Resource Allocation and Scheduling Strategies using Utility and the Knapsack Problem on ...

154 Pages·2008·5.31 MB·English

Resource Allocation and Scheduling Strategies
using Utility and the Knapsack Problem on
Computational Grids
by
Daniel Colin Vanderster
B.Eng., University of Victoria, 2003
A Dissertation Submitted in Partial Fulfillment of the
Requirements for the Degree of
DOCTOR OF PHILOSOPHY
in the Department of Electrical and Computer Engineering
(cid:13)c Daniel Colin Vanderster, 2008
University of Victoria
All rights reserved. This dissertation may not be reproduced in whole or in part by
photocopy or other means, without the permission of the author.
ii
Resource Allocation and Scheduling Strategies
using Utility and the Knapsack Problem on
Computational Grids
by
Daniel Colin Vanderster
B.Eng., University of Victoria, 2003
Supervisory Committee
Dr. Nikitas J. Dimopoulos, Supervisor
Department of Electrical and Computer Engineering
Dr. Randall J. Sobie, Supervisor and Outside Member
Department of Physics and Astronomy
Dr. Kin F. Li, Departmental Member
Department of Electrical and Computer Engineering
Dr. Amirali Baniasadi, Departmental Member
Department of Electrical and Computer Engineering
Dr. Rajkumar Buyya, External Member
Department of Computer Science and Software Engineering,
University of Melbourne
iii
Supervisory Committee
Dr. Nikitas J. Dimopoulos, Supervisor
Department of Electrical and Computer Engineering
Dr. Randall J. Sobie, Supervisor and Outside Member
Department of Physics and Astronomy
Dr. Kin F. Li, Departmental Member
Department of Electrical and Computer Engineering
Dr. Amirali Baniasadi, Departmental Member
Department of Electrical and Computer Engineering
Dr. Rajkumar Buyya, External Member
Department of Computer Science and Software Engineering,
University of Melbourne
Abstract
Computational grids are distributed systems composed of heterogeneous computing
resources which are distributed geographically and administratively. These highly
scalable systems are designed to meet the large computational demands of many
users from scientific and business orientations. This dissertation address problems
related to the allocation of the computing resources which compose a grid.
First, the design of a pan-Canadian grid is presented. The design exploits the
maturing stability of grid deployment toolkits, and introduces novel services for effi-
ciently allocating the grids resources. The challenges faced by this grid deployment
motivate further exploration in optimizing grid resource allocations.
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The primary contribution of this dissertation is one such technique for allocating
gridresources. Byapplyingautilitymodeltothegridallocationoptions, itispossible
to quantify the relative merits of the various possible scheduling decisions. Indeed,
a number of utility heuristics are derived to provide quality-of-service policies on
the grid; these implement scheduling policies which favour efficiency and also allow
users to intervene with urgent tasks. Using this model, the allocation problem is
then formulated as a knapsack problem. Formulation in this manner allows for rapid
solution times and results in nearly optimal allocations.
The combined utility/knapsack approach to grid resource allocation is first pre-
sented in the allocation of single resource type, processors. By evaluating the ap-
proach withnovel utility heuristics usingbothrandom andreal workloads, itis shown
toresultinefficientscheduleswhichhavecharacteristicsthatmatchtheintendedpoli-
cies. Additionally, two design and analysis techniques are performed to optimize the
design of the utility/knapsack scheduler; these techniques play a significant role in
practical adoption of the approach.
Next, the utility/knapsack approach is extended to the allocation of multiple
resource types. This extension generalizes the grid allocation solution a wider variety
of resources, including processors, disk storage, and network bandwidth. The general
technique, when combined with new heuristics for the varied resource types, is shown
to result in improved performance against reference strategies.
This dissertation concludes with a novel application of the utility/knapsack ap-
proach to fault-tolerant task scheduling. Computational grids typically feature many
techniques for providing fault tolerance to the grid tasks, including retrying failed
tasks or replicating running tasks. By applying the utility/knapsack approach, the
relative merits of these varied techniques can be quantified, and the overall number
of failures can be decreased subject to resource cost considerations.
