Grid Computing

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Grid Computing
Grid Computing

02/05/2008

• Grid Systems and scheduling

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Grid systems

• Many!!!
• Classification (depends on the author)
• Computational grid
• distributed supercomputing (parallel application
  execution on multiple machines)
• high throughput (stream of jobs)
• Data grid provides the way to solve large scale
  data management problems
• Service grid systems that provide services that
  are not provided by any single local machine.
• on demand aggregate resources to enable new
  services
• Collaborative connect users and applications via
  a virtual workspace
• Multimedia infrastructure for real-time
  multimedia applications

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Taxonomy of Applications

• Distributed supercomputing consume CPU cycles
  and memory
• High-Throughput Computing unused processor
  cycles
• On-Demand Computing meet short-term requirements
  for resources that cannot be cost-effectively or
  conveniently located locally.
• Data-Intensive Computing
• Collaborative Computing enabling and enhancing
  human-to-human interactions (eg CAVE5D system
  supports remote, collaborative exploration of
  large geophysical data sets and the models that
  generated them)

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Alternative classification

• independent tasks
• loosely-coupled tasks
• tightly-coupled tasks

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Application Management

• Description
• Partitioning
• Mapping
• Allocation

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Description

• Use a grid application description language
• Grid-ADL and GEL
• One can take advantage of loop construct to use
  compilation mechanisms for vectorization

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Grid-ADL
Traditional systems
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alternative systems
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..
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Partitioning/Clustering

• Application represented as a graph
• Nodes job
• Edges precedence
• Graph partitioning techniques
• Minimize communication
• Increase throughput or speedup
• Need good heuristics
• Clustering

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Graph Partitioning

• Optimally allocating the components of a
  distributed program over several machines
• Communication between machines is assumed to be
  the major factor in application performance
• NP-hard for case of 3 or more terminals

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Collapse the graph

• Given G N, E, M
• N is the set of Nodes
• E is the set of Edges
• M is the set of machine nodes

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Dominant Edge

• Take node n and its heaviest edge e
• Edges e1,e2,er with opposite end nodes not in M
• Edges e1,e2,ek with opposite end nodes in M
• If w(e) Sum(w(ei)) max(w(e1),,w(ek))
• Then the min-cut does not contain e
• So e can be collapsed

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Machine Cut

• Let machine cut Mi be the set of all edges
  between a machine mi and non-machine nodes N
• Let Wi be the sum of the weights of all edges in
  the machine cut Mi
• Wis are sorted so
• W1 W2
• Any edge that has a weight greater than W2 cannot
  be part of the min-cut

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Zeroing

• Assume that node n has edges to each of the m
  machines in M with weights
• w1 w2 wm
• Reducing the weights of each of the m edges from
  n to machines M by w1 doesnt change the
  assignment of nodes for the min-cut
• It reduces the cost of the minimum cut by (m-1)w1

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Order of Application

• If the previous 3 techniques are repeatedly
  applied on a graph until none of them are
  applicable
• Then the resulting reduced graph is independent
  of the order of application of the techniques

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Output

• List of nodes collapsed into each of the machine
  nodes
• Weight of edges connecting the machine nodes
• Source Graph Cutting Algorithms for Distributed
  Applications Partitioning, Karin Hogstedt, Doug
  Kimelman, VT Rajan, Tova Roth, and Mark Wegman,
  2001
• homepages.cae.wisc.edu/ece556/fall2002/PROJECT/di
  stributed_applications.ppt

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Graph partitioning

• Hendrickson and Kolda, 2000 edge cuts
• are not proportional to the total communication
  volume
• try to (approximately) minimize the total volume
  but not the total number of messages
• do not minimize the maximum volume and/or number
  of messages handled by any single processor
• do not consider distance between processors
  (number of switches the message passes through,
  for example)
• undirected graph model can only express symmetric
  data dependencies.

