Chronotopic Optimization: Temporal Embedding Strategies for Dynamic Resource Allocation in Ephemeral Computing Environments

Posted: Nov 27, 2024

• Dr. Elara Vance
• Professor Marcus Thorne
• Dr. Sienna Patel

Abstract

This paper introduces chronotopic optimization, a novel computational paradigm that addresses the challenge of resource allocation in transient computing environments where computational resources appear and disappear unpredictably. Unlike traditional static or dynamic optimization approaches, chronotopic optimization incorporates temporal persistence patterns as first-class optimization parameters, enabling systems to make allocation decisions based on both current availability and predicted temporal stability. Our methodology develops temporal embedding vectors that encode not only spatial resource characteristics but also their expected duration and reliability patterns. We formulate the allocation problem as a temporal graph optimization where edges represent not just connectivity but temporal compatibility between resource availability windows and computational task requirements. The core innovation lies in our temporal coherence function, which measures the alignment between task execution requirements and resource persistence patterns, allowing the system to prioritize resources that match both computational needs and temporal constraints. We evaluate our approach through extensive simulations of ephemeral computing scenarios, including mobile edge computing, volunteer computing networks, and satellite computing constellations. Results demonstrate that chronotopic optimization achieves 37% higher task completion rates and 42% better resource utilization compared to conventional dynamic allocation methods. Furthermore, our approach reduces computational migration overhead by 58% by selecting resources with temporal characteristics that align with task duration requirements. The temporal embedding strategy proves particularly effective in environments with heterogeneous resource persistence patterns, where it successfully identifies stable resources for long-running tasks while utilizing transient resources for short computational bursts. This research establishes temporal characteristics as fundamental optimization dimensions in modern computing environments and provides a mathematical foundation for reasoning about computational persistence in allocation decisions.