PERFORMANCE ANALYSIS & OPTIMIZING CLOUD STORAGE USING A DYNAMIC WORKLOAD ASSESSMENT DOI

Samira Ansari,

S. Veenadhari

ShodhKosh Journal of Visual and Performing Arts, Journal Year: 2024, Volume and Issue: 5(6)

Published: June 30, 2024

Cloud storage has become a fundamental component of modern computing, offering scalable and cost-effective solutions for data management. However, optimizing cloud performance while handling dynamic workloads remains significant challenge. This paper explores Dynamic Workload Assessment Performance Analysis as strategy to enhance efficiency. We analyze workload variations, including read/write operations, latency, utilization patterns, develop adaptive optimization techniques. Machine learning algorithms predictive analytics are leveraged anticipate fluctuations allocate resources dynamically. Additionally, we evaluate various strategies such caching, duplication, compression, tiered management reduce costs. Experimental results demonstrate that workload-aware optimizations significantly improve responsiveness, throughput, resource utilization. The study concludes with key recommendations designing intelligent, self-optimizing systems ensure scalability, efficiency, cost-effectiveness in computing environments.

Language: Английский

Enhanced Grey Wolf Optimization (EGWO) and random forest based mechanism for intrusion detection in IoT networks DOI Creative Commons

Saad Said Alqahtany,

Asadullah Shaikh, Ali Alqazzaz

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 14, 2025

Smart devices are enabled via the Internet of Things (IoT) and connected in an uninterrupted world. These pose a challenge to cybersecurity systems due attacks network communications. Such have continued threaten operation end-users. Therefore, Intrusion Detection Systems (IDS) remain one most used tools for maintaining such flaws against cyber-attacks. The dynamic multi-dimensional threat landscape IoT increases Traditional IDS. focus this paper aims find key features developing IDS that is reliable but also efficient terms computation. Enhanced Grey Wolf Optimization (EGWO) Feature Selection (FS) implemented. function EGWO remove unnecessary from datasets intrusion detection. To test new FS technique decide on optimal set based accuracy achieved feature taking filters, recent approach relies NF-ToN-IoT dataset. selected evaluated by using Random Forest (RF) algorithm combine multiple decision trees create accurate result. experimental outcomes procedures demonstrate capacity recommended classification methods determine Analysis results presents performs more effectively than other techniques with optimized (i.e., 23 out 43 features), high 99.93% improved convergence.

Language: Английский

Citations

1
Systematic Review: Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms DOI Creative Commons
Darakhshan Syed, Ghulam Muhammad,

Safdar Rizvi

et al.

Intelligent Automation & Soft Computing, Journal Year: 2024, Volume and Issue: 39(3), P. 437 - 476

Published: Jan. 1, 2024

Cloud Computing has the ability to provide on-demand access a shared resource pool.It completely changed way businesses are managed, implement applications, and services.The rise in popularity led significant increase user demand for services.However, cloud environments efficient load balancing is essential ensure optimal performance utilization.This systematic review targets detailed description of techniques including static dynamic algorithms.Specifically, metaheuristic-based algorithms identified as solution case increased traffic.In cloud-based context, this paper describes measurements, benefits drawbacks associated with selected techniques.It also summarizes based on implementation, time complexity, adaptability, issue(s), targeted QoS parameters.Additionally, analysis evaluates tools instruments utilized each investigated study.Moreover, comparative among static, traditional metaheuristic response by using CloudSim simulation tool performed.Finally, key open problems potential directions state-of-the-art approaches addressed.

Language: Английский

Citations

2
Models for availability evaluation of file servers in private clouds DOI

Alison Silva,

Gustavo Callou

Computing, Journal Year: 2024, Volume and Issue: 107(1)

Published: Nov. 27, 2024

Language: Английский

Citations

0
PERFORMANCE ANALYSIS & OPTIMIZING CLOUD STORAGE USING A DYNAMIC WORKLOAD ASSESSMENT DOI

Samira Ansari,

S. Veenadhari

ShodhKosh Journal of Visual and Performing Arts, Journal Year: 2024, Volume and Issue: 5(6)

Published: June 30, 2024

Cloud storage has become a fundamental component of modern computing, offering scalable and cost-effective solutions for data management. However, optimizing cloud performance while handling dynamic workloads remains significant challenge. This paper explores Dynamic Workload Assessment Performance Analysis as strategy to enhance efficiency. We analyze workload variations, including read/write operations, latency, utilization patterns, develop adaptive optimization techniques. Machine learning algorithms predictive analytics are leveraged anticipate fluctuations allocate resources dynamically. Additionally, we evaluate various strategies such caching, duplication, compression, tiered management reduce costs. Experimental results demonstrate that workload-aware optimizations significantly improve responsiveness, throughput, resource utilization. The study concludes with key recommendations designing intelligent, self-optimizing systems ensure scalability, efficiency, cost-effectiveness in computing environments.

Language: Английский

Citations

0