Comparison of analytic performance models using closed mean-value analysis versus open-queuing theory for estimating cycles per instruction of memory hierarchies

2003 ◽  
Vol 47 (4) ◽  
pp. 495-517 ◽  
Author(s):  
R. E. Matick
Keyword(s):  
Queuing Theory ◽  
Mean Value ◽  
Performance Models ◽  
Memory Hierarchies ◽  
Value Analysis ◽  
Mean Value Analysis

scholarly journals Improving Queuing System Throughput Using Distributed Mean Value Analysis to Control Network Congestion

2015 ◽  
Vol 07 (01) ◽  
pp. 21-29 ◽  
Author(s):  
Faisal Shahzad ◽  
Muhammad Faheem Mushtaq ◽  
Saleem Ullah ◽  
M. Abubakar Siddique ◽  
Shahzada Khurram ◽  
...  
Keyword(s):  
Mean Value ◽  
Queuing System ◽  
System Throughput ◽  
Control Network ◽  
Network Congestion ◽  
Value Analysis ◽  
Mean Value Analysis

scholarly journals Optimization Strategies for Dockless Bike Sharing Systems via two Algorithms of Closed Queuing Networks

Processes ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 345 ◽  
Author(s):  
Rui-Na Fan ◽  
Fan-Qi Ma ◽  
Quan-Lin Li
Keyword(s):  
Mean Value ◽  
Sustainable Transportation ◽  
Queuing Network ◽  
Value Analysis ◽  
Fleet Size ◽  
Closed Queuing Networks ◽  
Mean Value Analysis ◽  
Bike Sharing ◽  
The Mean ◽  
First Time

The dockless bike sharing system (DBSS) has been globally adopted as a sustainable transportation system. Due to the robustness and tractability of the closed queuing network (CQN), it is a well-behaved method to model DBSSs. In this paper, we view DBSSs as CQNs and use the mean value analysis (MVA) algorithm to calculate a small size DBSS and the flow equivalent server (FES) algorithm to calculate the larger size DBSS. This is the first time that the FES algorithm is used to study the DBSS, by which the CQN can be divided into different subnetworks. A parking region and its downlink roads are viewed as a subnetwork, so the computation of CQN is reduced greatly. Based on the computation results of the two algorithms, we propose two optimization functions for determining the optimal fleet size and repositioning flow, respectively. At last, we provide numerical experiments to verify the two algorithms and illustrate the optimal fleet size and repositioning flow. This computation framework can also be used to analyze other on-demand transportation networks.

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