October 16, 2015 1 Grid Computing: From Old Traces to New Applications Fribourg, Switzerland...

April 20, 20231

Grid Computing:From Old Traces to New Applications

Fribourg, Switzerland

Alexandru Iosup, Ozan Sonmez, Nezih Yigitbasi, Hashim Mohamed, Catalin Dumitrescu, Mathieu Jan, Dick Epema

Parallel and Distributed Systems Group, TU Delft

Many thanks to our collaborators: U Wisc./Madison, U Chicago, U Dortmund, U Innsbruck, LRI/INRIA Paris, INRIA Grenoble, U Leiden, Politehnica University of Bucharest, Technion, …

DGSimThe FailureTraceArchive

Alexandru Iosuphttp://pds.twi.tudelft.nl/~iosup/

• Systems• The Koala grid scheduler• The Tribler BitTorrent-compatible P2P file-sharing• The POGGI and CAMEO gaming platforms

• Performance• The Grid Workloads Archive (Nov 2006)• The Failure Trace Archive (Nov 2009)• The Peer-to-Peer Trace Archive (Apr 2010)• Tools: DGSim trace-based grid simulator, GrenchMark

workload-based grid benchmarking

• Team of 15+ active collaborators in NL, AT, RO, US• Happy to be in Berkeley until September

April 20, 20232

The Grid

An ubiquitous, always-on computational and data storage platform on which users can seamlessly run their (large-scale) applications

April 20, 20233

Shared capacity & costs, economies of scale

April 20, 20234

The Dutch Grid: DAS System and Extensions

VU (85 nodes)

TU Delft (68) Leiden (32)

SURFnet6

10 Gb/s lambdas

UvA/MultimediaN (46)

UvA/VL-e (41)

• 272 AMD Opteron nodes 792 cores, 1TB memory• Heterogeneous: 2.2-2.6 GHz single/dual core nodes• Myrinet-10G (excl. Delft)• Gigabit Ethernet

DAS-4 (upcoming)• Multi-cores: general purpose, GPU, Cell, …

DAS-3: a 5-cluster grid

Clouds• Amazon EC2+S3, Mosso, …

April 20, 20235

Many Grids Built

DAS, Grid’5000, OSG, NGS, CERN, …

Why grids and not The Grid?Why grids and not The Grid?

April 20, 20236

Agenda

1. Introduction2. Was it the System?3. Was it the Workload?4. Was it the System Designer?5. New Application Types6. Suggestions for Collaboration7. Conclusion

April 20, 20237

The Failure Trace ArchiveFailure and Recovery Events

20+ traces online

http://fta.inria.frhttp://fta.inria.fr

D. Kondo, B. Javadi, A. Iosup, D. Epema, The Failure Trace Archive: Enabling Comparative Analysis of Failures in Diverse Distributed Systems, CCGrid 2010 (Best Paper Award)

Was it the System?

• No• System can grow fast• Good data and models to support system

designers• Yes• Grid middleware unscalable

[CCGrid06,Grid09,HPDC09]• Grid middleware failure-prone [CCGrid07,Grid07]• Grid resources unavailable [CCGrid10]• Inability to load balance well [SC|07]• Poor online information about resource availability

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Agenda


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The Grid Workloads ArchivePer-Job Arrival, Start, Stop, Structure, etc.

6 traces online

http://gwa.ewi.tudelft.nlhttp://gwa.ewi.tudelft.nl

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.

April 20, 202311

Grid Systems

How Are Real Grids Used?

Data Analysis and Modeling• Grids vs. parallel production environments such

as clusters and (small) supercomputers• Bags of single-processor tasks vs. single parallel jobs• Bigger bursts of job arrivals• More jobs

Parallel production environments

April 20, 202312

Bags-of-Tasks (BoTs)

Grid Workloads

Analysis: Grid Workload Components

Time [units]

Workflows (WFs)

• BoT size = 2-70 tasks, most 5-20• Task runtime highly variable,

from minutes to tens of hours

• WF size = 2-1k tasks, most 30-40

• Task runtime of minutes

Was it the Workload?

• No• Similar workload characteristics across grids• High utilization possible due to single-node jobs• High load imbalance• Good data and models to support system designers

[Grid06,EuroPar08,HPDC08-10,FGCS08]• Yes• Too many tasks (system limitation)• Poor online information about job characteristics +

High variability of job resource requirements • How to schedule BoTs, WFs, mixtures in grids?

