Introduction to Warehouse-Scale Computers
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The document discusses warehouse-scale computers (WSCs) and their architectural differences from HPC clusters and traditional datacenters, focusing on their internet service capabilities and design considerations. Key design factors include cost-performance, energy efficiency, redundancy, and the handling of request-level parallelism. It also covers programming models like MapReduce, infrastructure requirements, and the advantages of cloud computing, highlighting significant cost reductions in storage, administration, and networking associated with WSCs.
Introduction to Warehouse-Scale Computers
- 1. Warehouse-Scale Computers CS4342 Advanced Computer Architecture Dilum Bandara [email protected] Slides adapted from “Computer Architecture, A Quantitative Approach” by John L. Hennessy and David A. Patterson, 5th Edition, 2012, MK Publishers and The Datacenter as a Computer:An Introduction to the Design of Warehouse-Scale Machines by Luiz André Barroso & Urs Hölzle
- 2. Outline Programming model & workloads Architectures Cloud computing 2
- 3. Warehouse-Scale Computers (WSC) 3 www.laserfocusworld.com/articles/print/volume-48/issue- 12/features/optical-technologies-scale-the-datacenter.html http://www.slashgear.com/google-data-center-hd-photos- hit-where-the-internet-lives-gallery-17252451/
- 6. Main Components of a WSC 6
- 7. Warehouse-Scale Computer (WSC) Provides Internet services Search, social networking, online maps, video sharing, online shopping, email, cloud computing, etc. Differences with HPC clusters Clusters use higher performance processors & network Clusters emphasize thread-level parallelism, WSCs emphasize request/task-level parallelism Differences with datacenters Datacenters consolidate different machines & software into a single location Datacenters emphasize virtual machines & hardware heterogeneity to serve varied customers 7
- 8. Design Factors for WSC Cost-performance Small savings add up Energy efficiency Affects power distribution & cooling Work per joule Operational costs count Power consumption is a primary constraint when designing a system Dependability via redundancy Many low-cost components 8
- 9. Design Factors (Cont.) Network I/O Interactive & batch processing workloads Web search – interactive Web indexing – batch Ample computational parallelism isn’t important Most jobs are totally independent, “Request-level parallelism” Scale – Its opportunities & problems Can afford to build customized systems as WSC require volume purchase Frequent failures 9
- 10. Failure Example Consider a WSC with 50,000 nodes. MTTF of a node is 5 years. How many failures be there for a day? MTTF in days = 5 x 365 = 1,825 Failure rate = 1/1,825 per day No of failures per day = 50,000/1,825 = 27.4 Consider a WSC with 50,000 nodes & each node with 4 hard disks. Suppose a annual failure rate of a disk is 4%. What is the time for a disk failure? No of disks = 50,000 x 4 = 200,000 No of failures per year = 200,000 x 0.04 = 8,000 Time for failure = 365 x 24 / 8,000 = 1.095 hours/failure 10
- 11. Programming Models & Workloads Batch processing framework – MapReduce Map Applies a programmer- supplied function to each logical input record Runs on thousands of computers Provides new set of (key, value) pairs as intermediate values Reduce Collapses values using another function 11 http://www.cbsolution.net/techniques/ontarget/mapredu ce_vs_data_warehouse
- 12. MapReduce Execution 12 Source: Dean et. al., “MapReduce, OSDI, 2004
- 13. Programming Models & Workloads (Cont.) 13 www.datanami.com/datanami/2012-07- 16/top_5_challenges_for_hadoop_mapreduce _in_the_enterprise.html
- 14. Programming Models & Workloads (Cont.) MapReduce runtime environment schedules map & reduce task to WSC nodes Availability Use replicas of data across different servers Use relaxed consistency No need for all replicas to always agree Workload demands Often vary considerably 14
- 15. Computer Architecture of WSC Often uses a hierarchy of networks for interconnection Each 19” rack holds 48 1U servers connected to a rack switch Rack switches are uplinked to a switch(es) higher in hierarchy Uplink has 48/n times lower bandwidth – Oversubscription n – No of uplink ports Goal is to maximize locality of communication relative to the rack 15
- 19. Infrastructure & Costs Location Proximity to Internet backbones, electricity cost, property tax rates, low risk from earthquakes, floods, & hurricanes Power distribution 19
- 20. Power Usage 20 U.S. EPA Report 2007 – 1.5% of total U.S. power consumption used by data centers which has more than doubled since 2000 & costs $4.5 billion
- 21. How Many Nodes can a WSC Support? Each node “Nameplate power rating” gives maximum power consumption To get actual, measure power under actual workloads Oversubscribe cumulative nodes power by 40%, but monitor power closely 21
- 22. Cooling 22 Typically operate around 18 – 22 0C
- 23. Cooling (Cont.) 23 Cooling system also uses water (evaporation & spills) e.g. 70,000 to 200,000 gallons per day for an 8 MW facility
- 24. Efficiency Power Utilization Effectiveness (PUE) = Total facility power / IT equipment power ≥ 1 Median PUE on 2006 study was 1.69 24 Source: http://hightech.lbl.gov/benchmarking-guides/data-a1.html
- 25. Performance Latency is important metric because it is seen by users Bing study Users will use search less as response time increases Service Level Objectives (SLOs) & Service Level Agreements (SLAs) Typically given at application level e.g., 99% of requests be below 100 ms In clouds typically given only for static resources CPU speed, no of cores, & memory 25
- 26. Cost Capital expenditures (CAPEX) Cost to build a WSC Hardware cost dominates Operational expenditures (OPEX) Cost to operate a WSC Power for nodes & cooling dominates 26
- 27. Cloud Computing 27 Clients Other Cloud Services Govt. Cloud Services Private Cloud Cloud Manager Public Cloud Green Cloud Computing by Dr. Rajkumar Buyya
- 28. Cloud Computing (Cont.) WSCs offer economies of scale that can’t be achieved with a datacenter 5.7 times reduction in storage costs 7.1 times reduction in administrative costs 7.3 times reduction in networking costs This has given rise to cloud services such as Amazon Web Services “Utility Computing” Based on using open source virtual machine & operating system software 28
- 29. Amazon Web Services Virtual machines XEN Very low cost $ 0.10 per hour per instance Primary rely on open source software No (initial) service guarantees No contract required Amazon S3 Simple Storage Service Amazon EC2 Elastic Computer Cloud 29
- 30. Amazon Web Services – Example 30 http://www.ryhug.com/free-art-available-on-amazon-amazon-web-services-that-is/