Slide - PDCC - Nanyang Technological University

Report
General and Effective Monetary
Optimizations for Workflows in IaaS
Clouds
presented by
Amelie Chi Zhou
[email protected]
Xtra Computing Group
http://pdcc.ntu.edu.sg/xtra
Nanyang Technological University, Singapore
1
Workflows for Scientific
Applications
• Workflows are structured
– Tasks have very different I/O and computational behavior.
• Real-world workflows
– Montage, Ligo, Epigenomics, water-simulation
Montage
Ligo
Epigenomics
• Workflow ensembles [Malawski et al., SC’12]
– Composition of workflows with similar structures and different
parameters and priorities
2
Running Workflows on IaaS
Clouds
• Define IaaS clouds
– Provide fundamental computing resources for users to provision
– Examples: Amazon EC2, Rackspace, OpenStack, Google
Compute Engine …
• Example projects
– Montage, Broadband, Epigenomics on Amazon EC2 [Juve et al.,
eScience’09]
– Astronomy applications on Nimbus, Eucalyptus, and EC2
[Vöckler et al., ScienceCloud’11]
– …
3
Workflows in IaaS Clouds
• Features of IaaS clouds
– Pay as you go (e.g., hourly pricing scheme)
– Rich and evolving cloud offerings
• Research problems
– Monetary cost optimizations
– Performance optimizations
– Elasticity
– Fault tolerance
– …
Are the current solutions ideal/sufficient?
4
Monetary Cost Opportunities
• Instance types
– Amazon EC2 provides 29 types of instances
• Instance reuse
– Hourly charging scheme
• Pricing schemes
– On-demand, spot and reserved pricing
V.S.
• Tasks can have very different I/O and computational behavior.
• Workflows have different deadline and monetary constraints.
• Users may have various workflow application scenarios.
5
Current Solutions are Far From
Ideal
• Problems of current approaches
– Auto-scaling [Mao et al., SC’11] resource
management
• More effective optimizations  29%
less cost
– Assume static cloud performance and
pricing
• Cloud dynamics + spot instances 
73% less cost
– Heuristic-based cost and performance
optimizations are specific.
• They are likely to be suboptimal in
evolving and diversified workflow
applications.
29%
73%
6
Our Research Efforts
• Effectiveness
– Dyna: Minimize the monetary cost of workflows, addressing both
the price and performance dynamics in clouds
• Generality
– ToF: Define transformation operations to model common cost
and performance optimizations
– Deco: Design a declarative language called WLog to specify
various workflow optimization problems
The focus of this presentation.
7
Overall Design
• We design general workflow optimization frameworks to fully
explore the optimization opportunities that lie in workflows
Problem specification layer
Wlog programs
Deco
Optimization layer
Transformationbased Optimizer
ToF
Execution layer
8
Outline
• Related Work
• Generalized Optimization Frameworks
– General transformations for cost and performance optimizations
– A declarative language for workflow optimization problems
• Conclusions
9
Related Work
• Performance and monetary cost optimization heuristics
– Auto-scaling [Mao et al., SC’11]
• Fixed sequence of workflow optimizations
– Workflow scheduling with performance and cost constraints
[Kllapi et al., SIGMOD’11]
• Consider only one on-demand instance type
The heuristics are specifically designed for specific optimization
problems and the optimization opportunities are not fully explored.
10
Related Work (cont’d)
• Generalized optimization frameworks: overhead is a problem
– Generalized bin-ball abstraction for resource allocation [Rai et
al., SoCC’12]
• GPU acceleration
• Not always convenient to model a problem with the bin-ball model
– Declarative language to model a wide range of COPs [Liu et al.,
VLDB’12]
• Distributed systems
• Ignorant to the special features and optimization opportunities in
workflows
There is no general optimization framework for workflows.
11
Outline
• Related Work
• Generalized Optimization Frameworks
– General transformations for cost and performance
optimizations
– A declarative language for workflow optimization problems
• Conclusions
12
ToF: A Transformation-based
Optimization Framework
• Outline
– Main contributions of this work
– System overview
– Design details
– Evaluation results
13
Main Contributions
• This study has two major contributions
– We define a series of common transformations for the
performance and cost optimizations of workflows.
– We design a light-weight optimizer to guide the
transformation process.
14
Workflow Transformation
• Definitions
– Instance assignment graph
• Each node represents instance configuration for a task.
