Resource allocation in cloud computing using agents

Shyam, Gopal K.; Manvi, Sunilkumar S.

doi:10.1109/iadcc.2015.7154750

Cited by 13 publications

(7 citation statements)

References 20 publications

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“…Genetic algorithm was modified to balance the load and to reduce the execution time and cost. An agent based best fit resource allocation scheme was proposed to increase the resource utilization [35]. The results showed that the best fit approach was better in terms of job execution time, cost, virtual machine allocation and resource utilization.…”

Section: Related Workmentioning

confidence: 98%

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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Section: Related Workmentioning

confidence: 98%

Optimized Scheduling and Resource Allocation Using Evolutionary Algorithms in Cloud Environment

Muthu¹,

Enoch²

2017

IJIES

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“…By this tactic, resource allocation advance to provision the sensitive deadline leases by decreasing the denial of the lease, in discrepancy to dual current algorithms via Haizea. Correspondingly, Shyam and Manvi [30] propose an efficient resource allocation scheme using cloud provider's resource agent and cloud user's task agent in IaaS Cloud. With maximizing the resource utility, reducing the total cost, and preserving the QoS, the minimum usage of the amount of VMs is ensured.…”

Section: Artificial Intelligent Resource Allocationmentioning

confidence: 99%

“…It is frequently generated to execute certain tasks by software competition ways or hardware virtualization techniques that are different than tasks are executed in a host environment. An-ping and Chun-xiang [31] Li and Li [34] Liang et al [33] Panda and Jana [29] Radhakrishnan and Kavitha [32] Shyam and Manvi [30] Vernekar and Game [35] Wang et al [36] Ali et al [44] Dai et al [48] Hu et al [45] Hadji and Zeghlache [49] Oddi et al [46] Saraswathi et al [39] Teng and MagoulFs [54] Wang and Liu [50] Wang and Su [40] Wolke and Ziegler [41] Wuhib et al [51] Wuhib et al [55] Xie and Liu [42] Xiao et al [47] Yin et al [52] Zhang et al [43] Zhang et al [ Ali et al [44] Dai et al [48] Hu et al [45] Hadji and Zeghlache [49] Oddi et al [46] Saraswathi et al [39] Teng and MagoulFs [54] Wang and Liu [50] Wang and Su [40] Wuhib et al [51] Wuhib et al [55] Xie and Liu [42] Xiao et al [47] Yin et al [52] Zhang et al [43] Zhang et al [53] Gu et al [65] Mashayekhy et al …”

Section: Analysis Of Resources and Parameters Used In Current Studiesmentioning

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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“…To address the issue mentioned above, many scheduling techniques have been proposed, with focus on enhancing multiple parameters simultaneously [32,[55][56][57][58].…”

Section: Multi-objective Scheduling Techniquesmentioning

confidence: 99%

Online Task Scheduling of Big Data Applications in the Cloud Environment

2023

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The development of big data has generated data-intensive tasks that are usually time-consuming, with a high demand on cloud data centers for hosting big data applications. It becomes necessary to consider both data and task management to find the optimal resource allocation scheme, which is a challenging research issue. In this paper, we address the problem of online task scheduling combined with data migration and replication in order to reduce the overall response time as well as ensure that the available resources are efficiently used. We introduce a new scheduling technique, named Online Task Scheduling algorithm based on Data Migration and Data Replication (OTS-DMDR). The main objective is to efficiently assign online incoming tasks to the available servers while considering the access time of the required datasets and their replicas, the execution time of the task in different machines, and the computational power of each machine. The core idea is to achieve better data locality by performing an effective data migration while handling replicas. As a result, the overall response time of the online tasks is reduced, and the throughput is improved with enhanced machine resource utilization. To validate the performance of the proposed scheduling method, we run in-depth simulations with various scenarios and the results show that our proposed strategy performs better than the other existing approaches. In fact, it reduces the response time by 78% when compared to the First Come First Served scheduler (FCFS), by 58% compared to the Delay Scheduling, and by 46% compared to the technique of Li et al. Consequently, the present OTS-DMDR method is very effective and convenient for the problem of online task scheduling.

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Resource allocation in cloud computing using agents

Cited by 13 publications

References 20 publications

Optimized Scheduling and Resource Allocation Using Evolutionary Algorithms in Cloud Environment

Optimized Scheduling and Resource Allocation Using Evolutionary Algorithms in Cloud Environment

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

Online Task Scheduling of Big Data Applications in the Cloud Environment

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