High Volume Throughput Computers (HVC): An ICT View of Datacenter Computers

Transcription

1 High Volume Throughput Computers (HVC): An ICT View of Datacenter Computers Jianfeng Zhan ( 詹剑锋 )

2 Outline Motivation Related work Challenges and Opportunities Work-in-the-progress in my group

3 Datacenter hosting computing

4 Three trends in datacenter hosting computing More and more services involving a large amount of data, are deployed in datacenters to serve the masses. More and more data are produced, stored, analyzed, and serviced. Lots of users tend to use streaming media or VoIP for fun or communications.

5 Trend of Big Data Data Warehouse: 20PB Daily Increase: 60TB User Number: 750 million Image Number: 260 Billion Weekly increase : 1 billion 5

6 Scales of deployed data centers

7 We coin a new term---high Volume Throughput Computing ( in short, HVC) to describe those workloads.

8 What is HVC? a datacenter based computing paradigm focusing on throughput-oriented workloads Workloads: consist of a large amount of loosely coupled jobs in stead of a big job. Nature: throughput computing Target : increase the volume of throughput in terms of Requests (Services) processed data (Data processing applications) the maximum number of simultaneous subscribers (Interactive real-time application) High Volume Throughput Computing: Identifying and Characterizing Throughput Oriented Workloads in Data Centers. Jianfeng Zhan. Lixin Zhang, Lei Wang, Ninghui Sun et al.. LSPP 2012.

9 Three categories of workloads in HVC Services A service is a group of applications that collaborate to receive user requests and return responses to end users. Data processing applications Only include loosely coupled data-intensive computing. Interactive real-time applications An interactive real-time application will maintain a user session of a long period while guaranteeing the real time quality of service.

10 The relationships between WSC and DISC Data Intensive Services (Web search) Loose coupled MapReduce jobs DISC WSC

11 The relationships among HVC, WSC and DISC Traditional server Workloads (SQL, Web Server) Services Data Intensive Services (Web search) WSC Data processing applications Interactive real-time Applications (Desktop Cloud, Stream media) HVC

12 The relationship between HVC and Cloud HPC in Cloud HVC server Consolidation systems Cloud computing

13 Differences of HVC from other computing

14 Differences of HVC from other computing

15 Outline Motivation Related work Challenges and Opportunities Work-in-the-progress in my group

16 Google Data Center Solution Low-end server clusters X86 CPU Single thread performance commodity-off-the-shelf networks Software built in house Software-level fault tolerance

17 A Large SMP Server vs. a Low-end, PC- Class Server

18 Why Google Objects to Hardware Specialization? The diverse requirements of multiple services The datacenter must be a general-purpose computing system. The breadth of requirements makes it less likely that specialized hardware can make a large overall impact in the operation. The speed of workload churn product requirements evolve rapidly Smart programmers learn from experience and rewrite the baseline algorithms and data structures much more rapidly than hardware itself can evolve. There is substantial risk that by the time a specialized hardware solution is implemented.

19 IBM Technologies Z-series Super I/O capability High redundancy Suitable for banking systems. P-series SMT after-thought Achieve high single thread performance and reliability. Suitable for high end commercial computing.

20 Sun UltraSparc T1/T2 Designed for high throughput. Poor singe thread performance. The single thread performance of T2 is about 1/5 2.6GHz Nehalem. Power consumption is still high. T2 processor: about 100watts. The Rock project is canceled.

21 Outline Motivation Related work Challenges and Opportunities Work-in-the-progress in my group

22 (1) HVC is a Multidiscipline Field Computer Science and Engineering Electrical Engineering Cooling Mechanical Engineering

23 Breakdown of a Datacenter

24 Categories of Datacenters Tier I datacenters A single path for power and cooling distribution, without redundant components. Tier II datacenters Redundant components to this design (N + 1), improving availability. Tier III datacenters Multiple power and cooling distribution paths but only one active path. Provide redundancy even during maintenance, usually with an N + 2 setup. Tier IV datacenters Two active power and cooling distribution paths, redundant components in each path.

25 Google s Power Efficiency Metrics The first term in the efficiency calculation (a) is the power usage effectiveness (PUE) the ratio of total building power to IT power IT power is the power consumed by the actual computing equipment (servers, network equipment, etc.). The second term (b) accounts for these overheads: server PUE (SPUE). Our CS guys only can improve the third term (c).

26 LBNL Survey of the PUE of 24 Datacenters

27 (2) No Benchmark High-performance computing (HPC) LINPACK The Green 500. Graph 500 No initiative for Internet services. We want to do, however its is very difficult No one would like to share data.

28 (3) Cost Challenges The differences of service systems from enterprise systems Data cost models.

