parallel job scheduling algorithms and interfaces
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Parallel Job SchedulingAlgorithms and Interfaces
Research Exam for
Cynthia Bailey Lee
Department of Computer Science and Engineering
University of California, San DiegoMay 27, 2004
Outline
• Introduction– Problem Overview– Why does this matter? – Problem Specification
• History– Early Approaches– Backfilling– Priorities
• Evaluation– Metrics– Metric Pitfalls– User Perspectives
• Future Directions
What Are We Trying to Do?
Introduction: Problem Overview Why Does This Matter? Problem Specification
Job:
BlueHorizon
CFD visualization: www.science-computing.de/products/powerviz.html
System:
Job Model: System Model:
Time
Pro
cesso
rs
Time
Pro
cesso
rs Running Jobs
Queued Job
Message-PassingParallel Scientific Code
Idle space
Why Does This Matter?
Introduction: Problem Overview Why Does This Matter? Problem Specification
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Time- sharingSpace- sharingOther
Systems in the Top500 typically range in price from $1 million to $50 million+
Top500 data: www.top500.org
Problem Specification
• Purpose process a workload parallel batch jobs
• Processor Homogeneity machine consists of N identical processors
• Job Specification processors by requested runtime
• Exclusivity jobs do not share processors
• Non-Preemption once begun, jobs run to completion
• Online jobs arrive stochastically, no knowledge of future
• Accounting there is a scheme to track users' resource consumption
• User Independence users are in competition for system resources
Introduction: Problem Overview Why Does This Matter? Problem Specification
Outline
• Introduction– Problem Overview– Why does this matter?– Problem Specification
• History– Early Approaches– Backfilling– Priorities
• Evaluation– Metrics– Metric Pitfalls– User Perspectives
• Future Directions
History
First Come First Serve(FCFS)
Job 1
Job 4Job 3
Time
Pro
cesso
rs
Job 2
History: Early Approaches Backfilling Priorities
Queue:
Tennis Court Scheduling[M93,P04]
Job 2
Job 3 Job 4
Job 7
Job 6
Time
Pro
cesso
rs
Job 1Job 5
History: Early Approaches Backfilling Priorities
• Allow backfills when the projected start of first job in the queue is not delayed
• No starvation—all jobs will eventually run• Claim: “Jobs in the queue are never delayed
from running by jobs submitted to the queue after them.”
• Disproved [MF01]
EASY Backfilling[SCZL96]
History: Early Approaches Backfilling Priorities
Conservative Backfilling• Allow backfills when the projected starts of all
preceding jobs in the queue are not delayed• Worst-case start time guaranteed at submittal• Claim: “guarantees that future arrivals do not
delay previously queued jobs.” [MF01]• Disproved—depending on semantics of “delay”
[JSC01]
History: Early Approaches Backfilling Priorities
Maui Scheduler [JS01]
• Priorities—a function of 20+ parameters (don’t read this chart)
History: Early Approaches Backfilling Priorities
• Parameterized backfills– Backfilling allowed when the projected starts of the N preceding jobs in the queue are not delayed
Maui is deployed on many major systems
Microeconomic Scheduler [SAWP95]
A Unifying PrincipleInfluence user behavior through accounting
and charges, allow users to influence system behavior through payments [FR96]
Job 1
Time
Pro
cesso
rs
History: Early Approaches Backfilling Priorities
Outline
• Introduction– Problem Overview– Why does this matter?– Problem Specification
• History– Early Approaches– Backfilling– Priorities
• Evaluation– Metrics– Metric Pitfalls– User Perspectives
• Future Directions
Evaluation
Common Metrics
• Makespan • Utilization• ResponseTime • Expansion Factor (Slowdown)• Bounded Slowdown • Weighted Response Time
Evaluation: Metrics Metric Pitfalls User Perspectives
Metric Pitfallsor “12 Ways to Fool the Masses When Giving Scheduler Performance Results” (Apologies to [B91])
1. Rely on a single number (e.g. average)• Don’t mention what happens to the unluckiest jobs
[CADV02]—especially avoid focusing on those hard-to-schedule big jobs [SKSS02, EHY02]
2. Use a workload that is unrealistic and shows off your scheduler’s strengths [MF01,FN95]
3. Avoid unpleasant related facts like internal fragmentation [PJN99]
4. Don’t waste time worrying about user-centric aspects of performance such as fairness and start-time guarantees [MF01]
5. Focus solely on performance, not user interface and implementation issues
Evaluation: Metrics Metric Pitfalls User Perspectives
» Citations noted are exemplary cases of doing the right thing
8 am 12–1pm 5 pm-8 am 9 am
Scheduling in Context: User Utility Functions
[FRSSW97]
Evaluation: Metrics Metric Pitfalls User Perspectives
u(t
)
Outline
• Introduction– Problem Overview– Why does this matter?– Problem Specification
• History– Early Approaches– Backfilling– Priorities
• Evaluation– Metrics– Metric Pitfalls– User Perspectives
• Future Directions
Future Directions
Scheduling Explicitly by User Utility Function
[L04, FrN95]
• If user utility functions can be collected, a scheduler can be designed to explicitly optimize the global utility– A survey of users at SDSC demonstrated
feasibility of collection for crude utility functions
• Formulated as a Linear program—with some integer constraints—finding the optimal solution is NP-hard– Commercially available solvers are able to
produce good solutions in reasonable timeframes (< 1 minute)
Future Directions
Empowering the User by Providing More
Information[L04]
Future Directions
User-Provided Inputs[MF01, LSHS04]
• Users are strongly motivated to overestimate in their requested runtimes– Jobs are killed when the time expires
• Can users be more accurate when not threatened with death, and with more tangible rewards?
Future Directions
Outline
• Introduction– Problem Overview– Why does this matter?– Problem Specification
• History– Early Approaches– Backfilling– Priorities
• Evaluation– Metrics– Metric Pitfalls– User Perspectives
• Future Directions
Conclusion
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