Thread Pool Parameters Tuning Using Simulation

Stetsenko, Inna V.; Dyfuchyna, Oleksandra

doi:10.1007/978-3-030-16621-2_8

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…In [32], the author proved by experiments that the performance of multithreaded application based on thread pool depends not only on thread pool parameters but also on computational complexity of thread pool tuning algorithm and available computational resources. The author suggested that an optimal pool size can be achieved by assessing both computational complexity and resources.…”

Section: Related Workmentioning

confidence: 99%

The Double Edge Sword Based Distributed Executor Service

Bahadur¹,

Umar²,

Ullah³

et al. 2022

Computer Systems Science and Engineering

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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.

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Section: Related Workmentioning

confidence: 99%

The Double Edge Sword Based Distributed Executor Service

Bahadur¹,

Umar²,

Ullah³

et al. 2022

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…They aimed to increase the performance concerning the speed of processing of the requests, by adjusts the number of threads and throughput according to the number of packets, the capacity of the queue, and retransmission time of each packet in IoT networks. Stetsenko and Dyfuchyna [36] designed a model of the multithreaded algorithm using Petri-object simulation technology based on stochastic Petri net and objectoriented approach. The model o+9-6f a thread pool was developed to prove the relationship between algorithm complexity, computing resources and parameters of the thread pool.…”

Section: Related Workmentioning

confidence: 99%

“…Braun and Krus [11] Power systems Load balancing and synchronisation Ágnes Bogárdi-Mészöly and Rövid [9] Software system Prediction performance Pasha et al [31] Power systems Prediction of power consumption Ahmad et al [1] Software system Prediction performance Altmann et al [3] Software system Interoperability Bahadur et al [4] Distributed system Load balancing Tarvo and Reiss [38] Software system Prediction performance Jeon and Jung [25] IoT networks Increase the performance Stetsenko and Dyfuchyna [36] Software system Prediction performance. Casini et al [12] Software system Evaluation of schedulability Berned et al [6] Software system Energy consumption [Our proposal] EAI Evaluation of performance…”

Section: Research Field Goalmentioning

confidence: 99%