Thesis on Dynamic Tuning Strategy for Thread Pool System

MS thesis on the topic Dynamic Tuning Strategy for Thread Pool System

In modern computing systems, dynamic tuning strategy for thread pool systems plays a pivotal role in optimizing performance, resource utilization, and responsiveness under varying workloads. As multi-threaded applications become increasingly complex, traditional static thread pool configurations often fail to adapt efficiently to fluctuating demands, leading to suboptimal throughput or excessive overhead. This MS thesis explores an advanced dynamic tuning strategy that leverages real-time performance metrics, machine learning, or heuristic-based approaches to adjust thread pool parameters such as size, queue capacity, and task scheduling policies. By implementing an adaptive framework, this research aims to enhance scalability, reduce latency, and improve energy efficiency in high-performance computing environments. The findings of this study could significantly contribute to the development of self-optimizing thread pool systems in cloud computing, big data processing, and real-time applications.

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Abstract

Server side applications are extensively developed by multithreading approach with the claim that it offers efficient resource utilization and promotes SMP architecture. Two competitive multithreading models are thread-per-request and thread pool. Thread pool architecture have been proved as a more responsive and more performance efficient, because of its pre-spawning and recycling a pool of threads. Many popular server applications have now adopted the thread pool architecture to tackle high loads and slowdown situations. But tuning the thread pool system at the optimal level and dynamically changing its size is still a challenging task of thread pool system. This thesis presents a dynamic optimization strategy for thread pool system which is named Frequency Based Optimization Strategy (FBOS) that can change the size of thread pool based on the set of quantitative measures and let the thread pool system running gracefully. The Frequency Based Optimization Strategy (FBOS) reacts on the basis of client’s request frequencies to gain more performance. FBOS is implemented in Java to adjust the size of thread pool dynamically so that wait time and turnaround time of requests can be reduced and performance of the server can be increased.

Maintaining concurrency in server applications

Concurrency is desirable in server side programming, as many users are submitting parallel requests to the server at the same time. Two basic approaches to develop concurrent programs are single-threaded approach and Multithreading approach. If a server is designed by the first approach then it would spawn a separate process (application instance) for every new request arrived at the server. But this is heavy weight form of concurrency as every process has its own code segment, data segment, stack, resources (files) and registers, that’s why process creation for every new request is very costly and since each process has its own address space, the communication between processes is difficult and involves explicit Inter- Process Communications (IPC) mechanism. Multithreading is encouraged by server side programmers because of its light weight nature then process.

If a server is designed by this approach then a single process within a server will spawn a new thread to handle new request arrived at the server and all the threads will share the code segment the data segment and other resources of the process with the constraint that each thread will have its own stack and register set and since all the threads share the address space of a single process so they can easily communicate directly with each other. Figure 1.1 illustrates the difference between these two approaches (Silberschatz et al., 2003).

Multithreading Architectures

Multithreading is more desirable form of concurrency model than process because of its light weight nature that makes it easy to create and maintain thread with low cost than process. For example, in Solaris 2 creating a thread is 30 times faster than creating a process and context switching of threads is five times faster than process context switching (Silberschatz et al., 2003). While multithreading is superior design approach than process, the architecture used to implement multithreading can significantly affect the performance of server. Two basic architectures to implement multithreading are thread-per-request and thread pool (Harkema et al., 2004).

Thread-per-request architecture creates a new thread for every request arrived at the server and the thread is destroyed after finishing the task, both operations of creation and destruction of a thread takes time and dynamic creation and destruction of a thread for every request involve excessive resource utilization when user’s request volume is high. In Windows NT and Solaris operating systems, creation of a single thread involves allocation of one megabyte virtual memory for thread stack and this operation will obviously take time, so high request rate will result in frequent memory allocation and de-allocation and that will ultimately result in performance bottleneck (Ling et al., 2000).

Thread-per-request architecture also increases response time as a thread must be created first before servicing the request which involves thread creation time overhead. Thread pool model on the other hand avoids these overheads by pre-spawning a reserve number of threads at system start-up that are waiting in the pool to service incoming requests.

On a request arrival, a free thread in the pool is picked up to handle the request and after finishing the request the thread is returned back to the pool again to service other requests. So there is no overhead involved in creation and destruction of threads for every request. Experimental studies have proved that thread pool architecture is more performance efficient and responsive than thread per request architecture and it reduces response time for the clients and improves system performance (Hu et al., 1997).

Thread Pool Tuning

Due to the run-time overheads of thread-per-request architecture, large number of server applications including Web servers, mail servers, file servers, database servers, and distributed object computing (DOC) infrastructures also known as distributed object computing middleware are build around thread pool architecture (Harkema et al., 2004). But the difficulty with this approach is to point out those factors on the basis of which the pool can be dynamically optimized and we can set the size of pool at an ideal level so that the pool can give high performance and improve Quality of Service. Fair and prompt response to all the client is achieved by only those thread pool systems which are tuned at the best level.

There are different strategies to dynamically tune a thread pool system each one uses its own set of factors for dynamic optimization. This thesis presents a new approach to dynamically optimize thread pool system on the basis of certain quantitative measures. The performance of thread pool system is tested through a Java Request Simulator that is build for underlying thread pool system and that will behave as a multithreaded server to the thread pool system. The simulator will submit the jobs to the thread pool system at different frequencies and the thread pool will change its size on the basis of certain quantitative measures discussed later.

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