Statistical Learning-Based Prediction of Execution Time of Data-Intensive Program Under Hadoop2.0

PEBIL: binary instrumentation for practical data-intensive program analysis

Cluster Computing ◽

10.1007/s10586-013-0307-2 ◽

2013 ◽

Vol 18 (1) ◽

pp. 1-14 ◽

Cited By ~ 7

Author(s):

Michael A. Laurenzano ◽

Joshua Peraza ◽

Laura Carrington ◽

Ananta Tiwari ◽

William A. Ward ◽

...

Keyword(s):

Program Analysis ◽

Binary Instrumentation ◽

Data Intensive ◽

Intensive Program

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Extended Balanced Scheduler with Clustering and Rep- lication for Data Intensive Scientific Workflow Applications in Cloud Computing

Journal of Electronic Research and Application ◽

10.26689/jera.v2i3.380 ◽

2018 ◽

Vol 2 (3) ◽

Author(s):

Satwinder Kaur ◽

Mehak Aggarwal

Keyword(s):

Cloud Computing ◽

Task Scheduling ◽

Execution Time ◽

Performance Metrics ◽

Scheduling Algorithm ◽

Research Work ◽

Scientific Workflow ◽

It Services ◽

Data Intensive ◽

Fine Grain

Cloud computing is an advance computing model using which several applications, data and countless IT services are provided over the Internet. Task scheduling plays a crucial role in cloud computing systems. The issue of task scheduling can be viewed as the finding or searching an optimal mapping/assignment of set of subtasks of different tasks over the available set of resources so that we can achieve the desired goals for tasks. With the enlargement of users of cloud the tasks need to be scheduled. Cloudâ€™s performance depends on the task scheduling algorithms used. Numerous algorithms have been submitted in the past to solve the task scheduling problem for heterogeneous network of computers. The existing research work proposes different methods for data intensive applications which are energy and deadline aware task scheduling method. As scientific workflow is combination of fine grain and coarse grain task. Every task scheduled to VM has system overhead. If multiple fine grain task are executing in scientific workflow, it increase the scheduling overhead. To overcome the scheduling overhead, multiple small tasks has been combined to large task, which decrease the scheduling overhead and improve the execution time of the workflow. Horizontal clustering has been used to cluster the fine grained task further replication technique has been combined. The proposed scheduling algorithm improves the performance metrics such as execution time and cost. Further this research can be extended with improved clustering technique and replication methods.

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Worst-case Execution Time Calculation for Query-based Monitors by Witness Generation

ACM Transactions on Embedded Computing Systems ◽

10.1145/3471904 ◽

2021 ◽

Vol 20 (6) ◽

pp. 1-36

Author(s):

Márton Búr ◽

Kristóf Marussy ◽

Brett H. Meyer ◽

Dániel Varró

Keyword(s):

Real Time ◽

Execution Time ◽

Timing Analysis ◽

Runtime Monitoring ◽

Worst Case ◽

Data Intensive ◽

Monitoring Programs ◽

Worst Case Execution Time ◽

Time Calculation ◽

Graph Queries

Runtime monitoring plays a key role in the assurance of modern intelligent cyber-physical systems, which are frequently data-intensive and safety-critical. While graph queries can serve as an expressive yet formally precise specification language to capture the safety properties of interest, there are no timeliness guarantees for such auto-generated runtime monitoring programs, which prevents their use in a real-time setting. While worst-case execution time (WCET) bounds derived by existing static WCET estimation techniques are safe, they may not be tight as they are unable to exploit domain-specific (semantic) information about the input models. This article presents a semantic-aware WCET analysis method for data-driven monitoring programs derived from graph queries. The method incorporates results obtained from low-level timing analysis into the objective function of a modern graph solver. This allows the systematic generation of input graph models up to a specified size (referred to as witness models ) for which the monitor is expected to take the most time to complete. Hence, the estimated execution time of the monitors on these graphs can be considered as safe and tight WCET. Additionally, we perform a set of experiments with query-based programs running on a real-time platform over a set of generated models to investigate the relationship between execution times and their estimates, and we compare WCET estimates produced by our approach with results from two well-known timing analyzers, aiT and OTAWA.

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The Successful Stuttering Management Program: A Preliminary Report on Outcomes

Perspectives on Fluency and Fluency Disorders ◽

10.1044/ffd20.1.20 ◽

2010 ◽

Vol 20 (1) ◽

pp. 20-25 ◽

Cited By ~ 2

Author(s):

Jim Tsiamtsiouris ◽

Kim Krieger

Keyword(s):

Preliminary Report ◽

Speech Rate ◽

Management Program ◽

Intensive Program ◽

Adults Who Stutter

Abstract The purpose of this study was to test the hypothesis that adults who stutter will exhibit significant improvements after attending a residential, 3-week intensive program that focuses on avoidance reduction and stuttering modification therapy. Preliminary analyses focused on four measures: (a) SSI-3, (b) speech rate, (c) S-24 Scale, and (d) OASES. Results indicated significant improvements on all of the measures.

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Rapid Serial Auditory Presentation

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000295 ◽

2015 ◽

Vol 62 (5) ◽

pp. 346-351 ◽

Cited By ~ 15

Author(s):

Ana Franco ◽

Julia Eberlen ◽

Arnaud Destrebecqz ◽

Axel Cleeremans ◽

Julie Bertels

Keyword(s):

Statistical Learning ◽

Reaction Times ◽

Visual Presentation ◽

Detection Task ◽

Speech Segmentation ◽

Auditory Presentation ◽

Indirect Measure ◽

Speech Stream ◽

Adult Participants ◽

Presentation Procedure

Abstract. The Rapid Serial Visual Presentation procedure is a method widely used in visual perception research. In this paper we propose an adaptation of this method which can be used with auditory material and enables assessment of statistical learning in speech segmentation. Adult participants were exposed to an artificial speech stream composed of statistically defined trisyllabic nonsense words. They were subsequently instructed to perform a detection task in a Rapid Serial Auditory Presentation (RSAP) stream in which they had to detect a syllable in a short speech stream. Results showed that reaction times varied as a function of the statistical predictability of the syllable: second and third syllables of each word were responded to faster than first syllables. This result suggests that the RSAP procedure provides a reliable and sensitive indirect measure of auditory statistical learning.

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