Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks

The success of modern applications depends on the insights they collect from their data repositories. Data repositories for such applications currently exceed exabytes and are rapidly increasing in size, as they collect data from varied sources - web applications, mobile phones, sensors and other connected devices. Distributed storage and data-centric compute frameworks have been invented to store and analyze these large datasets. This dissertation focuses on extending the applicability and improving the efficiency of distributed data-centric compute frameworks

Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies

We explore the trade-offs of performing linear algebra using Apache Spark, compared to traditional C and MPI implementations on HPC platforms. Spark is designed for data analytics on cluster computing platforms with access to local disks and is optimized for data-parallel tasks. We examine three widely-used and important matrix factorizations: NMF (for physical plausability), PCA (for its ubiquity) and CX (for data interpretability). We apply these methods to TB-sized problems in particle physics, climate modeling and bioimaging. The data matrices are tall-and-skinny which enable the algorithms to map conveniently into Spark's data-parallel model. We perform scaling experiments on up to 1600 Cray XC40 nodes, describe the sources of slowdowns, and provide tuning guidance to obtain high performance

The Glasgow raspberry pi cloud: a scale model for cloud computing infrastructures

Data Centers (DC) used to support Cloud services often consist of tens of thousands of networked machines under a single roof. The significant capital outlay required to replicate such infrastructures constitutes a major obstacle to practical implementation and evaluation of research in this domain. Currently, most research into Cloud computing relies on either limited software simulation, or the use of a testbed environments with a handful of machines. The recent introduction of the Raspberry Pi, a low-cost, low-power single-board computer, has made the construction of a miniature Cloud DCs more affordable. In this paper, we present the Glasgow Raspberry Pi Cloud (PiCloud), a scale model of a DC composed of clusters of Raspberry Pi devices. The PiCloud emulates every layer of a Cloud stack, ranging from resource virtualisation to network behaviour, providing a full-featured Cloud Computing research and educational environment

Understanding and Improving the Performance of Read Operations Across the Storage Stack

We live in a data-driven era, large amounts of data are generated and collected every day. Storage systems are the backbone of this era, as they store and retrieve data. To cope with increasing data demands (e.g., diversity, scalability), storage systems are experiencing changes across the stack. As other computer systems, storage systems rely on layering and modularity, to allow rapid development. Unfortunately, this can hinder performance clarity and introduce degradations (e.g., tail latency), due to unexpected interactions between components of the stack. In this thesis, we first perform a study to understand the behavior across different layers of the storage stack. We focus on sequential read workloads, a common I/O pattern in distributed le systems (e.g., HDFS, GFS). We analyze the interaction between read workloads, local le systems (i.e., ext4), and storage media (i.e., SSDs). We perform the same experiment over different periods of time (e.g., le lifetime). We uncover 3 slowdowns, all of which occur in the lower layers. When combined, these slowdowns can degrade throughput by 30%. We find that increased parallelism on the local le system mitigates these slowdowns, showing the need for adaptability in storage stacks. Given the fact that performance instabilities can occur at any layer of the stack, it is important that upper-layer systems are able to react. We propose smart hedging, a novel technique to manage high-percentile (tail) latency variations in read operations. Smart hedging considers production challenges, such as massive scalability, heterogeneity, and ease of deployment and maintainability. Our technique establishes a dynamic threshold by tracking latencies on the client-side. If a read operation exceeds the threshold, a new hedged request is issued, in an exponential back-off manner. We implement our technique in HDFS and evaluate it on 70k servers in 3 datacenters. Our technique reduces average tail latency, without generating excessive system load

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c

Computing at massive scale: Scalability and dependability challenges

Large-scale Cloud systems and big data analytics frameworks are now widely used for practical services and applications. However, with the increase of data volume, together with the heterogeneity of workloads and resources, and the dynamic nature of massive user requests, the uncertainties and complexity of resource management and service provisioning increase dramatically, often resulting in poor resource utilization, vulnerable system dependability, and user-perceived performance degradations. In this paper we report our latest understanding of the current and future challenges in this particular area, and discuss both existing and potential solutions to the problems, especially those concerned with system efficiency, scalability and dependability. We first introduce a data-driven analysis methodology for characterizing the resource and workload patterns and tracing performance bottlenecks in a massive-scale distributed computing environment. We then examine and analyze several fundamental challenges and the solutions we are developing to tackle them, including for example incremental but decentralized resource scheduling, incremental messaging communication, rapid system failover, and request handling parallelism. We integrate these solutions with our data analysis methodology in order to establish an engineering approach that facilitates the optimization, tuning and verification of massive-scale distributed systems. We aim to develop and offer innovative methods and mechanisms for future computing platforms that will provide strong support for new big data and IoE (Internet of Everything) applications

