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A Grid Research Toolbox

The Failure Trace Archive. and Cloud. A Grid Research Toolbox. DGSim. A. Iosup , O. Sonmez, N. Yigitbasi, H. Mohamed, S. Anoep, D.H.J. Epema. M. Jan. LRI/INRIA Futurs Paris, INRIA. PDS Group, ST/EWI, TU Delft. H. Li, L. Wolters. I. Raicu, C. Dumitrescu, I. Foster. LIACS, U. Leiden.

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A Grid Research Toolbox

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  1. The FailureTraceArchive and Cloud A Grid Research Toolbox • DGSim A. Iosup, O. Sonmez, N. Yigitbasi, H. Mohamed, S. Anoep, D.H.J. Epema M. Jan LRI/INRIA Futurs Paris, INRIA PDS Group, ST/EWI, TU Delft H. Li, L. Wolters I. Raicu, C. Dumitrescu, I. Foster LIACS, U. Leiden U. Chicago Paris, France

  2. A Layered View of the Grid World • Layer 1: Hardware + OS • Automated • Non-grid (XtreemOS?) • Layers 2-4: Grid Middleware Stack • Low Level: file transfers, local resource allocation, etc. • High Level: grid scheduling • Very High Level: application environments (e.g., distributed objects) • Automated/user control • Simple to complex • Layer 5: Grid Applications • User control • Simple to complex Grid Applications Grid Very High Level MW Grid High Level MW Grid MW Stack Grid Low Level MW HW + OS

  3. Grid Work: Science or Engineering? • Work on Grid Middleware and Applications • When is work in grid computing science? • Studying systems to uncover their hidden laws • Designing innovative systems • Proposing novel algorithms • Methodological aspects: repeatable experiments to verify and extend hypotheses • When is work in grid computing engineering? • Showing that the system works in a common case, or in a special case of great importance (e.g., weather prediction) • When our students can do it (H. Casanova’s argument)

  4. Grid Research Problem: We Are Missing Both Data and Tools • Lack of data • Infrastructure • number and type of resources, resource availability and failures • Workloads • arrival process, resource consumption • … • Lack of tools • Simulators • SimGrid, GridSim, MicroGrid, GangSim, OptorGrid, MONARC, … • Testing tools that operate in real environments • DiPerF, QUAKE/FAIL-FCI • … We have problems to solve in grid computing (as a science)!

  5. CERN LCG jobs 74.71% successful 25.29% unsuccessful Source: dboard-gr.cern.ch, May’07. Anecdote: Grids are far from being reliable job execution environments Server • 99.99999% reliable • 99.999% reliable Small Cluster So at the moment our students cannot work in grid computing engineering! Production Cluster • 5x decrease in failure rate after first year [Schroeder and Gibson, DSN‘06] DAS-2 • >10% jobs fail [Iosup et al., CCGrid’06] TeraGrid • 20-45% failures [Khalili et al., Grid’06] Grid3 • 27% failures, 5-10 retries [Dumitrescu et al., GCC’05]

  6. The Anecdote at Scale • NMI Build-and-Test Environment at U.Wisc.-Madison: 112 hosts, >40 platforms (e.g., X86-32/Solaris/5, X86-64/RH/9) • Serves >50 grid middleware packages: Condor, Globus, VDT, gLite, GridFTP, RLS, NWS, INCA(-2), APST, NINF-G, BOINC … • Two years of functionality tests (‘04-‘06): over 1:3 runs have at least one failure! • Test or perish! • In today’s grids, reliability is more important than performance! A. Iosup, D.H.J.Epema, P. Couvares, A. Karp, M. Livny, Build-and-Test Workloads for Grid Middleware: Problem, Analysis, and Applications, CCGrid, 2007.

  7. A Grid Research Toolbox • Hypothesis: (a) is better than (b). For scenario 1, … 1 3 DGSim 2

  8. Research Questions Q1: How to exchange grid/cloud data?(e.g., Grid/Cloud * Archive) Q2: What are the characteristics of grids/clouds? (e.g., infrastructure, workload) Q3: How to test and evaluate grids/clouds?

