Distributed applications have been developed using thread pool system (TPS) in order to improve system performance. The dynamic optimization and overload management of TPS are two crucial factors that affect overall performance of distributed thread pool (DTP). This paper presents a DTP, that is based on central management system, where a central manager forwards client's requests in round robin fashion to available set of TPSs running in servers. The dynamic tuning of each TPS is done based on request rate on the TPS. The overload condition at each TPS is detected by the TPS itself, by throughput decline. The overload condition is resolved by reducing the size of thread pool to previous value, at which it was producing throughput parallel to the request rates. By reducing the size of thread pool on high request rates, the context switches and thread contention overheads are eliminated that enables system resources to be utilized effectively by available threads in the pool. The result of evaluation proved the validity of proposed system.

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Scalability is one of the most important quality attribute of softwareintensive systems, because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload. In order to achieve scalability, thread pool system (TPS) (which is also known as executor service) has been used extensively as a middleware service in software-intensive systems. TPS optimization is a challenging problem that determines the optimal size of thread pool dynamically on runtime. In case of distributed-TPS (DTPS), another issue is the load balancing b/w available set of TPSs running at backend servers. Existing DTPSs are overloaded either due to an inappropriate TPS optimization strategy at backend servers or improper load balancing scheme that cannot quickly recover an overload. Consequently, the performance of software-intensive system is suffered. Thus, in this paper, we propose a new DTPS that follows the collaborative round robin load balancing that has the effect of a double-edge sword. On the one hand, it effectively performs the load balancing (in case of overload situation) among available TPSs by a fast overload recovery procedure that decelerates the load on the overloaded TPSs up to their capacities and shifts the remaining load towards other gracefully running TPSs. And on the other hand, its robust load deceleration technique which is applied to an overloaded TPS sets an appropriate upper bound of thread pool size, because the pool size in each TPS is kept equal to the request rate on it, hence dynamically optimizes TPS. We evaluated the results of the proposed system against state of the art DTPSs by a clientserver based simulator and found that our system outperformed by sustaining smaller response times.

Scalability is one of the utmost nonfunctional requirement of server applications, because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload. In order to achieve scalability, thread pool system (TPS) has been used extensively as a middleware service in server applications. The size of thread pool is the most significant factor, that affects the overall performance of servers. Determining the optimal size of thread pool dynamically on runtime is a challenging problem. The most widely used and simple method to tackle this problem is to keep the size of thread pool equal to the request rate, i.e., the frequencyoriented thread pool (FOTP). The FOTPs are the most widely used TPSs in the industry, because of the implementation simplicity, the negligible overhead and the capability to use in any system. However, the frequency-based schemes only focused on one aspect of changes in the load, and that is the fluctuations in request rate. The request rate alone is an imperfect knob to scale thread pool. Thus, this paper presents a workload profiling based FOTP, that focuses on request size (service time of request) besides the request rate as a knob to scale thread pool on runtime, because we argue that the combination of both truly represents the load fluctuation in server-side applications. We evaluated the results of the proposed system against state of the art TPS of Oracle Corporation (by a client-server-based simulator) and concluded that our system outperformed in terms of both; the response times and throughput.