Virtual Machine Instance’s Price Prediction using Machinelearning Techniques at the Cloud Data Center DOI Creative Commons
Neeraj Sharma, Tejodbhav Koduru,

Sai Yasheswini Kandimalla

et al.

Research Square (Research Square), Journal Year: 2022, Volume and Issue: unknown

Published: Sept. 14, 2022

Abstract Virtual Machine (VM) instance price prediction in cloud computing is an emerging and important research area. VM instance’s used for different purposes such as reducing energy consumption, maintaining Service Level Agreement (SLA), balancing workload at data centers. In this paper, we propose a Seasonal Auto-Regressive Moving Average (SARIMA) based prediction. We also investigate two models known Auto Regressive Integrated (ARIMA), Long ShortTerm Memory (LSTM). The experimental results show that the proposed SARIMA (0,1,0) (1,1,0) model outperforms ARIMA LSTM with MAPE percentage of 1.147.

Language: Английский

Intent-driven orchestration of serverless applications in the computing continuum DOI
Nikos Filinis, Ioannis Tzanettis, Dimitrios Spatharakis

et al.

Future Generation Computer Systems, Journal Year: 2024, Volume and Issue: 154, P. 72 - 86

Published: Jan. 2, 2024

Language: Английский

Citations

10
Live virtual machine migration: A survey, research challenges, and future directions DOI
Muhammad Imran, Muhammad Ibrahim, Muhammad Salah ud din

et al.

Computers & Electrical Engineering, Journal Year: 2022, Volume and Issue: 103, P. 108297 - 108297

Published: Aug. 12, 2022

Language: Английский

Citations

29
VMP-A3C: Virtual machines placement in cloud computing based on asynchronous advantage actor-critic algorithm DOI Creative Commons
Pengcheng Wei, Yushan Zeng, Bei Yan

et al.

Journal of King Saud University - Computer and Information Sciences, Journal Year: 2023, Volume and Issue: 35(5), P. 101549 - 101549

Published: April 20, 2023

Virtualization technology represented through Virtual Machines (VMs) is recognized as a key infrastructure in cloud computing. This developing rapidly and data centers face challenges such Machine Placement (VMP) for energy efficiency. VMP defined the efficient allocation of VMs to Host (HMs) achieve various objectives reducing consumption, load balancing avoid Service Level Agreement Violations (SLAV). In this paper, addressed using Deep Reinforcement Learning (DRL) based strategy determine best mapping between HMs. We present VMP-A3C, an effective solve Asynchronous Advantage Actor-Critic (A3C) algorithm new DRL approach. VMP-A3C aims at HMs without SLAV, where consumption reduced much possible. learns dynamically consolidate migration techniques minimum number believe that there scope improvements shutting down little-workload migration. The effectiveness proposed has been evaluated from aspects deployment rate, shutdown migrated VMs. main difference terms required existing state-of-the-art method 2.54% 7.14%, respectively.

Language: Английский

Citations

12
An Intelligent VM Placement Method for Minimizing Energy Cost and Carbon Emission in Distributed Cloud Data Centers DOI

Ehsan Rasoulpour Shabestari,

Alireza Shameli‐Sendi

Journal of Grid Computing, Journal Year: 2025, Volume and Issue: 23(1)

Published: March 1, 2025

Language: Английский

Citations

0
Intelligent Resource Orchestration for 5G Edge Infrastructures DOI Creative Commons
Rafael Moreno‐Vozmediano, Rubén Montero, Eduardo Huedo

et al.

Future Internet, Journal Year: 2024, Volume and Issue: 16(3), P. 103 - 103

Published: March 19, 2024

The adoption of edge infrastructure in 5G environments stands out as a transformative technology aimed at meeting the increasing demands latency-sensitive and data-intensive applications. This research paper presents comprehensive study on intelligent orchestration computing infrastructures. proposed Smart Edge-Cloud Management Architecture, built upon an OpenNebula foundation, incorporates ONEedge5G experimental component, which offers workload forecasting automation capabilities, for optimal allocation virtual resources across diverse locations. evaluated different models, based both traditional statistical techniques machine learning techniques, comparing their accuracy CPU usage prediction dataset machines (VMs). Additionally, integer linear programming formulation was to solve optimization problem mapping VMs physical servers distributed infrastructure. Different criteria such minimizing server usage, load balancing, reducing latency violations were considered, along with constraints. Comprehensive tests experiments conducted evaluate efficacy architecture.