  9. Outline • Introduction and Motivation • Q1: Exchange Data • The Grid Workloads Archive • The Failure Trace Archive • The Cloud Workloads Archive (?) • Q2: System Characteristics • Grid Workloads • Grid Infrastructure • Q3: System Testing and Evaluation

  10. Traces in Distributed Systems Research • “My system/method/algorithm is better than yours (on my carefully crafted workload)” • Unrealistic (trivial): Prove that “prioritize jobs from users whose name starts with A” is a good scheduling policy • Realistic? “85% jobs are short”; “10% Writes”; ... • Major problem in Computer Systems research • Workload Trace = recording of real activity from a (real) system, often as a sequence of jobs / requests submitted by users for execution • Main use: compare and cross-validate new job and resource management techniques and algorithms • Major problem: real workload traces from several sources August 26, 2010 10

  11. 2.1. The Grid Workloads Archive [1/3]Content http://gwa.ewi.tudelft.nl 6 traces online 1.5 yrs >750K >250 A. Iosup, H. Li, M. Jan, S. Anoep, C. Dumitrescu, L. Wolters, D. Epema, The Grid Workloads Archive, FGCS 24, 672—686, 2008.

  12. 2.1. The Grid Workloads Archive [2/3]Approach: Standard Data Format (GWF) • Goals • Provide a unitary format for Grid workloads; • Same format in plain text and relational DB (SQLite/SQL92); • To ease adoption, base on the Parallel Workloads Format (SWF). • Existing • Identification data: Job/User/Group/Application ID • Time and Status: Sub/Start/Finish Time, Job Status and Exit code • Request vs. consumption: CPU/Wallclock/Mem • Added • Job submission site • Job structure: bag-of-tasks, workflows • Extensions: co-allocation, reservations, others possible A. Iosup, H. Li, M. Jan, S. Anoep, C. Dumitrescu, L. Wolters, D. Epema, The Grid Workloads Archive, FGCS 24, 672—686, 2008.

  13. 2.1. The Grid Workloads Archive [3/3]Approach: GWF Example Used Req Submit Wait[s] Run #CPUs Mem [KB] #CPUs A. Iosup, H. Li, M. Jan, S. Anoep, C. Dumitrescu, L. Wolters, D. Epema, The Grid Workloads Archive, FGCS 24, 672—686, 2008.

  14. The FailureTraceArchive 2.2. The Failure Trace ArchivePresentation Types of systems • (Desktop) Grids • DNS servers • HPC Clusters • P2P systems Stats • 25 traces • 100,000 nodes • Decades of operation http://fta.inria.fr

  15. 2.2. The Cloud Workloads Archive [1/2]One Format Fits Them All CWJ CWJD CWT CWTD A. Iosup, R. Griffith, A. Konwinski, M. Zaharia, A. Ghodsi, I. Stoica, Data Format for the Cloud Workloads Archive, v.3, 13/07/10 • Flat format • Job and Tasks • Summary (20 unique data fields) and Detail (60 fields) • Categories of information • Shared with GWA, PWA: Time, Disk, Memory, Net • Jobs/Tasks that change resource consumption profile • MapReduce-specific (two-thirds data fields) 15

  16. Trace ID Total IO [MB] Rd. [MB] Wr [%] HDFS Wr[MB] CWA-01 10,934 6,805 38% 1,538 CWA-02 75,546 47,539 37% 8,563 2.2. The Cloud Workloads Archive [2/2]The Cloud Workloads Archive • Looking for invariants • Wr [%] ~40% Total IO, but absolute values vary • # Tasks/Job, ratio M:(M+R) Tasks, vary • Understanding workload evolution

  17. Outline • Introduction and Motivation • Q1: Exchange Data • The Grid Workloads Archive • The Failure Trace Archive • The Cloud Workloads Archive (?) • Q2: System Characteristics • Grid Workloads • Grid Infrastructure • Q3: System Testing and Evaluation

  18. 3.1. Grid Workloads [1/7]Analysis Summary:Grid workloads different, e.g., from parallel production envs. (HPC) • Traces: LCG, Grid3, TeraGrid, and DAS • long traces (6+ months), active environments (500+K jobs per trace, 100s of users), >4 million jobs • Analysis • System-wide, VO, group, user characteristics • Environment, user evolution • System performance • Selected findings • Almost no parallel jobs • Top 2-5 groups/users dominate the workloads • Performance problems: high job wait time, high failure rates A. Iosup, C. Dumitrescu, D.H.J. Epema, H. Li, L. Wolters, How are Real Grids Used? The Analysis of Four Grid Traces and Its Implications, Grid 2006.

