Design and Implementation of Scalable and Load-Balanced Virtual Machine Clusters

Chang, Jia-Hong; Cheng, Hui-Sheng; Chiang, Mei‐Ling

doi:10.1109/sc2.2017.14

Cited by 5 publications

(10 citation statements)

References 5 publications

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“…Our previous study [2] explores the issues of implementing server clusters based on VMs. Several kinds of VM-based server clusters with different architectures are developed and discussed, including single VM cluster, hierarchical multiple VM clusters, and distributed multiple VM clusters.…”

Section: Related Workmentioning

confidence: 99%

“…KVM is a virtualization module running in the Linux kernel that allows the kernel to function as a hypervisor. Being directly supported in the Linux kernel, they are shown to have excellent performance and are adopted in many studies [1,2,7,24]. Figure 1 shows the VMC/H architecture, which consists of multiple physical servers (i.e., Host 1-N) on which several VMs are built.…”

Section: System Architecturementioning

confidence: 99%

“…This allows a high degree of scalability, excellent reliability, and high efficiency. With the maturity of computer hardware and virtualization technology, administrators can maintain and deploy systems more conveniently and efficiently [1,2]. Hence, enterprises and organizations use virtualization technology to deploy their systems.…”

Section: Introductionmentioning

confidence: 99%

“…LVS and KVM technologies are adopted since they are directly supported in the Linux kernel. Being operated at the kernel layer, they are shown to have excellent performance in the related studies [1, 2,7]. It consists of a front-end server, also called a load balancer for dispatching requests, and several back-end servers to provide services.…”

Section: Introductionmentioning

confidence: 99%

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A Scalable Virtualized Server Cluster Providing Sensor Data Storage and Web Services

Chiang

Hou

2020

Symmetry

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With the rapid development of the Internet of Things (IoT) technology, diversified applications deploy extensive sensors to monitor objects, such as PM2.5 air quality monitoring. The sensors transmit data to the server periodically and continuously. However, a single server cannot provide efficient services for the ever-growing IoT devices and the data they generate. This study bases on the concept of symmetry of architecture and quantities in system design and explores the load balancing issue to improve performance. This study uses the Linux Virtual Server (LVS) and virtualization technology to deploy a virtual machine (VM) cluster. It consists of a front-end server, also a load balancer, to dispatch requests, and several back-end servers to provide services. These receive data from sensors and provide Web services for browsing real-time sensor data. The Hadoop Distributed File System (HDFS) and HBase are used to store the massive amount of received sensor data. Because load-balancing is critical for resource utilization, this study also proposes a new load distribution algorithm for VM-based server clusters that simultaneously provide multiple services, such as sensor services and Web service. It considers the aggregate load of all back-end servers on the same physical server that provides multiple services. It also considers the difference between physical machines and VMs. Algorithms such as those for LVS, which do not consider these factors, can cause load imbalance between physical servers. The experimental results demonstrate that the proposed system is fault tolerant, highly scalable, and offers high availability and high performance.

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Section: Related Workmentioning

confidence: 99%

Section: System Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Scalable Virtualized Server Cluster Providing Sensor Data Storage and Web Services

Chiang

Hou

2020

Symmetry

Self Cite

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“…However, what this method considered more was dynamic server workload balancing to compensate for the insufficient availability of individual servers with a lack of availability evaluation and analysis of virtualized resources. Chang J. H. et al [19] realized an extensible and workload balancing method of VM clusters by balancing server workloads through the difference between physical machine and VM, but this method lacked availability evaluation of VM resources, which affected task execution efficiency. Nowadays, these task scheduling algorithms based on these availability awareness have problems such as: the gap between available task processing capacity of VM resources in the cloud platform and task availability requirement is large, and accurately evaluating availability of VM resource is impossible; workload balancing of resources cannot be guaranteed under availability constraints; when task workload pressure is large among VMs, task completion time will be affected.…”

Section: State Of the Artmentioning

confidence: 99%

Task Scheduling Algorithm Based on Virtual Machine Availability Awareness in Cloud Platform

Li¹,

Liu²,

Tong³

2018

JESTR

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Virtual machine (VM) availability awareness is a key task scheduling technology in a cloud platform. However, the dynamic change and uncertainty of VM availability constitute difficulties for task scheduling, and quality requirements of task services cannot be satisfied and thus seriously affect the task scheduling capacity of the cloud platform. A task scheduling algorithm based on VM availability awareness was proposed to solve the unmatching problem between VM availability and quality of service (QoS) to improve task scheduling capacity of VMs. This algorithm combined available task processing capacities of VMs and task requirement features to establish a differential entropy model between VM availability and task availability requirement. Task availability matching and scheduling was realized through the principle of maximum entropy, and task scheduling was optimized from the aspect of balance of server workloads. Finally, a comparative verification between the task scheduling algorithm and Random and Minmin algorithms was implemented. Results demonstrate that through the task scheduling algorithm based on VM availability awareness, the task execution speed of VMs is higher than those of Random and Minmin algorithms by 28% and 6%, respectively. Therefore, task execution speed of the cloud platform is significantly elevated, and server workloads are more balanced than those in Random and Minmin algorithms. Within the same time, QoS satisfaction rate is higher than those of Random and Minmin algorithms by 10% and 2%, respectively. QoS task requirement is satisfied while task completion time is reduced. This study concludes that the VM availability awareness method satisfies the task requirements and improves task processing performance of the VM in the cloud platform. Relevant conclusions can provide technical support for task scheduling of the VM in the cloud platform.

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DALMIG: Matching-Based Data Center Allocation and Dual Live VM Migration in Cluster-Based Federated Cloud

Varghese,

Sreenivasaiah

2023

IETE Journal of Research

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Design and Implementation of Scalable and Load-Balanced Virtual Machine Clusters

Cited by 5 publications

References 5 publications

A Scalable Virtualized Server Cluster Providing Sensor Data Storage and Web Services

A Scalable Virtualized Server Cluster Providing Sensor Data Storage and Web Services

Task Scheduling Algorithm Based on Virtual Machine Availability Awareness in Cloud Platform

DALMIG: Matching-Based Data Center Allocation and Dual Live VM Migration in Cluster-Based Federated Cloud

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