Vincent W Freeh

Works (12)

Updated: July 5th, 2023 15:55

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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9. Industry, Innovation and Infrastructure (OpenAlex)
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

Dynamically controlling node-level parallelism in Hadoop

2015 IEEE 8TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, pp. 309–316.

By: K. Kc n & V. Freeh n

TL;DR: This work develops an approach to dynamically change the parallelism for concurrent containers to suit an application and improves performance of MapReduce applications by as much as 28% and 60% respectively when compared to the best practice and default settings. (via Semantic Scholar)
Source: Web Of Science
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 chapter

Tuning Hadoop Map Slot Value Using CPU Metric

In Big Data Benchmarks, Performance Optimization, and Emerging Hardware (pp. 141–153).

By: K. Kc n & V. Freeh n

TL;DR: A low-overhead method is found to predict the best MSV using a new Hadoop counter that measures per-map task CPU utilization and shows that using a single MSV for all applications results in performance degradation up to 132 % when compared to using the bestMSV for each application. (via Semantic Scholar)
Source: Crossref
Added: February 24, 2020

2011 journal article

Adaptive, transparent CPU scaling algorithms leveraging inter-node MPI communication regions

PARALLEL COMPUTING, 37(10-11), 667–683.

By: M. Lim*, V. Freeh n & D. Lowenthal *

author keywords: Power-aware computing; Message passing interface (MPI)
TL;DR: An MPI runtime system that dynamically reduces CPU frequency and voltage during communication phases in MPI programs and, without a priori knowledge, selects the CPU frequency in order to minimize energy-delay product is presented. (via Semantic Scholar)
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7. Affordable and Clean Energy (OpenAlex)
Source: Web Of Science
Added: August 6, 2018

2011 chapter

On the Expressiveness of Return-into-libc Attacks

In Lecture Notes in Computer Science (pp. 121–141).

By: M. Tran  n, M. Etheridge n, T. Bletsch n, X. Jiang n, V. Freeh n & P. Ning  n

TL;DR: This paper presents a generalized R ILC attack called Turing complete RILC (TC-RILC) that allows for arbitrary computations and demonstrates that TC-R ILC satisfies formal requirements of Turing-completeness. (via Semantic Scholar)
Source: Crossref
Added: August 28, 2020

2011 chapter

Taming Information-Stealing Smartphone Applications (on Android)

In Trust and Trustworthy Computing (pp. 93–107).

By: Y. Zhou  n, X. Zhang *, X. Jiang n & V. Freeh n

TL;DR: A system called TISSA is developed that implements a new privacy mode in smartphones that can empower users to flexibly control in a fine-grained manner what kinds of personal information will be accessible to an application. (via Semantic Scholar)
Source: Crossref
Added: August 28, 2020

2008 journal article

Just-in-time dynamic voltage scaling: Exploiting inter-node slack to save energy in MPI programs

JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 68(9), 1175–1185.

By: V. Freeh n, N. Kappiah n, D. Lowenthal * & T. Bletsch n

author keywords: power-aware; distributed computing; message passing interface (MPI)
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7. Affordable and Clean Energy (OpenAlex)
Source: Web Of Science
Added: August 6, 2018

2007 journal article

Analyzing the energy-time trade-off in high-performance computing applications

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 18(6), 835–848.

By: V. Freeh n, D. Lowenthal *, F. Pan n, N. Kappiah n, R. Springer*, B. Rountree *, M. Femal n

author keywords: high-performance computing; power-aware computing
TL;DR: The results show that, for programs that have a memory or communication bottleneck, a power-scalable cluster can save significant energy with only a small time penalty, and it is possible to both consume less energy and execute in less time by increasing the number of nodes while reducing the frequency-voltage setting of each node. (via Semantic Scholar)
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UN Sustainable Development Goal Categories
7. Affordable and Clean Energy (OpenAlex)
Source: Web Of Science
Added: August 6, 2018

2005 chapter

Safe overprovisioning: Using power limits to increase aggregate throughput

In T. N. V. B. Falsafi (Ed.), Power-aware computer systems: 4th International Workshop, PACS 2004, Portland, OR, USA, December 5, 2004 (Lecture notes in computer science; 3471) (Vol. 3471, pp. 150–164).

By: M. Femal n & V. Freeh n

Ed(s): T. B. Falsafi

TL;DR: Host-based and network-centric models are proposed to monitor and coordinate the distribution of power with the fundamental goal of increasing throughput and initial results with a synthetic benchmark indicate throughput increases of nearly 6% from a staticly assigned, power managed environment and over 30% from an unmanaged environment. (via Semantic Scholar)
Source: NC State University Libraries
Added: August 6, 2018

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