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

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.