Uppsala, April 12-16th 2010EGEE 5th User Forum1 A Business-Driven Cloudburst Scheduler for Bag-of-Task Applications Francisco Brasileiro, Ricardo Araújo, David Candeia Maia, Raquel Lopes Federal University of Campina Grande, Brazil Department of Systems and Computing Distributed Systems Lab

Outline Motivation Problem Statement Business-driven heuristics for cloudbursting Evaluation Implementation Conclusions Uppsala, April 12-16th 20102EGEE 5th User Forum

Motivation Many e-Science applications can be easily parallelised – They fall in the so called, bag-of-tasks class of applications They have little QoS requirements – In particular, they can be executed on opportunistic infrastructures, since fault tolerance mechanism are trivially implemented Yet, the research cycle could be speeded up, if applications could complete faster – Can we leverage on the availability of resources in cloud computing providers, so to speed up the execution of such applications? – How much should one pay for that? Uppsala, April 12-16th 20103EGEE 5th User Forum

Computation infrastructure Uppsala, April 12-16th 2010EGEE 5th User Forum4 Free resources from opportunistic Desktop Grids (eg. Condor, OurGrid, XtremWeb, etc.) Resources acquired from a Cloud Computing provider (eg. AWS EC2 on-demand instances) Local resources, possibly used in an opportunistic way with a fairly small additional cost BoT user

Research question Uppsala, April 12-16th 2010EGEE 5th User Forum5 Free resources from opportunistic Desktop Grids (eg. Condor, OurGrid, XtremWeb, etc.) Resources acquired from a Cloud Computing provider (eg. AWS EC2 on-demand instances) Local resources, possibly used in an opportunistic way with a fairly small additional cost BoT user Where shall I run my application???

A business-driven approach Running the application will incur costs, except when it is executed on the idle time of the local resources or on the best-effort grid infrastructure Completing the execution of the application by a given time yields utility – These are described by monotonically decreasing utility functions that associate a utility to each different value of the application’s makespan A solution to the problem should maximise the profit, where: Profit = Utility – Cost Uppsala, April 12-16th 2010EGEE 5th User Forum6

Examples of utility functions Uppsala, April 12-16th 20107EGEE 5th User Forum Let t r be the time the application is ready for submission and t d -t r be the largest makespan for which there is some utility to be gained by the execution of the application

A family of heuristics for cloudbursting From time to time, observe the system past behaviour Calculate the system throughput (number of tasks processed per unit of time) Maximise the profit function: – Assuming that the current throughput will be maintained – Considering the system “acceleration” The output of the maximisation procedure is the number of cloud computing instances that should be acquired/released for the next period Uppsala, April 12-16th 2010EGEE 5th User Forum8

Evaluation methodology We have built a discrete-event simulator to evaluate the proposed heuristics – It works with the notion of a turn whose length is equal to the minimal time window for which resources can be acquired from a cloud computing provider (eg. 1 hour for AWS EC2 on-demand instances) – At each turn it decides how many recourses need to be acquired from the cloud provider for the next turns in order to maximise the profit The simulator also performs the cloudburst scheduling with full knowledge about the future, leading to an optimal solution – The profit yield by the optimal solution is used to compute the efficiency of the schedule provided by the heuristics E(h) = P(h)/P o, where E(h) is the efficiency of heuristic h, P(h) is the profit achieved by heuristic h, and P o is the optimal profit for the scenario evaluated Uppsala, April 12-16th 20109EGEE 5th User Forum

Evaluation scenarios Three different heuristics – Conservative, derivative, midpoint derivative Two utility functions – Decay and exponential Three different grid sizes Four machine availability traces AWS EC2 on-demand instances pricing model Three level of task heterogeneity for BoT applications – Homogeneous (10 minutes per task), U[5,15], U(0,20] Uppsala, April 12-16th 2010EGEE 5th User Forum10

Evaluation results Uppsala, April 12-16th EGEE 5th User Forum Efficiency Number of machines in the grid Efficiency Number of machines in the grid Efficiency Number of machines in the grid Efficiency Number of machines in the grid Efficiency Number of machines in the grid Efficiency Number of machines in the grid Decay utility functionExponential utility function

Implementation The best heuristic has been implemented in the OurGrid grid middleware The new user interface allows users to perform cloudbursting using both the AWS EC2 cloud computing provider and private/public cloud providers based on Eucalyptus Uppsala, April 12-16th EGEE 5th User Forum

Implementation

Peers

Implementation Workers

Implementation Broker

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Cloud Provider Peer Cloud Provider Peer

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OurGrid Broker – User set up a “Cloud Provider Peer”

Conclusions We have shown that cloudbursting is a feasible approach to speed up the execution of BoT applications Simple heuristics perform very well The software is not yet available in the latest release of OurGrid but can be provided upon requests sent to The use of the system by real users will help us to improve its design Uppsala, April 12-16th EGEE 5th User Forum

Thanks for your attention! I will be glad to answer your questions For more information about this project visit For more information about the OurGrid middelware visit For more information about other projects developed by LSD/UFCG, visit Uppsala, April 12-16th 2010EGEE 5th User Forum26