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Agenda


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Problems in Grid Scheduling and Resource Management

The System1. Grid schedulers do not own resources

themselves• They have to negotiate with autonomous local schedulers

• Authentication/multi-organizational issues

2. Grid schedulers interface to local schedulers• Some may have support for reservations, others are queuing-based

3. Grid resources are heterogeneous and dynamic• Hardware (processor architecture, disk space, network)

• Basic software (OS, libraries)

• Grid software (middleware)

• Resources may fail

• Lack of complete and accurate resource information

April 20, 202316


The Workloads

4. Workloads are heterogeneous and dynamic• Grid schedulers may not have control over the full

workload (multiple submission points)

• Jobs may have performance requirements

• Lack of complete and accurate job information

5. Application structure is heterogeneous• Single sequential job

• Bags of Tasks; parameter sweeps (Monte Carlo), pilot jobs

• Workflows, pipelines, chains-of-tasks

• Parallel jobs (MPI); malleable, coallocated

April 20, 202317

The Koala Grid Scheduler

• Developed in the DAS system• Has been deployed on the DAS-2 in September 2005• Ported to DAS-3 in April 2007• Independent from grid middlewares such as Globus• Runs on top of local schedulers

• Objectives:• Data and processor co-allocation in grids• Supporting different application types• Specialized application-oriented scheduling policies

Koala homepage: http://www.st.ewi.tudelft.nl/koala/

April 20, 202318

Koala in a Nutshell

• Parallel Applications• MPI, Ibis,…• Co-Allocation• Malleability

• Parameter Sweep Applications• Cycle Scavenging• Run as low-priority jobs

• Workflows

A bridge between theory and practice

Euro-Par 2008, Las Palmas, 27 August 200819

Inter-Operating Grids Through Delegated MatchMaking

Inter-Operation Architectures

Hybrid hierarchical/ decentralize

d

Decentralized

Hierarchical

Independent

Centralized

Delegated MatchMakin

g

April 20, 202320

Inter-Operating Grids Through Delegated MatchMaking

The Delegated MatchMaking Mechanism

1. Deal with local load locally (if possible)2. When local load is too high, temporarily bind resources from

remote sites to the local environment. • May build delegation chains. • Delegate resource usage rights, do not migrate jobs.

3. Deal with delegations each delegation cycle (delegated matchmaking)

Delegate

Local load too high

Resource request

Resource usage rights

Bind remote resource

The Delegated MatchMaking Mechanism=The Delegated MatchMaking Mechanism=Delegate Resource Usage Rights, Delegate Resource Usage Rights,

Do Not Delegate JobsDo Not Delegate Jobs

April 20, 202321

• DMM• High goodput• Low wait time• Finishes all jobs

• Even better for load imbalance between grids

• Reasonable overhead• [see thesis]

What is the Potential Gain of Grid Inter-Operation?

Delegated MatchMaking vs. Alternatives

Independent

Centralized

Decentralized

DMM

(Higher is better)

Grid Inter-Operation (through DMM)Grid Inter-Operation (through DMM)delivers good performancedelivers good performance

April 20, 202322

4.2. Studies on Grid Scheduling [5/5]

Scheduling under Cycle Stealing

Scheduler

CS-Runner

Node

submits PSA(s)

JDF

grow/shrink

messagesregisters

Clusters

Launcher

Launcher

Head Node

KCMKCM

submitslaunchers

deploys, monitors,

and preempts

tasks

monitors/informs

idle/demanded resources

CS Policies:• Equi-All: grid-wide basis • Equi-PerSite: per cluster

CS Policies:• Equi-All: grid-wide basis • Equi-PerSite: per cluster

Application Level Scheduling:• Pull-based approach• Shrinkage policy

Application Level Scheduling:• Pull-based approach• Shrinkage policy

Launcher

Launcher

O. Sonmez, B. Grundeken, H. Mohamed, A. Iosup, D. Epema: Scheduling Strategies for Cycle Scavenging in Multicluster Grid Systems. CCGRID 2009: 12-19

Requirements1.Unobtrusiveness Minimal delay for (higher priority) local and grid jobs

2.Fairness3. Dynamic Resource Allocation4. Efficiency5. Robustness and Fault

Tolerance

Deployed as Koala Runner

Was it the System Designer?

• No• Mechanisms to inter-operate grids: DMM [SC|07], …• Mechanisms to run many grid application types:

WFs, BoTs, parameter sweeps, cycle scavenging, …• Scheduling algorithms with inaccurate information

[HPDC ‘08, ‘09, ‘10]• Tools for empirical and trace-based experimentation

• Yes• Still too many tasks• What about new application types?

April 20, 202323

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Agenda


Cloud Futures Workshop 2010 – Cloud Computing Support for Massively Social Gaming 25

MSGs are a Popular, Growing Market

• 25,000,000 subscribed players (from 150,000,000+ active)

• Over 10,000 MSGs in operation

• Market size 7,500,000,000$/year

Sources: MMOGChart, own research. Sources: ESA, MPAA, RIAA.


