Active Resource Management Services

ARMS is reaching out to you

Mentor

Dr. Yaohang Li

Dr. Li is an Associate Professor in the Department of Computer Science at Old Dominion University.

His research interests are in Computational Biology, Markov Chain Monte Carlo (MCMC) methods and Parallel Distributed Grid Computing.

He has acquired over $15.3 million in grants over his research career.

“Sometimes we can do in one day by computer what might require a couple of years in a lab. It feels good knowing I might make a contribution to human health.”
-Dr. Li

Dr. Li's Research

Computing for Cures Article

Problem Statement

Processing time on big data sets is computationally expensive and as the volume of queries grows the system will progressively drop in performance until the system fails.
Researchers have difficulties handling and managing the massive amounts of data that they are collecting.

Solution Statement

We aim to configure a distributed resource management system (D-RMS), in this case Sun Grid Engine (SGE), to handle resource allocation on the Dinosolve cluster.
We hope to prepare the system to be able to continue to support the research community in light of its expected growth in requests.

Objectives

Configure, utilize, and optimize the SGE
Design an aesthetically pleasing and professional user interface
Conform to 508 Compiance standards
Improve the existing database schema by normalizing and adding user accounts

Benefits of Solution

Efficient utilization of available resources and increased throughput of the cluster
Professional user interface leading to a rise in popularity
Accessibility
Security and efficient access of previous submissions

Overall Goal

With the updated user interface and correctly configured Sun Grid Engine, Dr. Li hopes to establish a reputable, reliable, and aesthetically pleasing Disulfide Bonding Prediction Server.

Big Data

Big data spans four dimensions: Volume, Velocity, Variety, and Veracity.

Volume

Enterprises are awash with ever-growing data of all types, easily amassing terabytes—even petabytes—of information.
Turn 12 terabytes of Tweets created each day into improved product sentiment analysis Convert 350 billion annual meter readings to better predict power consumption

Veracity

1 in 3 business leaders don’t trust the information they use to make decisions. How can you act upon information if you don’t trust it? Establishing trust in big data presents a huge challenge as the variety and number of sources grows.

Variety

Big data is any type of data - structured and unstructured data such as text, sensor data, audio, video, click streams, log files and more. New insights are found when analyzing these data types together.
Monitor 100’s of live video feeds from surveillance cameras to target points of interest Exploit the 80% data growth in images, video and documents to improve customer satisfaction

Velocity

Sometimes 2 minutes is too late. For time-sensitive processes such as catching fraud, big data must be used as it streams into your enterprise in order to maximize its value.
Scrutinize 5 million trade events created each day to identify potential fraud Analyze 500 million daily call detail records in real-time to predict customer churn faster