Grid Scheduling

1
Grid Scheduling

• Cécile Germain-Renaud

2
Scheduling

• Job
• A computation to run on a machine
• Possibly with network access e.g. input/output
  file (coarse grain) or communication with other
  jobs (the DAG model)
• Schedule
• s(J) date to begin execution of task J
• Alloc(J) machine assigned to J
• One of the oldest Computer Science problems
• Principles of classification
• Graham et al. Optimization and approximation in
  deterministic sequencing and scheduling A
  survey. Ann. Discrete Math. 5, (1979), 287-326
• Computer-aided classification of complexity
  results (4536 at the time of the paper) Lageweg
  et al. Computer-Aided complexity classification
  of combinational problems. CACM 112, 1892

3
Classical scheduling in HPC
T

• Context parallel computing/computers
• Application Direct Acyclic Graph (T, E, w, c)
• T set of sequential tasks
• E dependence constraints
• w(t) computational cost of task t
• c(t,t) communication cost (data sent from t to
  t)
• Infrastructure
• P identical processors
• With or without preemption, dedicated (no
  sharing)
• An optimization problem with objective function
• Makespan Total execution time S(T) max (s(t)
  w(t))
• Complexity
• NP-complete for independant tasks and no
  communication E vide, p 2 and c 0
• NP-complete for UET-UCT graphs (w c 1)
• Very old without communication, list scheduling
  provides a (2-1/p) approximation

T
4
Scheduling in Institutional Grids

• Institutional federation of ressources
• accounted-for fair-share on the medium to long
  time scale is a premium constraint
• Partially autonomous local policies must be
  allowed
• Grid
• Permanent regime on-line decisions
• Large scale strongly distributed
• Information system
• Scheduling services
• Relevant contexts
• Autonomous, multi-agents systems
• Auction algorithms
• Service Level Agreement (SLA) technology

5
EGEE gLite Scheduling
Site (node)
Proc
Broker
CE
Broker
Local scheduler
Broker
6
EGEE gLite Scheduling
Site (node)
Proc
BDII
Broker
Publish
CE
Broker
Local scheduler
Broker
7
EGEE gLite Scheduling
Site (node)
Proc
BDII
Publish
CE
Query
Rank
Broker
Local scheduler
The information published is Static eg which
type of VO is accepted Dynamic expected
traversal time
8
EGEE gLite Scheduling
Site (node)
Proc
BDII
Publish
CE
Query
Rank
Broker
Local scheduler
Rank may be any user-defined function, e.g.
avoid  bad  machines Default is first locality,
second expected traversal time
9
EGEE gLite Scheduling
Site (node)
Proc
BDII
Publish
CE
Update
Broker
Local scheduler
BDII broker cache
10
Not only academic
Overhead Ratio

• Long waiting times
• When EGEE was not so heavily loaded

Execution time (s)
11
Batch scheduling

• Very complex policies
• Maximise throughput under constraints
• Weighted fair-share VOs, type of jobs
• Priorities
• Hardware requirements
• Advance reservations
• An indication of job duration is given by the
  type of queue infinite, long, medium, short, and
  exotic ones
• B. Bode et al.The Portable Batch Scheduler and
  the Maui Scheduler on Linux Clusters

12
Classical vs Grid

• (Relatively) easy
• Throughput instead of makespan Master-slave
  graph instead of DAG allow for instance to define
  cyclic schedules in polynomial time which are
  asymptotically optimal, but not local
• Y. Robert A. Rosenberg
• Moderately difficult information about
• Applications
• Infrastructures
• The same program on different data may run at
  very different speed
• The network performance is dynamic
• Really difficult
• Queues managed by local policies
• On-line decision

13
Information and Scheduling (I)

• Considerable work has been done in predicting CPU
  load in shared environments desktops, clusters,
  desktop grids P.A. Dinda, R. Wolski, J. Schopf
• The basic technique is linear time-series
  analysis
• Self-similarity and epochal behavior
• Usual goal is the prediction of the next value
• Applied to soft real-time scheduling on shared
  clusters
• Practical application in NWS

14
Information and scheduling (III)

• Less work on predicting the behavior of dedicated
  systems
• Papers are on parallel systems, mostly based on
  time-series techniques, but at least one based on
  a genetic algorithm Downey, Foster, Wolski
• The traces are much more difficult to access
• No time slice - Irregular time series the
  records are event-driven
• Which analysis
• Average waiting time clear but not very useful
  for prediction
• Fitting a distribution not convincing for //
  systems
• Predicting an upper bound with a confidence
  interval metric of success?

15
Information and grid

• We cannot directly log the entire state of the
  system
• Access rights
• Size
• Currently available data
• The lifecycle of jobs going through certain
  brokers
• The job ranking at the same brokers
• The detailed behavior of the queues on certain
  sites
• Certain LAL possibly other mainstream
• Easy to get
• Summary data about the lifecycle of all jobs
• From which it could be possible to reconstruct
  the detailed state and dynamic of the CE

16
What should we learn ?

• Learning besides time series make sense in a
  grid massive use of community programs instead
  of (?) sparse runs of a very long and complex
  digital experiment
• Information as sketched before
• Beware not be a steady-state system
• New users, new machines, new software is the
  expected regime for some years from now
• A community-based resource will tend display
  correlated activity
• Is there an invariant social graph? Is it a
  feature?
• System algorithms e.g. a site scheduler or the
  broker
• Validation ?
• Scheduling algorithms
• Validation ?