Providing Security of Real Time Data Intensive Applications on Grids Using Dynamic Scheduling

2009 ◽  
pp. 69-78 ◽  
Author(s):  
Rafiqul Islam ◽  
Toufiq Hasan ◽  
Md. Ashaduzzaman
Keyword(s):  
Real Time ◽  
Dynamic Scheduling ◽  
Time Data ◽  
Data Intensive ◽  
Real Time Data ◽  
Data Intensive Applications

Advanced Data Management for real-time data intensive applications and services

2014 ◽  
Vol 6 (6) ◽  
pp. 741-742 ◽  
Author(s):  
Luca Carafoli ◽  
Federica Mandreoli ◽  
Riccardo Martoglia
Keyword(s):  
Data Management ◽  
Real Time ◽  
Time Data ◽  
Data Intensive ◽  
Real Time Data ◽  
Data Intensive Applications

scholarly journals QoS Management for Embedded Databases in Multicore-Based Embedded Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Woochul Kang ◽  
Jaeyong Chung
Keyword(s):  
Embedded Systems ◽  
Real Time ◽  
Control Method ◽  
Network Connectivity ◽  
Time Data ◽  
Quality Of Data ◽  
Multiple Control ◽  
Data Intensive ◽  
Real Time Data

With ubiquitous deployment of sensors and network connectivity, amounts of real-time data for embedded systems are increasing rapidly and database capability is required for many embedded systems for systematic management of real-time data. In such embedded systems, supporting the timeliness of tasks accessing databases is an important problem. However, recent multicore-based embedded architectures pose a significant challenge for such data-intensive real-time tasks since the response time of accessing data can be significantly affected by potential intercore interferences. In this paper, we propose a novel feedback control scheme that supports the timeliness of data-intensive tasks against unpredictable intercore interferences. In particular, we use multiple inputs/multiple outputs (MIMO) control method that exploits multiple control knobs, for example, CPU frequency and the Quality-of-Data (QoD) to handle highly unpredictable workloads in multicore systems. Experimental results, using actual implementation, show that the proposed approach achieves the target Quality-of-Service (QoS) goals, such as task timeliness and Quality-of-Data (QoD) while consuming less energy compared to baseline approaches.

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