Failure-awareness and dynamic adaptation in data scheduling

Over the years, scientific applications have become more complex and more data intensive. Especially large scale simulations and scientific experiments in areas such as physics, biology, astronomy and earth sciences demand highly distributed resources to satisfy excessive computational requirements. Increasing data requirements and the distributed nature of the resources made I/O the major bottleneck for end-to-end application performance. Existing systems fail to address issues such as reliability, scalability, and efficiency in dealing with wide area data access, retrieval and processing. In this study, we explore data-intensive distributed computing and study challenges in data placement in distributed environments. After analyzing different application scenarios, we develop new data scheduling methodologies and the key attributes for reliability, adaptability and performance optimization of distributed data placement tasks. Inspired by techniques used in microprocessor and operating system architectures, we extend and adapt some of the known low-level data handling and optimization techniques to distributed computing. Two major contributions of this work include (i) a failure-aware data placement paradigm for increased fault-tolerance, and (ii) adaptive scheduling of data placement tasks for improved end-to-end performance. The failure-aware data placement includes early error detection, error classification, and use of this information in scheduling decisions for the prevention of and recovery from possible future errors. The adaptive scheduling approach includes dynamically tuning data transfer parameters over wide area networks for efficient utilization of available network capacity and optimized end-to-end data transfer performance

Assessing hyper parameter optimization and speedup for convolutional neural networks

The increased processing power of graphical processing units (GPUs) and the availability of large image datasets has fostered a renewed interest in extracting semantic information from images. Promising results for complex image categorization problems have been achieved using deep learning, with neural networks comprised of many layers. Convolutional neural networks (CNN) are one such architecture which provides more opportunities for image classification. Advances in CNN enable the development of training models using large labelled image datasets, but the hyper parameters need to be specified, which is challenging and complex due to the large number of parameters. A substantial amount of computational power and processing time is required to determine the optimal hyper parameters to define a model yielding good results. This article provides a survey of the hyper parameter search and optimization methods for CNN architectures

Hierarchical ResNeXt Models for Breast Cancer Histology Image Classification

Microscopic histology image analysis is a cornerstone in early detection of breast cancer. However these images are very large and manual analysis is error prone and very time consuming. Thus automating this process is in high demand. We proposed a hierarchical system of convolutional neural networks (CNN) that classifies automatically patches of these images into four pathologies: normal, benign, in situ carcinoma and invasive carcinoma. We evaluated our system on the BACH challenge dataset of image-wise classification and a small dataset that we used to extend it. Using a train/test split of 75%/25%, we achieved an accuracy rate of 0.99 on the test split for the BACH dataset and 0.96 on that of the extension. On the test of the BACH challenge, we've reached an accuracy of 0.81 which rank us to the 8th out of 51 teams

Quality of Service over Specific Link Layers: state of the art report

The Integrated Services concept is proposed as an enhancement to the current Internet architecture, to provide a better Quality of Service (QoS) than that provided by the traditional Best-Effort service. The features of the Integrated Services are explained in this report. To support Integrated Services, certain requirements are posed on the underlying link layer. These requirements are studied by the Integrated Services over Specific Link Layers (ISSLL) IETF working group. The status of this ongoing research is reported in this document. To be more specific, the solutions to provide Integrated Services over ATM, IEEE 802 LAN technologies and low-bitrate links are evaluated in detail. The ISSLL working group has not yet studied the requirements, that are posed on the underlying link layer, when this link layer is wireless. Therefore, this state of the art report is extended with an identification of the requirements that are posed on the underlying wireless link, to provide differentiated Quality of Service

Data transfer scheduling with advance reservation and provisioning

Over the years, scientific applications have become more complex and more data intensive. Although through the use of distributed resources the institutions and organizations gain access to the resources needed for their large-scale applications, complex middleware is required to orchestrate the use of these storage and network resources between collaborating parties, and to manage the end-to-end processing of data. We present a new data scheduling paradigm with advance reservation and provisioning. Our methodology provides a basis for provisioning end-to-end high performance data transfers which require integration between system, storage and network resources, and coordination between reservation managers and data transfer nodes. This allows researchers/users and higher level meta-schedulers to use data placement as a service where they can plan ahead and reserve time and resources for their data movement operations. We present a novel approach for evaluating time-dependent structures with bandwidth guaranteed paths. We present a practical online scheduling model using advance reservation in dynamic network with time constraints. In addition, we report a new polynomial algorithm presenting possible reservation options and alternatives for earliest completion and shortest transfer duration. We enhance the advance network reservation system by extending the underlying mechanism to provide a new service in which users submit their constraints and the system suggests possible reservation requests satisfying users\u27 requirements. We have studied scheduling data transfer operation with resource and time conflicts. We have developed a new scheduling methodology considering resource allocation in client sites and bandwidth allocation on network link connecting resources. Some other major contributions of our study include enhanced reliability, adaptability, and performance optimization of distributed data placement tasks. While designing this new data scheduling architecture, we also developed other important methodologies such as early error detection, failure awareness, job aggregation, and dynamic adaptation of distributed data placement tasks. The adaptive tuning includes dynamically setting data transfer parameters and controlling utilization of available network capacity. Our research aims to provide a middleware to improve the data bottleneck in high performance computing systems