11,548 research outputs found
Enhancing Failure Propagation Analysis in Cloud Computing Systems
In order to plan for failure recovery, the designers of cloud systems need to
understand how their system can potentially fail. Unfortunately, analyzing the
failure behavior of such systems can be very difficult and time-consuming, due
to the large volume of events, non-determinism, and reuse of third-party
components. To address these issues, we propose a novel approach that joins
fault injection with anomaly detection to identify the symptoms of failures. We
evaluated the proposed approach in the context of the OpenStack cloud computing
platform. We show that our model can significantly improve the accuracy of
failure analysis in terms of false positives and negatives, with a low
computational cost.Comment: 12 pages, The 30th International Symposium on Software Reliability
Engineering (ISSRE 2019
Fault Injection Analytics: A Novel Approach to Discover Failure Modes in Cloud-Computing Systems
Cloud computing systems fail in complex and unexpected ways due to unexpected
combinations of events and interactions between hardware and software
components. Fault injection is an effective means to bring out these failures
in a controlled environment. However, fault injection experiments produce
massive amounts of data, and manually analyzing these data is inefficient and
error-prone, as the analyst can miss severe failure modes that are yet unknown.
This paper introduces a new paradigm (fault injection analytics) that applies
unsupervised machine learning on execution traces of the injected system, to
ease the discovery and interpretation of failure modes. We evaluated the
proposed approach in the context of fault injection experiments on the
OpenStack cloud computing platform, where we show that the approach can
accurately identify failure modes with a low computational cost.Comment: IEEE Transactions on Dependable and Secure Computing; 16 pages. arXiv
admin note: text overlap with arXiv:1908.1164
Fine-Grained Reliability for V2V Communications around Suburban and Urban Intersections
Safe transportation is a key use-case of the 5G/LTE Rel.15+ communications,
where an end-to-end reliability of 0.99999 is expected for a vehicle-to-vehicle
(V2V) transmission distance of 100-200 m. Since communications reliability is
related to road-safety, it is crucial to verify the fulfillment of the
performance, especially for accident-prone areas such as intersections. We
derive closed-form expressions for the V2V transmission reliability near
suburban corners and urban intersections over finite interference regions. The
analysis is based on plausible street configurations, traffic scenarios, and
empirically-supported channel propagation. We show the means by which the
performance metric can serve as a preliminary design tool to meet a target
reliability. We then apply meta distribution concepts to provide a careful
dissection of V2V communications reliability. Contrary to existing work on
infinite roads, when we consider finite road segments for practical deployment,
fine-grained reliability per realization exhibits bimodal behavior. Either
performance for a certain vehicular traffic scenario is very reliable or
extremely unreliable, but nowhere in relatively proximity to the average
performance. In other words, standard SINR-based average performance metrics
are analytically accurate but can be insufficient from a practical viewpoint.
Investigating other safety-critical point process networks at the meta
distribution-level may reveal similar discrepancies.Comment: 27 pages, 6 figures, submitted to IEEE Transactions on Wireless
Communication
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