Language: Английский

Citations

3
Deep learning BiLSTM and Branch-and-Bound based multi-objective virtual machine allocation and migration with profit, energy, and SLA constraints DOI
Neeraj Sharma, Sriramulu Bojjagani, Ravi Uyyala

et al.

Sustainable Computing Informatics and Systems, Journal Year: 2025, Volume and Issue: unknown, P. 101128 - 101128

Published: May 1, 2025

Language: Английский

Citations

0
Multi-resource management using an advanced scheduling algorithm to the least amount of time DOI
Santosh Shakya, Priyanka Tripathi

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: 16(4), P. 2283 - 2293

Published: Feb. 28, 2024

Language: Английский

Citations

2
Automated cloud resources provisioning with the use of the proximal policy optimization DOI Creative Commons
Włodzimierz Funika, Paweł Koperek, Jacek Kitowski

et al.

The Journal of Supercomputing, Journal Year: 2022, Volume and Issue: 79(6), P. 6674 - 6704

Published: Nov. 10, 2022

Abstract Many modern applications, both scientific and commercial, are deployed to cloud environments often employ multiple types of resources. That allows them efficiently allocate only the resources which actually needed achieve their goals. However, in many workloads actual usage infrastructure varies over time, results over-provisioning unnecessarily high costs. In such cases, automatic resource scaling can provide significant cost savings by provisioning amount necessary support current workload. Unfortunately, due complex nature distributed systems, remains a challenge. Reinforcement learning domain has been recently very active field research. Thanks combining it with Deep Learning, newly designed algorithms improve state art domains. this paper we present our attempt use recent advancements Learning optimize running compute-intensive evolutionary process automating heterogeneous compute environment. We describe architecture system evaluation results. The experiments include autonomous management sample workload comparison its performance traditional threshold-based approach. also details training policy using proximal optimization algorithm. Finally, discuss feasibility extend presented approach further scenarios.

Language: Английский

Citations

11
Multi-Objective Reinforcement Learning for Virtual Machines Placement in Cloud Computing DOI Open Access

Chayan Bhatt,

Sunita Singhal

International Journal of Advanced Computer Science and Applications, Journal Year: 2024, Volume and Issue: 15(3)

Published: Jan. 1, 2024

The rapid demand for cloud services has provoked providers to efficiently resolve the problem of Virtual Machines Placement in cloud. This paper presents a VM using Reinforcement Learning that aims provide optimal resource and energy management data centers. provides better decision-making as it solves complexity caused due tradeoff among objectives hence is useful mapping requested on minimum number Physical Machines. An enhanced Tournament-based selection strategy along with Roulette Wheel sampling been applied ensure optimization goes through balanced exploration exploitation, thereby giving solution quality. Two heuristics have used ordering VM, considering impact CPU memory utilizations over placement. Moreover, concept Pareto approximate set considered both are prioritized according perspective users. proposed technique implemented MATLAB 2020b. Simulation analysis showed VMRL performed preferably well shown improvement 17%, 20% 18% terms consumption, utilization fragmentation respectively comparison other multi-objective algorithms.

Language: Английский

Citations

1
Optimizing pre-copy live virtual machine migration in cloud computing using machine learning-based prediction model DOI Creative Commons

Raseena M. Haris,

Mahmoud Barhamgi, Armstrong Nhlabatsi

et al.

Computing, Journal Year: 2024, Volume and Issue: 106(9), P. 3031 - 3062

Published: July 8, 2024

Abstract One of the preconditions for efficient cloud computing services is continuous availability to clients. However, there are various reasons temporary service unavailability due routine maintenance, load balancing, cyber-attacks, power management, fault tolerance, emergency incident response, and resource usage. Live Virtual Machine Migration (LVM) an option address by moving virtual machines between hosts without disrupting running services. Pre-copy memory migration a common LVM approach used in systems, but it faces challenges high rate frequently updated pages known as dirty pages. Transferring these during pre-copy prolongs overall time. If large numbers remaining after predefined iteration page transfer, stop-and-copy phase initiated, which significantly increases downtime negatively impacts availability. To mitigate this issue, we introduce prediction-based that optimizes process dynamically halting when predicted falls below threshold. Our proposed machine learning method was rigorously evaluated through experiments conducted on dedicated testbed using KVM/QEMU technology, involving different VM sizes memory-intensive workloads. A comparative analysis against methods default reveals remarkable improvement, with average 64.91% reduction RAM configurations high-write-intensive workloads, along total time approximately 85.81%. These findings underscore practical advantages our reducing disruptions live systems.

Language: Английский

Citations

1