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Short time-to-localize and time-to-fix for production bugs is extremely important for any 24x7 service-oriented application (SOA). Debugging buggy behavior in deployed applications is hard, as it requires careful reproduction of a similar environment and workload. Prior approaches for automatically reproducing production failures do not scale to large SOA systems. Our key insight is that for many failures in SOA systems (e.g., many semantic and performance bugs), a failure can automatically be reproduced solely by relaying network packets to replicas of suspect services, an insight that we validated through a manual study of 16 real bugs across five different systems. This paper presents Parikshan, an application monitoring framework that leverages user-space virtualization and network proxy technologies to provide a sandbox “debug” environment. In this “debug” environment, developers are free to attach debuggers and analysis tools without impacting performance or correctness of the production environment. In comparison to existing monitoring solutions that can slow down production applications, Parikshan allows application monitoring at significantly lower overhead. 

Abstract We demonstrate that the key components of cognitive architectures (declarative and procedural memory) and their key capabilities (learning, memory retrieval, probability judgment, and utility estimation) can be implemented as algebraic operations on vectors and tensors in a high‐dimensional space using a distributional semantics model. High‐dimensional vector spaces underlie the success of modern machine learning techniques based on deep learning. However, while neural networks have an impressive ability to process data to find patterns, they do not typically model high‐level cognition, and it is often unclear how they work. Symbolic cognitive architectures can capture the complexities of high‐level cognition and provide human‐readable, explainable models, but scale poorly to naturalistic, non‐symbolic, or big data. Vector‐symbolic architectures, where symbols are represented as vectors, bridge the gap between the two approaches. We posit that cognitive architectures, if implemented in a vector‐space model, represent a useful, explanatory model of the internal representations of otherwise opaque neural architectures. Our proposed model, Holographic Declarative Memory (HDM), is a vector‐space model based on distributional semantics. HDM accounts for primacy and recency effects in free recall, the fan effect in recognition, probability judgments, and human performance on an iterated decision task. HDM provides a flexible, scalable alternative to symbolic cognitive architectures at a level of description that bridges symbolic, quantum, and neural models of cognition.