Chronotopic Optimization: A Temporal Sequencing Approach to Dynamic Resource Allocation in Edge Computing Environments

Posted: Oct 28, 2025

• Dr. Elara Vance
• Prof. Kenji Tanaka
• Dr. Isabella Rossi

Abstract

The exponential growth of distributed computational systems, particularly in edge computing environments, has exposed fundamental limitations in traditional resource allocation methodologies. Current approaches predominantly treat resource allocation as a spatial distribution problem, focusing on load balancing across available computational nodes. While effective for static or predictable workloads, these methods struggle with the dynamic, heterogeneous nature of modern computational demands. The temporal dimension of computational tasks remains largely unexplored as a primary optimization parameter, representing a significant gap in resource management theory. This paper introduces a paradigm shift in how we conceptualize and implement resource allocation in distributed systems. Rather than viewing computational tasks as static entities with fixed resource requirements, we propose treating them as temporal sequences with evolving characteristics. This perspective enables a fundamentally different approach to optimization that prioritizes temporal coordination over spatial distribution. Our methodology, termed Chronotopic Optimization, addresses the critical challenge of managing computational resources in environments characterized by uncertainty, heterogeneity, and dynamic demand patterns. The research presented herein was motivated by observing that traditional resource allocation algorithms consistently underperform in edge computing scenarios where workload patterns exhibit complex temporal dependencies.