DEPLOYING AND MONITORING HADOOP MAP-REDUCE ANALYTICS ON SINGLE-CHIP CLOUD COMPUTER

Transcription

1 DEPLOYING AND MONITORING HADOOP MAP-REDUCE ANALYTICS ON SINGLE-CHIP CLOUD COMPUTER ANDREAS-LAZAROS GEORGIADIS, SOTIRIOS XYDIS, DIMITRIOS SOUDRIS MICROPROCESSOR AND MICROSYSTEMS LABORATORY ELECTRICAL AND COMPUTER ENGINEERING DEPARTMENT NATIONAL TECHNICAL UNIVERSITY OF ATHENS

2 TIME IS BYTES

3 NEW OPEN QUESTIONS FOR COMPUTER ARCHITECTS How Big Data and Scale-Out Workloads Performs on Manycores? DATA SCALE UP WORKLOADS SCALE OUT 50-core Intel 2015 Single 2000 TECHNOLOGY SCALE DOWN Single-core Pentium-Pro@ 2003

4 SCOPE OF THIS PAPER HADOOP ANALYTICS ON CHIP: HOW TO DEPLOY & MONITOR HADOOP MAPREDUCE CLUSTERS INTEL SINGLE-CLOUD-CHIP (SCC) MANYCORE WORKLOAD CHARACTERIZATION: ANALYSIS OF HADOOP ANALYTIC WORKLOADS ON REAL- SILICON INTEL-SCC MANYCORE PERFORMANCE-POWER TUNING: HADOOP CONFIGURATIONS FOR EFFICIENT PERFORMANCE- POWER TRADE-OFFS W.R.T. CLUSTER TOPOLOGIES AND FREQUENCY SETTINGS

5 INTEL SCC: ARCHITECTURAL SPECIFICATION Intel SCC Power Management RESEARCH CHIP BUILT IN INTEL LABS. 48 P54C IA CORES ORGANIZED IN 24 TILES. CORE FREQUENCY FROM 100 MHZ TO 800 MHZ. ON-DIE 2D MESH NETWORK. 24 PACKET-SWITCHED ROUTERS. MESH NETWORK FREQUENCY 800 MHZ OR 1.6 GHZ. 32 GB OF DRAM THROUGH 4 DDR3 MEMORY CONTROLLERS. MEMORY CONTROLLER FREQUENCY 800 MHZ OR 1066 MHZ. 16 KB OF FAST LOCAL SRAM ON EACH TILE, CALLED THE MESSAGE PASSING BUFFER (MPB). BOARD MANAGEMENT MICROCONTROLLER (BMC). INITIALIZES AND SHUTS DOWN CRITICAL SYSTEM FUNCTIONS. CONNECTED TO A MANAGEMENT CONTROL PC (MCPC) BY A PCI- EXPRESS CABLE.

6 HADOOP CLUSTER: HDFS + MAPREDUCE HDFS: DISTRIBUTED FILE SYSTEM [NAMENODE, DATANODES] EFFICIENT AND RELIABLE ACCESS TO DATA NAMENODE: MANAGES FILE SYSTEM NAMESPACE AND REGULATES ACCESS TO FILES BY CLIENTS. DATANODE: MANAGES STORAGE ATTACHED TO THE NODES THAT THEY RUN ON. BLOCK CREATION, DELETION, AND REPLICATION UPON INSTRUCTION FROM THE NAMENODE. MAPREDUCE: SCALABLE PARALLLEL PROGRAMMING MODEL MAP TASKS PROCESS INDEPENDENT SPITS OF INPUT DATA AS <KEY,VALUE> PAIRS TO GENERATE A SET OF INTERMEDIATE <KEY, VALUE> PAIRS. REDUCE TASKS MERGE ALL INTERMEDIATE VALUES ASSOCIATED WITH THE SAME INTERMEDIATE KEY, SO AS TO PRODUCE THE FINAL OUTPUT <KEY, VALUE> PAIRS. INPUT AND THE OUTPUT FILES ARE STORED IN HDFS. TASK SCHEDULING WHERE THE DATA IS ALREADY PRESENT, VERY HIGH AGGREGATE BANDWIDTH ACROSS THE CLUSTER.

