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Pooyan Jamshidi
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DICE Demo in Y1 Review, European Commission, Brussels, Belgium, April 2016
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Configuration Optimization Tool
1.
DICE Horizon 2020 Research & Innovation Action Grant Agreement no. 644869 http://www.dice-h2020.eu Funded by the Horizon 2020 Framework Programme of the European Union Configuration Optimization Tool Pooyan Jamshidi Imperial College London
2.
Big Data Technologies Cloud (Priv/Pub) ` DICE Framework 2©DICE DICE IDE Profile Plugins Sim Ver Opt DPIM DTSM DDSM TOSCAMethodology Deploy
Config Test M o n Anomaly Trace Iter. Enh. Data Intensive Application (DIA) Cont.Int. Fault Inj. WP4 WP3 WP2 WP5 WP1 WP6 - Demonstrators
3.
Configuration Optimization Tool The problem: o Big Data technologies have 100s of
tuneable parameters o A knowledge gap is faced by SMEs in configuring these technologies What does the tool do? o Automatically runs experiments on DIAs o Returns recommended configuration parameters for Big Data technologies 3©DICE Innovation: o Automate DIA configuration across release cycles o Prior art focuses on manual configuration Impact & stakeholders: o Reduce cost and time of testing between releases o Support operators of DIAs
4.
CO Tool Architecture 4©DICE Configuration Optimisation Tool performance repository Monitoring Deployment Service Data
Preparation configuration parameters values configuration parameters values Experimental Suite Testbed Doc Data Broker Tester experiment time polling interval previous versions configuration parameters GP model Kafka Vn V1 V2 System Under Test historical data Workload Generator Technology Interface Storm Cassandra Spark Two impementations of CO: BO4CO, TL4CO
5.
Tool Input (Parameters and Options) 5©DICE 1- Information about the experiment:
budget, config file, duration of each experiment 2- Information about the configuration parameters and their options that testers determine
6.
CO Tool Architecture 6©DICE Configuration Optimisation Tool performance repository Monitoring Deployment Service Data
Preparation configuration parameters values configuration parameters values Experimental Suite Testbed Doc Data Broker Tester experiment time polling interval previous versions configuration parameters GP model Kafka Vn V1 V2 System Under Test historical data Workload Generator Technology Interface Storm Cassandra Spark
7.
Optimization Component (Matlab) 7©DICE - This component
select the next configuration to experiment considering the current measurements, - This continues until optimum configuration located or experimental budget finished. The optimization overhead is negligable comparing with measurements This componly relies on rayality free MCR component
8.
CO Tool Architecture 8©DICE Configuration Optimisation Tool performance repository Monitoring Deployment Service Data
Preparation configuration parameters values configuration parameters values Experimental Suite Testbed Doc Data Broker Tester experiment time polling interval previous versions configuration parameters GP model Kafka Vn V1 V2 System Under Test historical data Workload Generator Technology Interface Storm Cassandra Spark
9.
Experimental Suite 9©DICE This component runs the experiments and measures the performance of the system under test, the data are flushed to csv file and communicated with the optimization component
10.
CO Tool Architecture 10©DICE Configuration Optimisation Tool performance repository Monitoring Deployment Service Data
Preparation configuration parameters values configuration parameters values Experimental Suite Testbed Doc Data Broker Tester experiment time polling interval previous versions configuration parameters GP model Kafka Vn V1 V2 System Under Test historical data Workload Generator Technology Interface Storm Cassandra Spark
11.
Performance Repository 11©DICE spouts max_spout sorters
emit_freq chunk_size message_size throughput latency 1 10 1 1 1.00E+05 1000 22657 3.9833 1 10 1 1 1.00E+05 10000 3596.3 18.415 1 10 1 1 1.00E+05 1.00E+05 112.56 217.63 1 10 1 1 1.00E+06 1000 12273 5.1952 1 10 1 1 1.00E+06 10000 1174.9 24.247 1 10 1 1 1.00E+06 1.00E+05 111.88 205.49 1 10 1 1 2.00E+06 1000 12024 5.2935 1 10 1 1 2.00E+06 10000 1151.3 25.039 1 10 1 1 2.00E+06 1.00E+05 94.294 220.62 1 10 1 1 1.00E+07 1000 11552 6.2867 1 10 1 1 1.00E+07 10000 1228.1 24.975 1 10 1 1 1.00E+07 1.00E+05 102.29 236.19 1 10 1 10 1.00E+05 1000 25978 3.4782 1 10 1 10 1.00E+05 10000 10112 9.2847 1 10 1 10 1.00E+05 1.00E+05 1023.8 83.236 1 10 1 10 1.00E+06 1000 24147 3.6594 1 10 1 10 1.00E+06 10000 8400.2 11.804 1 10 1 10 1.00E+06 1.00E+05 1197.4 73.786 1 10 1 10 2.00E+06 1000 22858 3.7151 1 10 1 10 2.00E+06 10000 7141.3 10.755 1 10 1 10 2.00E+06 1.00E+05 1095.1 78.624 1 10 1 10 1.00E+07 1000 22693 4.3637 1 10 1 10 1.00E+07 10000 6281.5 14.308 1 10 1 10 1.00E+07 1.00E+05 951.27 71.492 1 10 1 60 1.00E+05 1000 25862 3.8521 1 10 1 60 1.00E+05 10000 10859 8.6452 1 10 1 60 1.00E+05 1.00E+05 1128.8 79.862 1 10 1 60 1.00E+06 1000 23553 3.9048 1 10 1 60 1.00E+06 10000 9734.3 9.345 1 10 1 60 1.00E+06 1.00E+05 982 66.852 1 10 1 60 2.00E+06 1000 25408 3.5738 1 10 1 60 2.00E+06 10000 7993.9 9.2784 Configuration Metrics Measured The performance repository mediates between the optimization component and experimental suite
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Configuration Parameters (Output) 12©DICE
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Technology Support 13©DICE Configuration Optimisation Tool performance repository Monitoring Deployment Service Data
Preparation configuration parameters values configuration parameters values Experimental Suite Testbed Doc Data Broker Tester experiment time polling interval previous versions configuration parameters GP model Kafka Vn V1 V2 System Under Test historical data Workload Generator Technology Interface Storm Cassandra Spark Technologies: Storm, Spark, Cassandra
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