ADAPTT: An Adaptive Algorithm for AutoTuning Worker Thread Pool Size in Application Servers Modern application servers run on multicore hardware and these servers use multithreading to achieve high performance. Performance of such servers is highly dependent on the number of processing threads in the worker thread pool. The optimal thread pool size (that would result in the best performance) depends on workload properties (both the application and the incoming arrival pattern). In this paper, we propose ADAPTT, a lightweight, adaptive algorithm that can autoadjust the thread pool size to optimize the latency. ADAPTT exploits the concave upward property of system performance and optimizes the latency online by periodically monitoring the serverxe2x80x99s behaviour and then adjusts the thread pool size based on monitored statistics. It has a special locking mechanism to minimize the frequent fluctuations that may occur in latency while tuning the thread pool size. We perform extensive experiments under different workload patterns and show that ADAPTT performs well under a wide range of workload scenarios compared to other policies.

Resource Utilization Analysis of Early Scheduling in Parallel State Machine Replication Most approaches to scaling the throughput of state machine replication resort to concurrent execution of nonconflicting requests. While these approaches enhance throughput, conflict detection and handling introduce overhead. Early scheduling is a promising technique that trades concurrency for expeditious scheduling decisions. In this technique, requests are grouped in classes and a fixed subset of threads is assigned to each class, respecting request dependencies. Early scheduling has been shown to provide significant performance improvements in state machine replication. This paper evaluates the impact of the restrictions imposed by the early scheduling technique. In particular, it shows that threads may be idle while pending independent requests are available to be executed, leading to poor processor utilization. We characterize resource underutilization for workloads with different rates of conflicting requests, number of threads, and number of request classes. The paper opens up new opportunities to further enhance the early scheduling technique.