• Same structure as the workflow DAG
– Transformation operation
• Structural change in the instance assignment graph
0
0
0
Transformations
1,2
1
2
3
3
0
2
2,3
1
0
1,3
1,2,3
15
System Overview
• Design ideas
– Two types of transformations
• Main schemes: reduce cost
• Auxiliary schemes: help main
schemes to reduce cost
– Use cost model to guide the
transformation optimization
– Periodical batch optimization
• Maximize instance sharing
and reuse
• Reduce optimizer overhead
Main
Schemes
Cost
model
Auxiliary
Schemes
No
Termin
ation?
Yes
Output
Optimization process in one plan period
16
Design Details
• Transformation operations
– Main schemes: Merge,
Demote
– Auxiliary schemes: Move,
Promote, Split, Coscheduling
– Transformations can
combine with each other
17
Using Transformations
• Example of using Move and Merge operations
Only transform
shape
Reduces cost
Charging hours:  +  +  =   ( +  + )/ = 
18
Experimental Setup
• Workload
– Montage, Ligo and Mixed
– Workflow submission rate
follows Poisson distribution
• Comparisons
–
–
–
–
ToF
Baseline: only implement the initial instance configuration
Auto-scaling [Mao et al., SC’11]
Greedy: randomly select the transformation during
optimization
• All results are normalized to Baseline
19
Evaluation Results on Cost
Optimizations
29%
17%
21%
16%
28%
15%
Optimization results under the pricing scheme of Amazon EC2.
ToF obtains the lowest monetary cost on all workflows.
• Over Auto-scaling by 29%
• Over Baseline by 27%
• Over Greedy by 17%
20
Evaluation Results on Performance
Optimizations
21%
18%
21%
8%
16%
12%
Performance optimization results.
ToF obtains the lowest average execution time on all workflows.
• Over Auto-scaling by 21%
• Over Baseline by 21%
• Over Greedy by 18%
21
Outline
• Related Work
• Generalized Optimization Frameworks
– General transformations for cost and performance optimizations
– A declarative language for workflow optimization problems
• Conclusions
22
Deco: A Declarative Optimization
Framework
• Outline
– Main contributions of this work
– System overview
– A declarative language for workflows
– GPU-accelerated search engine
– Evaluation results
23
Main Contributions
• This work has three main contributions
– A declarative language for resource provisioning of scientific
workflows in IaaS clouds
– A generalized optimization framework to serve a wide range of
optimization problems
– Fast GPU-based implementation for low optimization overhead
24
Motivating Ideas
• Why declarative language?
– Declarative languages like HTML, SQL, Prolog
– Concise and clear
– Focus on what to do rather than how to do it
• Why GPU acceleration?
– Generic search has large runtime overhead
– Monte Carlo method is used for probabilistic approximation
[Raedt et al. 2007] which is suitable for GPU acceleration
25
System Overview
• Overview of the Deco system
– WLog, a declarative language for workflows
– GPU-Accelerated search engine
26
WLog – A Declarative Language for
Workflows
• WLog is designed based on Prolog
• A WLog program describing a workflow scheduling problem
goal minimize Ct in totalcost(Ct).
deadline(P, D) A probabilistic deadline
cons deadline(95%, 10h).
requirement that D is at the P-th percentile of
var configs(Tid, Vid) forall task(Tid)
and Vm(Vid).
workflow
execution time.
r1
r2
r3
r4
r5
r6
r7
problem specific keywords:
import(amazonec2).• goal Optimization goal defined by the user.
import(montage). • cons Problem constraint defined by the user.
path(X,Y,Y,C) :- edge(X,Y),
exetime(X,Vid,T),
C is
T.
• var Problem
variable to be
optimized.
path(X,Y,Z,C) :- edge(X,Z),
Zn==Y, path(Z,Y,Z2,C1),
exetime(X,Vid,T),
import(cloud)
Import the cloud-related
facts from
C is T+C1.
the cloud metadata.
maxtime(Path,T) :- setof([Z,C],path(root,tail,Z,C),Set),
max(Set,[Path,T]).import(daxfile) Import the workflow-related facts
generated from
a DAX file.
cost(Tid,Vid,C) :- price(Vid,Up),
exetime(Tid,Vid,T),
C is
ceil(T/60.0)*Up.
totalcost(Ct) :- findall(C,cost(Tid,Vid,C),Bag), sum(Bag,Ct).
27
GPU Accelerations
• Explore vs. exploit
– By exploit, partial results are prioritized.
– Exploration traverses the search tree level by level which offers
GPU a opportunity to parallel the searching process.
