International Journal of Advance Computational Engineering and Networking (IJACEN)
.
Follow Us On :
current issues
Volume-12,Issue-9  ( Sep, 2024 )
Past issues
  1. Volume-12,Issue-8  ( Aug, 2024 )
  2. Volume-12,Issue-7  ( Jul, 2024 )
  3. Volume-12,Issue-6  ( Jun, 2024 )
  4. Volume-12,Issue-5  ( May, 2024 )
  5. Volume-12,Issue-4  ( Apr, 2024 )
  6. Volume-12,Issue-3  ( Mar, 2024 )
  7. Volume-12,Issue-2  ( Feb, 2024 )
  8. Volume-12,Issue-1  ( Jan, 2024 )
  9. Volume-11,Issue-12  ( Dec, 2023 )
  10. Volume-11,Issue-11  ( Nov, 2023 )

Statistics report
Feb. 2025
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 141
Paper Published : 1672
No. of Authors : 4423
  Journal Paper


Paper Title :
Predictive Task Scheduling Strategies for Cost Optimization in Multi-Cloud Deployments

Author :Devarapalli Keerthi Priya, Dudipala Jaswanth, Samhitha Putteti, Josna Tamma, Arun Reddy Pothireddy

Article Citation :Devarapalli Keerthi Priya ,Dudipala Jaswanth ,Samhitha Putteti ,Josna Tamma ,Arun Reddy Pothireddy , (2024 ) " Predictive Task Scheduling Strategies for Cost Optimization in Multi-Cloud Deployments " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 40-46, Volume-12,Issue-8

Abstract : In today's dynamic cloud computing landscape, organizations increasingly rely on multi-cloud deployments to leverage diverse services and mitigate risks associated with vendor lock-in and service outages. However, optimizing resource allocation and minimizing costs across multiple cloud providers pose significant challenges. This paper presents a novel approach to address these challenges through predictive task scheduling strategies tailored for cost optimization in multi-cloud environments. Leveraging machine learning techniques, our proposed framework analyzes historical data on task executions, resource utilization, and cost metrics to forecast future workload patterns and make informed scheduling decisions. By dynamically allocating tasks across multiple clouds based on predicted resource demands and pricing fluctuations, our approach aims to maximize cost-efficiency while meeting performance requirements. We evaluate the effectiveness of our strategy through extensive simulations and real-world experiments, demonstrating significant cost savings compared to traditional scheduling methods. Our findings underscore the potential of predictive task scheduling in multi-cloud deployments to drive cost optimization and enhance overall resource utilization in cloud computing ecosystems. Keywords - Predictive, Task Scheduling, Strategies, Cost Optimization, Multi-Cloud Deployments, Cloud Computing, Machine Learning, Resource Allocation, Workload Patterns, Performance, Dynamic, Resource Utilization, Pricing Fluctuations, Simulation, Experiments, Cost Savings, Resource Efficiency.

Type : Research paper

Published : Volume-12,Issue-8


DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-21174   View Here

Copyright: © Institute of Research and Journals

| PDF |
Viewed - 20
| Published on 2024-11-19
   
   
IRAJ Other Journals
IJACEN updates
Paper Submission is open now for upcoming Issue.
The Conference World

JOURNAL SUPPORTED BY