A Preference-based Resource Allocation in Cloud Computing Systems

Kumar, Narander; Saxena, Swati

doi:10.1016/j.procs.2015.07.375

Cited by 30 publications

(17 citation statements)

References 15 publications

(16 reference statements)

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“…Compared to PSO, GA and BFO the hybrid GABFO produced better result. The work mentioned in [24] compared with our result. Fig.2 shows the result of total execution time of jobs.…”

Section: Simulation and Analytical Resultssupporting

confidence: 63%

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Optimized Scheduling and Resource Allocation Using Evolutionary Algorithms in Cloud Environment

Muthu¹,

Enoch²

2017

IJIES

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Cloud computing is a powerful computing technology, which render a flexible services at anywhere to the user. One of the major issue of cloud computing was scheduling. In this work, a bacterial foraging optimization algorithm with genetic algorithm (GABFO) was combined to find out trustworthy scheduling problems in cloud workflow. Generally job scheduling and resource allocation in cloud is a tedious optimization problem at the time of considering QoS requirements. Lot of existing works under scheduling only concentrates on cost optimization and deadline problems, and it ignores the importance of reliability, availability and robustness. The main subscription of my work is to state a new optimized approach to schedule the jobs efficiently and allocate the resources in a efficient manner by introducing GABFO algorithm. Experiments were done in PSO, Genetic, BFO and then Genetic and BFO was combined to generate a hybrid optimized result, and the work was compared with above mentioned algorithms. The algorithms were executed for 52 iterations and totally 10 runs are calculated. The size of the job as well as virtual machines was varied for each iteration to calculate performance variation. We considered the optimization parameter as time and cost, and throughput. The work is implemented in cloudsim to create a simulated cloud environment. Final result shows better performance and maximum resource utilization in GABFO when compared to PSO, GA, BFO.

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“…Compared to PSO, GA and BFO the hybrid GABFO produced better result. The work mentioned in [24] compared with our result. Fig.2 shows the result of total execution time of jobs.…”

Section: Simulation and Analytical Resultssupporting

confidence: 63%

“…A scheduling algorithm based on berger model [24,25] was designed to establish the dual fairness constraint in virtualized cloud. The fairness of resource allocation was judged by the application of justice function.…”

Section: Related Workmentioning

confidence: 99%

Optimized Scheduling and Resource Allocation Using Evolutionary Algorithms in Cloud Environment

Muthu¹,

Enoch²

2017

IJIES

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“…Experimental results show a considerable improvement in the CPU utilization and reduced AR leases as compared to the standard model. Similar approach is attempted by Kumar, Narander, and Swati Saxena (Elsevier 2015) [9] in their work but with preference based approach. Their work involves bidding for resources and options for payment for using cloud services.…”

Section: B Haizeamentioning

confidence: 92%

Resource Allocation Strategy with Lease Policy and Dynamic Load Balancing

Kshirsagar¹,

Pujar²

2017

IJMECS

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Abstract-CloudComputing has managed to attract the entire buzz in the growing era of technology due to its ondemand services for resource request. Despite of the enormous growth of cloud computing, there are many problems related to resource allocation in cloud that are still unaddressed. Current work for resource allocation strategy focuses on various methods to place Virtual Machine per appropriate requests. The current state of art focuses on the dynamic nature of the work load on cloud. But there is still scope of improvement in the resource allocation strategies that have been proposed in terms of well-balanced network even at the resource contention. This study proposes a hybrid model composed of lease methodology and dynamic load balancing algorithm, with an attempt to overcome the problems of resource contention and starvation and a well-balanced network even at the input of varying loads. An attempt to increase the CPU utilization and throughput along with no request rejection is taken. The work also retains the lease options for its clients thus maintaining the anti-starvation for preemptible requests.

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“…A demand based preferential resource allocation method is proposed in [67], that proposes for resource allocation a market driven auction mechanism based on their capacities.…”

Section: Cost Aware Resource Allocationmentioning

confidence: 99%

Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

et al. 2016

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There are two actors in cloud computing environment cloud providers and cloud users. On one hand cloud providers hold enormous computing resources in the cloud large data centers that rent the resources out to the cloud users on a pay-per-use basis to maximize the profit by achieving high resource utilization. On the other hand cloud users who have applications with loads variation and lease the resources from the providers they run their applications within minimum expenses. One of the most critical issues of cloud computing is resource management in infrastructure as a service (IaaS). Resource management related problems include resource allocation, resource adaptation, resource brokering, resource discovery, resource mapping, resource modeling, resource provisioning and resource scheduling. In this review we investigated resource allocation schemes and algorithms used by different researchers and categorized these approaches according to the problems addressed schemes and the parameters used in evaluating different approaches. Based on different studies considered, it is observed that different schemes did not consider some important parameters and enhancement is required to improve the performance of the existing schemes. This review contributes to the existing body of research and will help the researchers to gain more insight into resource allocation techniques for IaaS in cloud computing in the future.

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A Preference-based Resource Allocation in Cloud Computing Systems

Cited by 30 publications

References 15 publications

Optimized Scheduling and Resource Allocation Using Evolutionary Algorithms in Cloud Environment

Optimized Scheduling and Resource Allocation Using Evolutionary Algorithms in Cloud Environment

Resource Allocation Strategy with Lease Policy and Dynamic Load Balancing

Recent advancements in resource allocation techniques for cloud computing environment: a systematic review

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