VM Capacity-Aware Scheduling within Budget Constraints in IaaS Clouds

Area of Science:

• Computer Science
• Cloud Computing
• Distributed Systems

Background:

• Cloud computing, particularly Infrastructure-as-a-Service (IaaS), offers elastic resource provisioning.
• Dynamic changes in resource demand pose challenges for efficient budget utilization and performance maintenance.
• Task scheduling and resource provisioning under budget constraints remain a critical issue.

Purpose of the Study:

• To develop novel algorithms for task scheduling and resource provisioning in IaaS clouds.
• To ensure efficient budget utilization while maintaining adequate performance for executing tasks.
• To minimize task slowdown caused by budget limitations.

Main Methods:

• Development of new provisioning algorithms for dynamic resource allocation.
• Design of novel scheduling algorithms to optimize task execution within budget constraints.
• Simulation studies to evaluate the performance of the proposed algorithms.

Main Results:

• The proposed algorithms effectively manage resources to stay within a given budget.
• A significant reduction in task slowdown, up to 70%, was observed in simulations.
• The algorithms demonstrate efficient utilization of cloud resources under fluctuating workloads.

Conclusions:

• The developed algorithms provide an effective solution for budget-constrained task scheduling in IaaS clouds.
• The research contributes to optimizing cloud resource management for cost-efficiency and performance.
• The findings have practical implications for managing cloud computing costs and performance.