Using quality of service bounds for effective multi-objective software architecture optimization

Author(s):  
Qais Noorshams ◽  
Anne Martens ◽  
Ralf Reussner
Author(s):  
Wei Guo ◽  
Pingyu Jiang

For adapting the socialization, individuation and servitization in manufacturing industry, a new manufacturing paradigm called social manufacturing has received a lot of attention. Social manufacturing can be seen as a network that enterprises with socialized resources self-organized into communities that provide personalized machining and service capabilities to customers. Since a community of social manufacturing has multiple enterprises and emphasizes on the importance of service, manufacturing service order allocation must be studied from the new perspective considering objectives on service cost and quality of service. The manufacturing service order allocation can be seen as a one-to-many game model with multi-objective. In this article, a Stackelberg game model is proposed to tackle the manufacturing service order allocation problem with considering the payoffs on cost and quality of service. Since this Stackelberg game can be mapped to a multi-objective bi-level programming, a modified multi-objective hierarchical Bird Swarm Algorithm is used to find the Nash equilibrium of the game. Finally, a case from a professional printing firm is analyzed to validate the proposed methodology and model. The objective of this research is to find the Nash equilibrium on the manufacturing service order allocation and provide strategies guidance for customer and small- and medium-sized enterprises with optimal service cost and lead time. According to the game process and Nash equilibrium, some rules are revealed, and they are useful for guiding practical production.


2019 ◽  
Vol 29 (10) ◽  
pp. 2050167
Author(s):  
Xiumin Zhou ◽  
Gongxuan Zhang ◽  
Tian Wang ◽  
Mingyue Zhang ◽  
Xiji Wang ◽  
...  

Most popular scientific workflow systems can now support the deployment of tasks to the cloud. The execution of workflow on cloud has become a multi-objective scheduling in order to meet the needs of users in many aspects. Cost and makespan are considered to be the two most important objects. In addition to these, there are some other Quality-of-Service (QoS) parameters including system reliability, energy consumption and so on. Here, we focus on three objectives: cost, makespan and system reliability. In this paper, we propose a Multi-objective Evolutionary Algorithm on the Cloud (MEAC). In the algorithm, we design some novel schemes including problem-specific encoding and also evolutionary operations, such as crossover and mutation. Simulations on real-world and random workflows are conducted and the results show that MEAC can get on average about 5% higher hypervolume value than some other workflow scheduling algorithms.


2020 ◽  
Vol 44 (7) ◽  
pp. 1183-1198
Author(s):  
Samira Hayat ◽  
Evşen Yanmaz ◽  
Christian Bettstetter ◽  
Timothy X. Brown

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1906
Author(s):  
Amarjeet Prajapati ◽  
Zong Woo Geem

The success of any software system highly depends on the quality of architectural design. It has been observed that over time, the quality of software architectural design gets degraded. The software system with poor architecture design is difficult to understand and maintain. To improve the architecture of a software system, multiple design goals or objectives (often conflicting) need to be optimized simultaneously. To address such types of multi-objective optimization problems a variety of metaheuristic-oriented computational intelligence algorithms have been proposed. In existing approaches, harmony search (HS) algorithm has been demonstrated as an effective approach for numerous types of complex optimization problems. Despite the successful application of the HS algorithm on different non-software engineering optimization problems, it gained little attention in the direction of architecture reconstruction problem. In this study, we customize the original HS algorithm and propose a multi-objective harmony search algorithm for software architecture reconstruction (MoHS-SAR). To demonstrate the effectiveness of the MoHS-SAR, it has been tested on seven object-oriented software projects and compared with the existing related multi-objective evolutionary algorithms in terms of different software architecture quality metrics and metaheuristic performance criteria. The experimental results show that the MoHS-SAR performs better compared to the other related multi-objective evolutionary algorithms.


Sign in / Sign up

Export Citation Format

Share Document