Redundancy management for efficient fault recovery in NASA's distributed computing system

The management of redundancy in computer systems was studied and guidelines were provided for the development of NASA's fault-tolerant distributed systems. Fault recovery and reconfiguration mechanisms were examined. A theoretical foundation was laid for redundancy management by efficient reconfiguration methods and algorithmic diversity. Algorithms were developed to optimize the resources for embedding of computational graphs of tasks in the system architecture and reconfiguration of these tasks after a failure has occurred. The computational structure represented by a path and the complete binary tree was considered and the mesh and hypercube architectures were targeted for their embeddings. The innovative concept of Hybrid Algorithm Technique was introduced. This new technique provides a mechanism for obtaining fault tolerance while exhibiting improved performance

Reliability Modelling of the Redundancy Allocation Problem in the Series-parallel Systems and Determining the System Optimal Parameters

Considering the increasingly high attention to quality, promoting the reliability of products during designing process has gained significant importance. In this study, we consider one of the current models of the reliability science and propose a non-linear programming model for redundancy allocation in the series-parallel systems according to the redundancy strategy and considering the assumption that the failure rate depends on the number of the active elements. The purpose of this model is to maximize the reliability of the system. Internal connection costs, which are the most common costs in electronic systems, are used in this model in order to reach the real-world conditions. To get the results from this model, we used meta-heuristic algorithms such as genetic algorithm and simulation annealing after optimizing their operators’ rates by using response surface methodology

Genetic Algorithm and Simulated Annealing for Redundancy Allocation Problem with Cold-standby Strategy

This paper presents a new mathematical model for a redundancyallocation problem (RAP) withcold-standby redundancy strategy and multiple component choices.The applications of the proposed model arecommon in electrical power, transformation,telecommunication systems,etc.Manystudies have concentrated onone type of time-to-failure, butin thispaper, two components of time-to-failures which follow hypo-exponential and exponential distributionare investigated. The goal of the RAP is to select available components and redundancy level for each subsystem for maximizing system reliability under cost and weight constraints.Sincethe proposed model belongs to NP-hard class, we proposed two metaheuristic algorithms; namely, simulated annealing and genetic algorithm to solve it. In addition, a numerical example is presented to demonstrate the application of the proposed solution methodology.</p

Project scheduling under undertainty – survey and research potentials.

The vast majority of the research efforts in project scheduling assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. However, in the real world, project activities are subject to considerable uncertainty, that is gradually resolved during project execution. In this survey we review the fundamental approaches for scheduling under uncertainty: reactive scheduling, stochastic project scheduling, stochastic GERT network scheduling, fuzzy project scheduling, robust (proactive) scheduling and sensitivity analysis. We discuss the potentials of these approaches for scheduling projects under uncertainty.Management; Project management; Robustness; Scheduling; Stability;

Risk-based reliability allocation at component level in non-repairable systems by using evolutionary algorithm

