Paper Info
Title
A Declarative Optimization Engine for Resource Provisioning of Scientific Workflows in IaaS Clouds
Abstract
Resource provisioning for scientific workflows in Infrastructure-as-a-service (IaaS) clouds is an important and complicated problem for budget and performance optimizations of workflows. Scientists are facing the complexities resulting from severe cloud performance dynamics and various user requirements on performance and cost. To address those complexity issues, we propose a declarative optimization engine named Deco for resource provisioning of scientific workflows in IaaS clouds. Deco allows users to specify their workflow optimization goals and constraints of specific problems with an extended declarative language. We propose a novel probabilistic optimization approach for evaluating the declarative optimization goals and constraints in dynamic clouds. To accelerate the solution finding, Deco leverages the available power of GPUs to find the solution in a fast and timely manner. We evaluate Deco with several common provisioning problems. We integrate Deco into a popular workflow management system (Pegasus) and show that Deco can achieve more effective performance/cost optimizations than the state-of-the-art approaches.
Year
DOI
Venue
2015
10.1145/2749246.2749251
High-Performance Distributed Computing
Field
DocType
Citations 
Workflow optimization,Computer science,Real-time computing,Provisioning,Probabilistic optimization,Declarative programming,Workflow,Workflow management system,User requirements document,Distributed computing,Cloud computing
Conference
5
PageRank 
References 
Authors
0.42
33
4
Name
Order
Citations
PageRank
Amelie Chi Zhou11039.11
Bingsheng He22810179.09
Xuntao Cheng3596.40
Chiew Tong Lau440635.82