Chin-Jung Hsu

Works (5)

Updated: July 5th, 2023 15:38

2018 article

Micky: A Cheaper Alternative for Selecting Cloud Instances

PROCEEDINGS 2018 IEEE 11TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING (CLOUD), pp. 409–416.

By: C. Hsu n, V. Nair n, T. Menzies n  & V. Freeh n

TL;DR: A collective-optimizer is created, MICKY, that reformulates the task of finding the near-optimal cloud configuration as a multi-armed bandit problem and can achieve on average 8.6 times reduction in measurement cost as compared to the state-of-the-art method while finding near-Optimal solutions. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: January 21, 2019

2017 conference paper

Trilogy: data placement to improve performance and robustness of cloud computing

2017 IEEE International Conference on Big Data (Big Data), 2442–2451.

By: C. Hsu n, V. Freeh n & F. Villanustre

TL;DR: Evaluations show that maximizing the number of unique partitions per node increases robustness to tolerate workload deviation while minimizing this number reduces storage footprint, and a surprisingly small increase in granularity is sufficient to obtain most benefits. (via Semantic Scholar)
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Source: NC State University Libraries
Added: August 6, 2018

2016 conference paper

Inside-out: Reliable performance prediction for distributed storage systems in the cloud

Proceedings of 2016 ieee 35th symposium on reliable distributed systems (srds), 127–136.

By: C. Hsu n, R. Panta*, M. Ra * & V. Freeh n

TL;DR: In-depth evaluation shows that Inside-Out is a robust solution that enables SDS to predict end-to-end performance even in challenging conditions, e.g., changes in workload, storage configuration, available cloud resources, size of the distributed storage service, and amount of interference due to multi-tenants. (via Semantic Scholar)
Source: NC State University Libraries
Added: August 6, 2018

2015 article

Evaluation of MapReduce in a large cluster

2015 IEEE 8TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, pp. 461–468.

By: K. Kc n, C. Hsu n & V. Freeh n

TL;DR: The findings of running applications on Pivotal's Analytics Workbench, which consists of a 540-node Hadoop cluster, show that IO-intensive applications do not scale as data size increases and MapReduce applications require different amounts of parallelism and overlap to minimize completion time. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2014 journal article

Maintenance of cooperative overlays in multi-overlay networks

IET Communications, 8(15), 2676–2683.

By: W. Chung, C. Hsu, K. Lai, K. Li & Y. Chung

Source: NC State University Libraries
Added: August 6, 2018

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