Search

Identifying performance problems in modern software systems is nontrivial, even more so when looking at specific application domains, such as cyber-physical systems. The heterogeneity of software and hardware components makes the process of performance evaluation more challenging, and traditional software performance engineering techniques may fail while dealing with interacting and heterogeneous components. The goal of this paper is to introduce a model-based approach to understand software performance problems in cyber-physical systems. In our previous work, we listed some common bad practices, namely software performance antipatterns, that may occur. Here we are interested in shedding light on these antipatterns by means of performance models, i.e., queuing network models, that provide evidence of how antipatterns may affect the overall system performance. Starting from the specification of three software performance antipatterns tailored for cyber-physical systems, we provide the queuing network models capturing the corresponding bad practices. The analysis of these models demonstrates their usefulness in recognizing performance problems early in the software development process. This way, performance engineers are supported in the task of detecting and fixing the performance criticalities.

As the COVID-19 outbreak evolves around the world, the World Health Organization (WHO) and its Member States have been heavily relying on staying at home and lock down measures to control the spread of the virus. In last months, various signs showed that the COVID-19 curve was flattening, but the premature lifting of some containment measures (e.g., school closures and telecommuting) are favouring a second wave of the disease. The accurate evaluation of possible countermeasures and their well-timed revocation are therefore crucial to avoid future waves or reduce their duration. In this paper, we analyze patient and route data collected by the Korea Centers for Disease Control & Prevention (KCDC). We extract information from real-world data sets and use them to parameterize simulations and evaluate different what-if scenarios.

During the past few years, all leading cloud providers introduced burstable instances that can sprint their performance for a limited period to address sudden workload variations. Despite the availability of burstable instances, there is no clear understanding of how to minimize the waste of resources by regulating their burst capacity to the workload requirements. This is especially true when it comes to non-CPU-intensive applications. In this paper, we investigate how to limit network and I/O usage to optimize the efficiency of the bursting process. We also study which resource shall be controlled to benefit both cloud providers and end-users. We design MRburst (Multi-Resource burstable performance scheduler) to automatically limit multiple resources (i.e., network, I/O, and CPU) and make the application comply with a user-defined service level objective (SLO) while minimizing wasted resources. MRburst is evaluated on Amazon EC2 using two multi-resource applications: an FTP server and a Ceph system. Experimental results show that MRburst outperforms state-of-the-art approaches by allowing instances to speed up their performance for up to 2.4 times longer period while meeting SLO.