Task scheduling and VM placement to resource allocation in Cloud computing: challenges and opportunities

Cluster Computing, Journal Year: 2023, Volume and Issue: 26(5), P. 3069 - 3087

Published: July 8, 2023

Language: Английский

---

Resource-Efficient and Quality-Aware Virtual Machine Consolidation Method

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

Published: Jan. 8, 2025

Language: Английский

---

Optimizing Cloud Resource Management with an IoT-enabled Optimized Virtual Machine Migration Scheme for Improved Efficiency

Journal of Network and Computer Applications, Journal Year: 2025, Volume and Issue: unknown, P. 104137 - 104137

Published: Feb. 1, 2025

Language: Английский

---

Perspective of virtual machine consolidation in cloud computing: a systematic survey

Telecommunication Systems, Journal Year: 2024, Volume and Issue: 87(2), P. 257 - 285

Published: June 18, 2024

Language: Английский

---

An Efficient Model based on Machine Learning Algorithms for Virtual Machines Classification in Cloud Computing Environment

Published: May 16, 2024

In cloud computing, virtual machines consolidation (VMC) techniques are commonly used to improve resource utilization and reduce energy consumption. Task scheduling in systems is a crucial aspect of VMC as it involves mapping clients' requirements the appropriate computing resources such Virtual Machines (VMs) or Physical (PMs). The provider must ensure that tasks executed efficiently using available shared while maintaining quality service (QoS) minimizing carbon footprint. Therefore, good VM migration based on customer's needs IT capacity required maintain best performance system. this work, we introduce an approach leverage machine learning-based algorithms for VMs classification their latency sensitivity facilitate subsequent into suitable PM better VMC. These categorize two groups: potentially inter-active (exhibiting periodic behavior daily scale) latency-insensitive (for example, batch workloads, development, test workloads). Our model demonstrated robust performance, achieving accuracy around 83%, thus establishing itself most proficient classifier study.

Language: Английский

---

Time Series Cross-Sequence Prediction

WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS, Journal Year: 2024, Volume and Issue: 21, P. 1611 - 1618

Published: July 19, 2024

In the modern transport industry, vast and diverse information arrays, particularly those including time series data, are rapidly expanding. This growth presents an opportunity to improve quality of forecasting. Researchers practitioners continuously developing innovative tools predict their future values. The goal research is performance automated forecasting environments in a systematic structured way. paper investigates effect substituting initial with another similar nature, during training phase model’s development. A financial data set Prophet model employed for this objective. It observed that impact on accuracy predicted values promising, albeit not significant. Based obtained results, valuable conclusions drawn, recommendations further improvements provided. By highlighting importance incorporation, assists making informed choices leveraging full potential available more precise outcomes.

Language: Английский

---

Weight factor and priority-based virtual machine load balancing model for cloud computing

International Journal of Information Technology, Journal Year: 2024, Volume and Issue: 16(8), P. 5271 - 5276

Published: Aug. 23, 2024

Language: Английский

---

Stochastic measurement-based multi-cloud consolidation for efficient resource distribution

Measurement, Journal Year: 2024, Volume and Issue: 242, P. 115973 - 115973

Published: Oct. 18, 2024

Language: Английский

---