• Memory optimizations
– Minimize the usage of global memory
– Reduce accesses to shared memory
28
Evaluation Settings
• Three use cases
– Workflow scheduling problem
– Workflow ensemble [Malawski et al., SC’12]
• Goal: execute more workflows with high priorities within given
budget and deadline
– Follow-the-cost: multiple workflows, multiple datacenters
• Comparison for workflow ensemble problem
– Algorithms: Deco vs. SPSS [Malawski et al., SC’12]
– Ensemble types: constant, Uniform(Un)sorted, Pareto(Un)sorted
– Generate 5 budgets between [MinBudget, MaxBudget]
• All results are normalized to that of SPSS
29
Evaluation Results
• Under all ensemble types and budget constraints
– Deco obtains better score metric value than SPSS
Obtained score results of SPSS and Deco with different ensemble types under budget 1
to 5 and fixed deadline. Workflow type is Ligo.
30
Evaluation Results (cont’d)
• Programmability of WLog in Deco (lines of codes)
– Users (re-)implement the workflow application in C++.
– With Deco, users implement in WLog.
Use Case
C++
Implementation
WLog
Workflow Scheduling
1950
10
Workflow Ensemble
1960
13
Follow-the-Cost
2230
15
Deco allows much lower coding complexity than manual implementation.
31
Performance Speedup of GPUs
• Performance speedup of GPU implementation over CPU
implementation on a single core for the three applications
GPU Accelerations
500
437x
400
300
200
93x
100
31x
0
Montage
Epigenomics
Ligo
32
Outline
• Related Work
• Generalized Optimization Frameworks
– General transformations for cost and performance optimizations
– A declarative language for workflow optimization problems
• Conclusions
33
Conclusions
• IaaS clouds have become an attractive platform for hosting
workflows.
• Despite recent efforts in monetary cost optimizations of
workflows in the cloud, there is still a large room for further
improvements.
• Due to the complex cloud offerings and problem
specifications, we develop general optimization frameworks.
– ToF achieves up to 29% improvement over the state-ofthe-art algorithm.
– Deco achieves up to 77% improvement over the state-ofthe-art algorithm.
34
Future Work
• Energy-efficient Cloud
– Reduce the investment cost of cloud provider to potentially
reduce instance price with energy-efficient hardware/software
• Optimization opportunities in Multi-Cloud
– Utilize different cloud offerings, e.g., instance types, to further
reduce cost
35
References
•
•
•
•
•
•
•
•
•
•
•
Maciej Malawski, Gideon Juve, Ewa Deelman, and Jarek Nabrzyski. 2012. Cost- and deadlineconstrained provisioning for scientific workflow ensembles in IaaS clouds. SC '12. 11 pages.
Juve, G.; Deelman, E.; Vahi, K.; Mehta, G.; Berriman, B.; Berman, B.P.; Maechling, P., "Scientific
workflow applications on Amazon EC2," E-Science Workshops, pp.59,66, 9-11 Dec. 2009.
Jens-Sönke Vöckler, Gideon Juve, Ewa Deelman, Mats Rynge, and Bruce Berriman. 2011. Experiences
using cloud computing for a scientific workflow application. ScienceCloud '11. P15-P24. 2011.
Ming Mao, Marty Humphrey: Auto-scaling to minimize cost and meet application deadlines in cloud
workflows. SC 2011: 49.
Herald Kllapi, Eva Sitaridi, Manolis M. Tsangaris, and Yannis Ioannidis. 2011. Schedule optimization for
data processing flows on the cloud. SIGMOD '11. 289-300.
Anshul Rai, Ranjita Bhagwan, and Saikat Guha. 2012. Generalized resource allocation for the cloud.
SoCC '12. Article 15 , 12 pages.
Changbin Liu, Lu Ren, Boon Thau Loo, Yun Mao, and Prithwish Basu. 2012. Cologne: a declarative
distributed constraint optimization platform. Proc. VLDB Endow. 5, 8 752-763.
L. De Raedt, A. Kimmig, and H. Toivonen, ProbLog: A probabilistic Prolog and its application in link
discovery, IJCAI 2007, pages 2462-2467, 2007.
Amelie Chi Zhou, Bingsheng He, Transformation-based Monetary Cost Optimizations for Workflows in
the Cloud, accepted by TCC, Dec 2013.
Amelie Chi Zhou, Bingsheng He, A declarative optimization framework for workflows in IaaS clouds,
submitted to SC 2014.
Amelie Chi Zhou, Bingsheng He, Cheng Liu, Monetary Cost Optimizations for Hosting Workflow-as-aService in IaaS Clouds, submitted to ToC, 2014.
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Thank you!
Amelie Chi Zhou
[email protected]
Advisor: Bingsheng He
[email protected]
Xtra Computing Group
http://pdcc.ntu.edu.sg/xtra
Nanyang Technological University, Singapore
37

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