The safety case and the lessons learned for the reliability and maintainability case

This paper examine the safety case and the lessons learned for the reliability and maintainability case

Scalable and Reliable Sparse Data Computation on Emergent High Performance Computing Systems

Heterogeneous systems with both CPUs and GPUs have become important system architectures in emergent High Performance Computing (HPC) systems. Heterogeneous systems must address both performance-scalability and power-scalability in the presence of failures. Aggressive power reduction pushes hardware to its operating limit and increases the failure rate. Resilience allows programs to progress when subjected to faults and is an integral component of large-scale systems, but incurs significant time and energy overhead. The future exascale systems are expected to have higher power consumption with higher fault rates. Sparse data computation is the fundamental kernel in many scientific applications. It is suitable for the studies of scalability and resilience on heterogeneous systems due to its computational characteristics. To deliver the promised performance within the given power budget, heterogeneous computing mandates a deep understanding of the interplay between scalability and resilience. Managing scalability and resilience is challenging in heterogeneous systems, due to the heterogeneous compute capability, power consumption, and varying failure rates between CPUs and GPUs. Scalability and resilience have been traditionally studied in isolation, and optimizing one typically detrimentally impacts the other. While prior works have been proved successful in optimizing scalability and resilience on CPU-based homogeneous systems, simply extending current approaches to heterogeneous systems results in suboptimal performance-scalability and/or power-scalability. To address the above multiple research challenges, we propose novel resilience and energy-efficiency technologies to optimize scalability and resilience for sparse data computation on heterogeneous systems with CPUs and GPUs. First, we present generalized analytical and experimental methods to analyze and quantify the time and energy costs of various recovery schemes, and develop and prototype performance optimization and power management strategies to improve scalability for sparse linear solvers. Our results quantitatively reveal that each resilience scheme has its own advantages depending on the fault rate, system size, and power budget, and the forward recovery can further benefit from our performance and power optimizations for large-scale computing. Second, we design a novel resilience technique that relaxes the requirement of synchronization and identicalness for processes, and allows them to run in heterogeneous resources with power reduction. Our results show a significant reduction in energy for unmodified programs in various fault situations compared to exact replication techniques. Third, we propose a novel distributed sparse tensor decomposition that utilizes an asynchronous RDMA-based approach with OpenSHMEM to improve scalability on large-scale systems and prove that our method works well in heterogeneous systems. Our results show our irregularity-aware workload partition and balanced-asynchronous algorithms are scalable and outperform the state-of-the-art distributed implementations. We demonstrate that understanding different bottlenecks for various types of tensors plays critical roles in improving scalability

Mathematics in Software Reliability and Quality Assurance

This monograph concerns the mathematical aspects of software reliability and quality assurance and consists of 11 technical papers in this emerging area. Included are the latest research results related to formal methods and design, automatic software testing, software verification and validation, coalgebra theory, automata theory, hybrid system and software reliability modeling and assessment

A critical review of voluntary environmental initiatives in tourism: policy implications for rejuvenation

Environmental policy is one of the key management dimensions of rejuvenation strategies in mature tourism destinations. Among these, voluntary environmental initiatives are increasingly becoming a relevant theoretical possibility and are an empirical reality. Voluntary environmental initiatives are primarily undertaken in developed countries, mostly in Europe, that is on itself a mature tourism destination. This paper critically reviews the literature on voluntary environmental initiatives in tourism and extract policy implications for rejuvenation strategies. The main argument is that environmental management at tourism destinations can become a social dilemma for stakeholders at the destination, though these dilemmas are not inevitable. This paper shows that there are economic and non-economic incentives for different stakeholders to undertake voluntary environmental initiatives, and that many successful examples exist where voluntary action has fostered rejuvenation of degraded destinations. Voluntary initiatives should not be taken as a new panacea solution for tourism rejuvenation, but rather one strategy that can contribute in conjunction with others, to a better management of natural resources at destinations, improving environmental quality to guarantee the long-run competitiveness of tourism destinations

A survey on elasticity management in PaaS systems

[EN] Elasticity is a goal of cloud computing. An elastic system should manage in an autonomic way its resources, being adaptive to dynamic workloads, allocating additional resources when workload is increased and deallocating resources when workload decreases. PaaS providers should manage resources of customer applications with the aim of converting those applications into elastic services. This survey identifies the requirements that such management imposes on a PaaS provider: autonomy, scalability, adaptivity, SLA awareness, composability and upgradeability. This document delves into the variety of mechanisms that have been proposed to deal with all those requirements. Although there are multiple approaches to address those concerns, providers main goal is maximisation of profits. This compels providers to look for balancing two opposed goals: maximising quality of service and minimising costs. 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Redundant VoD Streaming Service in a Private Cloud: Availability Modeling and Sensitivity Analysis

For several years cloud computing has been generating considerable debate and interest within IT corporations. Since cloud computing environments provide storage and processing systems that are adaptable, efficient, and straightforward, thereby enabling rapid infrastructure modifications to be made according to constantly varying workloads, organizations of every size and type are migrating to web-based cloud supported solutions. Due to the advantages of the pay-per-use model and scalability factors, current video on demand (VoD) streaming services rely heavily on cloud infrastructures to offer a large variety of multimedia content. Recent well documented failure events in commercial VoD services have demonstrated the fundamental importance of maintaining high availability in cloud computing infrastructures, and hierarchical modeling has proved to be a useful tool for evaluating the availability of complex systems and services. This paper presents an availability model for a video streaming service deployed in a private cloud environment which includes redundancy mechanisms in the infrastructure. Differential sensitivity analysis was applied to identify and rank the critical components of the system with respect to service availability. The results demonstrate that such a modeling strategy combined with differential sensitivity analysis can be an attractive methodology for identifying which components should be supported with redundancy in order to consciously increase system dependability