  19. 3.1. Grid Workloads [2/7]Analysis Summary: Grids vs. Parallel Production Systems LCG cluster daily peak: 22.5k jobs • Similar CPUTime/Year, 5x larger arrival bursts Grids Parallel Production Environments (Large clusters, supercomputers) A. Iosup, D.H.J. Epema, C. Franke, A. Papaspyrou, L. Schley, B. Song, R. Yahyapour, On Grid Performance Evaluation using Synthetic Workloads, JSSPP’06.

  20. 3.1. Grid Workloads [3/7]More Analysis: Special Workload Components Workflows (WFs) Bags-of-Tasks (BoTs) Time [units] BoT = set of jobs… WF = set of jobs with precedence(think Direct Acyclic Graph) …that start at most Δs after the first job Parameter Sweep App. = BoT with same binary

  21. 3.1. Grid Workloads [4/7]BoTs are predominant in grids • Selected Findings • Batches predominant in grid workloads; up to 96% CPUTime • Average batch size (Δ≤120s) is 15-30 (500 max) • 75% of the batches are sized 20 jobs or less A. Iosup, M. Jan, O. Sonmez, and D.H.J. Epema, The Characteristics and Performance of Groups of Jobs in Grids, Euro-Par, LNCS, vol.4641, pp. 382-393, 2007.

  22. 3.1. Grid Workloads [5/7]Workflows exist, but they seem small • Traces • Selected Findings • Loose coupling • Graph with 3-4 levels • Average WF size is 30/44 jobs • 75%+ WFs are sized 40 jobs or less, 95% are sized 200 jobs or less S. Ostermann, A. Iosup, R. Prodan, D.H.J. Epema, and T. Fahringer. On the Characteristics of Grid Workflows, CoreGRID Integrated Research in Grid Computing (CGIW), 2008.

  23. 3.1. Grid Workloads [6/7]Modeling Grid Workloads: Feitelson adapted • Adapted to grids: percentage parallel jobs, other values. • Validated with 4 grid and 7 parallel production env. traces A. Iosup, D.H.J. Epema, T. Tannenbaum, M. Farrellee, and M. Livny. Inter-Operating Grids Through Delegated MatchMaking, ACM/IEEE Conference on High Performance Networking and Computing (SC), pp. 13-21, 2007.

  24. 3.1. Grid Workloads [7/7]Modeling Grid Workloads: adding users, BoTs • Single arrival process for both BoTs and parallel jobs • Reduce over-fitting and complexity of “Feitelson adapted” by removing the RunTime-Parallelism correlated model • Validated with 7 grid workloads A. Iosup, O. Sonmez, S. Anoep, and D.H.J. Epema. The Performance of Bags-of-Tasks in Large-Scale Distributed Systems, HPDC, pp. 97-108, 2008.

  25. 3.2. Grid Infrastructure [1/5]Existing resource models and data • Compute Resources • Commodity clusters[Kee et al., SC’04] • Desktop grids resource availability[Kondo et al., FCFS’07] • Network Resources • Structural generators: GT-ITM [Zegura et al., 1997] • Degree-based generators: BRITE [Medina et al., 2001] • Storage Resources, other resources • ? Static! Resource dynamic, evolution, … NOT considered Source: H. Casanova

  26. Grid-level availability: 70% 3.2. Grid Infrastructure [2/5]Resource dynamics in cluster-based grids • Environment: Grid’5000 traces • jobs 05/2004-11/2006 (30 mo., 950K jobs) • resource availability traces 05/2005-11/2006 (18 mo., 600K events) • Resource availability model for multi-cluster grids A. Iosup, M. Jan, O. Sonmez, and D.H.J. Epema, On the Dynamic Resource Availability in Grids, Grid 2007, Sep 2007.