Massively Social Gaming as New Grid/Cloud Application

1. Virtual worldExplore, do, learn, socialize, compete+

2. ContentGraphics, maps, puzzles, quests, culture+

3. Game analyticsPlayer stats and relationshipsRomeo and

Juliet

Massively Social Gaming(online) games with massive numbers of players (100K+), for which social interaction helps the gaming experience

[SC|08, TPDS’10]

[EuroPar09

BPAward, CPE10]

[ROIA09]

April 20, 202327

Suggestions for CollaborationSuggestions for Collaboration

• Scheduling mixtures of grid/HPC/cloud workloads• Scheduling and resource management in practice• Modeling aspects of cloud infrastructure and workloads

• Condor on top of Mesos

• Massively Social Gaming and Mesos• Step 1: Game analytics and social network analysis in Mesos

• The Grid Research Toolbox• Using and sharing traces: The Grid Workloads Archive and

The Failure Trace Archive• GrenchMark: testing large-scale distributed systems• DGSim: simulating multi-cluster grids

April 20, 202328

Alex Iosup, Ozan Sonmez, Nezih Yigitbasi, Hashim Mohamed, Dick Epema

Thank you! Questions? Observations?

More Information:• The Koala Grid Scheduler: www.st.ewi.tudelft.nl/koala

• The Grid Workloads Archive: gwa.ewi.tudelft.nl

• The Failure Trace Archive: fta.inria.fr

• The DGSim simulator: www.pds.ewi.tudelft.nl/~iosup/dgsim.php

• The GrenchMark perf. eval. tool: grenchmark.st.ewi.tudelft.nl

• Cloud research: www.st.ewi.tudelft.nl/~iosup/research_cloud.html

• Gaming research: www.st.ewi.tudelft.nl/~iosup/research_gaming.html

• see PDS publication database at: www.pds.twi.tudelft.nl/

email: [email protected]

Many thanks to our collaborators: U. Wisc.-Madison, U Chicago, U Dortmund, U Innsbruck, LRI/INRIA Paris, INRIA Grenoble, U Leiden, Politehnica University of Bucharest, Technion, …

DGSim

Additional Slides

April 20, 202329

April 20, 202330

The 1M-CPU Machine with Shared Resource Ownership

• The 1M-CPU machine• eScience (high-energy physics, earth sciences,

financial services, bioinformatics, etc.)

• Shared resource ownership• Shared resource acquisition• Shared maintenance and operation• Summed capacity higher (more efficiently used) than

sum of individual capacities

April 20, 202331

How to Build the 1M-CPU Machine with Shared Resource Ownership?• Clusters of resources are ever more

present• Top500 SuperComputers: cluster systems from 0% to

75% share in 10 years (also from 0% to 50% performance)

• CERN WLCG: from 100 to 300 clusters in 2½ years

Source: http://www.top500.org/overtime/list/29/archtyp

e/

Source: http://goc.grid.sinica.edu.tw/gstat//table.html

April 20, 202332

How to Build the 1M-CPU Machine with Shared Resource Ownership?Cluster size distribution over time, Top500, 1997-2007

1

10

100

1000

10000

100000

Nov-97

May-98

Nov-98

May-99

Nov-99

May-00

Nov-00

May-01

Nov-01

May-02

Nov-02

May-03

Nov-03

May-04

Nov-04

May-05

Nov-05

May-06

Nov-06

May-07

Date

Clu

ster

siz

e

Median Average Q1 Q3 Max

Median: 10x

Averge: 20x

Max:100x

Last 10 years

Data source: http://www.top500.org

Last 4 years

Now:0.5x/yr

To build the 1M-CPU cluster:To build the 1M-CPU cluster:- At last 10 years rate, another 10 years- At last 10 years rate, another 10 years

- At current rate, another 200 years- At current rate, another 200 years

April 20, 202333

How to Build the 1M-CPU Machine with Shared Resource Ownership?• Cluster-based Computing Grids

• CERN’s WLCG cluster size over time

Median: +5 procs/yr

Avg: +15 procs/yr

Max: 2x/yrShared clusters grow on average Shared clusters grow on average

slower than Top500 cluster systems!slower than Top500 cluster systems!

Data source: http://goc.grid.sinica.edu.tw/gstat/

Year 1 Year 2

April 20, 202334

How to Build the 1M-CPU Machine with Shared Resource Ownership?

• Physics• Dissipate heat from large clusters

• Market• Pay industrial power consumer rate, pay special system

building rate• Collaboration

• Who pays for the largest cluster?

