CONCRETE: A benchmarking framework to CONtrol and Classify REpeatable Testbed Experiments
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1 CONCRETE: A benchmarking framework to CONtrol and Classify REpeatable Testbed Experiments Stratos Keranidis* Wei Liu, Michael Mehari, Pieter Becue, Stefan Bouckaert, Ingrid Moerman, Thanasis Korakis*, Iordanis Koutsopoulos* and Leandros Tassiulas* University of Thessaly and CERTH, Greece* Ghent University - iminds, Gent, Belgium
2 Introduction Ø The Problem: n During experimentation in networking testbeds several different factors may impact the monitored performance of networks under consideration. n As a result high variation exists among several executions of the same experiment. Ø The Need: Stable experimental conditions have to be guaranteed, in order to arrive at solid conclusions. Ø Our Solution: The novel CONRETE benchmarking framework that provides for evaluation of experimental stability. 2
3 Outline Ø Related Projects Ø Basic Experimental Scenario Ø Interfering Factors Ø Building Blocks Ø CONCRETE Benchmarking Framework Ø Insights and Future work 3
4 Related Projects Ø CREW Ø Establishes an open federated test platform, which facilitates experimentally-driven research on advanced spectrum sensing, cognitive radio and cognitive networking strategies in view of horizontal and vertical spectrum sharing in licensed and unlicensed bands. Ø OPENLAB Ø Delivers control and experimental plane middleware to facilitate early use of testbeds and exploiting proven technologies, developed in the OneLab and Panlab initiatives. Ø OPENLAB CREW Collaboration Ø In order to improve the reproducibility of wireless experiments, OpenLab is interested to augment the OpenLab facilities with the CREW spectrum sensing benchmarking scenarios. 4
5 Outline Ø Related Projects Ø Basic Experimental Scenario Ø Interfering Factors Ø Building Blocks Ø CONCRETE Benchmarking Framework Ø Insights and Future work 5
6 Basic Experimental Scenario Ø 2 pairs of nodes contending for channel use. Ø AP2 - > STA2: saturated traffic conditions Ø AP1 -> STA1: varying traffic rate (TR RATE ) conditions Ø We monitor the throughput performance of the AP2-STA2 pair 6
7 Outline Ø Related Projects Ø Basic Experimental Scenario Ø Interfering Factors Ø Building Blocks Ø CONCRETE Benchmarking Framework Ø Insights and Future work 7
8 Interfering Factors (1/5) Specific executions of the same experiment may present different performance, due to: Internal Interference generated by testbed nodes, operated by other experimenters, which simultaneously transmit on the same or overlapping frequencies. 8
9 Interfering Factors (2/5) Specific executions of the same experiment may present different performance, due to: External Interference generated by collocated commercial devices belonging to external networks, which simultaneously transmit on the same or overlapping frequencies. 9
10 Interfering Factors (3/5) Specific executions of the same experiment may present different performance, due to various factors, such as: stopping of normal execution due to hardware or software failure 10
11 Interfering Factors (4/5) Specific executions of the same experiment may present different performance, due to various factors, such as: Different node positioning (etc. mobile nodes behind obstacles) 11
12 Interfering Factors (5/5) The Result 12
13 Outline Ø Related Projects Ø Basic Experimental Scenario Ø Interfering Factors Ø Building Blocks Ø CONCRETE Benchmarking Framework Ø Insights and Future work 13
14 Building Blocks Advanced Spectrum Sensing Techniques OMF Control and Management Framework CONCRETE iminds w-ilab.t Cognitive Testbed Long experience with instrumentation of testbed experiments 14
15 Building Blocks Correlation Ø The well known measure of dependence is Pearson's correlation, which indicates the extent to which two random variables covary. Ø Ø The µx and µy represents the mean of the data set X and Y respectively. The σx and σy represents the standard deviation of the data set X and Y respectively 15
16 Outline Ø Related Projects Ø Basic Experimental Scenario Ø Interfering Factors Ø Building Blocks Ø CONCRETE Benchmarking Framework Ø Insights and Future work 16
17 CONCRETE Benchmarking Framework CONtrol and Classify REpeatable Testbed Experiments The 6 main functionalities that are currently supported, are: 1. Scheduling the execution of several runs for the same experiment 2. Visualization of prevailing Channel Conditions before each run and moreover visualization of the Performance achieved in each run 3. Estimation of Correlation among the different runs, in order to provide an appropriate benchmarking score that describes the stability of each run 4. Calculation of average performance and st. deviation values for each run 5. Automatic mechanism that selects the most stable runs, based on their correlation score 6. Calculation of performance over all executed rounds in comparison with the performance achieved only in the subset of selected rounds. 17
18 CONCRETE Benchmarking Framework (1/6) 1. Scheduling the execution of several runs for the same experiment 18
19 CONCRETE Benchmarking Framework (2/6) 2. Visualization of Channel Conditions before each run and moreover visualization of the Performance achieved in each run 19
20 CONCRETE Benchmarking Framework (3/6) 3. Estimation of Correlation among the different runs 20
21 CONCRETE Benchmarking Framework (4/6) 4. Calculation of AVG performance and ST. DEV. for each run 21
22 CONCRETE Benchmarking Framework (5/6) 5. Automatic mechanism that selects the most stable runs, based on their correlation score 22
23 CONCRETE Benchmarking Framework (6/6) 6. Calculation of performance over all executed rounds in comparison with the performance achieved only in the subset of selected rounds. 23
24 Outline Ø Related Projects Ø Basic Experimental Scenario Ø Interfering Factors Ø Building Blocks Ø CONCRETE Benchmarking Framework Ø Insights and Future work 24
25 Insights and Future Work Ø Experimental Insights: Ø Due to the high variation of wireless channel conditions there is a clear need for environment monitoring mechanisms Ø that aid in arriving at CONCRETE conclusions. Ø Future Work: Ø Ø Ø Enable channel monitoring during the experiment execution through Wi-Fi Monitor nodes. Implement Feature detection mechanism to enable detection of transmissions generated by devices using heterogeneous technologies Examine performance under various experiments and metrics (energy etc.) and propose possible enhancements 25
26 Thank You! 26
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