Optimal multilevel redundancy allocation in series and series–parallel systems

To achieve truly optimal system reliability, the design of a complex system must address multilevel reliability configuration concerns at the earliest possible design stage, to ensure that appropriate degrees of reliability are allocated to every unit at all levels. However, the current practice of allocating reliability at a single level leads to inferior optimal solutions, particularly in the class of multilevel redundancy allocation problems. Multilevel redundancy allocation optimization problems frequently occur in optimizing the system reliability of multilevel systems. It has been found that a modular scheme of redundancy allocation in multilevel systems not only enhances system reliability but also provides fault tolerance to the optimum design. Therefore, to increase the efficiency, reliability and maintainability of a multilevel reliability system, the design engineer has to shift away from the traditional focus on component redundancy, and deal more effectively with issues pertaining to modular redundancy. This paper proposes a method for optimizing modular redundancy allocation in two types of multilevel reliability configurations, series and series–parallel. A modular design variable is defined to handle modular redundancy in these two types of multilevel redundancy allocation problem. A customized genetic algorithm, namely, a hierarchical genetic algorithm (HGA), is applied to solve the modular redundancy allocation optimization problems, in which the design variables are coded as hierarchical genotypes. These hierarchical genotypes are represented by two nodal genotypes, ordinal and terminal. Using these two genotypes is extremely effective, since this allows representation of all possible modular configurations. The numerical examples solved in this paper demonstrate the efficacy of a customized HGA in optimizing the multilevel system reliability. Additionally, the results obtained in this paper indicate that achieving modular redundancy in series and series–parallel systems provides significant advantages when compared with component redundancy. The demonstrated methodology also indicates that future research may yield significantly better solutions to the technological challenges of designing more fault-tolerant systems that provide improved reliability and lower lifecycle cost

Optimal Multilevel redundancy allocation in series and series-parallel systems

To achieve truly optimal system reliability, the design of a complex system must address multilevel reliability configuration concerns at the earliest possible design stage, to ensure that appropriate degrees of reliability are allocated to every unit at all levels. However, the current practice of allocating reliability at a single level leads to inferior optimal solutions, particularly in the class of multilevel redundancy allocation problems. Multilevel redundancy allocation optimization problems frequently occur in optimizing the system reliability of multilevel systems. It has been found that a modular scheme of redundancy allocation in multilevel systems not only enhances system reliability but also provides fault tolerance to the optimum design. Therefore, to increase the efficiency, reliability and maintainability of a multilevel reliability system, the design engineer has to shift away from the traditional focus on component redundancy, and deal more effectively with issues pertaining to modular redundancy. This paper proposes a method for optimizing modular redundancy allocation in two types of multilevel reliability configurations, series and series–parallel. A modular design variable is defined to handle modular redundancy in these two types of multilevel redundancy allocation problem. A customized genetic algorithm, namely, a hierarchical genetic algorithm (HGA), is applied to solve the modular redundancy allocation optimization problems, in which the design variables are coded as hierarchical genotypes. These hierarchical genotypes are represented by two nodal genotypes, ordinal and terminal. Using these two genotypes is extremely effective, since this allows representation of all possible modular configurations. The numerical examples solved in this paper demonstrate the efficacy of a customized HGA in optimizing the multilevel system reliability. Additionally, the results obtained in this paper indicate that achieving modular redundancy in series and series–parallel systems provides significant advantages when compared with component redundancy. The demonstrated methodology also indicates that future research may yield significantly better solutions to the technological challenges of designing more fault-tolerant systems that provide improved reliability and lower lifecycle cost

Improving reliability of service oriented systems with consideration of cost and time constraints in clouds

Web service technology is more and more popular for the implementation of service oriented systems. Additionally, cloud computing platforms, as an efficient and available environment, can provide the computing, networking and storage resources in order to decrease the budget of companies to deploy and manage their systems. Therefore, more service oriented systems are migrated and deployed in clouds. However, these applications need to be improved in terms of reliability, for certain components have low reliability. Fault tolerance approaches can improve software reliability. However, more redundant units are required, which increases the cost and the execution time of the entire system. Therefore, a migration and deployment framework with fault tolerance approaches with the consideration of global constraints in terms of cost and execution time may be needed. This work proposes a migration and deployment framework to guide the designers of service oriented systems in order to improve the reliability under global constraints in clouds. A multilevel redundancy allocation model is adopted for the framework to assign redundant units to the structure of systems with fault tolerance approaches. An improved genetic algorithm is utilised for the generation of the migration plan that takes the execution time of systems and the cost constraints into consideration. Fault tolerant approaches (such as NVP, RB and Parallel) can be integrated into the framework so as to improve the reliability of the components at the bottom level. Additionally, a new encoding mechanism based on linked lists is proposed to improve the performance of the genetic algorithm in order to reduce the movement of redundant units in the model. The experiments compare the performance of encoding mechanisms and the model integrated with different fault tolerance approaches. The empirical studies show that the proposed framework, with a multilevel redundancy allocation model integrated with the fault tolerance approaches, can generate migration plans for service oriented systems in clouds with the consideration of cost and execution time