Tag: z-wang

Making Data Retrieval More Efficient

When a user performs a search in social media, the request doesn’t stay within that platform. It calls upon the resources of a data center. “When someone sends a request to a data center, they want an immediate answer—they don’t want to wait,” Zhenlin Wang explains.

We designed upon open-source software and memcached that was adopted by Facebook and Twitter. They modified their approach to adapt to user demand. Our method beats their current practices.

Together with colleagues from Peking University, the University of Rochester, Wayne State University, and Michigan Tech, Wang looked to improve the internal structure,
theory, and algorithm of memory cache to make it more efficient.

This work is an offspring of his 2007 CAREER award.

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“Currently, bulky disks store the data and are slow to react. When smaller, in-memory cache is used, the search is much faster,” he adds. “We designed upon open-source software and memcached that was adopted by Facebook and Twitter. They modified their approach to adapt to user demand. Our method beats their current practices,” Wang says.

“Imagine inviting 100 people over to your house for dinner, but only four will fit in your dining room. When we think about data resource management, it’s a similar scenario.”


Transfer Learning in Data Centers

Faster apps. More memory. Laura Brown and Zhenlin Wang bring efficiency to Big Data.

What memory resources will be available if applications A, B, and C all run together?

Big companies like Amazon and Google have even bigger data centers. Think 30 data centers each with 50,000 to 80,000 servers. And the underlying computer processors are not all identical; each year new improvements are integrated and added. Brown, Wang, and computer science colleagues from Western Michigan University are digging deep into the management of memory resources in these larger-than-life data centers.

The researchers use machine-learning techniques to create models that predict the cache and memory requirements of an application.

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The challenge is how to make accurate predictions with such a massive variety of applications using the data center, and the different computers the application runs on. Applications might include Netflix streaming a movie, Airbnb running database queries, or NASA processing satellite images. Each app is not run in isolation with a dedicated machine. To maximize resources, data centers may have two or more applications all running on a single machine.

“If we learn the memory requirements of application A on computer X, what if the same app runs on machine Y or machine Z? Or, what memory resources will be available if A, B, and C all run together?” Brown asks.


Professor Zhenlin Wang received external funding

Zhenlin WangProfessor Zhenlin Wang received an NSF research award with a total budget of $375,000.

This is a 3-year project with a title of “CSR:Small: Effective Sampling-Based Miss Ratio Curves: Theory and Practice”. In this project, Zhenlin and his students will use miss ratio curves (MRCs), which relate cache miss ratio to cache size, to model working set and cache locality.

The project develops a new cache locality theory to construct MRCs effectively and then applies it to several caching or memory management systems.


Three Faculty Receive External Funding

Dear All,

Please join me in congratulating Zhenlin, Tim, and Philart on receiving external research funding during the summer!

Zhenlin received an NSF research award with a total budget of $375,000. This is a 3-year project with a title of “CSR:Small: Effective Sampling-Based Miss Ratio Curves: Theory and Practice”. In this project, Zhenlin and his students will use miss ratio curves (MRCs), which relate cache miss ratio to cache size, to model working set and cache locality. The project develops a new cache locality theory to construct MRCs effectively and then applies it to several caching or memory management systems.

Tim received a DoD Army Research Office research award with a budget of $99,779 during the first year. This is also a 3-year project with a total budget of $1,066,799. The project is titled “Multisensor Analysis and Algorithm Development for Detection and Classification of Buried and Obscured Targets.” Tim and his students will develop new algorithms to detect and classify buried objects, one of the important research areas for ARO.

Philart received a research award from Hyundai Motor Company in the amount of $130,236. The project is entitled, “Novel In-vehicle Interaction Design and Evaluation”. Philart and his students will investigate the effectiveness of an in-vehicle control system and culture-specific sound preference.

Congratulations Zhenlin, Tim, and Philart! Thanks for the great job!

Best,
Min Song


Promotions for Onder, Wang, and Kuhl

Michigan Tech Board of Control Adopts New Strategic Plan

At its regular meeting on Friday, May 1, 2015, the Board of Control promoted 11 associate professors with tenure to professor with tenure. Among them are Soner Onder and Zhenlin Wang.

The Board also promoted 18 assistant professors to associate professor with tenure and one associate professor without tenure to associate professor with tenure. Among them is Scott Kuhl.

Read more at Michigan Tech News, by Jennifer Donovan.

Zhenlin Wang
Zhenlin Wang
Soner Onder
Soner Onder
Scott Kuhl
Scott Kuhl

Recent Grants

Philart Jeon: PI, National Health Institute. “NRI: Colloborative: Interactive Robotic Orchestration – Music-based emotion and social interaction therapy for children with ASD,” 2014-2017.

Philart Jeon: Co-PI, US DOT-OST, National University Rail Center Project. “NURail-Tier I,” 2014-2017

Robert Pastel & Charles Wallace: CI-Team, National Science Foundation.”Environmental CyberCitizens: Engaging Citizen Scientists in Global Environmental Change through Crowdsensing and Visualization,” 2011 and on-going

Laura Brown & Zhenlin Wang: Co-PI, National Science Foundation. “Adaptive Memory Resource Management in a Data Center – A Transfer Learning Approach,” 2014-2017

Leo Ureel: Recipient, Jackson Blended Learning Grant. “Canvas TA: Auto Program Feedback,” 2014-2015