• We don’t know how to exploit multi-cores yet• Executing large batches of independent jobs

Why doesn’t CERN WLCG use larger clusters?Why doesn’t CERN WLCG use larger clusters?

Why doesn’t CERN WLCG opt for multi-cores?Why doesn’t CERN WLCG opt for multi-cores?

April 20, 202335

• 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

4.1. Grid Workloads [2/5]

BoTs are predominant in grids

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.

Grid’5000 NorduGrid GLOW (Condor)

Submissions

26k 50k 13k

Jobs 808k (951k)

738k (781k) 205k (216k)

CPU time 193y (651y)

2192y (2443y)

53y (55y)

Euro-Par 2008, Las Palmas, 27 August 200836

System Availability CharacteristicsResource Evolution: Grids Grow by Cluster

April 20, 202337

System Availability CharacteristicsGrid Dynamics: Grids Shrink Temporarily

Grid-level view

Average availability:

69%

April 20, 202338

Resource Availability Model

• Assume no correlation of failure occurrence between clusters

• Which site/cluster? • fs, fraction of failures at cluster s

MTBF MTTR Correl.

• Weibull distribution for IAT• the longer a node is online, the higher the chances that it

will fail

April 20, 202339

Grid Workloads

Load Imbalance Across Sites and Grids

• Overall workload imbalance: normalized daily load (5:1)

• Temporary workload imbalance: hourly load (1000:1)

Overall imbalanc

e Temporary imbalance

April 20, 202340

• Adapted to grids: percentage parallel jobs, other values.• Validated with 4 grid and 7 parallel production env. traces

4.1. Grid Workloads [4/5]

Modeling Grid Workloads: Feitelson adapted

A. Iosup, T. Tannenbaum, M. Farrellee, D. Epema, M. Livny: Inter-operating grids through Delegated MatchMaking. SC|07 (Nominated for Best Paper Award)

April 20, 202341

• 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

Grid Workloads

Modeling Grid Workloads: adding users, BoTs

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.

April 20, 202342

How To Compare Existing and New Grid Systems?

The Delft Grid Simulator (DGSim)

DGSimDGSim……tudelft.nl/~iosup/dgsim.phptudelft.nl/~iosup/dgsim.php

Discrete event generator

Generate realistic workloads

Automate simulation process (10,000s of tasks)

April 20, 202343

How to Inter-Operate Grids?

Existing (, Working?) Alternatives

Independent Centralized

HierarchicalDecentralized

Condor

Globus GRAM Alien

Koala

OAR

CCS

Moab/Torque

OAR2

NWIRE

OurGrid

Condor Flocking

Load imbalance? Resource selection? Scale?

Root ownership?

Node failures?

Accounting?Trust? Scale?

April 20, 202344

3

3

3

333

2

Inter-Operating Grids Through Delegated MatchMaking [1/3]

The Delegated MatchMaking Architecture

1. Start from a hierarchical architecture2. Let roots exchange load3. Let siblings exchange load

Delegated MatchMaking Architecture=Delegated MatchMaking Architecture=Hybrid hierarchical/decentralized Hybrid hierarchical/decentralized

architecture for grid inter-operationarchitecture for grid inter-operation


Massively Social Gaming on Clouds

Current TechnologyCurrent Technology

The FutureThe Future• Happy players• Happy cloud operators

• Million-user, multi-bn market

• Content, World Sim, Analytics

MSGsMSGs

• Upfront payment• Cost and scalability

problems• Makes players unhappy

Our VisionOur Vision

• Scalability & Automation• Economy of scale with

cloudsOngoing WorkOngoing Work

• Content: POGGI Framework

• Platform: edutain@grid• Analytics: CAMEO

Framework

Publications Gaming and Clouds

2008: ACM SC, TR Perf

2009: ROIA, CCGrid, NetGames,

EuroPar (Best Paper Award),

CloudComp, TR variability

2010: IEEE TPDS, Elsevier CCPE

2011: Book Chapter CAMEO

Graduation Forecast

2010/2011: 1PhD, 2Msc, 4BSc

April 20, 202346

6. Clouds• Large-scale, loosely coupled infrastructure and/or

platform• Computation and storage has fixed costs (?)• Guaranteed good performance, e.g., no wait time (?)• Easy to port grid applications to clouds (?)

7. Multi-cores• Small- and mid-scale, tightly-coupled infrastructure• Computation and storage has lower cost than grid (?)• Good performance (?)• Easy to port grid applications to multi-cores (?)


New Hypes, New Focus for Designers

October 16, 2015 1 Grid Computing: From Old Traces to New Applications Fribourg, Switzerland...

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