Assessing Intel's Memory Bandwidth Allocation for resource limitation in real-time systems

TL;DR

This paper evaluates Intel's Memory Bandwidth Allocation technology on Xeon processors to understand its effectiveness in limiting memory bandwidth and ensuring predictability for safety-critical applications in cloud environments.

Contribution

It provides a systematic measurement approach to assess the indirect memory bandwidth limitations achievable with MBA delays on real hardware.

Findings

Effective MBA delay values are 70, 80, and 90 in our setting.

Bandwidth assurance varies with interference from concurrent workloads.

Results support designing predictable memory sharing strategies for safety-critical cloud applications.

Abstract

Industries are recently considering the adoption of cloud computing for hosting safety critical applications. However, the use of multicore processors usually adopted in the cloud introduces temporal anomalies due to contention for shared resources, such as the memory subsystem. In this paper we explore the potential of Intel's Memory Bandwidth Allocation (MBA) technology, available on Xeon Scalable processors. By adopting a systematic measurement approach on real hardware, we assess the indirect memory bandwidth limitation achievable by applying MBA delays, showing that only given delay values (namely 70, 80 and 90) are effective in our setting. We also test the derived bandwidth assured to a hypothetical critical core when interfering cores (e.g., generating a concurrent memory access workload) are present on the same machine. Our results can support designers by providing understanding of impact of the shared memory to enable predictable progress of safety critical applications in cloud environments.