Controlling quality-of-service in distributed real-time and embedded systems via adaptive middleware

2006 ◽  
Vol 36 (11-12) ◽  
pp. 1189-1208 ◽  
Author(s):  
Richard E. Schantz ◽  
Joseph P. Loyall ◽  
Craig Rodrigues ◽  
Douglas C. Schmidt
Keyword(s):  
Quality Of Service ◽  
Embedded Systems ◽  
Real Time ◽  
Adaptive Middleware

Timely Autonomic Adaptation of Publish/Subscribe Middleware in Dynamic Environments

2011 ◽  
Vol 2 (4) ◽  
pp. 1-24 ◽  
Author(s):  
Joe Hoffert ◽  
Aniruddha Gokhale ◽  
Douglas C. Schmidt
Keyword(s):  
Quality Of Service ◽  
Embedded Systems ◽  
Real Time ◽  
Data Dissemination ◽  
Response Times ◽  
Dynamic Environments ◽  
Power Grids ◽  
Disaster Relief Operations ◽  
Relief Operations

Quality-of-service enabled publish/subscribe (pub/sub) middleware provides powerful support for scalable data dissemination. It is difficult to maintain key quality of service properties (such as reliability and latency) in dynamic environments for distributed real-time and embedded systems (such as disaster relief operations or power grids). Managing quality of service manually is often not feasible in dynamic environments due to slow response times, the complexity of managing multiple interrelated quality of service settings, and the scale of the systems being managed. For certain domains, distributed real-time and embedded systems must be able to reflect on the conditions of their environment and adapt accordingly in a bounded amount of time. This paper describes an architecture of quality of service-enabled middleware and corresponding algorithms to support specified quality of service in dynamic environments.

Timely Autonomic Adaptation of Publish/Subscribe Middleware in Dynamic Environments

2012 ◽  
pp. 172-195 ◽  
Author(s):  
Joe Hoffert ◽  
Aniruddha Gokhale ◽  
Douglas C. Schmidt
Keyword(s):  
Quality Of Service ◽  
Embedded Systems ◽  
Real Time ◽  
Data Dissemination ◽  
Response Times ◽  
Dynamic Environments ◽  
Power Grids ◽  
Disaster Relief Operations ◽  
Relief Operations

Quality-of-service enabled publish/subscribe (pub/sub) middleware provides powerful support for scalable data dissemination. It is difficult to maintain key quality of service properties (such as reliability and latency) in dynamic environments for distributed real-time and embedded systems (such as disaster relief operations or power grids). Managing quality of service manually is often not feasible in dynamic environments due to slow response times, the complexity of managing multiple interrelated quality of service settings, and the scale of the systems being managed. For certain domains, distributed real-time and embedded systems must be able to reflect on the conditions of their environment and adapt accordingly in a bounded amount of time. This paper describes an architecture of quality of service-enabled middleware and corresponding algorithms to support specified quality of service in dynamic environments.

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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