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Using Multivariate Statistics

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

[서평]「The Unified Modeling Language User Guide」

Technical foundations introduction software reliability and system reliability the operational profile software reliability modelling survey model evaluation and recalibration techniques practices and experiences best current practice of SRE software reliability measurement experience measurement-based analysis of software reliability software fault and failure classification techniques trend analysis in validation and maintenance software reliability and field data analysis software reliability process assessment emerging techniques software reliability prediction metrics software reliability and testing fault-tolerant SRE software reliability using fault trees software reliability process simulation neural networks and software reliability. Appendices: software reliability tools software failure data set repository.

/pdf/handbook-of-software-reliability-engineering-32dwvaljmm.pdf

Handbook of software reliability engineering

コンピュータ・サイエンス : ACM computing surveys

Software failures, workload-related failures and job overload conditions bring about SLA violations in software-as-a-service (SaaS) systems. Existing work does not address mitigation of SLA violations completely as (i) none of them address mitigation of SLA violations in business specific scenarios (SaaS, in our case), (ii) while some do not address software and workload-related failures, other approaches do not address the problem of target PM selection for workload migration comprehensively (leaving out vital considerations like workload compatibility checks between migrating VM and VMs at the target PM) and (iii) a clear mathematical mapping between workload, resource demand and SLA is lacking. In this paper, we present the Keep It Moving (KIM) software framework for the cloud controller that helps minimize service failures due to SLA violation of availability, utilization and response time in SaaS cloud data centers. Though we consider migration to be the primary mitigation technique, we also try to mitigate SLA violations without migration. We achieve this by performing a capacity check on the host physical machine (PM) before the migration to identify if enough capacity is available on the current PM to address the upcoming SLA violations by restart/reboot or VM resizing. In certain cases such as workload-related failures due to corrupt files, we prefer workload rerouting to a replica VM over migration. We formulate the selection of a target PM as a multi-objective optimization problem. We validate our proposed approach by using a trace-based discrete event simulation of a virtualized data center where failure and workload characteristics are simulated from data extracted from a real SaaS business server logs. We found that a 60% reduction in SLA violation is possible using our approach as well as reducing VM downtime by approximately 10%.

Keep it moving: Proactive workload management for reducing SLA violations in large scale SaaS clouds

High-level design from requirements continues to follow a manual, process centric approach, with heavy dependence on experts. In this paper we propose "Design Assistant Tool" (DAT) that combines natural language processing techniques and common design heuristics to create a functional design from a set of relatively structured textual requirements and use cases. To create a functional design, DAT processes a set of structured requirements and use cases using a set of heuristics and identifies a set of coarse grained modules, classes, and data entities as UML models. In this process, DAT produces two important design artifacts, namely a set of functional modules and classes and a set of entity classes. The functional modules form the basis of application logic and entity classes form the basis of data model. Our early investigations have shown that the resulting design is a good starting point for the detailed design and implementation.

Creating design from requirements and use cases: bridging the gap between requirement and detailed design

Internet of Things (IoT) has a disruptive impact on variety of application domains like healthcare, logistics, transportation, etc., where network-enabled embedded devices work proactively and collaboratively to solve a complex decision-making problem in real time. Many of these applications are business critical, and require the underlying technology to be dependable, that is, it must deliver its service even in the presence of failures. In this chapter we discuss the notion of reliability and recovery oriented systems in general and then explain why this is important for an IoT-based system. We discuss various failure scenarios and reliability challenges that exist at different layers of an IoT-based system. Next, we discuss various failure-prevention and fault-tolerance approaches to make an IoT-based system robust. Energy consumption by IoT devices plays a major role in the overall system reliability. In this chapter we discuss various reliability challenges due to the low-power devices and solutions that have emerged to improve the overall reliability of such devices and the whole system.

Internet of Things—robustness and reliability

Developers of GPU kernels, such as FFT, linear solvers, etc, tune their code extensively in order to obtain optimal performance, making efficient use of different resources available on the GPU. Complex applications are composed of several such kernel components. The software engineering community has performed extensive research on component based design to build generic and flexible components, such that a component can be reused across diverse applications, rather than optimizing its performance. Since a GPU is used primarily to improve performance, application performance becomes a key design issue. The contribution of our work lies in extending component based design research in a new direction, dealing with the performance impact of refactoring an application consisting of the composition of highly tuned kernels. Such refactoring can make the composition more effective with respect to GPU resource usage especially when combined with suitable scheduling. Here we propose a methodology where developers of highly tuned kernels can enable application designers to optimize performance of the composition. Kernel developers characterize the performance of a kernel through its "performance signature". The application designer combines these kernels such that the performance of the refactored kernel is better than the sum of the performances of the individual kernels. This is partly based on the observation that different kernels may make unbalanced use of different GPU resources like different types of memory. Kernels may also have the potential to share data. Refactoring the kernels, combining them, and scheduling them suitably can improve performance. We study different types of potential design optimizations and evaluate their effectiveness on different types of kernels. This may even involve choosing non-optimal parameters for an individual kernel. We analyze how the performance signature of the composition changes from that of the individual kernels through our techniques. We demonstrate that our techniques lead to over 50% improvement with some kernels. Furthermore, the performance of a basic molecular dynamics application can be improved by around 25.7%, on a Fermi GPU, compared with an un-refactored implementation.

/pdf/reuse-and-refactoring-of-gpu-kernels-to-design-complex-3ixkblctg7.pdf

Reuse and Refactoring of GPU Kernels to Design Complex Applications

This technique determines the possible Service Level Agreement (SLA) violation during migrating a workload from a physical environment to a virtualized environment and also migrating a virtual machine (VM) from one physical machine to another within a virtualized environment. This technique uses capacity factor, i.e. the ratio of host physical machine (host PM) capacity and target physical machine (target PM) capacity, to determine the service rate change in the target PM compared to host PM and eventually calculates the VM utilization and response time in the target PM. It then checks if the VM utilization and response time in the target PM lies within the SLA limit. It further checks if the migration in the target virtualized PM is violating a predefined target PM utilization threshold or not and finally causes the migration in the target physical machine if all the SLA parameters are satisfied.

Santonu Sarkar

Papers

Keep it moving: Proactive workload management for reducing SLA violations in large scale SaaS clouds

Creating design from requirements and use cases: bridging the gap between requirement and detailed design

Internet of Things—robustness and reliability

Reuse and Refactoring of GPU Kernels to Design Complex Applications

System and method for detecting and preventing service level agreement violation in a virtualized environment