NFSBST Framework Requirements perspective

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NFSBST Framework Requirements perspective により Mind Map: NFSBST Framework Requirements perspective

1. Quality of Information/ Quality of Service(QoS)

1.1. Systems developed using JAVA

1.1.1. Multiobjective Optimisation Genetic Algorithms(MOGA)

1.2. Mobile Applications

1.2.1. Tabu Search

2. Performance

2.1. Systems developed using JAVA

2.1.1. Multiobjective Optimisation Genetic Algorithms(MOGA)

2.2. Cloud Systems

2.2.1. GA, SA and TS

2.3. Safety critical systems

2.3.1. Hill Climbing

2.4. Service oriented systems

2.4.1. Genetic Algorithms

2.5. Algorithms( Knapsack problem, Travelling Salesman)

2.5.1. Genetic Algorithms

2.6. Embedded Systems

2.6.1. Genetic Programming

2.6.1.1. C: Encoding the problem for the fitness function to work is challenging.

2.6.1.2. C: Solutions (Metrics & Fitness functions) developed for another SUT cannot be reused.

3. Availability

3.1. Cloud Systems

3.1.1. GA, SA and TS

3.2. Service Oriented systems (Web Service composition)

3.2.1. Particle Swarm Optimisation

3.2.1.1. C: Finding a right balance between the behavior of the algorithm during search space formulation and the right search space is difficult [P2].

3.2.1.1.1. AS: Experimenting different search methods could help in overcoming this issue.

3.2.1.1.2. AS: Using Hybrid approaches could potentially solve this issue. Refer [P3]

4. Response Time

4.1. Component based systems in Automotive domain

4.1.1. Multi-objective ant colony optimisation

4.2. Systems optimised using GISMOE framework

4.2.1. Genetic Programming

5. Memory Consumption

5.1. Systems optimised using GISMOE framework

5.1.1. Genetic Programming

5.2. Software systems in general

5.2.1. Genetic Improvement

5.2.1.1. C: Finding an effective fitness function by preserving the core functionality of the system is challenging [P7].

5.2.1.1.1. AS: Finding the right metrics and combining them effectively needs to be done to formulate an efficient fitness function.

6. Processor Utilization/ Throughput

6.1. Systems optimised using GISMOE framework

6.1.1. Genetic Programming

6.1.1.1. C: Measuring Non-functional properties as a fitness function. Problem arises when interpreting these complex properties as a fitness function [P1].

6.1.1.1.1. AS: Finding the right metrics that would form the system should be know very well. To do that it is very important to get to know the SUT well.

6.1.1.1.2. AS: Simulation is recently gaining popularity and can be used to solve this. Cloud-based platforms can also be used.

7. Cost

7.1. Cloud Systems

7.1.1. GA, SA and TS

7.2. Component based systems in Automotive domain

7.2.1. Multi-objective ant colony optimisation

7.2.1.1. C: Applying Redundant allocation to improve the reliability of the system brings in negative impact on other non-functional attributes [P8].

7.2.1.1.1. AS: Solution lies out in trying out Multi-objective optimization algorithms.

7.2.1.2. Additional overheads incur into the response time of the system which itself is an important attribute for automobiles.

8. Wall-clock Execution time

8.1. Unix/Linux based systems

8.1.1. Hill Climbing

8.1.1.1. C: This properties are very brittle to measure. They might break at any point during the execution

8.1.1.1.1. AS: The lower level architecture (OS or hardware) must be robust enough to be easy for optimization.