29 The Difference between Amazon Service and Enterprise Systems Data source from James Hamilton, VP of Amazon, ISCA 09 Keynote

30 Cost Models of Datacenters Spending (US$B) $120 $100 $80 Worldwide Server Market Power and Cooling New Server Spending $60 $40 $20 $0 Source: werinlargescaledatacenter.aspx Source: IDC, The Impact of Power and Cooling on Data Center Infrastructure, May 2006

31 Main Concerns Server costs Workload spike Power related costs Power and cooling infrastructure Energy-proportional energy-proportional systems will consume almost no power when idle and gradually consume more power as the activity level increases.

32 Human Energy Usage vs. Activity Levels

33 Subsystem Power Usage in an x86 Server

34 Activity Profile of a Sample of 5,000 Google Servers over a Period of 6 Months.

35 Space-constrained Energy Efficiency Most of Chinese service companies depends upon hosted data centers. Space-constrained. The fixed power supply. How to improve performance gains under these constraints?

36 (4) System Architecture Challenges Limitations Code investments Rapidly changing workloads Opportunities Ample parallelism The power concerns.

37 Rapidly Changing Workloads Internet services are in their infancy as an application area New products appear and gain popularity at a very fast pace. YouTube video sharing site exploded in popularity in a period of a few months Some services have different architectural need. The difficult mismatch between The time scale for radical workload behavior changes The design and life cycles for data centers.

38 Building Balanced Systems from Imbalanced Components Processors continue to get faster and more energy efficient. Memory systems and magnetic storage are not evolving at the same pace, whether in performance or energy efficiency. Non-CPU components dominate computer performance and energy usage. Architects must try to build efficient large-scale systems. Some balance in performance and cost despite the shortcomings of the available components.

39 (5) Massive Data Challenges How to store data? How to process data? How to present data as insight?

40 Outline Motivation Related work Challenges and Opportunities Work-in-the-progress in my group

41 Benchmarks

42 Current Benchmarks SPEC CPU SPEC Web HPCC PARSEC TPCC Gridmix YCSB

43 Current Benchmarks SPEC CPU SPEC Web HPCC TPCC Gridmix YCSB PARSEC No Benchmark For HVC

44 Different benchmarks and metrics

45 Benchmark requirements Target Benchmark Represent- ative Diverse Program m-ing Models Distribute d State-of of-art

46 Current benchmark suits Search: Benchmark Category: service Details: Real workload-based benchmark Nutch based benchmark Open source: available from ks BigDataBench: Benchmark Category: big data processing Details: 21 representative workloads 4 programming models: MapReduce MPI AllPairs WorkQueue Characterization of Real Workloads of Web Search Engines. Huafeng Xi, Jianfeng Zhan.etc IEEE International Symposium on Workload Characterization (IISWC 2011). CloudRank-D : Benchmarking and Ranking Cloud Computing Systems For Data Processing Applications. Chunjie Luo, Jianfeng Zhan etc. To appear in Frontier of Computer Sciences.

47 Benchmark: Search Client Workloads generator Search service Web Server Search Server

48 BigDataBench Basic operation Classification Cluster Recommendation Association rule mining BigDataBench Sequence learning Warehouse operation Feature reduction Vector calculate Bioinformati cs

49 Detailed information of BigDataBench

50 Applications

51 An ideal application Data-intensive TB or PB A challenging application Machine Reading of the World Wide Web Valuable services attracts more searches Professional search Information extraction of professionals

52

53 根据研究方向搜索 用 prof.ict.ac.cn 搜索 多核系统 or 突厥文学 or 计划生育

54 多核系统

55 信息抽取的结果

56 突厥文学

57 计划生育

58 Data Analysis Processes

59 Summary High-level talk on the ICT view of datacenter computers. Challenges and opportunities. Some relevant work in my group.

60 Reading lists Vaquero, L. M., Rodero-Merino, L., Caceres, J., and Lindner, M A break in the clouds: towards a cloud definition. SIGCOMM Comput. Commun. Rev. 39, 1 (Dec. 2008), Jianfeng Zhan, Lei Wang, Xianna Li, Weisong Shi, Chuliang Weng, Wenyao Zhang, and Xiutao Zang. Cost-aware Cooperative Resource Provisioning for Heterogeneous Workloads in Data Centers. Accepted by IEEE Transaction on Computers (TC). U. Hoelzle et al The Datacenter as a Computer: an introduction to the Design of Warehouse-Scale Machines. 1st. Morgan and Claypool Publishers. Armbrust, M., et al. Above the clouds: A Berkeley view of cloud computing. Tech. Rep. UCB/EECS , EECS Department, UC Berkeley, Feb 2009 Chunjie Luo, Jianfeng Zhan, Zhen Jia et al. CloudRank-D : Benchmarking and Ranking Cloud Computing Systems For Data Processing Applications. To appear in Frontier of Computer Sciences

61 Thank you!