Towards achieving execution time predictability in web services middleware

Web services middleware are typically designed optimised for throughput. Requests are accepted unconditionally and no differentiation is made in processing. Many use the thread-pool pattern to execute requests in parallel using processor sharing. Clusters hosting web services dispatch requests only to balance out the load among the executors. Such optimisations for throughput work out negatively on the predictability of execution. Processor sharing results in the increase of execution time with the number of concurrent requests, making it impossible to predict or control the execution of a request. Existing works fail to address the need for predictability in web service execution. Some achieve a level of differentiated processing, but fail to consider predictability as their main quality attribute. Some give a probabilistic guarantee on service levels. However, from a predictability perspective they are inconsistent. A few achieve predictable execution times, though only in closed systems where request properties are known at system design time. Web services operate on the Internet, where request properties are relatively unknown. This thesis investigates the problem of achieving predictable times in web service executions. We introduce the notion of a processing deadline for service execution, which the web services engine must adhere to in completing the request in a repeatable and a consistent manner. Reaching such execution deadlines by the services engine is made possible by three main features. Firstly a deadline based scheduling algorithm introduced, ensures the processing deadlines are followed. A laxity based analytical model and an admission control algorithm it is based on, selects requests for execution, resulting in a wider range of laxities to enable more requests with overlapping executions to be scheduled together. Finally, a real-time scheduler component introduced in to the server uses a priority model to schedule the execution of requests by controlling the execution of individual worker threads in custom-made thread pools. Predictability of execution in cluster based deployments is further facilitated by four dispatching algorithms that consider the request deadlines and laxity property in the dispatching process. A performance model derived for a similar system approximates the waiting time where requests with smaller deadlines (having higher priority) experience smaller waiting times than requests with longer deadlines. These techniques are implemented in web services middleware in standalone and cluster-based configurations. They are evaluated against their unmodified versions and techniques such as round-robin and class based dispatching, to measure their predictability gain. Empirical evidence indicate the enhancements enable the middleware to achieve more than 90% of the deadlines, while accepting at least 20% of the requests in high traffic conditions. The enhancements additionally prevent the middleware from reaching overloaded conditions in heavy traffic, while maintaining comparable throughput rates to the unmodified versions of the middleware. Analytical and simulation results for the performance model confirms that deadline based preemptive scheduling results in a better balance of waiting times where high priority requests experience lower waiting times while lower priority requests are not over-starved compared to other techniques such as static priority ordering, First-Come-First-Served, Round-Robin and non-preemptive deadline based scheduling