BanditWare: A Contextual Bandit-based Framework for Hardware Prediction
This addresses resource optimization for users of distributed computing systems, but it is incremental as it builds on existing contextual bandit methods.
The paper tackles the problem of misallocating resources in distributed computing systems by introducing BanditWare, an online recommendation system that dynamically selects suitable hardware using a contextual multi-armed bandit algorithm, achieving efficient resource allocation without needing large historical datasets.
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 online, learning and adapting in real-time as new workloads arrive. We evaluated BanditWare on three workflow applications: Cycles (an agricultural science scientific workflow) BurnPro3D (a web-based platform for fire science) and a matrix multiplication application. Designed for seamless integration with the National Data Platform (NDP), BanditWare enables users of all experience levels to optimize resource allocation efficiently.