Cloud Load Balancing Algorithm

Rai, Himanshu; Ojha, Sanjeev Kumar; Nazarov, A. S.

doi:10.1109/icacccn51052.2020.9362810

Cited by 16 publications

(5 citation statements)

References 11 publications

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“…In the future, the authors plan to consider other ways of distributing VMs and control mechanisms known from the literature [37][38][39][40][41], as well as the possibility of moving VMs between physical machines to minimize the loss factor. In addition, they assume the use of queuing, compression in the system, and the use of traffic distributions other than…”

Section: Discussionmentioning

confidence: 99%

High-Accuracy Analytical Model for Heterogeneous Cloud Systems with Limited Availability of Physical Machine Resources Based on Markov Chain

Hanczewski,

Stasiak,

Weissenberg

2024

Electronics

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The article presents the results of a study on modeling cloud systems. In this research, the authors developed both analytical and simulation models. System analysis was conducted at the level of virtual machine support, corresponding to Infrastructure as a Service (IaaS). The models assumed that virtual machines of different sizes are offered as part of IaaS, reflecting the heterogeneous nature of modern systems. Additionally, it was assumed that due to limitations in access to physical server resources, only a portion of these resources could be used to create virtual machines. The model is based on Markov chain analysis for state-dependent systems. The system was divided into an external structure, represented by a collection of physical machines, and an internal structure, represented by a single physical machine. The authors developed a novel approach to determine the equivalent traffic, approximating the real traffic appearing at the input of a single physical machine under the assumptions of request distribution. As a result, it was possible to determine the actual request loss probability in the entire system. The results obtained from both models (simulation and analytical) were summarized in common graphs. The studies were related to the actual parameters of commercially offered physical and virtual machines. The conducted research confirmed the high accuracy of the analytical model and its independence from the number of different instances of virtual machines and the number of physical machines. Thus, the model can be used to dimension cloud systems.

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Section: Discussionmentioning

confidence: 99%

High-Accuracy Analytical Model for Heterogeneous Cloud Systems with Limited Availability of Physical Machine Resources Based on Markov Chain

Hanczewski,

Stasiak,

Weissenberg

2024

Electronics

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“…There are several algorithms used in cloud computing to distribute Virtual Machines (VMs) to Physical Machines (PMs). Some of the commonly used algorithms are [14][15][16][17] Ant Colony Optimization (ACO): This algorithm is inspired by the behavior of ants in finding the shortest path between two points. In the cloud computing environment, ACO can be used to find the optimal placement of VMs based on resource utilization, energy consumption, and other criteria.…”

Section: Management In the Cloudmentioning

confidence: 99%

“…According to the International Energy Agency (IEA), the entirety of data centers worldwide consumed about 200 TWh of electricity in 2020 [13]. To this end, advanced load-balancing algorithms, including Opportunistic Load Balancing (OLB), Round Robin (RR), and Central Load Balancing Decision Models (CLBDM), have been developed to ensure equitable use of resources and minimize energy losses [14][15][16][17]. A detailed description of the various loadbalancing models is provided in Section 2.1.…”

Section: Introductionmentioning

confidence: 99%

An Analytical Model of IaaS Architecture for Determining Resource Utilization

Hanczewski,

Stasiak,

Weissenberg

2024

Sensors

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Cloud computing has become a major component of the modern IT ecosystem. A key contributor to this has been the development of Infrastructure as a Service (IaaS) architecture, in which users’ virtual machines (VMs) are run on the service provider’s physical infrastructure, making it possible to become independent of the need to purchase one’s own physical machines (PMs). One of the main aspects to consider when designing such systems is achieving the optimal utilization of individual resources, such as processor, RAM, disk, and available bandwidth. In response to these challenges, the authors developed an analytical model (the ARU method) to determine the average utilization levels of the aforementioned resources. The effectiveness of the proposed analytical model was evaluated by comparing the results obtained by utilizing the model with those obtained by conducting a digital simulation of the operation of a cloud system according to the IaaS paradigm. The results show the effectiveness of the model regardless of the structure of the emerging requests, the variability of the capacity of individual resources, and the number of physical machines in the system. This translates into the applicability of the model in the design process of cloud systems.

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“…However, depending on the suggested work or goal of the cloud environment, the algorithms' efficacy is unclear [13]. Each algorithm varies depending on the type of load-balancing within the bounds of cloud computing [14]. The turn-based Round Robin load-balancing solution has its unique algorithm [15].…”

Section: Introductionmentioning

confidence: 99%

Equally Spread Current Execution Load Modelling with Optimize Response Time Brokerage Policy for Cloud Computing

Zamri¹,

Pakhrudin²,

Saaidin³

et al. 2023

IJACSA

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Cloud computing is one of the significant technologies that is used to provide seamless internet surfing for large-scale applications and data storing purposes. The cloud is described as a large platform that enables users to access data from the internet without needing to buy storing space in their equipment such as a computer. Many studies have analysed the load-balancing technique on the cloud to distribute tasks equally between servers using the Equally Spread Current Execution (ESCE) algorithm. ESCE, which is a dynamic load balancer, has quite a few problems such as average level performance and too long of response time which affected the Quality of Services. This research has simulated a cloud computing concept using the ESCE Load Modelling technique with the CloudAnalyst simulator for three servers of Data Center (DC) locations. The ESCE was simulated to enhance its algorithm's performance as a load balancer and higher throughput in the cloud environment. The result shows that ESCE average overall response time is shortest when the DC is located at R0 with response times of 15.05s, 13.05s with 10 VMs, and 8.631s with the Optimize Response Time brokerage policy. This research is significant to promote notable load-balancing technique testing for virtualized cloud machines data centers on Quality of Services (QoS) aware tasks for Internet of Things (IoT) services.

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Cloud Load Balancing Algorithm

Cited by 16 publications

References 11 publications

High-Accuracy Analytical Model for Heterogeneous Cloud Systems with Limited Availability of Physical Machine Resources Based on Markov Chain

High-Accuracy Analytical Model for Heterogeneous Cloud Systems with Limited Availability of Physical Machine Resources Based on Markov Chain

An Analytical Model of IaaS Architecture for Determining Resource Utilization

Equally Spread Current Execution Load Modelling with Optimize Response Time Brokerage Policy for Cloud Computing

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