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14th Annual International Conference on Computational Science

作者： Czarnul, Pawel Gdansk Univ Technol Fac Elect Telecommun & Informat Gdansk Poland

The paper presents a workflow application for efficient parallel processing of data downloaded from an Internet portal. The workflow partitions input files into subdirectories which are further split for parallel processing by services installed on distinct computer nodes. This way, analysis of the first ready subdirectories can start fast and is handled by services implemented as parallel multithreaded applications using multiple cores of modern CPUs. The goal is to assess achievable speed-ups and determine which factors influence scalability and to what degree. Data processing services were implemented for assessment of context (positive or negative) in which the given keyword appears in a document. The testbed application used these services to determine how a particular brand was recognized by either authors of articles or readers in comments in a specific Internet portal focused on new technologies. Obtained execution times as well as speed-ups are presented for data sets of various sizes along with discussion on how factors such as load imbalance and memory/disk bottlenecks limit performance.

关键词： parallel data processing parallel performance Internet parallel workflow application

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A workflow application for parallel Processing of Big Data from an Internet Portal

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Procedia Computer Science 2014年 29卷 499-508页

作者： Paweł Czarnul Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Poland

The paper presents a workflow application for efficient parallel processing of data downloaded from an Internet portal. The workflow partitions input files into subdirectories which are further split for parallel processing by services installed on distinct computer nodes. This way, analysis of the first ready sub-directories can start fast and is handled by services implemented as parallel multithreaded applications using multiple cores of modern CPUs. The goal is to assess achievable speed-ups and determine which factors influence scalability and to what degree. Data processing services were implemented for assessment of context (positive or negative) in which the given keyword appears in a document. The testbed application used these services to determine how a particular brand was recognized by either authors of articles or readers in comments in a specific Internet portal focused on new technologies. Obtained execution times as well as speed-ups are presented for data sets of various sizes along with discussion on how factors such as load imbalance and memory/disk bottlenecks limit performance.

关键词： parallel data processing parallel performance Internet parallel workflow application

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Energy Aware Workload Scheduling Metrics for Execution of parallel application in Heterogeneous Cloud Computing Platform

Energy Aware Workload Scheduling Metrics for Execution of Pa...

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Computing Conference

作者： Divyaprabha, K. N. Sudarshan, T. S. B. PES Univ Dept CSE Bangalore Karnataka India

ISBN: (纸本)9783031622717;9783031622694;9783031622687

The modern workload application is generally being executed on Heterogeneous (CPU-GPU) Cloud Computing (HCC) environment. Minimizing execution cost with high quality of service is most important considering economic perspective of Cloud Service Provider (CSP). The energy consumption plays a major part in cost and also makespan, meeting application deadlines. This work addresses the challenge involved in scheduling parallel workload under heterogeneous cloud computing for minimizing energy and makespan meeting task deadline prerequisite. Existing model are efficient in minimizing either energy or makespan;thus, resulting higher service provisioning cost. In addressing the research problem, this work present Energy Optimized Scheduling (EOS) for parallel workload application in heterogeneous cloud computing platform. Experiment results shows the proposed model is efficient in minimizing energy and makespan in comparison with existing workload scheduling approach. The result shows an average energy reduction of 23.22%, overall power consumption reduction of 85.06%, and makespan reduction of 78.2% for montage workflow in comparison with energy minimized scheduling.

关键词： Deadline Energy Heterogeneous cloud computing Multi-objective parallel workflow application workflow scheduling

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