Multi-Objective Memory Bandwidth Regulation and Cache Partitioning for Multicore Real-Time Systems

TL;DR

This paper introduces a multi-objective heuristic and a 0-1 linear program for efficient task-resource co-allocation in multicore real-time systems, improving schedulability and resource utilization through cache and bandwidth partitioning.

Contribution

It proposes a novel multi-layer heuristic and a 0-1 linear program for optimized resource co-allocation, enhancing predictability and efficiency in real-time multicore systems.

Findings

The 0-1 linear program outperforms existing mixed-integer programs in solution quality.

The multi-objective heuristic achieves better schedulability and resource efficiency.

Experimental results demonstrate improved computational efficiency and solution optimality.

Abstract

Memory bandwidth regulation and cache partitioning are widely used techniques for achieving predictable timing in real-time computing systems. Combined with partitioned scheduling, these methods require careful co-allocation of tasks and resources to cores, as task execution times strongly depend on available allocated resources. To address this challenge, this paper presents a 0-1 linear program for task-resource co-allocation, along with a multi-objective heuristic designed to minimize resource usage while guaranteeing schedulability under a preemptive EDF scheduling policy. Our heuristic employs a multi-layer framework, where an outer layer explores resource allocations using Pareto-pruned search, and an inner layer optimizes task allocation by solving a knapsack problem using dynamic programming. To evaluate the performance of the proposed optimization algorithm, we profile real-world benchmarks on an embedded AMD UltraScale+ ZCU102 platform, with fine-grained resource partitioning enabled by the Jailhouse hypervisor, leveraging cache set partitioning and MemGuard for memory bandwidth regulation. Experiments based on the benchmarking results show that the proposed 0-1 linear program outperforms existing mixed-integer programs by finding more optimal solutions within the same time limit. Moreover, the proposed multi-objective multi-layer heuristic performs consistently better than the state-of-the-art multi-resource-task co-allocation algorithm in terms of schedulability, resource usage, number of non-dominated solutions, and computational efficiency.