A proactive autoscaling and energy-efficient VM allocation framework using online multi-resource neural network for cloud data center

Saxena, Deepika; Singh, Anurag

doi:10.1016/j.neucom.2020.08.076

Cited by 79 publications

(37 citation statements)

References 33 publications

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“…There are different algorithms for improving the load balancing technique like Static Load balancing algorithm (SLBA), Dynamic load balancing algorithms (DLBA), and dynamic nature-inspired load balancing algorithm (NDLBA). The results proved that NDLBA and DLBA are efficient in their working [8].…”

Section: Introductionmentioning

confidence: 71%

Load Balancing Framework for Cross-Region Tasks in Cloud Computing

Nazir¹,

Iqbal²,

Alyas³

et al. 2022

Computers, Materials &Amp; Continua

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Load balancing is a technique for identifying overloaded and underloaded nodes and balancing the load between them. To maximize various performance parameters in cloud computing, researchers suggested various load balancing approaches. To store and access data and services provided by the different service providers through the network over different regions, cloud computing is one of the latest technology systems for both end-users and service providers. The volume of data is increasing due to the pandemic and a significant increase in usage of the internet has also been experienced. Users of the cloud are looking for services that are intelligent, and, can balance the traffic load by service providers, resulting in seamless and uninterrupted services. Different types of algorithms and techniques are available that can manage the load balancing in the cloud services. In this paper, a newly proposed method for load balancing in cloud computing at the database level is introduced. The database cloud services are frequently employed by companies of all sizes, for application development and business process. Load balancing for distributed applications can be used to maintain an efficient task scheduling process that also meets the user requirements and improves resource utilization. Load balancing is the process of distributing the load on various nodes to ensure that no single node is overloaded. To avoid the nodes from being overloaded, the load balancer divides an equal amount of computing time to all nodes. The results of two different scenarios showed the cross-region traffic management and significant growth in revenue of restaurants by using load balancer decisions on application traffic gateways.

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Section: Introductionmentioning

confidence: 71%

Load Balancing Framework for Cross-Region Tasks in Cloud Computing

Nazir¹,

Iqbal²,

Alyas³

et al. 2022

Computers, Materials &Amp; Continua

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“…The pioneering works presented for population based VMP approaches includes Genetic Algorithm (GA), swarm intelligence such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and whale optimization (WOA) [30]. The GA based approaches are applied in numerous research works including [31], [32], and [33] etc. Multi-objective GA for resource prediction and allocation is proposed in [32], that predicts resource usage before VM allocation to maximize resource utilization and energy saving.…”

Section: Related Workmentioning

confidence: 99%

A Secure and Multi-objective Virtual Machine Placement Framework for Cloud Data Centre

Saxena,

Gupta,

Kumar

et al. 2021

Preprint

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To facilitate cost-effective and elastic computing benefits to the cloud users, the energy-efficient and secure allocation of virtual machines (VMs) plays a significant role at the data centre. The inefficient VM Placement (VMP) and sharing of common physical machines among multiple users leads to resource wastage, excessive power consumption, increased inter-communication cost and security breaches. To address the aforementioned challenges, a novel secure and multi-objective virtual machine placement (SM-VMP) framework is proposed with an efficient VM migration. The proposed framework ensures an energy-efficient distribution of physical resources among VMs that emphasizes secure and timely execution of user application by reducing inter-communication delay. The VMP is carried out by applying the proposed Whale Optimization Genetic Algorithm (WOGA), inspired by whale evolutionary optimization and nondominated sorting based genetic algorithms. The performance evaluation for static and dynamic VMP and comparison with recent state-of-the-arts observed a notable reduction in shared servers, inter-communication cost, power consumption and execution time up to 28.81%, 25.7%, 35.9% and 82.21%, respectively and increased resource utilization up to 30.21%.

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“…The HCI appliances provide improved reliability and performance as these appliances are passed through various testing and validating processes. The HCI systems are easy to deploy and they come with all the packages that are required for upgrading purposes or scaling the system [8].…”

Section: Introductionmentioning

confidence: 99%

Hyper-Convergence Storage Framework for EcoCloud Correlates

Tabassum¹,

Alyas²,

Hamid³

et al. 2022

Computers, Materials &Amp; Continua

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Cloud computing is an emerging domain that is capturing global users from all walks of life-the corporate sector, government sector, and social arena as well. Various cloud providers have offered multiple services and facilities to this audience and the number of providers is increasing very swiftly. This enormous pace is generating the requirement of a comprehensive ecosystem that shall provide a seamless and customized user environment not only to enhance the user experience but also to improve security, availability, accessibility, and latency. Emerging technology is providing robust solutions to many of our problems, the cloud platform is one of them. It is worth mentioning that these solutions are also amplifying the complexity and need of sustenance of these rapid solutions. As with cloud computing, new entrants as cloud service providers, resellers, tech-support, hardware manufacturers, and software developers appear on a daily basis. These actors playing their role in the growth and sustenance of the cloud ecosystem. Our objective is to use convergence for cloud services, software-defined networks, network function virtualization for infrastructure, cognition for pattern development, and knowledge repository. In order to gear up these processes, machine learning to induce intelligence to maintain ecosystem growth, to monitor performance, and to become able to make decisions for the sustenance of the ecosystem. Workloads may be programmed to "superficially" imitate most business applications and create large numbers using lightweight workload generators that merely stress the storage. In today's current IT environment, when many enterprises use the cloud to service some of their application demands, a different performance testing technique that assesses more than the storage is necessary. Compute and storage are merged into a single building block with HCI(Hyper-converged infrastructure), resulting in a huge pool of compute and storage resources when clustered with other building blocks. The novelty of this work to design and test cloud storage using the measurement of This work is licensed under a Creative Commons Attribution 4.

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A proactive autoscaling and energy-efficient VM allocation framework using online multi-resource neural network for cloud data center

Cited by 79 publications

References 33 publications

Load Balancing Framework for Cross-Region Tasks in Cloud Computing

Load Balancing Framework for Cross-Region Tasks in Cloud Computing

A Secure and Multi-objective Virtual Machine Placement Framework for Cloud Data Centre

Hyper-Convergence Storage Framework for EcoCloud Correlates

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