parallel job scheduling algorithms and interfaces
DESCRIPTION
Parallel Job Scheduling Algorithms and Interfaces. Research Exam for Cynthia Bailey Lee Department of Computer Science and Engineering University of California, San Diego May 27, 2004. Outline. Introduction Problem Overview Why does this matter? Problem Specification History - PowerPoint PPT PresentationTRANSCRIPT
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
0
50
100
150
200
250
300
350
400
19
93
19
93
19
94
19
94
19
95
19
95
19
96
19
96
19
97
19
97
19
98
19
98
19
99
19
99
20
00
20
00
20
01
20
01
20
02
20
02
20
03
20
03
Nu
mb
er
of
Syst
em
s in
Top
50
0
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