Center for Machine Learning and Intelligent Systems
About  Citation Policy  Donate a Data Set  Contact


Repository Web            Google
View ALL Data Sets

× Check out the beta version of the new UCI Machine Learning Repository we are currently testing! Contact us if you have any issues, questions, or concerns. Click here to try out the new site.

Optical Interconnection Network Data Set
Download: Data Folder, Data Set Description

Abstract: This dataset contains 640 performance measurements from a simulation of 2-Dimensional Multiprocessor Optical Interconnection Network.

Data Set Characteristics:  

Multivariate

Number of Instances:

640

Area:

Computer

Attribute Characteristics:

Integer, Real

Number of Attributes:

10

Date Donated

2018-03-29

Associated Tasks:

Classification, Regression

Missing Values?

N/A

Number of Web Hits:

32280


Source:

Cigdem Inan ACI- Mersin University, Dept of Computer Engineering, caci '@' mersin.edu.tr
Mehmet Fatih AKAY- Cukurova University, Dept of Computer Engineering, mfakay '@' cu.edu.tr


Data Set Information:

All simulations have done under the software named OPNET Modeler. Message passing is used as the communication mechanism in which any processor can submit to the network a point-to-point message destined at any other processor. M/M/1 queue is considered in the calculations which consist of a First-in First-Out buffer with packet arriving randomly according to a Poisson arrival process, and a processor, that retrieves packets from the buffer at a specified service rate. In all simulations, it is assumed that the processor at each node extracts a packet from an input queue, processes it for a period of time and when that period expires, it generates an output data message. The size of each input queue is assumed as infinite. A processor becomes idle only when all its input queues are empty.


Attribute Information:

The summary of the attributes is given below. Please read the paper ([Web Link]) for detailed descriptions of the attributes.

Node Number: The number of the nodes in the network. (8x8 or 4x4).

Thread Number: The number of threads in each node at the beginning of the simulation.

Spatial Distribution: The performance of the network is evaluated using synthetic traffic workloads. Uniform (UN), Hot Region (HR), Bit reverse (BR) and Perfect Shuffle (PS) traffic models have been included.

Temporal Distribution: Temporal distribution of packet generation is implemented by independent traffic sources. In our simulations, we utilized client–server traffic (i.e., a server node sends packets to respond to the reception of packets from clients) and asynchronous traffic (i.e., initially, all nodes generate traffic independently of the others; as time progresses, traffic generation at the source/destination nodes depends
on the receipt of messages from destination/source nodes).

T/R: Message transfer time (T ) Uniformly distributed with mean in range from 20 to 100 clock cycles. Thread run time (R) Exponentially distributed with a mean of 100 clock cycles.

Processor Utilization: The average processor utilization measures the percent of time that threads are running in the processor.

Channel Waiting Time: Average waiting time of a packet at the output channel queue until it is serviced by the channel.

Input Waiting Time: Average waiting time of a packet until it is serviced by the processor.

Network Response Time: The time between a request message is enqueued at the output channel and the corresponding data message is received in the input queue.

Channel Utilization: The percent of time that the channel is busy transferring packets to the network.







Relevant Papers:

M.F. Akay, C¸.I. Aci, F. Abut, Predicting the Performance Measures of a 2-Dimensional Message Passing Multiprocessor Architecture by Using Machine Learning Methods. Neural Network World 71(5):1907-1931. DOI: 10.14311/NNW.2015.25.013.



Citation Request:

Acı, Ç.İ. & Akay, M.F. A hybrid congestion control algorithm for broadcast-based architectures with multiple input queues. J Supercomput (2015) 71: 1907. [Web Link]


Supported By:

 In Collaboration With:

About  ||  Citation Policy  ||  Donation Policy  ||  Contact  ||  CML