MOCHA: Modelling and Optimising Complex Heterogenous Architectures

(2019 - 2022) RA, University of York
Funded by Huawei Technologies Co. Ltd, £985,926

Project Overview

The applications and resources (processors, networks and memory) for real-time systems are becoming ever more complex to understand, control and maintain. This has led to research into building statistical models of systems and adaptive policies based on these statistical models.

The key challenges that emerge are whether the models reflect how the system would behave during operation, how systems should deal with unexpected or rarely occurring scenarios, and then how to optimise systems based on the behaviours of the systems. It is specifically to address the high overheads of current systems and the low cache hit rates that are currently achieved.

cpc-model

Project Objectives

The MOCHA project focuses on:

Statistical Modeling: Building accurate statistical models of complex heterogeneous architectures
Adaptive Policies: Developing adaptive policies based on statistical models
System Optimization: Optimizing systems based on behavioral analysis
Cache Performance: Addressing low cache hit rates in current systems
Overhead Reduction: Minimizing high overheads in current systems

Key Research Areas

Cache-aware scheduling for multi-core systems
Digital twin development for real-time systems
Statistical modeling of system behavior
Adaptive resource allocation strategies
Performance optimization techniques

Project Outcomes

The project has contributed to several publications and research advances in:

Cache-aware DAG scheduling methods
Digital twin applications for real-time systems
Statistical modeling approaches for complex architectures

More Information

For more information: [Project Website]

This project contributes to our research focus on:

Large scale allocation and scheduling for cyber-physical systems
Digital twins for real-time embedded systems
Multi-core and many-core scheduling techniques