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Graph partitioning

• To avoid message contention and improve the
  overall throughput of the message traffic, it is
  preferable to have communication restricted to
  processors which are near each other
• But, edge-cut is appropriate to applications
  whose graph has locality and few neighbors

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Kwok and Ahmad, 1999 multiprocessor scheduling
taxonomy
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List Scheduling

• make an ordered list of processes by assigning
  them some priorities
• repeatedly execute the following two steps until
  a valid schedule is obtained
• Select from the list, the process with the
  highest priority for scheduling.
• Select a resource to accommodate this process.
• priorities are determined statically before the
  scheduling process begins. The first step chooses
  the process with the highest priority, the second
  step selects the best possible resource.
• Some known list scheduling strategies
• Highest Level First algorithm or HLF
• Longest Path algorithm or LP
• Longest Processing Time
• Critical Path Method
• List scheduling algorithms only produce good
  results for coarse-grained applications

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Static scheduling task precedence graphDSC
Dominance Sequence Clustering

• Yang and Gerasoulis, 1994 two step method for
  scheduling with communication(focus on the
  critical path)
• schedule an unbounded number of completely
  connected processors (cluster of tasks)
• if the number of clusters is larger than the
  number of available processors, then merge the
  clusters until it gets the number of real
  processors, considering the network topology
  (merging step).

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Graph partitioning

• Kumar and Biswas, 2002 MiniMax
• multilevel graph partitioning scheme
• Grid-aware
• consider two weighted undirected graphs
• a work-load graph (to model the problem domain)
• a system graph (to model the heterogeneous system)

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Resource Management
(1988)
Source P. K. V. Mangan, Ph.D. Thesis, 2006
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Resource Management

• The scheduling algorithm has four components
• transfer policy when a node can take part of a
  task transfer
• selection policy which task must be transferred
• location policy which node to transfer to
• information policy when to collect system state
  information.

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Resource Management

• Location policy
• Sender-initiated
• Receiver-initiated
• Symetrically-initiated

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Scheduling mechanisms for grid

• Berman, 1998 (ext. by Kayser, 2006)
• Job scheduler
• Resource scheduler
• Application scheduler
• Meta-scheduler

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Scheduling mechanisms for grid

• Legion
• University of Virginia (Grimshaw, 1993)
• Supercomputing 1997
• Currently Avaki commercial product

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Legion

• is an object oriented infrastructure for grid
  environments layered on top of existing software
  services.
• uses the existing operating systems, resource
  management tools, and security mechanisms at host
  sites to implement higher level system-wide
  services
• design is based on a set of core objects

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Legion

• resource management is a negotiation between
  resources and active objects that represent the
  distributed application
• three steps to allocate resources for a task
• Decision considers tasks characteristics and
  requirements, resources properties and policies,
  and users preferences
• Enactment the class object receives an
  activation request if the placement is
  acceptable, start the task
• Monitoring ensures that the task is operating
  correctly

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Globus

• Toolkit with a set of components that implement
  basic services
• Security
• resource location
• resource management
• data management
• resource reservation
• Communication
• From version 1.0 in 1998 to the 2.0 release in
  2002 and the latest 3.0, the emphasis is to
  provide a set of components that can be used
  either independently or together to develop
  applications
• The Globus Toolkit version 2 (GT2) design is
  highly related to the architecture proposed by
  Foster et al.
• The Globus Toolkit version 3 (GT3) design is
  based on grid services, which are quite similar
  to web services. GT3 implements the Open Grid
  Service Infrastructure (OGSI).
• The current version, GT4, is also based on grid
  services, but with some changes in the standard

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Globus scheduling

• GRAM Globus Resource Allocation Manager
• Each GRAM responsible for a set of resources
  operating under the same site-specific allocation
  policy, often implemented by a local resource
  management
• GRAM provides an abstraction for remote process
  queuing and execution with several powerful
  features such as strong security and file
  transfer
• It does not provide scheduling or resource
  brokering capabilities but it can be used to
  start programs on remote resources, despite local
  heterogeneity due to the standard API and
  protocol.
• Resource Specification Language (RSL) is used to
  communicate requirements.
• To take advantage of GRAM, a user still needs a
  system that can remember what jobs have been
  submitted, where they are, and what they are
  doing.
• To track large numbers of jobs, the user needs
  queuing, prioritization, logging, and accounting.
  These services cannot be found in GRAM alone, but
  are provided by systems such as Condor-G

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MyGrid and OurGrid

• Mainly for bag-of-tasks (BoT) applications
• uses the dynamic algorithm Work Queue with
  Replication (WQR)
• hosts that finished their tasks are assigned to
  execute replicas of tasks that are still running.
  
• Tasks are replicated until a predefined maximum
  number of replicas is achieved (in MyGrid, the
  default is one).