The approach for setting system reliability in the risk-based reliability allocation (RBRA) method is driven solely by the amount of ‘total losses’ (sum of reliability investment and risk of failure) associated with a non-repairable system failure. For a system consisting of many components, reliability allocation by RBRA method becomes a very complex combinatorial optimisation problem particularly if large numbers of alternatives, with different levels of reliability and associated cost, are considered for each component. Furthermore, the complexity of this problem is magnified when the relationship between cost and reliability assumed to be nonlinear and non-monotone. An optimisation algorithm (OA) is therefore developed in this research to demonstrate the solution for such difficult problems. The core design of the OA originates from the fundamental concepts of basic Evolutionary Algorithms which are well known for emulating Natural process of evolution in solving complex optimisation problems through computer simulations of the key genetic operations such as 'reproduction', ‘crossover’ and ‘mutation’. However, the OA has been designed with significantly different model of evolution (for identifying valuable parent solutions and subsequently turning them into even better child solutions) compared to the classical genetic model for ensuring rapid and efficient convergence of the search process towards an optimum solution. The vital features of this OA model are 'generation of all populations (samples) with unique chromosomes (solutions)', 'working exclusively with the elite chromosomes in each iteration' and 'application of prudently designed genetic operators on the elite chromosomes with extra emphasis on mutation operation'. For each possible combination of alternatives, both system reliability and cost of failure is computed by means of Monte-Carlo simulation technique. For validation purposes, the optimisation algorithm is first applied to solve an already published reliability optimisation problem with constraint on some target level of system reliability, which is required to be achieved at a minimum system cost. After successful validation, the viability of the OA is demonstrated by showing its application in optimising four different non-repairable sample systems in view of the risk based reliability allocation method. Each system is assumed to have discrete choice of component data set, showing monotonically increasing cost and reliability relationship among the alternatives, and a fixed amount associated with cost of failure. While this optimisation process is the main objective of the research study, two variations are also introduced in this process for the purpose of undertaking parametric studies. To study the effects of changes in the reliability investment on system reliability and total loss, the first variation involves using a different choice of discrete data set exhibiting a non-monotonically increasing relationship between cost and reliability among the alternatives. To study the effects of risk of failure, the second variation in the optimisation process is introduced by means of a different cost of failure amount, associated with a given non-repairable system failure. The optimisation processes show very interesting results between system reliability and total loss. For instance, it is observed that while maximum reliability can generally be associated with high total loss and low risk of failure, the minimum observed value of the total loss is not always associated with minimum system reliability. Therefore, the results exhibit various levels of system reliability and total loss with both values showing strong sensitivity towards the selected combination of component alternatives. The first parametric study shows that second data set (nonmonotone) creates more opportunities for the optimisation process for producing better values of the loss function since cheaper components with higher reliabilities can be selected with higher probabilities. In the second parametric study, it can be seen that the reduction in the cost of failure amount reduces the size of risk of failure which also increases the chances of using cheaper components with lower levels of reliability hence producing lower values of the loss functions. The research study concludes that the risk-based reliability allocation method together with the optimisation algorithm can be used as a powerful tool for highlighting various levels of system reliabilities with associated total losses for any given system in consideration. This notion can be further extended in selecting optimal system configuration from various competing topologies. With such information to hand, reliability engineers can streamline complicated system designs in view of the required level of system reliability with minimum associated total cost of premature failure. In all cases studied, the run time of the optimisation algorithm increases linearly with the complexity of the algorithm and due to its unique model of evolution, it appears to conduct very detailed multi-directional search across the solution space in fewer generations - a very important attribute for solving the kind of problem studied in this research. Consequently, it converges rapidly towards optimum solution unlike the classical genetic algorithm which gradually reaches the optimum, when successful. The research also identifies key areas for future development with the scope to expand in various other dimensions due to its interdisciplinary applications

Using NSGA II Algorithm for a Three Objectives Redundancy Allocation Problem with k-out-of-n Sub-Systems

in the new production systems, finding a way to improving the product and system reliability in design is a very important. The reliability of the products and systems may improve using different methods. One of this methods is redundancy allocation problem. In this problem by adding redundant component to sub-systems under some constraints, the reliability improved. In this paper we worked on a three objectives redundancy allocation problem. The objectives are maximizing system reliability and minimizing the system cost and weight. The structure of sub-systems are k-out-of-n and the components have constant failure rate. Because this problem belongs to Np. Hard problems, we used NSGA II multi-objective Meta-heuristic algorithm to solving the presented problem

The Project Scheduling Problem with Non-Deterministic Activities Duration: A Literature Review

Purpose: The goal of this article is to provide an extensive literature review of the models and solution procedures proposed by many researchers interested on the Project Scheduling Problem with nondeterministic activities duration. Design/methodology/approach: This paper presents an exhaustive literature review, identifying the existing models where the activities duration were taken as uncertain or random parameters. In order to get published articles since 1996, was employed the Scopus database. The articles were selected on the basis of reviews of abstracts, methodologies, and conclusions. The results were classified according to following characteristics: year of publication, mathematical representation of the activities duration, solution techniques applied, and type of problem solved. Findings: Genetic Algorithms (GA) was pointed out as the main solution technique employed by researchers, and the Resource-Constrained Project Scheduling Problem (RCPSP) as the most studied type of problem. On the other hand, the application of new solution techniques, and the possibility of incorporating traditional methods into new PSP variants was presented as research trends. Originality/value: This literature review contents not only a descriptive analysis of the published articles but also a statistical information section in order to examine the state of the research activity carried out in relation to the Project Scheduling Problem with non-deterministic activities duration.Peer Reviewe

Lifetime reliability of multi-core systems: modeling and applications.

