ΕΠΛ372 Παράλληλη Επεξεργάσια

Transcription

1 ΕΠΛ372 Παράλληλη Επεξεργάσια Warehouse Scale Computing and Services Γιάννος Σαζεϊδης Εαρινό Εξάμηνο 2014 READING 1. Read Barroso The Datacenter as a Computer Slides based on Hennesy and Patterson CAQA 5 th edition Morgan and Kaufman

2 Introduction Introduction Warehouse-scale computer (WSC) Provides Internet services Search, social networking, online maps, video sharing, online shopping, , cloud computing, etc. Differences with HPC clusters : Clusters have higher performance processors and network Clusters emphasize thread-level parallelism, WSCs emphasize requestlevel parallelism Differences with co-located-datacenters: Datacenters consolidate different machines and software into one location Datacenters emphasize virtual machines and hardware heterogeneity in order to serve varied customers Private and public cloud Often term DC refers to both WSC and co-located-dc

3 Introduction Introduction Important design factors for WSC: (same) Cost-performance Small savings add up Energy efficiency Affects power distribution and cooling Work per joule Dependability via redundancy Commodity HW and software based redundancy Network I/O Consistency and interface to world Interactive and batch processing workloads Search but also calculate meta data (rank pages)

4 Introduction Introduction Important design factors for WSC: (differ.) Ample computational parallelism is not important Most jobs are totally independent Request-level parallelism Mostly read and often write to not shared data Data in storage for batch Operational costs count Power consumption is a primary, not secondary, constraint when designing system Scale and its opportunities and problems Can afford to build customized systems since WSC require volume purchase. Failures more common

5 Causes of Outages and Anomalies (2400 servers 1 st yr) Number Cause 1-2 Power outage 4 Upgrades Hard-disk 1000s Dram Problematic machines 5000 Server crashes

6 Figure 6.3 Average CPU utilization of more than 5000 servers during a 6-month period at Google. Servers are rarely completely idle or fully utilized, in-stead operating most of the time at between 10% and 50% of their maximum utilization. (From Figure 1 in Barroso and Hölzle [2007].) The column the third from the right in Figure 6.4 calculates percentages plus or minus 5% to come up with the weightings; thus, 1.2% for the 90% row means that 1.2% of servers were between 85% and 95% utilized.

7 Computer Architecture of WSC WSC often use a hierarchy of networks for interconnection Each 19 rack holds 48 1U servers connected to a rack switch Rack switches are uplinked to switch higher in hierarchy Uplink has 48 / n times lower bandwidth, where n = # of uplink ports Oversubscription Goal is to maximize locality of communication relative to the rack Computer Ar4chitecture of WSC

8 Figure 6.5 Hierarchy of switches in a WSC. (Based on Figure 1.2 of Barroso and Hölzle [2009].)

9 Storage Storage options: Use disks inside the servers, or Network attached storage through Infiniband Computer Ar4chitecture of WSC WSCs generally rely on local disks Google File System (GFS) uses local disks and maintains at least three replicas

10 Array Switch Switch that connects an array of racks Array switch should have 10 X the bisection bandwidth of rack switch Cost of n-port switch grows as n 2 Often utilize content addressable memory chips and FPGAs Computer Ar4chitecture of WSC

11 WSC Memory Hierarchy Servers can access DRAM and disks on other servers using a NUMA-style interface Computer Ar4chitecture of WSC

12 Figure 6.7 Graph of latency, bandwidth, and capacity of the memory hierarchy of a WSC for data in Figure 6.6 [Barroso and Hölzle 2009]. Copyright 2011, Elsevier Inc. All rights Reserved.

13 Infrastructure and Costs of WSC Location of WSC Proximity to Internet backbones, electricity cost, property tax rates, low risk from earthquakes, floods, and hurricanes Power distribution Physcical Infrastrcuture and Costs of WSC

14 Infrastructure and Costs of WSC Cooling Air conditioning used to cool server room 64 F 71 F Keep temperature higher (closer to 71 F) Cooling towers can also be used Physcical Infrastrcuture and Costs of WSC

15 Infrastructure and Costs of WSC Cooling system also uses water (evaporation and spills) E.g. 70,000 to 200,000 gallons per day for an 8 MW facility Power cost breakdown: Chillers: 30-50% of the power used by the IT equipment Air conditioning: 10-20% of the IT power, mostly due to fans How many servers can a WSC support? Each server: Nameplate power rating gives maximum power consumption To get actual, measure power under actual workloads Oversubscribe cumulative server power by 40%, but monitor power closely Physcical Infrastrcuture and Costs of WSC

16 Measuring Efficiency of a WSC Power Utilization Effectiveness (PUE) = Total facility power / IT equipment power Median PUE on 2006 study was 1.69 Today large facilities close 1.1 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) E.g. 99% of requests be below 100 ms Physcical Infrastrcuture and Costs of WSC

17 Figure 6.22 Power usage effectiveness (PUE) of 10 Google WSCs over time. Google A is the WSC described in this section. It is the highest line in Q3 07 and Q2 10. (From Facebook recently announced a new datacenter that should deliver an impressive PUE of 1.07 (see The Prineville Oregon Facility has no air conditioning and no chilled water. It relies strictly on outside air, which is brought in one side of the building, filtered, cooled via misters, pumped across the IT equipment, and then sent out the building by exhaust fans. In addition, the servers use a custom power supply that allows the power distribution system to skip one of the voltage conversion steps in Figure 6.9. Copyright 2011, Elsevier Inc. All rights Reserved.

18 Cost of a WSC Capital expenditures (CAPEX) Cost to build a WSC Acquire servers, switchets Operational expenditures (OPEX) Cost to operate a WSC Physcical Infrastrcuture and Costs of WSC

19 Cloud Computing WSCs offer economies of scale that cannot 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 and operating system software Cloud Computing

20 Prgrm g Models and 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 programmer-supplied function Programming Models and Workloads for WSCs

21 Prgrm g Models and Workloads Example: map (String key, String value): // key: document name // value: document contents for each word w in value EmitIntermediate(w, 1 ); // Produce list of all words reduce (String key, Iterator values): // key: a word // value: a list of counts int result = 0; for each v in values: result += ParseInt(v); // get integer from key-value pair Emit(AsString(result)); Programming Models and Workloads for WSCs

22 Prgrm g Models and Workloads MapReduce runtime environment schedules map and reduce task to WSC nodes Availability: Use replicas of data across different servers If one slow or fails start on another Use relaxed consistency: No need for all replicas to always agree Programming Models and Workloads for WSCs Workload demands Often vary considerably

23 Search CloudSuite Benchmark Overview Client Multithreaded client Frontend server Tomcat running Apache Nutch to n Index servers Hadoop 0.20 IPC server running Apache Nutch to n Document servers Hadoop 0.20 IPC server running Apache Nutch 1.2 Notes Index and document dataset is distributed to the number of index and document servers Apache Nutch 1.2 gives you the choice of running index and document server seperated or on the same process together Query flow description Client send query request to frontend server Frontend asks each index server to search in it s own part of index dataset for document matches All index servers search their part of index dataset in parallel Frontend server collects, sorts the replies and asks the index servers the title and url of top results (detail request) Frontend receives the urls and asks the document servers to search in it s own part of dataset for the summaries of the top documents. Frontend receives the summaries and assemblies the reply HTML page and sends 23 the answer back to client