v
Table of Contents
Supervisory Committee ii
Abstract iii
Table of Contents v
List of Tables viii
List of Figures x
Acknowledgements xii
Dedication xiv
1 Introduction 1
2 Background and Related Research 5
2.1 Introduction to Grid Computing . . . . . . . . . . . . . . . . . . . . . 5
2.2 Computational Grids: Components, Standards, and Toolkits . . . . . 8
2.3 Resource Allocation and Task Scheduling on Computational Grids . . 12
2.4 Utility Models and the Knapsack Problem . . . . . . . . . . . . . . . 17
3 GridX1: A Pan-Canadian Computational Grid 20
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2 GridX1 Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
vi
3.3 The GridX1 Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . 23
3.4 User Applications on GridX1 . . . . . . . . . . . . . . . . . . . . . . 34
3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4 Grid Resource Allocation using a Utility Model and Knapsack For-
mulation 38
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.2 Grid Resource Allocation and Utility . . . . . . . . . . . . . . . . . . 40
4.3 The Knapsack Formulation . . . . . . . . . . . . . . . . . . . . . . . . 44
4.4 Allocation Policies and Utility Heuristics . . . . . . . . . . . . . . . . 46
4.5 Experimentation via Simulation . . . . . . . . . . . . . . . . . . . . . 50
4.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
5 Design and Analysis of the Utility/Knapsack Scheduling System 72
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
5.2 Sensitivity of the Allocation Policies . . . . . . . . . . . . . . . . . . . 74
5.3 Plackett-Burman Design of the Utility/Knapsack Scheduler . . . . . . 81
5.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
6 Allocation of Multiple Resource Types 91
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
6.2 Example Allocation Problem . . . . . . . . . . . . . . . . . . . . . . . 92
6.3 Allocating Multiple Resource Types using the MMKP . . . . . . . . . 94
6.4 Multi-Resource Policies and Utility Function Heuristics . . . . . . . . 95
6.5 Experimentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
6.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
7 SelectionbetweenFaultToleranceOptionsusing the Utility/Knapsack
Approach 104
vii
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
7.2 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
7.3 Intelligent Selection of Fault Tolerance Techniques . . . . . . . . . . . 107
7.4 Utility Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
7.5 Experimentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
7.6 Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . 120
8 Conclusions 121
8.1 Summary of Contributions . . . . . . . . . . . . . . . . . . . . . . . . 121
8.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
Bibliography 126
A Full Data Tables 133
A.1 Data tables for chapter 4 . . . . . . . . . . . . . . . . . . . . . . . . . 133
A.2 Data tables for chapter 6 . . . . . . . . . . . . . . . . . . . . . . . . . 133
viii
List of Tables
4.1 Overall completion time (units of 107s) for the random workload and
τ = 6hr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.2 Correlation of credit-value with speedup for the random workload and
τ = 6hr. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.3 Overall completion time (units of 107s) for the NPACI workload and
τ = 6hr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.4 Correlation of credit-value with speedup for the NPACI workload and
τ = 6hr. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.5 Comparison between Overall Completion Times and Delay-Value Cor-
relation for Small and Large Grids . . . . . . . . . . . . . . . . . . . . 64
4.6 ComparisonoftheAverageRandomWorkloadResultsandtheNPACI-
derived Workload Results . . . . . . . . . . . . . . . . . . . . . . . . 66
4.7 Comparison between Overall Completion Times and Delay-Value Cor-
relation for Homogeneous and Heterogeneous Grids . . . . . . . . . . 68
5.1 PB Design with Fold Over for 7 Parameters having 16 Experiments . 81
5.2 High and Low Values for the Metascheduler Parameters . . . . . . . . 84
5.3 Overall Completion Time Results of the Metascheduler Design (Seed 1) 86
5.4 Ranking the Design Parameter Effects on Overall Completion Time . 87
5.5 Ranking the Design Parameter Effects on Mean Queue Delay . . . . . 87
5.6 Ranking the Design Parameter Effects on Money-Delay Correlation . 88
ix
7.1 Utility functions for the Value, Value/Cost, and Value/ERT heuristics 113
7.2 Simulator configuration and parameters . . . . . . . . . . . . . . . . . 114
7.3 Mean correlations between task value (V), value/length (V/L) and
startup delay (D) for each heuristic at queue length 640 . . . . . . . . 119
A.1 Chapter 4: Random workload, overall completion time (s ∗107) . . . . 134
A.2 Chapter 4: Random workload, correlation of credit-value-metric with
speedup relative to FCFS. . . . . . . . . . . . . . . . . . . . . . . . . 135
A.3 Chapter 4: NPACI workload, utility-based strategies: overall comple-
tion time (s ∗106). . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
A.4 Chapter 4: NPACI workload, correlation of credit-value-metric with
speedup relative to FCFS. . . . . . . . . . . . . . . . . . . . . . . . . 137
A.5 Chapter 6: Overall Completion Time Normalized to Basic FCFS . . . 138
A.6 Chapter 6: Total Transfer Time Normalized to Basic FCFS . . . . . . 139
A.7 Chapter 6: Correlation Between Money Offered and Startup Delay . . 140
x
List of Figures
2.1 The Grid provides users with access to distributed resources of many
types. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.1 The GridX1 Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2 The GridX1 metascheduling architecture . . . . . . . . . . . . . . . . 26
3.3 The Estimate-Wait-Time algorithm. . . . . . . . . . . . . . . . . . . . 28
3.4 A web-based monitoring system . . . . . . . . . . . . . . . . . . . . . 33
3.5 GridX1 resource federation . . . . . . . . . . . . . . . . . . . . . . . . 34
4.1 Example sigmoidal utility functions of the form U(x) = 2 −1. 43
e−α(x−θ)+1
4.2 Random and NPACI task length histograms . . . . . . . . . . . . . . 51
4.3 Task speedup vs. value for the random workload using (a) Gang, (b)
UM policy 1, (c) BF, and (d) UM+BF policy 1. . . . . . . . . . . . . 56
4.4 Task startup delay vs. value for the random workload using (a) Gang,
(b) UM policy 1, (c) BF, and (d) UM+BF policy 1. . . . . . . . . . . 57
4.5 Task speedup vs. value for the NPACI workload using (a) Gang, (b)
UM policy 1, (c) BF, and (d) UM+BF policy 1. . . . . . . . . . . . . 61
4.6 Task startup delay vs. value for the NPACI workload using (a) Gang,
(b) UM policy 1, (c) BF, and (d) UM+BF policy 1. . . . . . . . . . . 62
4.7 Task startup delay vs. value for the NPACI workload and UM policy
1 zoomed to show the startup of early tasks. . . . . . . . . . . . . . . 62
4.8 Knapsack problem solution time for various queue lengths and grid sizes. 69