Logo des Repositoriums

Auflistung nach Schlagwort "load"

1 - 2 von 2
  • Konferenzbeitrag
    Optimization of Automotive Software Distribution on Multi-core Systems using Machine Learning Approaches
    (Softwaretechnik-Trends Band 40, Heft 2, 2020) Raza, Syed Aoun; Vallavanthara, Amal Jose; Nidavan, Rakesh
    Multi-core software should be partitioned under different constraints e.g., balanced execution load on cores, timing behavior and optimized level of communication/ synchronization among different system components. The objective is to efficiently distribute the processes onto multi-core hardware such that the system has reduced communication/ synchronization complexity. Moreover, a bad distribution strategy during migration from single- to multi-core and from multi- to many-core hardware does not always return the expected performance gain. This paper presents two novel AI-based approaches for optimal distribution (minimal inter-core communication inspite of no deadline misses) of software system on multi-core hardware architecture. We discuss the comparisons of our machine learning solutions based on unsupervised and reinforcement learning. We share the benefits and limitations of using unsupervised learning and reinforcement learning based on our experience.
  • Konferenzbeitrag
    Toward Efficient Scalability Benchmarking of Event-Driven Microservice Architectures at Large Scale
    (Softwaretechnik-Trends Band 40, Heft 3, 2020) Henning, Sören; Hasselbring, Wilhelm
    Over the past years, an increase in software architectures containing microservices, which process data streams of a messaging system, can be observed. We present Theodolite, a method accompanied by an open source implementation for benchmarking the scalability of such microservices as well as their employed stream processing frameworks and deployment options. According to common scalability definitions, Theodolite provides detailed insights into how resource demands evolve with increasing load intensity. However, accurate and statistically rigorous insights come at the cost of long execution times, making it impracticable to execute benchmarks for large sets of systems under test. To overcome this limitation, we raise three research questions and propose a research agenda for executing scalability benchmarks more time-efficiently and, thus, for running scalability benchmarks at large scale.