CIS : Scalable Data Analysis

Transcription

1 CIS : Scalable Data Analysis Cloud Workloads Dr. David Koop

2 Scaling Up PC [Haeberlen and Ives, 2015] 2

3 Scaling Up PC Server [Haeberlen and Ives, 2015] 2

4 Scaling Up PC Server Cluster [Haeberlen and Ives, 2015] 2

5 Scaling Up PC Server Cluster Data center [Haeberlen and Ives, 2015] 2

6 Scaling Up PC Server Cluster Data center Network of data centers [Haeberlen and Ives, 2015] 2

7 Scale Problems 1.Difficult to dimension - Load can vary considerably - Waste resources or lose customers 2.Expensive - Hardware costs - Personnel costs - Maintenance costs 3.Difficult to scale - scaling up (new machines, new buildings) - scaling down (energy, fixed costs) [Haeberlen and Ives, 2015] 3

8 Energy Company Servers Electricity Cost ebay 16K ~0.6*10 5 MWh ~$3.7M/yr Akamai 40K ~1.7*10 5 MWh ~$10M/yr Rackspace 50K ~2*10 5 MWh ~$12M/yr Microsoft >200K >6*10 5 MWh >$36M/yr Google >500K >6.3*10 5 MWh >$38M/yr USA (2006) Data centers consume a lot of energy 10.9M 610*10 5 MWh $4.5B/yr Source: Qureshi et al., SIGCOMM Makes sense to build them near sources of cheap electricity - Example: Price per KWh is 3.6ct in Idaho (near hydroelectric power), 10ct in California (long distance transmission), 18ct in Hawaii (must ship fuel) - Most of this is converted into heat Cooling is a big issue! [Haeberlen and Ives, 2015] University of Pennsylvania 4

9 Modular data centers Need more capacity? Just deploy another container! University of Pennsylvania [Haeberlen and Ives, 2015] 5

10 Power Plant to Cloud Analogy Power source Power source Power source directly connected Network Meter Customer [Haeberlen and Ives, 2015] 6

11 Cloud Computing Architecture [Zhang et al., 2010] 7

12 Cloud Comparison Cloud Provider Amazon EC2 Windows Azure Google App Engine Classes of Utility Computing Infrastructure service Platform service Platform service Target Applications General-purpose applications General-purpose Windows applications Traditional web applications with supported framework Computation OS Level on a Xen Virtual Machine Microsoft Common Language Runtime (CLR) VM; Predefined roles of app. instances Predefined web application frameworks Storage Elastic Block Store; Amazon Simple Storage Service (S3); Amazon SimpleDB Azure storage service and SQL Data Services BigTable and MegaStore Auto Scaling Automatically changing the number of instances based on parameters that users specify Automatic scaling based on application roles and a configuration file specified by users Automatic Scaling which is transparent to users 8

13 Cloud Adoption 9

14 Public Cloud Adoption 10

15 Assignment 2 cis fa/assignment2.html New York City Trees - 680,000+ trees - Use WebGL for visualization - Use a Python bridge (mapboxgl) - Use the fork! - Smaller data versions available - Keep using pandas - Label subproblems and answers - Due Thursday, October 19 11

16 What is virtualization? Alice Physical machine Suppose Alice has a machine with 4 CPUs and 8 GB of memory, and three customers: - Bob wants 1 CPU and 3GB of memory - Charlie wants 2 CPUs and 1GB of memory - Daniel wants 1 CPU and 4GB of memory What should Alice do? University of Pennsylvania Bob Charlie Daniel [Haeberlen and Ives, 2015] 12

17 What is virtualization? Alice Alice can sell each customer a virtual machine (VM) with the requested resources - From each customer's perspective, it appears as if they had a physical machine all by themselves (isolation) Physical machine Virtual machine monitor University of Pennsylvania Virtual machines Bob Charlie Daniel [Haeberlen and Ives, 2015] 13

18 How does it work? VM Virt Phys Translation table App App VM 1 VM 2 OS 1 OS 2 VMM App Physical machine Resources (CPU, memory,...) are virtualized - VMM ("Hypervisor") has translation tables that map requests for virtual resources to physical resources - Ex: VM 1 accesses memory cell #323; VMM maps to memory cell For which resources does this (not) work? - How do VMMs differ from OS kernels? [Haeberlen and Ives, 2015] University of Pennsylvania 14