  27. 3.2. Grid Infrastructure [3/5] Correlated Failures • Correlated failure Maximal set of failures (ordered according to increasing event time), of time parameter in which for any two successive failures E and F, where returns the timestamp of the event; = 1-3600s. • Grid-level view • Range: 1-339 • Average: 11 • Cluster span • Range: 1-3 • Average: 1.06 • Failures “stay” within cluster Average CDF Grid-level view Size of correlated failures A. Iosup, M. Jan, O. Sonmez, and D.H.J. Epema, On the Dynamic Resource Availability in Grids, Grid 2007, Sep 2007.

  28. 3.2. Grid Infrastructure [4/5] Dynamics Model MTBF MTTR Correl. • Assume no correlation of failure occurrence between clusters • Which site/cluster? • fs, fraction of failures at cluster s • Weibull distribution for IAT • Shape parameter > 1: increasing hazard ratethe longer a node is online, the higher the chances that it will fail A. Iosup, M. Jan, O. Sonmez, and D.H.J. Epema, On the Dynamic Resource Availability in Grids, Grid 2007, Sep 2007.

  29. 3.2. Grid Infrastructure [5/5] Evolution Model A. Iosup, O. Sonmez, and D. Epema, DGSim: Comparing Grid Resource Management Architectures through Trace-Based Simulation, Euro-Par 2008.

  30. Q1,Q2: What are the characteristics of grids (e.g., infrastructure, workload)? • Grid workloads very different from those of other systems, e.g., parallel production envs. (large clusters, supercomputers) • Batches of jobs are predominant [Euro-Par’07,HPDC’08] • Almost no parallel jobs [Grid’06] • Workload model [SC’07, HPDC’08] • Clouds? (upcoming) • Grid resources are not static • Resource dynamics model [Grid’07] • Resource evolution model [EuroPar’08] • Clouds? [CCGrid’11] • Archives: easy to share traces and associated research http://gwa.ewi.tudelft.nl

  31. Outline • Introduction and Motivation • Q1: Exchange Data • The Grid Workloads Archive • The Failure Trace Archive • The Cloud Workloads Archive (?) • Q2: System Characteristics • Grid Workloads • Grid Infrastructure • Q3: System Testing and Evaluation

  32. 4.1. GrenchMark:Testing in LSDCSsAnalyzing, Testing, and Comparing Systems • Use cases for automatically analyzing, testing, and comparing systems (or middleware) • Functionality testing and system tuning • Performance testing/analysis of applications • Reliability testing of middleware • … • For grids and clouds, this problem is difficult! • Testing in real environments is difficult/costly/both • Grids/clouds change rapidly • Validity and reproducibility of tests • …

  33. 4.1. GrenchMark:Testing LSDCSs Architecture Overview GrenchMark = Grid Benchmark

  34. 4.1. GrenchMark:Testing LSDCSsTesting a Large-Scale Environment (1/2) • Testing a 1500-processors Condor environment • Workloads of 1000 jobs, grouped by 2, 10, 20, 50, 100, 200 • Test finishes 1h after the last submission • Results • >150,000 jobs submitted • >100,000 jobs successfully run, >2 yr CPU time in 1 week • 5% jobs failed (much less than other grids’ average) • 25% jobs did not start in time and where cancelled

  35. 4.1. GrenchMark:Testing LSDCSsTesting a Large-Scale Environment (2/2) • Performance metrics system-, job-, operational-, application-, and service-level

  36. 4.1. GrenchMark:Testing in LSDCSsServMark: Scalable GrenchMark DiPerF • Blending DiPerF and GrenchMark. • Tackles two orthogonal issues: • Multi-sourced testing(multi-user scenarios, scalability) • Generate and run dynamic test workloads with complex structure(real-world scenarios, flexibility) • Adds • Coordination and automation layers • Fault tolerance module GrenchMark ServMark

