Publications

Choe, Yung R.; Bierma, Michael B.; Ward, Nicholas P.; Wu, Kevin W.

Abstract not provided.

Choe, Yung R.; choi, sung c.; Bierma, Michael B.; Bigg, Jeffrey B.; Narayanan, Shrinithi N.; Gandhi, Vansh G.

Abstract not provided.

Bierma, Michael B.; Erickson, Jeremy L.; Fritz, David J.; Choe, Yung R.

Abstract not provided.

Choe, Yung R.

Abstract not provided.

Lloyd, Levi L.; Fritz, David J.; Erickson, Jeremy L.; Choe, Yung R.; Dirks, David H.; Hulette, Geoffrey C.; Armstrong, Robert C.; Vorobeychik, Yevgeniy V.; Deccio, Casey T.

Abstract not provided.

Choe, Yung R.; Wilke, Jeremiah J.; Hendry, Gilbert H.

Abstract not provided.

Choe, Yung R.

Abstract not provided.

Choe, Yung R.

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Choe, Yung R.

Abstract not provided.

Yoshimura, Ann S.; Choe, Yung R.

Abstract not provided.

GLOBECOM - IEEE Global Telecommunications Conference

Chen, Chao C.; Choe, Yung R.; Chuah, Chen N.; Mohapatra, Prasant

Network measurement is a discipline that provides the techniques to collect data that are fundamental to many branches of computer science. While many capturing tools and comparisons have made available in the literature and elsewhere, the impact of these packet capturing tools on existing processes have not been thoroughly studied. While not a concern for collection methods in which dedicated servers are used, many usage scenarios of packet capturing now requires the packet capturing tool to run concurrently with operational processes. In this paper we perform experimental evaluations of the performance impact that packet capturing process have on webbased services; in particular, we observe the impact on web servers. We find that packet capturing processes indeed impact the performance of web servers, but on a multi- core system the impact varies depending on whether the packet capturing and web hosting processes are co-located or not. In addition, the architecture and behavior of the web server and process scheduling is coupled with the behavior of the packet capturing process, which in turn also affect the web server's performance. © 2011 IEEE.

Choe, Yung R.

Abstract not provided.

Choe, Yung R.

Abstract not provided.

Choe, Yung R.

Abstract not provided.

Choe, Yung R.

Network measurement is a discipline that provides the techniques to collect data that are fundamental to many branches of computer science. While many capturing tools and comparisons have made available in the literature and elsewhere, the impact of these packet capturing tools on existing processes have not been thoroughly studied. While not a concern for collection methods in which dedicated servers are used, many usage scenarios of packet capturing now requires the packet capturing tool to run concurrently with operational processes. In this work we perform experimental evaluations of the performance impact that packet capturing process have on web-based services; in particular, we observe the impact on web servers. We find that packet capturing processes indeed impact the performance of web servers, but on a multi-core system the impact varies depending on whether the packet capturing and web hosting processes are co-located or not. In addition, the architecture and behavior of the web server and process scheduling is coupled with the behavior of the packet capturing process, which in turn also affect the web server's performance.

Vanderveen, Keith V.; Minnich, Ronald G.; Deccio, Casey T.; Floren, John F.; Sweeney, Andrew J.; Watts, Kristopher K.; Rudish, Don W.; Fritz, David J.; Choe, Yung R.

Abstract not provided.

Choe, Yung R.

Abstract not provided.

Roe, Diana C.; Choe, Yung R.; Ulmer, Craig D.

As scientific computing users migrate to petaflop platforms that promise to generate multi-terabyte datasets, there is a growing need in the community to be able to embed sophisticated analysis algorithms in the computing platforms' storage systems. Data Warehouse Appliances (DWAs) are attractive for this work, due to their ability to store and process massive datasets efficiently. While DWAs have been utilized effectively in data-mining and informatics applications, they remain largely unproven in scientific workloads. In this paper we present our experiences in adapting two mesh analysis algorithms to function on five different DWA architectures: two Netezza database appliances, an XtremeData dbX database, a LexisNexis DAS, and multiple Hadoop MapReduce clusters. The main contribution of this work is insight into the differences between these DWAs from a user's perspective. In addition, we present performance measurements for ten DWA systems to help understand the impact of different architectural trade-offs in these systems.

Proceedings of the Annual Hawaii International Conference on System Sciences

Ulmer, Craig; Bayer, Gregory B.; Choe, Yung R.; Roe, Diana C.

As scientific computing users migrate to petaflop platforms that promise to generate multi-terabyte datasets, there is a growing need in the community to be able to embed sophisticated data analysis algorithms in the storage systems for the computing platforms. Data Warehouse Appliances (DWAs) are an attractive option for this work, due to their ability to process massive datasets efficiently. While DWAs have been proven effective in data mining and informatics applications, there are relatively few examples of how DWAs can be integrated into the scientific computing workflow. In this paper we present our experiences in adapting two mesh analysis algorithms to function on two different DWAs: a SQL-based Netezza database appliance and a Map/Reduce-based Hadoop cluster. The main contribution of this work is insight into the differences between the two platforms' programming environments. In addition, we present performance measurements for entry-level DWAs to help provide a first-order comparison of the hardware. © 2010 IEEE.

Ulmer, Craig D.; Bayer, Gregory B.; Roe, Diana C.; Choe, Yung R.

Abstract not provided.