Supporting Load Balancing for Distributed Data-Intensive Applications Leonid Glimcher, Vignesh Ravi, and Gagan Agrawal Department of ComputerScience and Engg. The Ohio State University Columbus, Ohio

Outline Introduction Motivation FREERIDE-G Processing Structure Run-time Load Balancing System Experimental Results Conclusions December 24, 20152

Introduction Growing abundance of data –Sensors, scientific simulations and business transactions Data Analysis –Translate raw data into knowledge Grid/Cloud Computing –Enables distributed processing December 24, 20153

Motivation Resources are geographically distributed –Data nodes –Compute nodes –Middleware user Remote data analysis is important Heterogeneity of resources –Difference in network bandwidth –Difference in compute power December 24, Data Nodes Compute Nodes Middleware user Grid/Cloud Environment

FREERIDE-G Processing Structure (Framework for Rapid Implementation of Datamining Engines – Grid) December 24, While( ) { forall( data instances d) { (I, d’) = process(d) R(I) = R(I) op d’ } ……. } A Map-reduce like system Remote data analysis Middleware API Process Reduce Global Combine Reduction Object

A Real-time Grid/Cloud Scenario December 24, A B C D Compute Data

Run-time Load Balancing December 24, Two factors of load imbalance Computational factor, w1 Remote data transfer (wait time), w2 Case 1: w1 > w2 Case 2: w2 > w1 We use sum of weights to account for both the components

Dynamic Load Balancing Algorithm December 24, Consider every chunk, Ci Calculate Compute cost, Cc Calculate Data transfer cost, Tc Input Bandwidth matrix, W1 & W2 Total cost = W1*Cc + W2*Tc If Total cost < Min Update Min Assign Ci to Pj

Experimental Setup Settings Organizational Grid Wide Area Network (WAN) Goals are to evaluate Scalability Dynamic Load balancing overhead Adaptability to scenarios –compute bound, –I/O bound, –WAN setting Applications K-means Vortex Detection December 24, 20159

10 Scalability and Overhead of Dynamic Balancing Vortex detection 14.8 GB data Organizational setting Bandwidth –50mb/sec –100mb/sec 31% benefit Overhead within 10% December 24,

Model Adaptability – Compute Bound Scenario Kmeans clustering 25.6 GB data Bandwidth –50 MB –200 MB Best result combination skewed towards work load component Initial (unbalanced) overhead 57% over balanced Dynamic overhead 5% over balanced December 24, Ideal Case Dynamic case Compute Data transfer

Model Adaptability – I/O Bound Scenario December 24, Kmeans clustering 25.6 GB data Bandwidth –15 mb/s –60 mb/s Best result combination skewed towards data transfer component Initial (unbalanced) overhead 40% over balanced Dynamic overhead 4% over balanced

Model Adaptability – WAN setting Vortex Detection 14.6 GB Best result combination results in lowest overhead (favoring data delivery component) Unbalanced configuration 20% overhead over balanced Our approach Overhead reduced to 8% December 24,

Conclusions Dynamic load balancing solution for grid environments Both workload and data transfer factors are important Scalability is good and overheads are within 10% Adaptable to compute-bound, I/O bound, and WAN settings December 24,

December 24, Thank You! Questions? Contacts: Leonid Glimcher Vignesh Ravi- Gagan Agrawal-

P. 16 DataGrid Lab Setup 1: Organizational Grid Data hosted on Opteron 250’s Processed on Opteron 254’s 2 clusters connected through two 10 GB optical fibers Both clusters within same city (0.5 mile apart) Evaluating: Scalability Adaptability Integration overhead Compute cluster (cse-ri) Repository cluster (bmi-ri)

P. 17 DataGrid Lab Setup 2: WAN Data Repository: Opteron 250’s (OSU) Opteron 258’s (Kent St) Processed on Opteron 254’s No dedicated link between processing and repository clusters Evaluating: Scalability Adaptability Compute cluster (OSU ) Repository cluster (Kent ST) Repository cluster (OSU)

FREERIDE-G System Design December 24,