Modeling of Concurrent Web Sessions with Bounded Inconsistency in Shared Data
NEW YORK UNIV NY COURANT INST OF MATHEMATICAL SCIENCES
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Client interactions with modern web-accessible network services are typically organized into sessions involving multiple requests that read and write shared application data. Therefore when executed concurrently, web sessions may invalidate each others data. Depending on the nature of the business represented by the service, allowing the session with invalid data to progress might lead to financial penalties for the service provider, while blocking the sessions progress and deferring its execution e.g., by relaying its handling to the customer service will most probably result in user dissatisfaction. A compromise would be to tolerate some bounded data inconsistency, which would allow most of the sessions to progress, while limiting the potential financial loss incurred by the service. In order to quantitatively reason about these tradeoffs, the service provider can benefit from models that predict metrics, such as the percentage of successfully completed sessions, for a certain degree of tolerable data inconsistency. This paper develops such analytical models of concurrent web sessions with bounded inconsistency in shared data for three popular concurrency control algorithms. We illustrate our models using the sample buyer scenario from the TPC-W e-Commerce benchmark, and validate them by showing their close correspondence to measured results of concurrent session execution in both a simulated and a real web server environment. Our models take as input parameters of service usage, which can be obtained through profiling of incoming client requests. We augment our web application server environment with a profiling and automated decision making infrastructure which is shown to successfully choose, based on the specified performance metric, the best concurrency control algorithm in real time in response to changing service usage patterns.
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