Search

The application of deep reinforcement learning (DRL) to computer and networked systems has recently gained significant popularity. However, the obscurity of decisions by DRL policies renders it hard to ascertain that learning-augmented systems are safe to deploy, posing a significant obstacle to their real-world adoption. We observe that specific characteristics of recent applications of DRL to systems contexts give rise to an exciting opportunity: applying formal verification to establish that a given system provably satisfies designer/user-specified requirements, or to expose concrete counter-examples. We present whiRL, a platform for verifying DRL policies for systems, which combines recent advances in the verification of deep neural networks with scalable model checking techniques. To exemplify its usefulness, we employ whiRL to verify natural equirements from recently introduced learning-augmented systems for three real-world environments: Internet congestion control, adaptive video streaming, and job scheduling in compute clusters. Our evaluation shows that whiRL is capable of guaranteeing that natural requirements from these systems are satisfied, and of exposing specific scenarios in which other basic requirements are not.

Deep learning (DL) [4] is dramatically changing the world of software. The rapid improvement in deep neural network (DNN) technology now enables engineers to train models that achieve superhuman results, often surpassing algorithms that have been carefully hand-crafted by domain experts [19, 20]. There is even an intensifying trend of incorporating DNNs in safety-critical systems, e.g. as controllers for autonomous vehicles and drones [1, 12].

Behavioral programming (BP) is a decentralized scenario-based paradigm for the programming of reactive software, geared towards incremental and intuitive development. In this work we apply the principles of BP to a large, real-world case-study: a web-server. We discuss the conclusions learned from our attempt and propose several extension idioms to BP, aimed at improving the framework's scalability. Specifically, we propose extending BP with a timeout idiom for handling various time constraints, program-specific execution strategies, dynamic thread creation for efficiently allocating system resources, and support for parameterized events to handle inputs with infinite domains. Our extensions and case-study are implemented in a new framework for behavioral programming in C++.

In this demo we present a recommender benchmark framework that serves as an infrastructure for comparing and examining the performance and feasibility of different recommender algorithms on various datasets with a variety of measures. The extendable infrastructure aims to provide easy plugging of novel recommendation-algorithms, datasets and compare their performance using visual tools and metrics with other algorithms in the benchmark. It also aims at generating a WEKA-type workbench [1] for the recommender systems field to enable usage and application of common recommender systems (RS) algorithms for research and practice. The demo movie is available at: http://www.youtube.com/watch?v=fsDITf6s0WY