Evaluation of Dynamic Vector Bin Packing for Virtual Machine Placement

2026-02-16

AI Summary

This study addresses the virtual machine placement problem in cloud data centers with the objective of minimizing the total usage time of physical machines. The problem is formulated as a MinUsageTime variant of the Dynamic Vector Bin Packing (DVBP) problem. The authors systematically evaluate and enhance existing algorithms under three distinct online settings: non-clairvoyant, clairvoyant, and learning-augmented. By integrating a learning-augmented prediction mechanism and leveraging real-world Azure workload traces, the work identifies key design principles for high-performance algorithms and demonstrates the superior efficacy of the proposed approach across varying information assumptions. The findings offer practical and effective strategies for resource scheduling in real-world cloud environments.

Technology Category

• Machine Learning: Online Learning & Bandits
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Search and Optimization: Algorithm Configuration

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Virtual machine placement is a crucial challenge in cloud computing for efficiently utilizing physical machine resources in data centers. Virtual machine placement can be formulated as a MinUsageTime Dynamic Vector Bin Packing (DVBP) problem, aiming to minimize the total usage time of the physical machines. This paper evaluates state-of-the-art MinUsageTime DVBP algorithms in non-clairvoyant, clairvoyant and learning-augmented online settings, where item durations (virtual machine lifetimes) are unknown, known and predicted, respectively. Besides the algorithms taken from the literature, we also develop several new algorithms or enhancements. Empirical experimentation is carried out with real-world datasets of Microsoft Azure. The insights from the experimental results are discussed to explore the structures of algorithms and promising design elements that work well in practice.

Problem

• Virtual Machine Placement
• Dynamic Vector Bin Packing
• Cloud Computing
• Resource Utilization
• MinUsageTime

Innovation

• Dynamic Vector Bin Packing
• Virtual Machine Placement
• Learning-Augmented Algorithms
• MinUsageTime Optimization
• Cloud Resource Management