Multi-objective design exploration approach for Ravenscar real-time systems

Bouaziz, Rafik; Lemarchand, Laurent; Singhoff, Frank; Zalila, Béchir; Jmaïel, Mohamed

doi:10.1007/s11241-018-9299-6

Cited by 8 publications

(7 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Latency is the time it takes for a command or message to move from one place in the software stack to another. Lower latency is preferable, particularly in real-time applications (Bouaziz et al, 2018).  Throughput is the speed at which information may be sent or processed via the software stack.…”

Section: Performance Metrics and Benchmarking Resultsmentioning

confidence: 99%

AUTOSAR Classic vs. AUTOSAR Adaptive: A Comparative Analysis in Stack Development

Shajahan,

Richardson,

Dhameliya

et al. 2019

Eng. int. (Dhaka)

View full text Add to dashboard Cite

This study aims to clarify the advantages, disadvantages, and implications of the AUTOSAR Classic and AUTOSAR Adaptive frameworks for stack development in the automotive software engineering domain. The study's primary goals are to examine the design concepts, performance traits, development processes, and implementation difficulties of the AUTOSAR Classic and AUTOSAR Adaptive frameworks. The methodology consists of a thorough literature evaluation, an analysis of market trends, a look at development workflows, and case studies highlighting implementation issues and their resolutions. The key findings show how the AUTOSAR Classic and AUTOSAR Adaptive frameworks differ in architecture, performance, resource usage, and development process. Recommendations for standardization, funding for education and training, R&D, and regulatory frameworks are among the policy implications that support the uptake and advancement of AUTOSAR technologies in automotive software engineering. This report is an invaluable resource for those involved in the automotive sector, legislators, and industry associations trying to make sense of the complicated world of stack development and mold the course of automotive software engineering.

show abstract

Section: Performance Metrics and Benchmarking Resultsmentioning

confidence: 99%

AUTOSAR Classic vs. AUTOSAR Adaptive: A Comparative Analysis in Stack Development

Shajahan,

Richardson,

Dhameliya

et al. 2019

Eng. int. (Dhaka)

View full text Add to dashboard Cite

show abstract

“…Most of the papers cited above produce a single design decision. DSE is performed in [17,22,24]. The authors propose approaches to find trade-offs for real-time systems based on a multi-objective evolutionary algorithm (MOEA) for different multiobjective optimization problems.…”

Section: Related Workmentioning

confidence: 99%

Partitioning of TSP systems based on security and schedulability trade-off

Atchadam,

Tran,

Lemarchand

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

Time and Space Partitioning (TSP) introduces the concept of partitions that allow application isolation. Applications can be assigned to partitions according to various objective functions or constraints related to safety, security, schedulability or energy requirements. Some of these objective functions may be conflicting, i.e. an improvement of one objective leads to a decrease of another. For example, improving the safety by active redundancy of a system may impact its schedulability. In this paper, we investigate the conflicting aspect between schedulability and security (confidentiality and integrity) in TSP real-time systems. We formulate 3 design space exploration algorithms with a meta-heuristic called Pareto Archived Evolutionary Strategy (PAES). These algorithms are implemented into Cheddar, a schedulability analysis tool. We investigate the effect of different architecture implementations to bring confidentiality and integrity in TSP systems. Our experiments reveal that the security architecture implementation alternatives offer opportunities for good trade-offs between schedulability and security. We also establish that our proposed mutation algorithms are adapted to large-scale problems. Comparison with the exact method for small size test-case shows that the proposed approach converges towards the Pareto front, missing only one non dominated solution.

show abstract

“…To quantify and compare the quality of solution sets produced by different MOEAs, numerous performance indicators have been proposed in the literature. These performance indicators consist in assigning scores to Pareto front approximations [14,15]. We quote, among others, Generational Distance, Inverted Generational Distance, Hypervolume, Maximum Pareto Front Error and Spacing.…”

Section: Performance Indicatorsmentioning

confidence: 99%

“…Hypervolume (HV), also called hyperarea, is one of the most popular measures for the performance assessment of multi-objective algorithms [77]. It encompasses the exhaustivity, the precision and the diversity metrics [14,15]. It computes the portion of the objective space dominated by all the solutions bounded by a reference point .…”

Section: Performance Indicatorsmentioning

confidence: 99%

Multi-objective Optimization of Data Placement in a Storage-as-a-Service Federated Cloud

et al. 2021

Self Cite

View full text Add to dashboard Cite

Cloud federation enables service providers to collaborate to provide better services to customers. For cloud storage services, optimizing customer object placement for a member of a federation is a real challenge. Storage, migration, and latency costs need to be considered. These costs are contradictory in some cases. In this article, we modeled object placement as a multi-objective optimization problem. The proposed model takes into account parameters related to the local infrastructure, the federated environment, customer workloads, and their SLAs. For resolving this problem, we propose CDP-NSGAII IR , a Constraint Data Placement matheuristic based on NSGAII with Injection and Repair functions. The injection function aims to enhance the solutions’ quality. It consists to calculate some solutions using an exact method then inject them into the initial population of NSGAII. The repair function ensures that the solutions obey the problem constraints and so prevents from exploring large sets of unfeasible solutions. It reduces drastically the execution time of NSGAII. Experimental results show that the injection function improves the HV of NSGAII and the exact method by up to 94% and 60%, respectively, while the repair function reduces the execution time by an average of 68%.

show abstract

Multi-objective design exploration approach for Ravenscar real-time systems

Cited by 8 publications

References 44 publications

AUTOSAR Classic vs. AUTOSAR Adaptive: A Comparative Analysis in Stack Development

AUTOSAR Classic vs. AUTOSAR Adaptive: A Comparative Analysis in Stack Development

Partitioning of TSP systems based on security and schedulability trade-off

Multi-objective Optimization of Data Placement in a Storage-as-a-Service Federated Cloud

Contact Info

Product

Resources

About