scholarly journals Optimal Routing of Fixed Size Jobs to Two Parallel Servers

2013 ◽  
Vol 51 (4) ◽  
pp. 215-224 ◽  
Author(s):  
Esa Hyytiä
2001 ◽  
Vol 12 (06) ◽  
pp. 775-790 ◽  
Author(s):  
K. OIDA ◽  
K. SHINJO

This paper presents characteristics of optimal routing that assigns each arriving packet to one of two heterogeneous parallel servers, each with its own queue. The characteristics are derived from numerical solutions to an optimization problem, which is to find optimal routing that minimizes the average packet delay under the condition that all of the packets' arrival times as well as all of the packets' sizes are completely known in advance. There are four characteristics: (1) Under light or moderate traffic, the average packet delay of optimal routing is almost the same as that of join the shortest delay (JSD) policy. (2) Under heavier traffic, optimal routing comes to more often use fix queue based on size (FS) policy. (3) Under heavy traffic, optimal routing assigns small packets to the slower server. (4) As the ratio of the slower server's service rate to the faster server's service rate decreases, optimal routing comes to more often use FS policy under light or moderated traffic. These characteristics are verified by the fact that a mimic optimal routing designed based on the four characteristics attains almost the same performance as optimal routing.


1988 ◽  
Author(s):  
Wei K. Tsai ◽  
G. Huang ◽  
John K. Antonio ◽  
Wei-T. Tsai

2021 ◽  
Vol 40 (1) ◽  
pp. 551-563
Author(s):  
Liqiong Lu ◽  
Dong Wu ◽  
Ziwei Tang ◽  
Yaohua Yi ◽  
Faliang Huang

This paper focuses on script identification in natural scene images. Traditional CNNs (Convolution Neural Networks) cannot solve this problem perfectly for two reasons: one is the arbitrary aspect ratios of scene images which bring much difficulty to traditional CNNs with a fixed size image as the input. And the other is that some scripts with minor differences are easily confused because they share a subset of characters with the same shapes. We propose a novel approach combing Score CNN, Attention CNN and patches. Attention CNN is utilized to determine whether a patch is a discriminative patch and calculate the contribution weight of the discriminative patch to script identification of the whole image. Score CNN uses a discriminative patch as input and predict the score of each script type. Firstly patches with the same size are extracted from the scene images. Secondly these patches are used as inputs to Score CNN and Attention CNN to train two patch-level classifiers. Finally, the results of multiple discriminative patches extracted from the same image via the above two classifiers are fused to obtain the script type of this image. Using patches with the same size as inputs to CNN can avoid the problems caused by arbitrary aspect ratios of scene images. The trained classifiers can mine discriminative patches to accurately identify some confusing scripts. The experimental results show the good performance of our approach on four public datasets.


2021 ◽  
Vol 118 (24) ◽  
pp. 241108
Author(s):  
E. Mejia ◽  
Y. Qian ◽  
S. A. Safiabadi Tali ◽  
J. Song ◽  
W. Zhou

2021 ◽  
Vol 11 (9) ◽  
pp. 4064
Author(s):  
Muktar Hussaini ◽  
Muhammad Ali Naeem ◽  
Byung-Seo Kim

Named data networking (NDN) is designed as a clean-slate Internet architecture to replace the current IP Internet architecture. The named data networking was proposed to offer vast advantages, especially with the advent of new content distributions in IoT, 5G and vehicular networking. However, the architecture is still facing challenges for managing content producer mobility. Despite the efforts of many researchers that curtailed the high handoff latency and signaling overhead, there are still some prominent challenges, such as non-optimal routing path, long delay for data delivery and unnecessary interest packet losses. This paper proposed a solution to minimize unnecessary interest packet losses, delay and provide data path optimization when the mobile producer relocates by using mobility update, broadcasting and best route strategies. The proposed solution is implemented, evaluated and benchmarked with an existing Kite solution. The performance analysis result revealed that our proposed Optimal Producer Mobility Support Solution (OPMSS) minimizes the number of unnecessary interest packets lost on average by 30%, and an average delay of 25% to 30%, with almost equal and acceptable signaling overhead costs. Furthermore, it provides a better data packet delivery route than the Kite solution.


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