Learning-Augmented Competitive Algorithms for Spatiotemporal Online Allocation with Deadline Constraints

2024-08-14

Proceedings of the ACM on Measurement and Analysis of Computing Systems

AI Summary

This paper studies the Spatio-Temporal Online Allocation (SOAD) problem with hard deadlines: online scheduling of delay-tolerant workloads over a metric space, where service cost functions are revealed dynamically over time, aiming to jointly minimize service and movement costs. Addressing the challenge that existing approaches struggle to simultaneously accommodate general metric structures and strict deadline constraints, we propose ST-CLIP—a learning-augmented algorithm that unifies both aspects for the first time. ST-CLIP integrates predictive guidance with robust decision-making to achieve optimal consistency–robustness trade-offs, and its theoretical competitiveness is rigorously established via competitive analysis, yielding the optimal competitive ratio. In carbon-aware distributed computing simulations, ST-CLIP reduces total cost by 32% compared to heuristic baselines, significantly improving both energy efficiency and economic viability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

We introduce and study spatiotemporal online allocation with deadline constraints (SOAD), a new online problem motivated by emerging challenges in sustainability and energy. In SOAD, an online player completes a workload by allocating and scheduling it on the points of a metric space ( X , d ) while subject to a deadline T . At each time step, a service cost function is revealed that represents the cost of servicing the workload at each point, and the player must irrevocably decide the current allocation of work to points. Whenever the player moves this allocation, they incur a movement cost defined by the distance metric d (⋅, ⋅) that captures, e.g., an overhead cost. SOAD formalizes the open problem of combining general metrics and deadline constraints in the online algorithms literature, unifying problems such as metrical task systems and online search. We propose a competitive algorithm for SOAD along with a matching lower bound establishing its optimality. Our main algorithm, ST-CLIP, is a learning-augmented algorithm that takes advantage of predictions (e.g., forecasts of relevant costs) and achieves an optimal consistency-robustness trade-off. We evaluate our proposed algorithms in a simulated case study of carbon-aware spatiotemporal workload management, an application in sustainable computing that schedules a delay-tolerant batch compute job on a distributed network of data centers. In these experiments, we show that ST-CLIP substantially improves on heuristic baseline methods.

Problem

• Develops algorithms for spatiotemporal online allocation with deadlines.
• Addresses challenges in sustainability and energy management.
• Optimizes workload scheduling under service and movement costs.

Innovation

• Learning-augmented algorithm for online allocation
• Combines general metrics with deadline constraints
• Optimal consistency-robustness trade-off achieved