Abstract
The rise in demand for cloud resources (network, hardware and software) requires cost effective scientific workflow scheduling algorithm to reduce cost and balance load of all jobs evenly for a better system throughput. Getting multiple scientific workflows scheduled with a reduced makespan and cost in a dynamic cloud computing environment is an attractive research area which needs more attention. Scheduling multiple workflows with the standard Max-Min algorithm is a challenge because of the high priority given to task with maximum execution time first. To overcome this challenge, we proposed a new mechanism call Expanded Max-Min (Expa-Max-Min) algorithm to effectively give equal opportunity to both cloudlets with maximum and minimum execution time to be scheduled for a reduce cost and time. Expa-Max-Min algorithm first calculates the completion time of all the cloudlets in the cloudletList to find cloudlets with minimum and maximum execution time, then it sorts and queue the cloudlets in two queues based on their execution times. The algorithm first select a cloudlet from the cloudletList in the maximum execution time queue and assign it to a resource that produces minimum completion time, while executing cloudlets in the minimum execution time queue concurrently. The experimented results demonstrats that our proposed algorithm, Expa-Max-Min algorithm, is able to produce good quality solutions in terms of minimising average cost and makespan and able to balance loads than Max-Min and Min-Min algorithms.
Highlights
The evolution of cloud computing has taken place in various phases which include grid computing, utility computing, software as a service and cloud computing (Sharma and Pariha, 2014)
Though cloud computing has gain a lot of successes since its inception, but scientific workflow allocation with the standard Max-Min algorithm in cloud is still a major issue in research because it is unable to select and assign both cloudlets with maximum execution time and minimum execution time concurrently
The processing speed of each workflow cloudlet is measured in Million Instructions Per Second (MIPS)
Summary
The evolution of cloud computing has taken place in various phases which include grid computing, utility computing, software as a service and cloud computing (Sharma and Pariha, 2014). The focus of this paper is on how to achieve the control of workload, makespan and cost on cloud resources by ensuring that all the workloads on cloud resources are distributed properly to allow free flow of cloudlets on all the network nodes and to guarantee that, all the resources are allocated to cloud users at a lower cost This will optimise the use of scientific workflows in cloud computing to ensure that multiple resources are made available to cloud users at a reduced cost and time. The proposed algorithm is able to boost up cloud scheduling processes by simultaneously selecting and assigning cloudlets with both maximum and minimum execution time concurrently at a reduced cost, makespan and balancing loads fairly.
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