7 HADOOP DEPLOYMENT OF INTEL SCC (1/2) LIMITATIONS SOLUTION RESTRICTED APPLICATION DEVELOPMENT API OF INTEL SCC LINUX GENTOO IMAGE FOR THE INTEL SCC ON EACH INTEL SCC CORE. ONLY 640 MB OF MAIN MEMORY FOR EACH CORE. JAVA HEAP SPACE OF 128 MB FOR HADOOP DAEMONS AND THE CHILD JVM LIMITED TCP/IP STACK TO SUPPORT CLUSTER SW NAT ROUTING WITH MODIFIED ROUTING TABLES INTERNET ACCESS FOR INTEL SCC CORES DIRECT ACCESS TO INTERNAL VIRTUAL NETWORK INTERFACES OF THE INTEL SCC CORES (MB0)

8 HADOOP DEPLOYMENT OF INTEL SCC (2/2) LIMITATIONS SOLUTION HIGH I/O LOAD AND VERY LOW FREE MAIN MEMORY SPACE CAUSES CORES TO FREEZE AND BECOME UNREACHABLE FREQUENTLY. HADOOP CONSIDERS CONSIDERS RACK PROXIMITY NODE-FAILOVER WATCHDOG. PINGS INTEL SCC CORES PERIODICALLY. IF CORE UNREACHABLE, INTEL SCC LINUX IS BOOTED AND CORRESPONDING HADOOP DAEMON IS STARTED ON-DIE CLUSTER EXPLICITLY DIVIDED TO HADOOP RACKS. NO PARMA-DITAM EFFICIENT 2016 FRAMEWORK RUNTIME MONITORING ADAPT GANGLIA MONITORING ON INTEL SCC

9 HADOOP CLUSTER TOPOLOGY EXPLORATION (1/4) 16-node Hadoop cluster

10 HADOOP CLUSTER TOPOLOGY EXPLORATION (2/4) 24-node Hadoop cluster

11 HADOOP CLUSTER TOPOLOGY EXPLORATION (3/4) 32-node Hadoop cluster

12 HADOOP CLUSTER TOPOLOGY EXPLORATION (4/4) 48-node Hadoop cluster

13 EXPERIMENTAL PROCESS THE PERFORMANCE OF FOUR MAPREDUCE APPLICATIONS (WORDCOUNT, BAYES CLASSIFICATION [COUDSUITE], K-MEANS CLUSTERING AND FREQUENT PATTERN GROWTH[DATACENTERBENCH]) IS INVESTIGATED WHEN THEY ARE EXECUTED ON THE INTEL SCC EXPERIMENTAL ANALYSIS EXPLORES SCALABILITY, PERFORMANCE AND POWER CONSUMPTION TRADEOFFS FOR DIFFERENT CLUSTER TOPOLOGY ORGANIZATIONS AND FREQUENCY CONFIGURATIONS.

14 WORKLOAD CHARACTERIZATION: BAYES CLASSIFIER (1/3) DCSDCSC

15 WORKLOAD CHARACTERIZATION: BAYES CLASSIFIER (2/3) DCSDCSC

16 WORKLOAD CHARACTERIZATION: BAYES CLASSIFIER (3/3) DCSDCSC

17 IMPACT OF RESOURCE ALLOCATION ON PERFORMANCE - ENERGY Frequent Pattern Growth: Experimental Analysis K-Means Clustering: Experimental [More...] Analysis (1/ 2) [More...] (a) WordCount (b) Bayes Classifier Andreas - Lazaros Georgiadis ( NTUA) Diploma T hesis Andreas - Lazaros Georgiadis April ( NTUA) 27, / 56Diploma T hesis Apr (c) K-Means Andreas - Lazaros Georgiadis (NT UA) Diploma T hesis Apri (d) Frequent-Pattern Groth

18 CORE-FREQUENCY ASSIGNMENT IN HADOOP CLUSTER LOW ENERGY REGION: TASKTRACKER: 800MHZ DATANODE: 200 MHZ HIGH PERFORMANCE REGION: TASKTRACKER: 800 MHZ DATANODE: MHZ

19 CONCLUSIONS HADOOP MAPREDUCE WORKLOADS DEPLOYED AND MONITORED ON THE INTEL SCC CORES. PLATFORM LIMITATIONS AND SOLUTIONS EXTENSIVE EXPERIMENTATION REGARDING TO THE ANALYSIS OF HADOOP MAPREDUCE WORKLOADS OVER DIVERSE CLUSTER TOPOLOGIES INTERESTING PERFORMANCE-ENERGY TRADE-OFFS IN RESPECT TO CORE- FREQUENCY ALLOCATION STRATEGY OF THE DATA-NODES VS. TASKTRACKERS

20 THANK YOU QUESTIONS?