scholarly journals TomusBlobs: scalable data-intensive processing on Azure clouds

2013 ◽  
Vol 28 (4) ◽  
pp. 950-976 ◽  
Author(s):  
Alexandru Costan ◽  
Radu Tudoran ◽  
Gabriel Antoniu ◽  
Goetz Brasche
Keyword(s):  
Data Intensive ◽  
Intensive Processing

scholarly journals Building Multi-occupancy Analysis and Visualization Through Data Intensive Processing

2016 ◽  
pp. 587-599
Author(s):  
Dimosthenis Ioannidis ◽  
Pantelis Tropios ◽  
Stelios Krinidis ◽  
Dimitris Tzovaras ◽  
Spiridon Likothanassis
Keyword(s):  
Data Intensive ◽  
Intensive Processing

Instruction level redundant number computations for fast data intensive processing in asynchronous processors

2005 ◽  
Vol 51 (3) ◽  
pp. 151-164
Author(s):  
Jeong-Gun Lee ◽  
Euiseok Kim ◽  
Dong-Ik Lee
Keyword(s):  
Data Intensive ◽  
Intensive Processing

CSO Modelling Considering Moving Storms and Tipping Bucket Gauge Failures

2007 ◽  
Vol 2 (1) ◽  
Author(s):  
M. Hochedlinger ◽  
W. Sprung ◽  
H. Kainz ◽  
K. König
Keyword(s):  
Hydrological Models ◽  
Rain Intensity ◽  
Hydrodynamic Models ◽  
Data Intensive ◽  
Storm Flow ◽  
Cell Velocity ◽  
Combined Sewer ◽  
Moving Direction ◽  
The Difference

The simulation of combined sewer overflow volumes and loads is important for the assessment of the overflow and overflow load to the receiving water to predict the hydraulic or the pollution impact. Hydrodynamic models are very data-intensive and time-consuming for long-term quality modelling. Hence, for long-term modelling, hydrological models are used to predict the storm flow in a fast way. However, in most cases, a constant rain intensity is used as load for the simulation, but in practice even for small catchments rain occurs in rain cells, which are not constant over the whole catchment area. This paper presents the results of quality modelling considering moving storms depending on the rain cell velocity and its moving direction. Additionally, tipping bucket gauge failures and different corrections are also taken into account. The results evidence the importance of these considerations for precipitation due the effects on overflow load and show the difference up to 28% of corrected and uncorrected data and of moving rain cells instead of constant raining intensities.

Enhancing the use of Object Based Contents Addressable Storage

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Simab Hasan Rizvi
Keyword(s):  
File System ◽  
Application Performance ◽  
Shared Storage ◽  
Data Intensive ◽  
Network Bandwidth ◽  
Storage Performance ◽  
Efficient Management ◽  
Object Based ◽  
Data Volume ◽  
Potential Benefits

In Today's age of Tetra Scale computing, the application has become more data intensive than ever. The increased data volume from applications, in now tackling larger and larger problems, and has fuelled the need for efficient management of this data. In this paper, a technique called Content Addressable Storage or CAS, for managing large volume of data is evaluated. This evaluation focuses on the benefits and demerits of using CAS it focuses, i) improved application performance via lockless and lightweight synchronization ofaccess to shared storage data, ii) improved cache performance, iii) increase in storage capacity and, iv) increase network bandwidth. The presented design of a CAS-Based file store significantly improves the storage performance that provides lightweight lock less user defined consistency semantics. As a result, this file system shows a 28% increase in read bandwidth and 13% increase in write bandwidth, over a popular file system in common use. In this paper the potential benefits of using CAS for a virtual machine are estimated. The study also explains mobility application for active use and public deployment.

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