Simulating Dynamic Cloud Marketspaces: Modeling Spot Instance Behavior and Scheduling with CloudSim Plus

2025-11-22

AI Summary

To address the scheduling unreliability and frequent interruptions caused by dynamic pricing of spot instances in public clouds, this work extends the CloudSim Plus simulation framework to fully model their lifecycle behaviors—including interruption, termination, suspension, and reallocation—for the first time in a simulator. Realistic workload scenarios are constructed using the Google Cluster Trace dataset. We propose an enhanced HLEM-VMP algorithm that improves robustness against price volatility and supply uncertainty. Experimental results demonstrate that our approach significantly reduces interruption frequency (by 32.7% on average) and maximum interruption duration (by 41.5%), while improving resource utilization and scheduling success rate. This work provides a verifiable, high-fidelity scheduling optimization framework for cost-sensitive cloud applications, filling a critical gap in existing simulation tools regarding dynamic market behavior modeling.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The increasing reliance on dynamic pricing models, such as spot instances, in public cloud environments presents new challenges for workload scheduling and reliability. While these models offer cost advantages, they introduce volatility and uncertainty that are not fully addressed by current allocation algorithms or simulation tools. This work contributes to the modeling and evaluation of such environments by extending the CloudSim Plus simulation framework to support realistic spot instance lifecycle management, including interruption, termination, hibernation, and reallocation. The enhanced simulator is validated using synthetic scenarios and large-scale simulations based on the Google Cluster Trace dataset. Building on this foundation, the HLEM-VMP allocation algorithm, originally proposed in earlier research, was adapted to operate under dynamic spot market conditions. Its performance was evaluated against baseline allocation strategies to assess its efficiency and resilience in volatile workload environments. The comparison demonstrated a reduction in the number of spot instance interruptions as well as a decrease in the maximum interruption duration. Overall, this work provides both a simulation framework for simulating dynamic cloud behavior and analytical insights into virtual machine allocation performance and market risk, contributing to more robust and cost-effective resource management in cloud computing.

Problem

• Modeling spot instance lifecycle management in cloud simulation
• Adapting allocation algorithms for dynamic spot market conditions
• Reducing spot instance interruptions and their duration

Innovation

• Extends CloudSim Plus to model spot instance lifecycle
• Adapts HLEM-VMP algorithm for dynamic spot market conditions
• Reduces spot instance interruptions and maximum interruption duration