1. Job Scheduling in a Grid Computing Environment
Colton Lewis

2. Agenda Last presentation: introduce grid computing
This presentation: address job scheduling techniques in detail
Review what is grid computing
Job scheduling challenges in grid computing
Case study in approaching these challenges

3. Components of Grid Computing
Multiple computers
Independently functioning hardware
Multiple locations and/or owners
Shared computational goal
Distributed resources over a network
Typically an already existing network

4. Benefits of Grid Computing
Large pool of resources
Large grids are comparable in FLOP/s to top 500 supercomputers
Distributed costs
Administration
Maintenance
Electricity
Space
Utilize existing infrastructure and avoid specialized hardware

5. Inherent Parallelism
Large numbers of computers means lots of possible parallelism
Great for handling large numbers of easily separable tasks
Easily parallelizable problems with little communication
Signal processing, graphics and animation, search and simulation, etc.
High volume of very similar tasks

6. Example Grid Project: SETI@home

7. Job Scheduling NP-Hard computer science problem
Optimality is computationally intractable in general
Combinatorial Optimization
Grids must consider even more factors

8. Heterogeneous Machines
Machines on the grid may have vastly different resources
Dedicated clusters
Desktop computer donating spare cycles
Embedded devices
Must account for this to balance load

9. Dynamic Network Resources may not be available
Computers may be shut off, software uninstalled
Resources may not be reliable
Hardware errors, malicious participants returning incorrect results

10. General Strategies Know as much as possible Use Heuristics
Job intensity
Client capabilities
Use Heuristics

11. Examining the BOINC Scheduler
Berkeley Open Infrastructure for Network Computing
Software behind many volunteer computing projects

12. Terminology Host – a worker machine
May work on multiple projects
Client – program for fetching jobs from servers
All server communication is issued by the client
Server – a task assignment program
Project – a long-running computation on the grid
May have its own server or share
SETI is a BOINC project
Job – subtask of a project assigned to a host
Application – program for performing a job
Supplied by project

13. BOINC Host Architecture

14. User Preferences Informs many scheduling decisions
Owner of host can specify
Resource share of projects
Limits on CPU, RAM, Network Bandwidth
Connection interval to server(s)

15. Credit Hosts are assigned credit for jobs completed before deadline
Based on estimated number of FLOPs
Each project awards credit
Provides a way to rank performance of hosts
Points toward possible grid improvements

16. Host Perspective Each host must solve two related problems
CPU scheduling – when to run currently assigned jobs
When to ask a project for more work
Works to maximize credit subject to constraints
User preferences
Hardware

17. Early Policies CPU Scheduling – Weighted Round Robin
Each project given CPU time according to user specified percentage
Does not account for deadlines, may waste lots of work
Work Fetch Scheduling – Keep enough work for full connection interval for all projects

18. Example Failure Consider the table to the right
Jobs complete in 250, 20, and 10 hours
CPU is never idle, but all work is wasted

19. Earliest Deadline First
Does not enforce desired resource sharing
Projects with long jobs will stave

20. Estimating CPU Time
Knowing CPU time means knowing which jobs can be completed by deadline
Project supplied FLOPs estimate divided by host CPU benchmark
Can be consistently wrong, real projects need memory, io, etc.
Duration correction factor per project
How much CPU time did last project take compared to estimate
CPU efficiency factor
How does actual CPU time compare to wall time
Applications may periodically report percentage done

21. Debt The amount of work “owed” to a project
Long term enforcement of resource shares while still attending to deadlines
Short term debt controls CPU scheduling over one connection interval
Long term debt controls Work Fetching

22. CPU Scheduling Periodically calculate debt to each project
CPU time expected by resource sharing minus CPU time spent
Deduct expected payoff from currently running jobs
Run earliest deadline job from project with most debt

23. Work Fetching Same general method as CPU Scheduling
Controls new jobs requested rather than CPU time

24. Server Perspective Must ensure correctness of results, if needed
Must deliver reasonable jobs to hosts requesting work

25. BOINC Server Architecture

26. Credit and Redundancy Many jobs require error checking
Solution: assign same job to two or more hosts
Answers are compared by project server
If enough hosts agree, answer is accepted
Credit is awarded to all correct hosts
When assigning new work, prioritize jobs waiting for an answer

27. Job Size Matching Assume jobs can be created in various size classes
Keep order statistics of known host performance
When assigning new work, prioritize jobs that are the right size for the requesting host
If possible, create jobs according the distribution of known hosts

28. Summary Effective grid computing must consider both host and server
Nature of grid means different interests may control each
Long running projects allow for predictive statistics
CPU time, job matching, etc.
The best known methods use heuristics to decide what to do
Human-like notions of “credit”, “debt”, etc.

29. Works Consulted
D. P. Anderson and J. McLeod, "Local Scheduling for Volunteer Computing," IEEE International Parallel and Distributed Processing Symposium, Long Beach, CA, 2007, pp. 1-8.
D. P. Anderson, E. Korpela and R. Walton, "High-performance task distribution for volunteer computing," First International Conference on e-Science and Grid Computing (e-Science'05), Melbourne, Vic., 2005, pp. 8 pp.-203.
E. Korpela, D. Werthimer, D. Anderson, J. Cobb and M. Leboisky, massively distributed computing for SETI," in Computing in Science & Engineering, vol. 3, no. 1, pp , Jan/Feb 2001.
Jacob, Bart, et al. Introduction to Grid Computing. United States: IBM, International Technical Support Organization, Web. <
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