general and effective monetary optimizations for workflows in iaas clouds
DESCRIPTION
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. Workflows for Scientific Applications . - PowerPoint PPT PresentationTRANSCRIPT
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General and Effective Monetary Optimizations for Workflows in IaaS Clouds
Amelie Chi Zhou [email protected]
Xtra Computing Grouphttp://pdcc.ntu.edu.sg/xtraNanyang Technological University, Singapore
presented by
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Workflows for Scientific Applications • Workflows are structured
– Tasks have very different I/O and computational behavior. • Real-world workflows
– Montage, Ligo, Epigenomics, water-simulation
• Workflow ensembles [Malawski et al., SC’12]– Composition of workflows with similar structures and different
parameters and priorities
Montage Ligo Epigenomics
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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]
– …
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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?
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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.
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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%
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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.
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Overall Design• We design general workflow optimization frameworks to fully
explore the optimization opportunities that lie in workflows
Wlog programs
Transformation-based Optimizer
Problem specification layer
Optimization layer
Execution layer
Deco
ToF
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Outline
• Related Work• Generalized Optimization Frameworks
– General transformations for cost and performance optimizations– A declarative language for workflow optimization problems
• Conclusions
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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.
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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.
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Outline
• Related Work• Generalized Optimization Frameworks
– General transformations for cost and performance optimizations
– A declarative language for workflow optimization problems• Conclusions
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ToF: A Transformation-based Optimization Framework
• Outline– Main contributions of this work– System overview– Design details– Evaluation results
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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.
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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
1 3
Transformations
0
1,22
3
0
2,31
0
1,32
0
1,2,3
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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
AuxiliarySchemes
Termination?
Output
Cost model
No
Yes
Optimization process in one plan period
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Design Details
• Transformation operations– Main schemes: Merge,
Demote– Auxiliary schemes: Move,
Promote, Split, Co-scheduling
– Transformations can combine with each other
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Using Transformations
• Example of using Move and Merge operations
Charging hours:
Only transform shape
Reduces cost
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Experimental Setup
• Workload– Montage, Ligo and Mixed – Workflow submission ratefollows 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
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Evaluation Results on Cost Optimizations
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%
29%17%
21%16%
28%15%
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12%
Evaluation Results on Performance Optimizations
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%18%
21%8%
16%
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Outline
• Related Work• Generalized Optimization Frameworks
– General transformations for cost and performance optimizations– A declarative language for workflow optimization problems
• Conclusions
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Deco: A Declarative Optimization Framework
• Outline– Main contributions of this work– System overview– A declarative language for workflows– GPU-accelerated search engine– Evaluation results
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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
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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
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System Overview
• Overview of the Deco system– WLog, a declarative language for workflows– GPU-Accelerated search engine
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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).cons deadline(95%, 10h).var configs(Tid, Vid) forall task(Tid) and Vm(Vid).
r1 import(amazonec2).r2 import(montage).r3 path(X,Y,Y,C) :- edge(X,Y), exetime(X,Vid,T), C is T.r4 path(X,Y,Z,C) :- edge(X,Z), Zn==Y, path(Z,Y,Z2,C1), exetime(X,Vid,T), C is T+C1.r5 maxtime(Path,T) :- setof([Z,C],path(root,tail,Z,C),Set), max(Set,[Path,T]).r6 cost(Tid,Vid,C) :- price(Vid,Up), exetime(Tid,Vid,T), C is ceil(T/60.0)*Up.r7 totalcost(Ct) :- findall(C,cost(Tid,Vid,C),Bag), sum(Bag,Ct).
problem specific keywords:• goal Optimization goal defined by the user.• cons Problem constraint defined by the user.• var Problem variable to be optimized.
deadline(P, D) A probabilistic deadline requirement that D is at the P-th percentile of workflow execution time.
import(cloud) Import the cloud-related facts from the cloud metadata.
import(daxfile) Import the workflow-related facts generated from a DAX file.
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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
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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
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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.
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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.
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Performance Speedup of GPUs
Montage Epigenomics Ligo0
100
200
300
400
500
GPU Accelerations
• Performance speedup of GPU implementation over CPU implementation on a single core for the three applications
437x
93x31x
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Outline
• Related Work• Generalized Optimization Frameworks
– General transformations for cost and performance optimizations– A declarative language for workflow optimization problems
• Conclusions
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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-of-the-art algorithm.
– Deco achieves up to 77% improvement over the state-of-the-art algorithm.
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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
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References• Maciej Malawski, Gideon Juve, Ewa Deelman, and Jarek Nabrzyski. 2012. Cost- and deadline-
constrained 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-a-
Service in IaaS Clouds, submitted to ToC, 2014.
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Thank you!Amelie Chi [email protected]
Advisor: Bingsheng [email protected]
Xtra Computing Grouphttp://pdcc.ntu.edu.sg/xtraNanyang Technological University, Singapore