Integration of firefly optimization and Pearson service correlation for efficient cloud resource utilization

2018 ◽  
Vol 31 (15) ◽  
pp. e3771
Author(s):  
S. Gokulraj ◽  
B.G. Geetha
2019 ◽  
Vol 214 ◽  
pp. 07011
Author(s):  
Nikita Balashov ◽  
Alexander Baranov ◽  
Sergey Belov ◽  
Ivan Kadochnikov ◽  
Vladimir Korenkov ◽  
...  

The complexity of modern software libraries and applications makes it hard to predict possible workloads generated by the software that may lead to significant underutilization of hardware in Infrastructure-as-a-Service (IaaS) clouds. In this paper, we give a review of an approach aimed to deal with resource underutilization in cloud environments, including description of a developed software framework and an example algorithm. IaaS clouds following this universal approach can help increase overall cloud resource utilization independently of the variety of cloud applications.


2019 ◽  
Vol 8 (3) ◽  
pp. 1863-1870 ◽  

Resource allocation (RA) is a significant aspect of Cloud Computing. The Cloud resource manager is responsible to assign available resources to the tasks for execution in an effective way that improves system performance, reduce response time, lessen makespan and utilize resources efficiently. To fulfil these objectives, an effective Tasks Scheduling algorithm is required. The standard Max-Min and Min-Min Task Scheduling algorithms are not able to produce better makespan and effective resource utilization. In this paper, a Resource-Aware Min-Min (RAMM) Algorithm is proposed based on basic Min-Min algorithm. The proposed RAMM Algorithm selects shortest execution time task and assigns it to the resource which takes shortest completion time. If minimum completion time resource is busy, then the RAMM Algorithm selects next minimum completion time resource to reduce waiting time of the task and improve resource utilization. The experiment results show that the proposed RAMM Algorithm produces better makespan and load balance than Max-Min, Min-Min and improved Max-Min Algorithms.


2018 ◽  
Vol 7 (2.4) ◽  
pp. 26
Author(s):  
Monika . ◽  
Vivek Jaglan ◽  
Jugnesh Kumar ◽  
Akshat Agrawal

Cloud computing has come up as a standout amongst the most encouraging &reliable advancements in the IT part. However by and by there exists a noteworthy issue of load adjusting in the distributed computing condition. This paper comprises of an answer for streamlining the heap utilizing hereditary calculation. Hereditary calculation which takes after the transformative system can build up an answer near ideal arrangement. The proposed calculation is produced by consolidating two existing calculations by considering cost an incentive as the wellness work. The workload is adjusted by the considering the mix of both the heap rate and cost estimation of the assets. Allotment of assets is performed by taking the best fit esteem and lessening the reaction time and general cost. Reenactment comes about are indicated utilizing the cloud examiner test system.


2006 ◽  
Vol 175 (4S) ◽  
pp. 4-4
Author(s):  
Gurkirpal Singh ◽  
Smriti Malla ◽  
Huijian Wang ◽  
Harcharan Gill ◽  
Kristijian H. Kahler ◽  
...  

2004 ◽  
Vol 171 (4S) ◽  
pp. 35-35
Author(s):  
Brent K. Hollenbeck ◽  
David C. Miller ◽  
Rodney L. Dunn ◽  
Willie Underwood ◽  
Shukri F. Khuri ◽  
...  

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