ľ _(đ?‘ đ?‘Žđ?‘™đ?‘™đ?‘œđ?‘¤đ?‘’đ?‘‘đ?‘‰đ?‘€đ?‘†) {(đ??ś _đ?‘ (đ?‘Ą) )^(đ?‘&#x; ∗ ) đ??żđ??ľ_(đ?‘ ^(1−đ?‘&#x;) )}
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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: đ??ś_đ?‘— (đ?‘Ą)=đ??ś_đ?‘— (đ?‘Ą)+đ??żâˆ—đ?‘†đ?‘…đ??š If a task goes wrong on VM, the capability weight is affected. đ??ś_đ?‘— (đ?‘Ą)=đ??ś_đ?‘— (đ?‘Ą)+đ??żâˆ—đ??šđ?‘…đ??š Copyright Š 2019 PhdAssistance. All rights reserved
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Implementation and Experimental Results
Table : Cloud Sim Parameters Copyright Š 2019 PhdAssistance. All rights reserved
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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. Copyright Š 2019 PhdAssistance. All rights reserved
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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 Copyright Š 2019 PhdAssistance. All rights reserved
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