scholarly journals Gemini: An Adaptive Performance-Fairness Scheduler for Data-Intensive Cluster Computing

2015 ◽  
Author(s):  
Zhaojie Niu ◽  
Shanjiang Tang ◽  
Bingsheng He
Keyword(s):  
Cluster Computing ◽  
Adaptive Performance ◽  
Data Intensive

On Construction of Cluster and Grid Computing Platforms for Parallel Bioinformatics Applications

2012 ◽  
pp. 841-861
Author(s):  
Chao-Tung Yang ◽  
Wen-Chung Shih
Keyword(s):  
Grid Computing ◽  
High Performance Computing ◽  
High Speed ◽  
High Performance ◽  
Cluster Computing ◽  
Structural Features ◽  
Performance Technology ◽  
Data Intensive ◽  
Computing Platforms ◽  
Performance Computing

Biology databases are diverse and massive. As a result, researchers must compare each sequence with vast numbers of other sequences. Comparison, whether of structural features or protein sequences, is vital in bioinformatics. These activities require high-speed, high-performance computing power to search through and analyze large amounts of data and industrial-strength databases to perform a range of data-intensive computing functions. Grid computing and Cluster computing meet these requirements. Biological data exist in various web services that help biologists search for and extract useful information. The data formats produced are heterogeneous and powerful tools are needed to handle the complex and difficult task of integrating the data. This paper presents a review of the technologies and an approach to solve this problem using cluster and grid computing technologies. The authors implement an experimental distributed computing application for bioinformatics, consisting of basic high-performance computing environments (Grid and PC Cluster systems), multiple interfaces at user portals that provide useful graphical interfaces to enable biologists to benefit directly from the use of high-performance technology, and a translation tool for converting biology data into XML format.

Adaptive Performance Modeling of Data-intensive Workloads for Resource Provisioning in Virtualized Environment

2021 ◽  
Vol 5 (4) ◽  
pp. 1-24
Author(s):  
Hosein Mohamamdi Makrani ◽  
Hossein Sayadi ◽  
Najmeh Nazari ◽  
Sai Mnoj Pudukotai Dinakarrao ◽  
Avesta Sasan ◽  
...  
Keyword(s):  
Optimization Technique ◽  
Big Data Analytics ◽  
Resource Provisioning ◽  
Optimal Configuration ◽  
Adaptive Performance ◽  
Data Intensive ◽  
Resource Matching ◽  
Cross Platform ◽  
The Cost ◽  
Data Intensive Applications

The processing of data-intensive workloads is a challenging and time-consuming task that often requires massive infrastructure to ensure fast data analysis. The cloud platform is the most popular and powerful scale-out infrastructure to perform big data analytics and eliminate the need to maintain expensive and high-end computing resources at the user side. The performance and the cost of such infrastructure depend on the overall server configuration, such as processor, memory, network, and storage configurations. In addition to the cost of owning or maintaining the hardware, the heterogeneity in the server configuration further expands the selection space, leading to non-convergence. The challenge is further exacerbated by the dependency of the application’s performance on the underlying hardware. Despite an increasing interest in resource provisioning, few works have been done to develop accurate and practical models to proactively predict the performance of data-intensive applications corresponding to the server configuration and provision a cost-optimal configuration online. In this work, through a comprehensive real-system empirical analysis of performance, we address these challenges by introducing ProMLB: a proactive machine-learning-based methodology for resource provisioning. We first characterize diverse types of data-intensive workloads across different types of server architectures. The characterization aids in accurately capture applications’ behavior and train a model for prediction of their performance. Then, ProMLB builds a set of cross-platform performance models for each application. Based on the developed predictive model, ProMLB uses an optimization technique to distinguish close-to-optimal configuration to minimize the product of execution time and cost. Compared to the oracle scheduler, ProMLB achieves 91% accuracy in terms of application-resource matching. On average, ProMLB improves the performance and resource utilization by 42.6% and 41.1%, respectively, compared to baseline scheduler. Moreover, ProMLB improves the performance per cost by 2.5× on average.

