BanditWare: A Contextual Bandit-based Framework for Hardware Prediction
Tain\~a Coleman, Hena Ahmed, Ravi Shende, Ismael Perez, \"Ilkay Altinta\c{s}

TL;DR
BanditWare is an online framework that uses a contextual bandit algorithm to dynamically recommend optimal hardware configurations for distributed systems, improving resource utilization and system performance in real-time.
Contribution
It introduces a real-time, online hardware recommendation system based on contextual bandits, differing from traditional methods that depend on large historical datasets.
Findings
Effective hardware recommendations for diverse applications
Improved resource utilization and system performance
Seamless integration with existing data platforms
Abstract
Distributed computing systems are essential for meeting the demands of modern applications, yet transitioning from single-system to distributed environments presents significant challenges. Misallocating resources in shared systems can lead to resource contention, system instability, degraded performance, priority inversion, inefficient utilization, increased latency, and environmental impact. We present BanditWare, an online recommendation system that dynamically selects the most suitable hardware for applications using a contextual multi-armed bandit algorithm. BanditWare balances exploration and exploitation, gradually refining its hardware recommendations based on observed application performance while continuing to explore potentially better options. Unlike traditional statistical and machine learning approaches that rely heavily on large historical datasets, BanditWare operates…
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Taxonomy
TopicsParallel Computing and Optimization Techniques · Adversarial Robustness in Machine Learning · Electrostatic Discharge in Electronics