Huang, Lin.Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (leaves 218-232).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Preface --- p.1Chapter 1.2 --- Background --- p.5Chapter 1.3 --- Contributions --- p.6Chapter 1.3.1 --- Lifetime Reliability Modeling --- p.6Chapter 1.3.2 --- Simulation Framework --- p.7Chapter 1.3.3 --- Applications --- p.9Chapter 1.4 --- Thesis Outline --- p.10Chapter I --- Modeling --- p.12Chapter 2 --- Lifetime Reliability Modeling --- p.13Chapter 2.1 --- Notation --- p.13Chapter 2.2 --- Assumption --- p.16Chapter 2.3 --- Introduction --- p.16Chapter 2.4 --- Related Work --- p.19Chapter 2.5 --- System Model --- p.21Chapter 2.5.1 --- Reliability of A Surviving Component --- p.22Chapter 2.5.2 --- Reliability of a Hybrid k-out-of-n:G System --- p.26Chapter 2.6 --- Special Cases --- p.31Chapter 2.6.1 --- Case I: Gracefully Degrading System --- p.31Chapter 2.6.2 --- Case II: Standby Redundant System --- p.33Chapter 2.6.3 --- Case III: l-out-of-3:G System with --- p.34Chapter 2.7 --- Numerical Results --- p.37Chapter 2.7.1 --- Experimental Setup --- p.37Chapter 2.7.2 --- Experimental Results and Discussion --- p.40Chapter 2.8 --- Conclusion --- p.43Chapter 2.9 --- Appendix --- p.44Chapter II --- Simulation Framework --- p.47Chapter 3 --- AgeSim: A Simulation Framework --- p.48Chapter 3.1 --- Introduction --- p.48Chapter 3.2 --- Preliminaries and Motivation --- p.51Chapter 3.2.1 --- Prior Work on Lifetime Reliability Analysis of Processor- Based Systems --- p.51Chapter 3.2.2 --- Motivation of This Work --- p.53Chapter 3.3 --- The Proposed Framework --- p.54Chapter 3.4 --- Aging Rate Calculation --- p.57Chapter 3.4.1 --- Lifetime Reliability Calculation --- p.58Chapter 3.4.2 --- Aging Rate Extraction --- p.60Chapter 3.4.3 --- Discussion on Representative Workload --- p.63Chapter 3.4.4 --- Numerical Validation --- p.65Chapter 3.4.5 --- Miscellaneous --- p.66Chapter 3.5 --- Lifetime Reliability Model for MPSoCs with Redundancy --- p.68Chapter 3.6 --- Case Studies --- p.70Chapter 3.6.1 --- Dynamic Voltage and Frequency Scaling --- p.71Chapter 3.6.2 --- Burst Task Arrival --- p.75Chapter 3.6.3 --- Task Allocation on Multi-Core Processors --- p.77Chapter 3.6.4 --- Timeout Policy on Multi-Core Processors with Gracefully Degrading Redundancy --- p.78Chapter 3.7 --- Conclusion --- p.79Chapter 4 --- Evaluating Redundancy Schemes --- p.83Chapter 4.1 --- Introduction --- p.83Chapter 4.2 --- Preliminaries and Motivation --- p.85Chapter 4.2.1 --- Failure Mechanisms --- p.85Chapter 4.2.2 --- Related Work and Motivation --- p.86Chapter 4.3 --- Proposed Analytical Model for the Lifetime Reliability of Proces- sor Cores --- p.88Chapter 4.3.1 --- "Impact of Temperature, Voltage, and Frequency" --- p.88Chapter 4.3.2 --- Impact of Workloads --- p.92Chapter 4.4 --- Lifetime Reliability Analysis for Multi-core Processors with Vari- ous Redundancy Schemes --- p.95Chapter 4.4.1 --- Gracefully Degrading System (GDS) --- p.95Chapter 4.4.2 --- Processor Rotation System (PRS) --- p.97Chapter 4.4.3 --- Standby Redundant System (SRS) --- p.98Chapter 4.4.4 --- Extension to Heterogeneous System --- p.99Chapter 4.5 --- Experimental Methodology --- p.101Chapter 4.5.1 --- Workload Description --- p.102Chapter 4.5.2 --- Temperature Distribution Extraction --- p.102Chapter 4.5.3 --- Reliability Factors --- p.103Chapter 4.6 --- Results and Discussions --- p.103Chapter 4.6.1 --- Wear-out Rate Computation --- p.103Chapter 4.6.2 --- Comparison on Lifetime Reliability --- p.105Chapter 4.6.3 --- Comparison on Performance --- p.110Chapter 4.6.4 --- Comparison on Expected Computation Amount --- p.112Chapter 4.7 --- Conclusion --- p.118Chapter III --- Applications --- p.119Chapter 5 --- Task Allocation and Scheduling for MPSoCs --- p.120Chapter 5.1 --- Introduction --- p.120Chapter 5.2 --- Prior Work and Motivation --- p.122Chapter 5.2.1 --- IC Lifetime Reliability --- p.122Chapter 5.2.2 --- Task Allocation and Scheduling for MPSoC Designs --- p.124Chapter 5.3 --- Proposed Task Allocation and Scheduling Strategy --- p.126Chapter 5.3.1 --- Problem Definition --- p.126Chapter 5.3.2 --- Solution Representation --- p.128Chapter 5.3.3 --- Cost Function --- p.129Chapter 5.3.4 --- Simulated Annealing Process --- p.130Chapter 5.4 --- Lifetime Reliability Computation for MPSoC Embedded Systems --- p.133Chapter 5.5 --- Efficient MPSoC Lifetime Approximation --- p.138Chapter 5.5.1 --- Speedup Technique I - Multiple Periods --- p.139Chapter 5.5.2 --- Speedup Technique II - Steady Temperature --- p.139Chapter 5.5.3 --- Speedup Technique III - Temperature Pre- calculation --- p.140Chapter 5.5.4 --- Speedup Technique IV - Time Slot Quantity Control --- p.144Chapter 5.6 --- Experimental Results --- p.144Chapter 5.6.1 --- Experimental Setup --- p.144Chapter 5.6.2 --- Results and Discussion --- p.146Chapter 5.7 --- Conclusion and Future Work --- p.152Chapter 6 --- Energy-Efficient Task Allocation and Scheduling --- p.154Chapter 6.1 --- Introduction --- p.154Chapter 6.2 --- Preliminaries and Problem Formulation --- p.157Chapter 6.2.1 --- Related Work --- p.157Chapter 6.2.2 --- Problem Formulation --- p.159Chapter 6.3 --- Analytical Models --- p.160Chapter 6.3.1 --- Performance and Energy Models for DVS-Enabled Pro- cessors --- p.160Chapter 6.3.2 --- Lifetime Reliability Model --- p.163Chapter 6.4 --- Proposed Algorithm for Single-Mode Embedded Systems --- p.165Chapter 6.4.1 --- Task Allocation and Scheduling --- p.165Chapter 6.4.2 --- Voltage Assignment for DVS-Enabled Processors --- p.168Chapter 6.5 --- Proposed Algorithm for Multi-Mode Embedded Systems --- p.169Chapter 6.5.1 --- Feasible Solution Set --- p.169Chapter 6.5.2 --- Searching Procedure for a Single Mode --- p.171Chapter 6.5.3 --- Feasible Solution Set Identification --- p.171Chapter 6.5.4 --- Multi-Mode Combination --- p.177Chapter 6.6 --- Experimental Results --- p.178Chapter 6.6.1 --- Experimental Setup --- p.178Chapter 6.6.2 --- Case Study --- p.180Chapter 6.6.3 --- Sensitivity Analysis --- p.181Chapter 6.6.4 --- Extensive Results --- p.183Chapter 6.7 --- Conclusion --- p.185Chapter 7 --- Customer-Aware Task Allocation and Scheduling --- p.186Chapter 7.1 --- Introduction --- p.186Chapter 7.2 --- Prior Work and Problem Formulation --- p.188Chapter 7.2.1 --- Related Work and Motivation --- p.188Chapter 7.2.2 --- Problem Formulation --- p.191Chapter 7.3 --- Proposed Design-Stage Task Allocation and Scheduling --- p.192Chapter 7.3.1 --- Solution Representation and Moves --- p.193Chapter 7.3.2 --- Cost Function --- p.196Chapter 7.3.3 --- Impact of DVFS --- p.198Chapter 7.4 --- Proposed Algorithm for Online Adjustment --- p.200Chapter 7.4.1 --- Reliability Requirement for Online Adjustment --- p.201Chapter 7.4.2 --- Analytical Model --- p.203Chapter 7.4.3 --- Overall Flow --- p.204Chapter 7.5 --- Experimental Results --- p.205Chapter 7.5.1 --- Experimental Setup --- p.205Chapter 7.5.2 --- Results and Discussion --- p.207Chapter 7.6 --- Conclusion --- p.211Chapter 7.7 --- Appendix --- p.211Chapter 8 --- Conclusion and Future Work --- p.214Chapter 8.1 --- Conclusion --- p.214Chapter 8.2 --- Future Work --- p.215Bibliography --- p.23