19 Benefit: Migration Emil Alice Virtual machine monitor Bob Charlie Virtual machines Daniel What if the machine needs to be shut down? - e.g., for maintenance, consolidation,... - Alice can migrate the VMs to different physical machines without any customers noticing Physical machines University of Pennsylvania [Haeberlen and Ives, 2015] 15

20 Benefit: Time sharing Emil Alice What if Alice gets another customer? - Multiple VMs can time-share the existing resources - Result: Alice has more virtual CPUs and virtual memory than physical resources (but not all can be active at the same time) Physical machine Virtual machine monitor University of Pennsylvania Virtual machines Bob Charlie Daniel [Haeberlen and Ives, 2015] 16

21 Benefit and challenge: Isolation Emil VMM Bob Alice Good: Emil can't access Charlie's data Bad: What if the load suddenly increases? - Example: Emil's VM shares CPUs with Charlie's VM, and Charlie suddenly starts a large compute job - Emil's performance may decrease as a result - VMM can move Emil's software to a different CPU, or migrate it to a different machine Physical machine University of Pennsylvania Virtual machines Charlie Daniel [Haeberlen and Ives, 2015] 17

22 Cloud Use by Industry How would you characterise the current presence of cloud in the following industries? % of respondents reporting a significant or pervasive presence Pervasive presence Significant presence 7 43 Banking Retail Manufacturing Education Healthcare Industry average Source: EIU Survey Cloud Computing and Economic Development, October 2015 [The Economist] 18

23 Banking Use How important is cloud in supporting sectors of the banking industry? % saying cloud will be somewhat or very important New ways to make payments Lowering banking costs Banking for remote populations New ways of saving Banking for poor populations New ways of lending Very important Somewhat important Source: EIU Survey Cloud Computing and Economic Development, October 2015 [The Economist] 19

24 Education and the Cloud Future cloud penetration of the education industry % saying cloud will be a moderate or major factor Major factor Moderate factor In one year In three years In five years Source: EIU Survey Cloud Computing and Economic Development, October 2015 [The Economist] 20

25 Supporting Education How important is cloud in supporting sectors of the education industry? % saying cloud will be somewhat or very important Higher education (university) Very important Somewhat important Education for remote areas Continuing/adult education Mid-level students (ages 11-17) Education for low-income Educating young students (ages 3-10) Source: EIU Survey Cloud Computing and Economic Development, October 2015 [The Economist] 21

26 Amazon GovCloud 22

27 Heterogeneity and Dynamicity of Clouds at Scale: Google Trace Analysis C. Reiss, A. Tumanov, G. R. Ganger, R. H. Katz, and M. A. Kozuch

28 Issues faced by cloud schedulers Machine and workload heterogeneity and variability Highly dynamic resource demand and availability Predictable, but poorly predicted, resource needs Resource class preferences and constraints [Reiss et al., 2012] 24

29 Previous Cluster Workload Studies Long-Running Services DAG-of-taks Systems (e.g. MapReduce) High-Performance Computing [Reiss et al., 2012] 25

30 Discussion 26

31 Machine Configuration Number of machines Platform CPUs Memory 6732 B B B C A B B B C B [Reiss et al., 2012] 27

32 CPU Usage (Production vs. Lower Priority) portion of cluster CPU time (hour) [Reiss et al., 2012] 28

33 Job Duration (Log-scale inverted CDF) all production non-production jobs longer job duration (hours) [Reiss et al., 2012] 29

34 Resource Requests portion of tasks requesting less CPU Memory instaneous resource request (normalized CPU/memory) [Reiss et al., 2012] 30

35 Task Submission (New vs. Rescheduled) 3M new tasks rescheduling events per day 2M 1M time (days) [Reiss et al., 2012] 31

36 Requested vs. Allocated Resources portion of memory portion of CPU used allocated time (days) time (days) [Reiss et al., 2012] 32

37 Jobs, Tasks, and Resource Requests number of jobs number of jobs task count memory request (per task) number of jobs CPU request (per task) [Reiss et al., 2012] 33

38 Discussion 34