The System of Load Optimal Distributing of Hydropower Station Based on Multi-Agent

Zhao, Tinghong; Man, Zibin; Bi, Guiquan

doi:10.1109/paciia.2008.372

Cited by 1 publication

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 1 shows the average waiting time of requests of STI-RD strategy with different T and dt when dt=0.25,0.5,1,2.5,5. As can be seen from the table that, If T~(0,5), the average waiting time of requests is shortest when dt=0.25; if T~ [5,25] , the average waiting time of requests is shortest when dt=0.5. So we should select dt=0.25 if T~(0,5) and select dt=0.5 if T~ [5,25].…”

Section: Experiments and Results Analysismentioning

confidence: 99%

“…As can be seen from the table that, If T~(0,5), the average waiting time of requests is shortest when dt=0.25; if T~ [5,25] , the average waiting time of requests is shortest when dt=0.5. So we should select dt=0.25 if T~(0,5) and select dt=0.5 if T~ [5,25]. Fig.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

“…The main current request distribution strategies for server cluster are [1][2][3][4][5]: (1) shortest strategy: sends the requests to the node with lightest load. (2) random strategy: randomly selects one node to service the requests.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Request Distribution Strategy Based on Static Time Interval

Xie

Zhao

Pan

2012

AMM

View full text Add to dashboard Cite

Cluster has become a new generation of distributed system. Among the server cluster technologies, request distribution technology is used to distribute requests among server nodes, providing support to large-scale concurrent access to server cluster. In order to overcome the weakness of existing request distribution strategies for server cluster in average response time and computation cost, this paper proposes a request distribution strategy based on static time interval. Its basic idea is that it divides the update interval into several subintervals and introduces randomness into the selection of server node for the requests arrived in a subinterval. We give an implementation of the strategy and use MATLAB as the simulation platform to carry out simulation experiments in testing the performance of the strategy. Our theoretical analysis and experimental results show that, the strategy we propose achieves shorter average waiting time, more convenient implementation and lower computation cost than other several existing request distribution strategies.

show abstract

Section: Experiments and Results Analysismentioning

confidence: 99%

Section: Experiments and Results Analysismentioning

confidence: 99%