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OurGrid

• An extension of MyGrid
• resource sharing system based on peer-to-peer
  technology
• resources are shared according to a network of
  favors model, in which each peer prioritizes
  those who have credit in their past history of
  interactions.

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GrADS

• is an application scheduler
• The user invokes the Grid Routine component to
  execute an application
• The Grid Routine invokes the component Resource
  Selector
• The Resource Selector accesses the Globus
  MetaDirectory Service (MDS) to get a list of
  machines that are alive and then contact the
  Network Weather Service (NWS) to get system
  information for the machines.
• The Grid Routine then invokes a component called
  Performance Modeler with the problem parameters,
  machines and machine information.
• The Performance Modeler builds the final list of
  machines and sends it to the Contract Developer
  for approval.
• The Grid Routine then passes the problem, its
  parameters, and the final list of machines to the
  Application Launcher.
• The Application Launcher spawns the job using the
  Globus management mechanism (GRAM) and also
  spawns the Contract Monitor.
• The Contract Monitor monitors the application,
  displays the actual and predicted times, and can
  report contract violations to a re-scheduler.
• Although the execution model is efficient from
  the application perspective, it does not take
  into account the existence of other applications
  in the system.

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GrADS

• Vadhiyar and Dongarra, 2002 proposed a
  metascheduling architecture in the context of the
  GrADS Project.
• The metascheduler receives candidate schedules of
  different application level schedulers and
  implements scheduling policies for balancing the
  interests of different applications.

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EasyGrid

• Mainly concerned with MPI applications
• Allows intercluster execution of MPI processes

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Nimrod

• uses a simple declarative parametric modeling
  language to express parametric experiments
• provides machinery that automates
• task of formulating,
• running,
• monitoring,
• collating results from the multiple individual
  experiments.
• incorporates distributed scheduling that can
  manage the scheduling of individual experiments
  to idle computers in a local area network
• has been applied to a range of application areas,
  e.g. Bioinformatics, Operations Research,
  Network Simulation, Electronic CAD, Ecological
  Modelling and Business Process Simulation.

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Nimrod/G
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AppLeS

• UCSD (Berman and Casanova)
• Application parameter Sweep Template
• Use scheduling based on min-min, min-max,
  sufferage, but with heuristics to estimate
  performance of resources and tasks
• Performance information dependent algorithms
  (pida)
• Main goal to minimize file transfers

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GRAnD Kayser et al., CCPE, 2007

• Distributed submission control
• Data locality
• automatic staging of data
• optimization of file transfer

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Distributed submission
Results of simulation with Monarc
http//monarc.web.cern.ch/MONARC/ Kayser, 2006
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GRAnD

• Experiments with Globus
• Discussion list discuss_at_globus.org (05/02/2004)
• Submission takes 2s per task
• Place 200 tasks in the queue 6min
• Maximum number of tasks few hundreds
• experiments in CERN (D. Foster et al. 2003)
• 16s to submit a task
• Saturation in the server 3.8 tasks/minute

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GRAnD

• Grid Robust Application Deployment

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GRAnD
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GRAnD data management
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GRAnD data management
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Comparison (Kayser, 2006)
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Comparison (Kayser, 2006)
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Condor performance
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Condor performance
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Condor x AppMan
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Condor performance

• exps on a cluster of 8 nodes (Sanches et al.
  2005)

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ReGS Condor performance
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ReGS Condor performance
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Toward Grid Operating Systems

• Vega GOS
• G SMA

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Vega GOS (the CNGrid OS)

• GOS overview
• A user-level middleware running on a client
  machine
• GOS has 2 components GOS and gnetd
• - GOS is a daemon running on the client
  machine
• - gnetd is a daemon on the grid server

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GOS

• Grid process and Grid thread
• Grid process is a unit for managing the whole
  resource of the Grid.
• Grid thread is a unit for executing computation
  on the Grid.
• GOS API
• GOS API for application developers
• grid() constructs a Grid process on the client
  machine.
• gridcon() grid process connects to the Grid
  system.
• gridclose() close a connected grid.
• gnetd API for service developer on Grid servers
• grid_register() register a service to Grid.
• grid_unregister() unregister a service.

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Grid

• Not yet mentioned
• Simulation SimGrid and GridSim
• Monitoring RTM, MonaLisa, ...
• Portals GridIce, Genius, ...