Toward Effective Multi-Capacity Resource Allocation in Distributed Real-Time and Embedded Systems

A Novel Rail-Network Hardware with simulation facilities is presented in this paper. The hardware is designed to facilitate the learning of application-oriented, logical, real-time programming in an embedded system environment. The platform enables the creation of multiple unique programming scenarios with variability in complexity without any hardware changes. Prior experimental hardware comes with static programming facilities that focus the students’ learning on hardware features and programming basics, leaving them ill-equipped to take up practical applications with more real-time constraints. This hardware complements and completes their learning to help them program real-world embedded systems. The hardware uses LEDs to simulate the movement of trains in a network. The network has train stations, intersections and parking slots where the train movements can be controlled by using a 16-bit Renesas RL78/G13 microcontroller. Additionally, simulating facilities are provided to enable the students to navigate the trains by manual controls using switches and indicators. This helps them get an easy understanding of train navigation functions before taking up programming. The students start with simple tasks and gradually progress to more complicated ones with real-time constraints, on their own. During training, students’ learning outcomes are evaluated by obtaining their feedback and conducting a test at the end to measure their knowledge acquisition during the training. Students’ Knowledge Enhancement Index is originated to measure the knowledge acquired by the students. It is observed that 87% of students have successfully enhanced their knowledge undergoing training with this rail-network simulator.

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Energy consumption optimization of processor scheduling for real-time embedded systems under the constraints of sequential relationship and reliability

Alexandria Engineering Journal ◽

10.1016/j.aej.2021.04.071 ◽

2021 ◽

Author(s):

Wei Xiong ◽

Bing Guo ◽

Shen Yan

Keyword(s):

Energy Consumption ◽

Embedded Systems ◽

Real Time ◽

Processor Scheduling ◽

Energy Consumption Optimization ◽

Sequential Relationship

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NetQoPE: A Model-Driven Network QoS Provisioning Engine for Distributed Real-time and Embedded Systems

2008 IEEE Real-Time and Embedded Technology and Applications Symposium ◽

10.1109/rtas.2008.32 ◽

2008 ◽

Cited By ~ 7

Author(s):

Jaiganesh Balasubramanian ◽

Sumant Tambe ◽

Balakrishnan Dasarathy ◽

Shrirang Gadgil ◽

Frederick Porter ◽

...

Keyword(s):

Embedded Systems ◽

Real Time ◽

Qos Provisioning ◽

Model Driven

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SAFER model: Strategic allocation of fundamental epidemic resources

Journal of Public Health ◽

10.1093/pubmed/fdaa179 ◽

2020 ◽

Author(s):

B W Weston ◽

Z N Swingen ◽

S Gramann ◽

D Pojar

Keyword(s):

Resource Allocation ◽

Real Time ◽

Community Based ◽

Broad Distribution ◽

Census Tracts ◽

Milwaukee County ◽

Strategic Allocation ◽

Four Quadrant ◽

Time Resource ◽

Critical Resources

Abstract Background To describe the Strategic Allocation of Fundamental Epidemic Resources (SAFER) model as a method to inform equitable community distribution of critical resources and testing infrastructure. Methods The SAFER model incorporates a four-quadrant design to categorize a given community based on two scales: testing rate and positivity rate. Three models for stratifying testing rates and positivity rates were applied to census tracts in Milwaukee County, Wisconsin: using median values (MVs), cluster-based classification and goal-oriented values (GVs). Results Each of the three approaches had its strengths. MV stratification divided the categories most evenly across geography, aiding in assessing resource distribution in a fixed resource and testing capacity environment. The cluster-based stratification resulted in a less broad distribution but likely provides a truer distribution of communities. The GVs grouping displayed the least variation across communities, yet best highlighted our areas of need. Conclusions The SAFER model allowed the distribution of census tracts into categories to aid in informing resource and testing allocation. The MV stratification was found to be of most utility in our community for near real time resource allocation based on even distribution of census tracts. The GVs approach was found to better demonstrate areas of need.

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