Comparing Four Approaches for Technical Debt Identification

Background: Software systems accumulate technical debt (TD) when short-term goals in software development are traded for long term goals (e.g., quick-and-dirty implementation to reach a release date vs. a well-refactored implementation that supports the long term health of the project). Some forms of TD accumulate over time in the form of source code that is difficult to work with and exhibits a variety of anomalies. A number of source code analysis techniques and tools have been proposed to potentially identify the code-level debt accumulated in a system. What has not yet been studied is if using multiple tools to detect TD can lead to benefits, i.e. if different tools will flag the same or different source code components. Further, these techniques also lack investigation into the symptoms of TD "interest" that they lead to. To address this latter question, we also investigated whether TD, as identified by the source code analysis techniques, correlates with interest payments in the form of increased defect- and change-proneness. Aims: Comparing the results of different TD identification approaches to understand their commonalities and differences and to evaluate their relationship to indicators of future TD "interest". Method: We selected four different TD identification techniques (code smells, automatic static analysis (ASA) issues, grime buildup, and modularity violations) and applied them to 13 versions of the Apache Hadoop open source software project. We collected and aggregated statistical measures to investigate whether the different techniques identified TD indicators in the same or different classes and whether those classes in turn exhibited high interest (in the form of a large number of defects and higher change proneness). Results: The outputs of the four approaches have very little overlap and are therefore pointing to different problems in the source code. Dispersed coupling and modularity violations were co-located in classes with higher defect proneness. We also observed a strong relationship between modularity violations and change proneness. Conclusions: Our main contribution is an initial overview of the TD landscape, showing that different TD techniques are loosely coupled and therefore indicate problems in different locations of the source code. Moreover, our proxy interest indicators (change- and defect-proneness) correlate with only a small subset of TD indicator

Understanding Spark System Performance for Image Processing in a Heterogeneous Commodity Cluster

In recent years, Apache Spark has seen a widespread adoption in industries and institutions due to its cache mechanism for faster Big Data analytics. However, the speed advantage Spark provides, especially in a heterogeneous cluster environment, is not obtainable out-of-the-box; it requires the right combination of configuration parameters from the myriads of parameters provided by Spark developers. Recognizing this challenge, this thesis undertakes a study to provide insight on Spark performance particularly, regarding the impact of choice parameter settings. These are parameters that are critical to fast job completion and effective utilization of resources. To this end, the study focuses on two specific example applications namely, flowerCounter and imageClustering, for processing still image datasets of Canola plants collected during the Summer of 2016 from selected plot fields using timelapse cameras in a heterogeneous Spark-clustered environments. These applications were of initial interest to the Plant Phenotyping and Imaging Research Centre (P2IRC) at the University of Saskatchewan. The P2IRC is responsible for developing systems that will aid fast analysis of large-scale seed breeding to ensure global food security. The flowerCounter application estimates the count of flowers from the images while the imageClustering application clusters images based on physical plant attributes. Two clusters are used for the experiments: a 12-node and 3-node cluster (including a master node), with Hadoop Distributed File System (HDFS) as the storage medium for the image datasets. Experiments with the two case study applications demonstrate that increasing the number of tasks does not always speed-up job processing due to increased communication overheads. Findings from other experiments show that numerous tasks with one core per executor and small allocated memory limits parallelism within an executor and result in inefficient use of cluster resources. Executors with large CPU and memory, on the other hand, do not speed-up analytics due to processing delays and threads concurrency. Further experimental results indicate that application processing time depends on input data storage in conjunction with locality levels and executor run time is largely dominated by the disk I/O time especially, the read time cost. With respect to horizontal node scaling, Spark scales with increasing homogeneous computing nodes but the speed-up degrades with heterogeneous nodes. Finally, this study shows that the effectiveness of speculative tasks execution in mitigating the impact of slow nodes varies for the applications