Dynamically updating language runtime and core libraries such as collections and threading is challenging since the update mechanism uses such libraries at the same time that it modifies them. To tackle this challenge, we present Dynamic Core Library Update (DCU) as an extension of Dynamic Software Update (DSU) and our approach based on a virtualization architecture. Our solution supports the update of core libraries as any other normal library, avoiding the circular dependencies between the updater and the core libraries. Our benchmarks show that there is no evident performance overhead in comparison with a default execution. Finally, we show that our approach can be applied to real life scenario by introducing a critical update inside a web application with 20 simulated concurrent users.
Dynamic Software Update (DSU) solutions update applications while they are executing. These solutions are typically used in production to minimize application downtime, or in integrated development environments to provide live programming support. Each of these scenarios presents different challenges, forcing existing solutions to be designed with only one of these use cases in mind. For example, DSUs for live programming typically do not implement safe point detection or instance migration, while production DSUs require manual generation of patches and lack IDE integration. Also, these solutions have limited ability to update themselves or the language core libraries, and some of them present execution penalties outside the update window. We propose a DSU (gDSU) that works for both live programming and production environments. Our solution implements safe update point detection using call stack manipulation and a reusable instance migration mechanism to minimize manual intervention in patch generation. Moreover, it also offers updates of core language libraries and the update mechanism itself. This is achieved by the incremental copy of the modified objects and an atomic commit operation. We show that our solution does not affect the global performance of the application and it presents only a run-time penalty during the update window. Our solution is able to apply an update impacting 100,000 instances in 1 second. In this 1 second, only during 250 milliseconds the application is not responsive. The rest of the time the application runs normally while gDSU is looking for the safe update point. The update only requires to copy the elements that are modified.
While the robotics community agrees that the benchmarking is of high importance to objectively compare different solutions, there are only few and limited tools to support it. To address this issue in the context of multi-robot systems, we have defined a benchmarking process based on experimental designs, which aimed at improving the reproducibility of experiments by making explicit all elements of a benchmark such as parameters, measurements and metrics. We have also developed a ROS (Robot Operating System)-based testbed with the goal of making it easy for users to validate, benchmark, and compare different algorithms including coordination strategies. Our testbed uses the MORSE (Modular OpenRobots Simulation Engine) simulator for realistic simulation and a computer cluster for decentralized computation. In this paper, we present our testbed in details with the architecture and infrastructure, the issues encountered in implementing the infrastructure, and the automation of the deployment. We also report a series of experiments on multi-robot exploration, in order to demonstrate the capabilities of our testbed.
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