Towards improving resource management in cloud systems using a multi-agent framework

Al‐Ayyoub, Mahmoud; Daraghmeh, Mustafa; Jararweh, Yaser; Althebyan, Qutaibah

doi:10.1504/ijcc.2016.075112

Cited by 20 publications

(5 citation statements)

References 15 publications

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“…The author suggested a technique for analyzing the conditions and splits the load balancing methodology into multiple layers. The multi-queue management policy is used to form and split requests in multiple queues depending upon their execution priorities and the rest of the layer handles internal requests of the queue in the cloud network with the help of the network administrator [ 20 ]. Due to the handling of both segments, the problem of heavy load processing reduces in many terms like energy utilization, average response time, and network load.…”

Section: Related Workmentioning

confidence: 99%

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Experimental Setup for Investigating the Efficient Load Balancing Algorithms on Virtual Cloud

Alankar

Sharma

Kaur

et al. 2020

Sensors

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Cloud computing has emerged as the primary choice for developers in developing applications that require high-performance computing. Virtualization technology has helped in the distribution of resources to multiple users. Increased use of cloud infrastructure has led to the challenge of developing a load balancing mechanism to provide optimized use of resources and better performance. Round robin and least connections load balancing algorithms have been developed to allocate user requests across a cluster of servers in the cloud in a time-bound manner. In this paper, we have applied the round robin and least connections approach of load balancing to HAProxy, virtual machine clusters and web servers. The experimental results are visualized and summarized using Apache Jmeter and a further comparative study of round robin and least connections is also depicted. Experimental setup and results show that the round robin algorithm performs better as compared to the least connections algorithm in all measuring parameters of load balancer in this paper.

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Section: Related Workmentioning

confidence: 99%

“…M. Al-Ayyoub (2016) [20] Analyses the conditions and splits the load balancing methodology into multiple layers.…”

Section: Selecting Virtual Machine With the Least Loadmentioning

confidence: 99%

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Experimental Setup for Investigating the Efficient Load Balancing Algorithms on Virtual Cloud

Alankar

Sharma

Kaur

et al. 2020

Sensors

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“…The adopted DRPM system is evaluated using the CloudSim tool and the results generated shows that the system allows the cloud providers in increasing resource utilization with less power consumption. [24] Venkata et. al.…”

Section: This Work Compared Variousmentioning

confidence: 99%

Implementation of cost effective hierarchical Hadoop cluster–a case study for education

Chakravarthy¹,

Sudhakar²,

Reddy³

2018

IJET

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To equip the younger generation of the province with computing skills and provide them access to a wide variety of modern educational resources such as multimedia based on educational content in government schools and colleges to set a strong foundation at an early stage of their education. Educational Media help to empower educational institutions by altering the way of using Information Communication Technology (ICT). To put this into practice, several challenges need to be addressed. It requires a scalable technological architecture and algorithms to form a cluster in which effective resource sharing like CPU, Memory among multiple nodes, tools to monitor, assess and evaluate data under hierarchical Hadoop cluster is needed. By analyzing text, audios, videos information, Periodic reports will be generated to assist the students, teachers and Government. In this case, a software framework is required to process big data stored in hierarchical nodes. As the architecture of Hadoop, an open source software framework doesn't support processing the data stored in hierarchical nodes. This case study proposes the Hierarchical Hadoop cluster to alter the way of using ICT. The proposed work helps in monitoring, reporting the usage of ICT and also acts a help desk to address the issues of the educational institutions. This establishes a novel communication media by generating reports on text, audio, video information based on analysis.

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“…With the great development of Internet and data center networking technologies [1], [2], there is a rapid increasing of tenant requests for cloud computing services, Infrastructureas-a-Service, Network-as-a-Service, and physical resources to host their applications [3].…”

Section: Introductionmentioning

confidence: 99%

Predicting Multi-Attribute Host Resource Utilization Using Support Vector Regression Technique

Abdullah

Al-Jamali

et al. 2020

IEEE Access

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Studies on the resource workload demand in cloud computing environment aim at reducing resource wastage by optimizing the resource utilization in a cloud data center. Based on this goal, most of the existing approaches rely on resource management mechanisms such as resource allocation and Virtual Machine (VM) consolidation to reach an ideal solution for reducing resource wastage. Because of instability and high variability of the cloud resource usage and workloads, there is a demand for cloud providers to apply the prediction methods for forecasting the future cloud resource utilization. This paper employs a supervised statistical learning method, i.e., Support Vector Regression Technique (SVRT), to forecast the future usage of multi-attribute host resource. The method is particularly suitable to handle a non-linear cloud resource workload. To improve the prediction accuracy of SVRT, we decide Radial Basis Function as the kernel function of SVRT and apply Sequential Minimal Optimization Algorithm (SMOA) for the training and regression estimation of the prediction method. Besides, compared with the existing work, we consider the multi-attribute cloud resources other than the single resource. The method is employed under eight sets of real-world workloads, which are collected from BitBrain (BB), PlanetLab (PL) and Google Cluster Workload Traces (GCWT). Series of experiments conducted on the workload dataset show the effectiveness of our approach. Based on evaluation metrics, the final results show that the accuracy was enhanced by approximately 4%-16% and the error percentage was reduced by approximately 8%-60% compared with the state-of-the-art methods.

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Towards improving resource management in cloud systems using a multi-agent framework

Cited by 20 publications

References 15 publications

Experimental Setup for Investigating the Efficient Load Balancing Algorithms on Virtual Cloud

Experimental Setup for Investigating the Efficient Load Balancing Algorithms on Virtual Cloud

Implementation of cost effective hierarchical Hadoop cluster–a case study for education

Predicting Multi-Attribute Host Resource Utilization Using Support Vector Regression Technique

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