  37. Performance Evaluation of Clouds [1/3]C-Meter: Cloud-Oriented GrenchMark Yigitbasi et al.: C-Meter: A Framework for Performance Analysis of Computing Clouds. Proc. of CCGRID 2009

  38. Performance Evaluation of Clouds [2/3]Low Performance for Sci.Comp. • Evaluated the performance of resources from four production, commercial clouds. • GrenchMark for evaluating the performance of cloud resources • C-Meter for complex workloads • Four production, commercial IaaS clouds: Amazon Elastic Compute Cloud (EC2), Mosso, Elastic Hosts, and GoGrid. • Finding: cloud performance low for sci.comp. S. Ostermann et al., A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing, Cloudcomp 2009, LNICST 34, pp.115–131, 2010. A. Iosup et al.,Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing, IEEE TPDS, vol.22(6), 2011.

  39. Performance Evaluation of Clouds [3/3]Cloud Performance Variability • Long-term performance variability of production cloud services • IaaS: Amazon Web Services • PaaS: Google App Engine • Year-long performance information for nine services • Finding: about half of the cloud services investigated in this work exhibits yearly and daily patterns; impact of performance variability depends on application. Amazon S3: GET US HI operations A. Iosup, N. Yigitbasi, and D. Epema, On the Performance Variability of Production Cloud Services, CCGrid 2011.

  40. 4.2. DGSim:Simulating Multi-Cluster GridsGoal and Challenges • Simulate various grid resource management architectures • Multi-cluster grids • Grids of grids (THE grid) • Challenges • Many types of architectures • Generating and replaying grid workloads • Management of simulations • Many repetitions of a simulation for statistical relevance • Simulations with many parameters • Managing results (e.g., analysis tools) • Enabling collaborative experiments Two GRM architectures DGSim

  41. 4.2. DGSim:Simulating Multi-Cluster GridsOverview Discrete-EventSimulator DGSim

  42. 4.2. DGSim:Simulating Multi-Cluster GridsSimulated Architectures (Sep 2007) Hybrid hierarchical/ decentralized Independent Centralized Hierarchical Decentralized A. Iosup, D.H.J.Epema, T. Tannenbaum, M. Farrellee, M. Livny, Inter-Operating Grids through Delegated MatchMaking, SC, 2007. DGSim

  43. Q3: How to test and evaluate grids/clouds? • GrenchMark+C-Meter: testing large-scale distrib. sys. • Framework • Testing in real environments performance, reliability, functionality • Uniform process: metrics, workloads • Real tool available • DGSim: simulating multi-cluster grids • Many types of architectures • Generating and replaying grid workloads • Management of the simulations grenchmark.st.ewi.tudelft.nl dev.globus.org/wiki/Incubator/ServMark

  44. Take Home Message: Research Toolbox • Understanding how real systems work • Modeling workloads and infrastructure • Compare grids and clouds with other platforms (parallel production env.,…) • The Archives: easy to share system traces and associated research • Grid Workloads Archive • Failure Trace Archive • Cloud Workloads Archive (upcoming) • Testing/Evaluating Grids/Clouds • GrenchMark • ServMark: Scalable GrenchMark • C-Meter: Cloud-oriented GrenchMark • DGSim: Simulating Grids (and Clouds?) Publications2006: Grid, CCGrid, JSSPP 2007: SC, Grid, CCGrid, …2008: HPDC, SC,Grid, …2009: HPDC, CCGrid, …2010: HPDC, CCGrid (Best Paper Award), EuroPar, …2011: IEEE TPDS, IEEE Internet Computing, CCGrid, …

  45. Thank you for your attention! Questions? Suggestions? Observations? More Info: Alexandru IosupA.Iosup@tudelft.nlhttp://www.pds.ewi.tudelft.nl/~iosup/ (or google “iosup”)Parallel and Distributed Systems GroupDelft University of Technology • http://www.st.ewi.tudelft.nl/~iosup/research.html • http://www.st.ewi.tudelft.nl/~iosup/research_gaming.html • http://www.st.ewi.tudelft.nl/~iosup/research_cloud.html Do not hesitate to contact me…

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