Intelligent Management of Mobile Systems through Computational Self-Awareness

TL;DR

This paper explores adaptive resource management in many-core systems using computational self-awareness, enabling systems to self-optimize and self-adapt amidst complex, dynamic workloads and resource constraints.

Contribution

It introduces a novel approach employing computational self-awareness, specifically reflection, to enhance robustness and adaptability in runtime resource management.

Findings

Supports self-optimization and self-adaptivity in resource management

Provides a formal framework for robustness against runtime unpredictability

Enables reasoning about conflicting objectives and changing conditions

Abstract

Runtime resource management for many-core systems is increasingly complex. The complexity can be due to diverse workload characteristics with conflicting demands, or limited shared resources such as memory bandwidth and power. Resource management strategies for many-core systems must distribute shared resource(s) appropriately across workloads, while coordinating the high-level system goals at runtime in a scalable and robust manner. To address the complexity of dynamic resource management in many-core systems, state-of-the-art techniques that use heuristics have been proposed. These methods lack the formalism in providing robustness against unexpected runtime behavior. One of the common solutions for this problem is to deploy classical control approaches with bounds and formal guarantees. Traditional control theoretic methods lack the ability to adapt to (1) changing goals at runtime (i.e., self-adaptivity), and (2) changing dynamics of the modeled system (i.e., self-optimization). In this chapter, we explore adaptive resource management techniques that provide self-optimization and self-adaptivity by employing principles of computational self-awareness, specifically reflection. By supporting these self-awareness properties, the system can reason about the actions it takes by considering the significance of competing objectives, user requirements, and operating conditions while executing unpredictable workloads.