2022
DOI: 10.17762/ijritcc.v10i1s.5846
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Cloud Host Selection using Iterative Particle-Swarm Optimization for Dynamic Container Consolidation

Abstract: A significant portion of the energy consumption in cloud data centres can be attributed to the inefficient utilization of available resources due to the lack of dynamic resource allocation techniques such as virtual machine migration and workload consolidation strategies to better optimize the utilization of resources. We present a new method for optimizing cloud data centre management by combining virtual machine migration with workload consolidation. Our proposed Energy Efficient Particle Swarm Optimization … Show more

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Cited by 5 publications
(2 citation statements)
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“…Furthermore, a dedicated hardware setup is required to run a full blockchain node, the specifications of which will vary based on the selected blockchain platform. On the software front, the system runs on a Linux distribution, such as Ubuntu 18.04 LTS [23], chosen for its stability and security features. The blockchain platform is a pivotal component, with Ethereum and Hyperledger Fabric [24] identified as prime candidates due to their robust smart contract capabilities.…”
Section: Mathematical Modelmentioning
confidence: 99%
“…Furthermore, a dedicated hardware setup is required to run a full blockchain node, the specifications of which will vary based on the selected blockchain platform. On the software front, the system runs on a Linux distribution, such as Ubuntu 18.04 LTS [23], chosen for its stability and security features. The blockchain platform is a pivotal component, with Ethereum and Hyperledger Fabric [24] identified as prime candidates due to their robust smart contract capabilities.…”
Section: Mathematical Modelmentioning
confidence: 99%
“…Nevertheless, the effectiveness of these approaches is limited in correctly forecasting traffic patterns since they fail to account for the stochastic and nonlinear characteristics inherent in traffic flow [14]. The conventional techniques employed for traffic flow prediction encompass ARIMA and regression models [16]. The Autoregressive Integrated Moving Average (ARIMA) model is a statistical framework that leverages historical observations from a time series to make projections about future values.…”
Section: Traffic Flow Predictionmentioning
confidence: 99%