Data Intensive Cloud Computing

Big Data ◽  
2016 ◽  
pp. 639-654
Author(s):  
Jayalakshmi D. S. ◽  
R. Srinivasan ◽  
K. G. Srinivasa
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Cluster Computing ◽  
Resource Provisioning ◽  
Data Intensive ◽  
Scientific Value ◽  
Data Intensive Applications ◽  
Cloud Applications ◽  
Problem Data ◽  
Huge Challenge

Processing Big Data is a huge challenge for today's technology. There is a need to find, apply and analyze new ways of computing to make use of the Big Data so as to derive business and scientific value from it. Cloud computing with its promise of seemingly infinite computing resources is seen as the solution to this problem. Data Intensive computing on cloud builds upon the already mature parallel and distributed computing technologies such HPC, grid and cluster computing. However, handling Big Data in the cloud presents its own challenges. In this chapter, we analyze issues specific to data intensive cloud computing and provides a study on available solutions in programming models, data distribution and replication, resource provisioning and scheduling with reference to data intensive applications in cloud. Future directions for further research enabling data intensive cloud applications in cloud environment are identified.

Data Intensive Cloud Computing

2015 ◽  
pp. 305-320
Author(s):  
Jayalakshmi D. S. ◽  
R. Srinivasan ◽  
K. G. Srinivasa
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Cluster Computing ◽  
Resource Provisioning ◽  
Data Intensive ◽  
Scientific Value ◽  
Data Intensive Applications ◽  
Cloud Applications ◽  
Problem Data ◽  
Huge Challenge

Processing Big Data is a huge challenge for today's technology. There is a need to find, apply and analyze new ways of computing to make use of the Big Data so as to derive business and scientific value from it. Cloud computing with its promise of seemingly infinite computing resources is seen as the solution to this problem. Data Intensive computing on cloud builds upon the already mature parallel and distributed computing technologies such HPC, grid and cluster computing. However, handling Big Data in the cloud presents its own challenges. In this chapter, we analyze issues specific to data intensive cloud computing and provides a study on available solutions in programming models, data distribution and replication, resource provisioning and scheduling with reference to data intensive applications in cloud. Future directions for further research enabling data intensive cloud applications in cloud environment are identified.

On Construction of Cluster and Grid Computing Platforms for Parallel Bioinformatics Applications

2013 ◽  
pp. 286-306
Author(s):  
Chao-Tung Yang ◽  
Wen-Chung Shih
Keyword(s):  
Grid Computing ◽  
High Performance Computing ◽  
High Speed ◽  
High Performance ◽  
Cluster Computing ◽  
Structural Features ◽  
Performance Technology ◽  
Data Intensive ◽  
Computing Platforms ◽  
Performance Computing

Biology databases are diverse and massive. As a result, researchers must compare each sequence with vast numbers of other sequences. Comparison, whether of structural features or protein sequences, is vital in bioinformatics. These activities require high-speed, high-performance computing power to search through and analyze large amounts of data and industrial-strength databases to perform a range of data-intensive computing functions. Grid computing and Cluster computing meet these requirements. Biological data exist in various web services that help biologists search for and extract useful information. The data formats produced are heterogeneous and powerful tools are needed to handle the complex and difficult task of integrating the data. This paper presents a review of the technologies and an approach to solve this problem using cluster and grid computing technologies. The authors implement an experimental distributed computing application for bioinformatics, consisting of basic high-performance computing environments (Grid and PC Cluster systems), multiple interfaces at user portals that provide useful graphical interfaces to enable biologists to benefit directly from the use of high-performance technology, and a translation tool for converting biology data into XML format.

Linking individual-level technical, contextual, and adaptive performance to team processes

2007 ◽  
Author(s):  
Meredith L. Cracraft ◽  
Gonzalo Ferro ◽  
David W. Dorsey ◽  
Johnathan Nelson
Keyword(s):  
Adaptive Performance ◽  
Team Processes ◽  
Individual Level

Site, Adapt, Perform: A Practice-as-Research Confrontation with Climate Change

2017 ◽  
Vol 35 (1) ◽  
pp. 111-129 ◽  
Author(s):  
Melanie Kloetzel
Keyword(s):  
Climate Change ◽  
Creative Process ◽  
Adaptive Performance ◽  
Bruno Latour ◽  
Research Methodologies ◽  
Arts Festivals ◽  
A Site

In recent years, arts festivals around the globe have become enamoured of touring, site-based performance. Such serialised site work is growing in popularity due to its accessibility, its spectacular characteristics, and its adaptive qualities. Employing practice-as-research methodologies to dissect the basis of such site-adaptive performances, the author highlights her discovery of the crumbling foundation of the adaptation discourse by way of her creative process for the performance work Room. Combining findings from the phenomenological explorations of her dancing body as well as from cultural analyses of the climate change debate by Dipesh Chakrabarty (2009), Claire Colebrook (2011, 2012), and Bruno Latour (2014), the author argues that only by fundamentally shifting the direction of the adaptation discourse – on scales from global to the personal – will we be able to build a site-adaptive performance strategy that resists the neoliberal drive towards ecological and economic precarity.

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