Limits...
Job Scheduling with Efficient Resource Monitoring in Cloud Datacenter.

Loganathan S, Mukherjee S - ScientificWorldJournal (2015)

Bottom Line: Since the resources are limited in these private clouds maximizing the utilization of resources and giving the guaranteed service for the user are the ultimate goal.For that, efficient scheduling is needed.This research reports on an efficient data structure for resource management and resource scheduling technique in a private cloud environment and discusses a cloud model.

View Article: PubMed Central - PubMed

Affiliation: Department of Information Science and Technology, Anna University, Tamil Nadu 600025, India.

ABSTRACT
Cloud computing is an on-demand computing model, which uses virtualization technology to provide cloud resources to users in the form of virtual machines through internet. Being an adaptable technology, cloud computing is an excellent alternative for organizations for forming their own private cloud. Since the resources are limited in these private clouds maximizing the utilization of resources and giving the guaranteed service for the user are the ultimate goal. For that, efficient scheduling is needed. This research reports on an efficient data structure for resource management and resource scheduling technique in a private cloud environment and discusses a cloud model. The proposed scheduling algorithm considers the types of jobs and the resource availability in its scheduling decision. Finally, we conducted simulations using CloudSim and compared our algorithm with other existing methods, like V-MCT and priority scheduling algorithms.

No MeSH data available.


Resource utilization in percentage.
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fig9: Resource utilization in percentage.

Mentions: From Figure 7, it can be observed that our algorithm gives high success rate to AR request and ensures a guaranteed service to AR request than IM request. IM requests get accepted when the resource is available, otherwise rejected. BE jobs are kept in the queue till execution and utilize the resources when it is free or idle. Therefore, the success rate of BE jobs is greater than IM request. The other scheduling metrics are compared and shown in Figures 8–11. From all the above results, we conclude that MBFCBS has achieved the highest success rate and utilization in all cases compared to the other algorithms. This is due to the fact that the MBFCBS algorithm attempts to select the most suitable VM that can rapidly respond and execute the given job. We observe from Figure 11 that makespan time and total completion time of MBFCBS are higher than that of V-MCT when the number of jobs increases. This is attributed to the fact that when more requests are submitted, MBFCBS preempts more BE jobs and puts these for backfilling later. This increases the average completion time of the over job requests, which also results in higher makespan. V-MCT algorithm performed better when more number of requests arrives compared to the proposed algorithm since V-MCT does not consider the preemption of BE jobs and arbitrarily chooses any finishing VM to assign the job. But V-MCT algorithm delays the other types of jobs to execute if any batch type of job is assigned on the VM. Since V-MCT algorithm does not support preemption of job requests. Hence, the success rate and throughput decrease which results in more failed job requests.


Job Scheduling with Efficient Resource Monitoring in Cloud Datacenter.

Loganathan S, Mukherjee S - ScientificWorldJournal (2015)

Resource utilization in percentage.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4587428&req=5

fig9: Resource utilization in percentage.
Mentions: From Figure 7, it can be observed that our algorithm gives high success rate to AR request and ensures a guaranteed service to AR request than IM request. IM requests get accepted when the resource is available, otherwise rejected. BE jobs are kept in the queue till execution and utilize the resources when it is free or idle. Therefore, the success rate of BE jobs is greater than IM request. The other scheduling metrics are compared and shown in Figures 8–11. From all the above results, we conclude that MBFCBS has achieved the highest success rate and utilization in all cases compared to the other algorithms. This is due to the fact that the MBFCBS algorithm attempts to select the most suitable VM that can rapidly respond and execute the given job. We observe from Figure 11 that makespan time and total completion time of MBFCBS are higher than that of V-MCT when the number of jobs increases. This is attributed to the fact that when more requests are submitted, MBFCBS preempts more BE jobs and puts these for backfilling later. This increases the average completion time of the over job requests, which also results in higher makespan. V-MCT algorithm performed better when more number of requests arrives compared to the proposed algorithm since V-MCT does not consider the preemption of BE jobs and arbitrarily chooses any finishing VM to assign the job. But V-MCT algorithm delays the other types of jobs to execute if any batch type of job is assigned on the VM. Since V-MCT algorithm does not support preemption of job requests. Hence, the success rate and throughput decrease which results in more failed job requests.

Bottom Line: Since the resources are limited in these private clouds maximizing the utilization of resources and giving the guaranteed service for the user are the ultimate goal.For that, efficient scheduling is needed.This research reports on an efficient data structure for resource management and resource scheduling technique in a private cloud environment and discusses a cloud model.

View Article: PubMed Central - PubMed

Affiliation: Department of Information Science and Technology, Anna University, Tamil Nadu 600025, India.

ABSTRACT
Cloud computing is an on-demand computing model, which uses virtualization technology to provide cloud resources to users in the form of virtual machines through internet. Being an adaptable technology, cloud computing is an excellent alternative for organizations for forming their own private cloud. Since the resources are limited in these private clouds maximizing the utilization of resources and giving the guaranteed service for the user are the ultimate goal. For that, efficient scheduling is needed. This research reports on an efficient data structure for resource management and resource scheduling technique in a private cloud environment and discusses a cloud model. The proposed scheduling algorithm considers the types of jobs and the resource availability in its scheduling decision. Finally, we conducted simulations using CloudSim and compared our algorithm with other existing methods, like V-MCT and priority scheduling algorithms.

No MeSH data available.