Ping-Chen Chang

Maintenance reliability estimation for a cloud computing network with nodes failure

Abstract The cloud computing network (CCN) has become a new paradigm for the business and clients as the development of information technology. To guarantee the CCN keep a good quality of service (QoS), the maintenance action is necessary when the CCN falls to a specific state such that it cannot afford enough capacity to meet demand d. In the CCN, edges and nodes have various capacities due to failure, partial failure, or maintenance; thus, the CCN has several possible states. This paper proposes an algorithm to estimate the performance of a CCN under maintenance budget with nodes failure. Hence, the maintenance reliability is developed to measure the capability that the CCN can send d units of data from the cloud to the client through multiple paths under the maintenance budget and time constraints. Furthermore, the system supervisor can conduct the sensitive analysis to improve/investigate the most important part in a large CCN afterwards.

System reliability of a manufacturing network with reworking action and different failure rates

From the perspective of network analysis, the manufacturing system can be constructed as a stochastic-flow network, since the capacity of each machine is stochastic (i.e. multistate) owing to the failure, partial failure, and maintenance. Considering reworking action and different failure rates of machines, the input flow (raw materials/work in process) processed by each machine might be defective, and therefore the output flow (work in process/products) would be less than the input amount. To evaluate the capability of the manufacturing system, we measure the probability that the manufacturing network can satisfy demand. Such a probability is defined as the system reliability. A decomposition method is first proposed to divide the manufacturing network into one general processing path and one reworking path. Subsequently, two algorithms are utilised for different network models to generate the lower boundary vector of machine capacity to guarantee that the manufacturing network is able to produce sufficient products fulfilling the demand. The system reliability of the manufacturing network can be derived in terms of such a capacity vector afterwards.

A Novel Reliability Evaluation Technique for Stochastic-Flow Manufacturing Networks With Multiple Production Lines

This paper presents a novel technique to measure the performance of a stochastic-flow manufacturing network (SMN) which violates the so-called flow conservation law due to the failure rates of stations. We address the mission reliability, the probability of demand satisfaction, as a performance indicator for the SMN while considering both the stochastic capacities and the multiple production lines. First, we construct a manufacturing system as an SMN through a graphical transformation, and decompose the transformed SMN into several paths for further analysis. Subsequently, two algorithms for different scenarios are designed to generate all minimal capacity vectors that stations should provide to satisfy the given demand. The first scenario is for the SMN with identical production lines in parallel. The second scenario is for distinct production lines with common stations in the SMN. We derive the mission reliability in terms of minimal capacity vectors by applying the recursive sum of disjoint products (RSDP) algorithm. A decision making issue is also discussed to decide a reliable production strategy.

Evaluation of system reliability for a cloud computing system with imperfect nodes

From the perspective of system design and quality of service (QoS), system reliability is one of the essential performance indicators to measure the probable reliability of a network. In terms of a practical cloud computing system (CCS), edges and nodes have various capacities or states due to failure, partial failure, or maintenance. Thus, the CCS is a typical capacitated-flow network. To guarantee a good level of quality and reliability, the CCS should be maintained, so as not to fall into a failed state whereby it cannot provide sufficient capacity to satisfy demand. Thus, system reliability is developed in this paper to evaluate the capability of the CCS to send d units of data from the cloud to the client through two paths under both the maintenance budget and time constraints. An algorithm with an adjusting procedure based on the branch-and-bound approach is proposed to evaluate the system reliability. The relevant proof shows that the proposed algorithm is reasonable and appropriate for measuring the system reliability of the CCS. According to different maintenance budgets and corresponding system reliability, the system supervisor could determine a reasonable maintenance budget to maintain a good level of quality and reliability of the CCS. From the perspective of system design, the system supervisor could further conduct a sensitivity analysis to improve or investigate the most important part in a large CCS based on system reliability.

Performance evaluation for a footwear manufacturing system with multiple production lines and different station failure rates

This paper develops a three-phase procedure to measure the performance of a highly value-added footwear manufacturing system taking reworking actions into account, in which the system consists of multiple production lines. We mainly address the system reliability as a performance indicator to evaluate the possibility of demand satisfaction. First, we construct the manufacturing system as a manufacturing network by graphical transformation and decomposition. Second, capability analysis is implemented to determine the input flow of each station based on the manufacturing network. Third, a simple algorithm is proposed to generate all minimal capacity vectors that stations should provide to satisfy the given demand. We evaluate the system reliability in terms of the minimal capacity vectors. A further decision making issue is discussed to decide a reliable production strategy. Whenever the system state changes, the proposed performance evaluation procedure can be implemented easily and flexibly.

