scholarly journals Uniform Traffic Patterns using Virtual Cut-Through Flow Control on VMMN

2015 ◽  
Vol 59 ◽  
pp. 400-409 ◽  
Author(s):  
Ala Ahmed Yahya Hag ◽  
M.M. Hafizur Rahman ◽  
Rizal Mohd Nor ◽  
Tengku Mohd Tengku Sembok ◽  
Yasuyuki Miura ◽  
...  
Keyword(s):  
Flow Control ◽  
Traffic Patterns ◽  
Through Flow

Efficiency improvement study for small wind turbines through flow control

2014 ◽  
Vol 7 ◽  
pp. 195-208 ◽  
Author(s):  
Jasvipul S. Chawla ◽  
Shashikanth Suryanarayanan ◽  
Bhalchandra Puranik ◽  
John Sheridan ◽  
Brian G. Falzon
Keyword(s):  
Flow Control ◽  
Wind Turbines ◽  
Efficiency Improvement ◽  
Through Flow ◽  
Small Wind Turbines

scholarly journals IcoNoClast: Tackling Voltage Noise in the NoC Power Supply Through Flow-Control and Routing Algorithms

2017 ◽  
Vol 25 (7) ◽  
pp. 2035-2044 ◽  
Author(s):  
Prabal Basu ◽  
Rajesh Jayashankara Shridevi ◽  
Koushik Chakraborty ◽  
Sanghamitra Roy
Keyword(s):  
Flow Control ◽  
Power Supply ◽  
Routing Algorithms ◽  
Voltage Noise ◽  
Through Flow

scholarly journals Network-Cognitive Traffic Control: A Fluidity-Aware On-Chip Communication

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1667
Author(s):  
Wen-Chung Tsai ◽  
Sao-Jie Chen ◽  
Yu-Hen Hu ◽  
Mao-Lun Chiang
Keyword(s):  
Flow Control ◽  
Congestion Control ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Performance Enhancement ◽  
Traffic Patterns ◽  
Control Scheme ◽  
Significant Performance ◽  
On Chip ◽  
Fill Level

A novel network-on-chip (NoC) integrated congestion control and flow control scheme, called Network-Cognitive Traffic Control (NCogn.TC), is proposed. This scheme is cognizant of the fluidity levels in on-chip router buffers and it uses this measurement to prioritize the forwarding of flits in the buffers. This preferential forwarding policy is based on the observation that flits with higher levels of fluidity are likely to arrive at their destinations faster, because they may require fewer routing steps. By giving higher priority to forward flits in high-fluidity buffers, scarce buffer resources may be freed-up sooner in order to relieve on-going traffic congestion. In this work, a buffer cognition monitor is developed to rapidly estimate the buffer fluidity level. An integrated congestion control and flow control algorithm is proposed based on the estimated buffer fluidity level. Tested with both synthetic traffic patterns as well as industry benchmark traffic patterns, significant performance enhancement has been observed when the proposed Network-Cognitive Traffic Control is compared against conventional traffic control algorithms that only monitor the buffer fill level.

scholarly journals Mitigation of impact of a major benzene spill into a river through flow control and in-situ activated carbon absorption

2020 ◽  
Vol 172 ◽  
pp. 115489
Author(s):  
Guangqiu Jin ◽  
Zhongtian Zhang ◽  
Yihang Yang ◽  
Shuheng Hu ◽  
Hongwu Tang ◽  
...  
Keyword(s):  
Activated Carbon ◽  
Flow Control ◽  
Through Flow ◽  
Carbon Absorption

scholarly journals Scheduling Fair Resource Allocation Policies for Cloud Computing through Flow Control

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1348 ◽  
Author(s):  
Stavros Souravlas ◽  
Stefanos Katsavounis
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Flow Control ◽  
Resource Utilization ◽  
Short Paper ◽  
Allocation Scheme ◽  
Through Flow ◽  
Resource Requirements ◽  
Efficient Resource ◽  
Resource Allocation Scheme

In this short paper, we discuss the problem of resource allocation for cloud computing. The cloud provides a variety of resources for users based on their requirements. Thus, one of the main issues in cloud computing is to design an efficient resource allocation scheme. Each job generated by a user in the cloud has some resource requirements. In this work, we propose a resource allocation method which aims at maximizing the resource utilization and distributing the system’s resources in a fast and fair way, by controlling the flow according to the resources available and by analyzing the dominant demands of each job. Moreover, by parallelizing the computations required, the runtime of the proposed strategy increases linearly as the number of jobs N increases. Here, we present some initial experimental results for small sets of users, that have shown that our strategy allocates the available resources among user jobs in a fair manner, while increasing the overall utilization of each resource.

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