Lecture notes on data engineering and communications technologies, Journal Year: 2023, Volume and Issue: unknown, P. 45 - 56
Published: Oct. 30, 2023
Language: Английский
Mathematics, Journal Year: 2024, Volume and Issue: 12(3), P. 468 - 468
Published: Feb. 1, 2024
The advancement of cloud computing technologies has positioned virtual machine (VM) migration as a critical area research, essential for optimizing resource management, bolstering fault tolerance, and ensuring uninterrupted service delivery. This paper offers an exhaustive analysis VM processes within infrastructures, examining various types, server load assessment methods, selection strategies, ideal timing, target determination criteria. We introduce queuing theory-based model to scrutinize dynamics between servers in environment. By reinterpreting resource-centric mechanisms into task-processing paradigm, we accommodate the stochastic nature demands, characterized by random task arrivals variable processing times. is specifically tailored scenarios with two three VMs. Through numerical examples, elucidate several performance metrics: blocking probability, average tasks processed VMs, managed servers. Additionally, examine influence arrival rates duration on these measures.
Language: Английский
Citations
4
Expert Systems with Applications, Journal Year: 2025, Volume and Issue: 273, P. 126856 - 126856
Published: Feb. 20, 2025
Language: Английский
Citations
0
PeerJ Computer Science, Journal Year: 2025, Volume and Issue: 11, P. e2739 - e2739
Published: March 18, 2025
Cloud computing provides an opportunity to gain access the large-scale and high-speed resources without establishing your own infrastructure for executing high-performance (HPC) applications. has ( i.e ., computation power, storage, operating system, network, database etc .) as a public utility services end users on pay-as-you-go model. From past several years, efficient utilization of compute cloud become prime interest scientific community. One key reasons behind inefficient resource is imbalance distribution workload while HPC applications in heterogenous environment. The static scheduling technique usually produces lower higher makespan, dynamic achieves better load-balancing by incorporating pool. techniques lead increased overhead requiring continuous system monitoring, job requirement assessments real-time allocation decisions. This additional load potential impact performance responsiveness system. In this article, enhanced resource-aware balancing algorithm (DE-RALBA) proposed mitigate load-imbalance considering capabilities all VMs computing. empirical are performed CloudSim simulator using instances two benchmark datasets heterogeneous problems (HCSP) Google Jobs (GoCJ) dataset). obtained results revealed that DE-RALBA mitigates significant improvement terms makespan against existing algorithms, namely PSSLB, PSSELB, Dynamic MaxMin, DRALBA. Using HCSP instances, up 52.35% improved compared technique, more superior achieved GoCJ dataset.
Language: Английский
Citations
0
Transactions on Emerging Telecommunications Technologies, Journal Year: 2025, Volume and Issue: 36(5)
Published: May 1, 2025
ABSTRACT Cloud computing is an innovative technology that provides services over the internet and replaces requirement to own physical hardware or software. Security threats present a wide range of risks cloud computing, security threat defense plays significant role in computing. Virtual machines (VM) serve as backbone, providing flexible scalable resources for running storing data. Moving Target Defense (MTD) Blockchain enhance privacy by reducing chances successful attacks minimizing impact attacks. To address these issues, we propose integrating MTD blockchain technologies within environment named Hybrid Secure Onlooker (HSO). The proposed work involves several entities, including Users (CUs), Centralized Subnet Manager (CSM), Distributed Group (DGM), Consortium Block Module (CBM) Private (PBM). Initially, perform Multi‐Factor Authentication (MFA) establish secure communication avoid malicious traffic. Followed this, utilize Komoda Miliphir optimization (KMO) algorithm CUs' task scheduling based upon types, sensitivity, size. Entrenched scheduled tasks, CSM performs classification grouping VMs, assigning them their capacity, protocols, availability, utilizing Residual Flowed Capsule Network (RFC‐Net). grouped subsets are overseen managed DGM, which handles operations such virtual switch placement VM migration subsets. Finally, transactions stored hybrid layer with CBM PBM ensure security. implementation tool realizing HSO model. model can be examined on metrics state‐of‐the‐art comparisons. results show outperforms models.
Language: Английский
Citations
0
Published: June 21, 2024
Language: Английский
Citations
1
2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), Journal Year: 2023, Volume and Issue: unknown, P. 1016 - 1023
Published: Nov. 14, 2023
In the contemporary era of cloud computing, efficient and precise prediction CPU utilization ensures optimal performance energy efficiency in data centers. Traditional predictive models often need to be improved as these centers grow complexity scale, necessitating more nuanced integrative solutions. This paper introduces an advanced multi-layered learning framework meticulously designed meet demands modern Our innovative approach synergistically combines anomaly detection, clustering, ensemble-based regression prediction. The Isolation Forest algorithm is used during preliminary stage identify address anomalies within data. Subsequent phases harness K-Means clustering algorithm, refine categorization based on recurrent usage patterns, employ multilevel for accurate forecasting rooted historical real-time trends. Through comprehensive evaluations, our model demonstrates significant improvements accuracy robustness against dynamism inherent environments. research paves way a resilient, proactive, prediction, laying foundational stone future innovations computing resource management.
Language: Английский
Citations
1
Service Oriented Computing and Applications, Journal Year: 2024, Volume and Issue: unknown
Published: May 10, 2024
Language: Английский
Citations
0
Published: April 18, 2024
Managing Electrical Energy has become crucial nowadays where everything we use works on electrical energy. Utilizing energy in an efficient and effective way can save a lot of Reducing unnecessary usage current lead to lowering carbon emission also directly benefit the production cost industries. This reduction is possible when continuously monitor it's being utilized. In order overcome this problem our novel idea proposes state art algorithm such as Autoregressive Integrated Moving Average (ARIMA) model which link between one observation several lagged observations same series modeled by autoregressive component. essence, it depicts how value linearly dependent its own historical values. Linear regression prevalent statistical technique utilized association variable or more independent variables. When comes consumption, linear be used examine forecast patterns depending variety influencing factors. To improve accuracy proposed work, Time Series Analysis aids making system reliable efficient. We have developed all-in-one management solution provides platform control wastage manage all equipment place. Our monitors using various IOT sensors utilization predicting earlier help industries make better financial plans.
Language: Английский
Citations
0
Published: April 19, 2024
Language: Английский
Citations
0
International Journal of Information Technology, Journal Year: 2024, Volume and Issue: 16(8), P. 5271 - 5276
Published: Aug. 23, 2024
Language: Английский
Citations
0