Evaluate the system reliability for a manufacturing network with reworking actions

To measure the system reliability of a manufacturing system with reworking actions is a crucial issue in industry, in which the system reliability could be one of the essential performance indicators to evaluate whether the manufacturing system is capable or not. In a manufacturing system, the input flow (raw materials/WIP) processed by each machine might be defective and thus the output flow (WIP/products) would be less than the input amount. Moreover, defective WIP/products are usually incentive to be reworked for reducing wasting and increasing output. Therefore, reworking actions are necessary to be considered in the manufacturing system. Based on the path concept, we revise such a manufacturing system as a stochastic-flow network in which the capacity of each machine is stochastic (i.e., multistate) due to the failure, partial failure, and maintenance. We decompose the network into one general processing path and several reworking paths. Subsequently, three algorithms for different network models are proposed to generate the lower boundary vector which affords to produce enough products satisfying the demand d. In terms of such a vector, the system reliability can be derived afterwards.

RELIABILITY EVALUATION OF A MANUFACTURING NETWORK WITH REWORKING ACTION

This paper focuses on performance evaluation of a manufacturing system from the network analysis perspective. Due to failure, partial failure, or maintenance, the capacity of each machine is stochastic (i.e., multistate). Hence, a manufacturing system can be constructed as a stochastic-flow network, named manufacturing network herein. Considering reworking action and failure rates of machines, this paper assesses the probability that the manufacturing network can satisfy demand. Such a probability is defined as the system reliability. First, a graphical technique is proposed to decompose the manufacturing network into one general processing path and one reworking path. Subsequently, two algorithms are utilized for different network models to generate the minimal capacity vector of machines that guarantee that the manufacturing network is able to produce sufficient products fulfilling the demand. The system reliability of the manufacturing network is derived in terms of such a capacity vector afterward.

System Performance and Reliability Modeling of a Stochastic-Flow Production Network: A Confidence-Based Approach

Production network performance and reliability are essential to satisfy customer orders in a timely manner. This paper proposes a statistical method for a production system to satisfy customer demand with a desired level of confidence, referred to as yield confidence, while simultaneously considering system reliability, defined as the probability that the amount of input can be processed based on the capacities of the individual workstations. The approach models a production system as a stochastic-flow production network, characterized by a discrete time Markov chain (DTMC), where one or more rework actions are possible. This model quantifies the probability that raw input is transformed into a finished product, which is subsequently used to calculate the amount of raw input needed to satisfy demand with a user-specified level of yield confidence. A pair of case studies, taken from the tile and circuit board industries, illustrates the assessment techniques as well as methods to identify workstation level enhancements that can improve network performance and reliability most significantly. Our results indicate that improving the reliability of workstations can enhance yield confidence because a lower volume of raw input can produce the desired volume of output, thereby minimizing the load placed on the production network.

System reliability evaluation of a touch panel manufacturing system with defect rate and reworking

In recent years, portable consumer electronic products, such as cell phone, GPS, digital camera, tablet PC, and notebook are using touch panel as interface. With the demand of touch panel increases, performance assessment is essential for touch panel production. This paper develops a method to evaluate system reliability of a touch panel manufacturing system (TPMS) with defect rate of each workstation and takes reworking actions into account. The system reliability which evaluates the possibility of demand satisfaction can provide to managers with an understanding of the system capability and can indicate possible improvements. First, we construct a capacitated manufacturing network (CMN) for a TPMS. Second, a decomposition technique is developed to determine the input flow of each workstation based on the CMN. Finally, we generate the minimal capacity vectors that should be provided to satisfy the demand. The system reliability is subsequently evaluated in terms of the minimal capacity vectors. A further decision making issue is discussed to decide a reliable production strategy.

Quantifying the Impact of Correlated Failures on Stochastic Flow Network Reliability

This paper develops two techniques to analyse the performance of a stochastic-flow network (SFN) model, considering correlated failures. The first approach utilizes a correlated binomial distribution to characterize the failure behavior of the physical lines and routers internal to the individual edges and nodes in the network. The second employs a simulation technique, which can characterize correlated failures between every pair of physical lines and routers in the different edges and nodes comprising the network. Both approaches quantify the probability that a given amount of data can be sent from a source to a sink through this network. This probability that the network satisfies a specified level of demand is referred to as the SFN reliability. The techniques are demonstrated in the context of two case studies, including the Taiwan Academic Network, the backbone of the national computer network connecting all educational institutions in Taiwan. Experimental results demonstrate that correlation can produce a significantly negative impact on reliability, especially when there is a high level of network demand. The proposed approaches, thus, capture the influence of correlation on SFN reliability, offering methods to quantify the utility of reducing correlation.