Time Varying Sensitivities on a GRID architecture

We estimate time varying risk sensitivities on a wide range of stocks' portfolios of the US market. We empirically test, on a 1926-2004 Monthly CRSP database, a classic one factor model augmented with a time varying specification of betas. Using a Kalman filter based on a genetic algorithm, we show that the model is able to explain a large part of the variability of stock returns. Furthermore we run a Risk Management application on a GRID computing architecture. By estimating a parametric Value at Risk, we show how GRID computing offers an opportunity to enhance the solution of computational demanding problems with decentralized data retrieval.

Passive network awareness as a means for improved grid scheduling

Grids enable sharing resources of heterogeneous nature and administration. In such distributed systems, the network is usually taken for granted which is potentially problematic due to the complexity and unpredictability of public networks that typically underlie grids. This article introduces GridMAP, a mechanism for considering the network state for enhancing grid scheduling. Network measurements are collected in a passive manner from a user-centric vantage point. This mechanism has been evaluated on a production e-science grid infrastructure, with results showing the ability of GridMAP to improve grid scheduling with minimal network, computational and deployment overheads

Towards High-Performance Big Data Processing Systems

The amount of generated and stored data has been growing rapidly, It is estimated that 2.5 quintillion bytes of data are generated every day, and 90% of the data in the world today has been created in the last two years. How to solve these big data issues has become a hot topic in both industry and academia. Due to the complex of big data platform, we stratify it into four layers: storage layer, resource management layer, computing layer, and methodology layer. This dissertation proposes brand-new approaches to address the performance of big data platforms like Hadoop and Spark on these four layers. We first present an improved HDFS design called SMARTH, which optimizes the storage layer. It utilizes asynchronous multi-pipeline data transfers instead of a single pipeline stop-and-wait mechanism. SMARTH records the actual transfer speed of data blocks and sends this information to the namenode along with periodic heartbeat messages. The namenode sorts datanodes according to their past performance and tracks this information continuously. When a client initiates an upload request, the namenode will send it a list of \u27\u27high performance\u27\u27 datanodes that it thinks will yield the highest throughput for the client. By choosing higher performance datanodes relative to each client and by taking advantage of the multi-pipeline design, our experiments show that SMARTH significantly improves the performance of data write operations compared to HDFS. Specifically, SMARTH is able to improve the throughput of data transfer by 27-245% in a heterogeneous virtual cluster on Amazon EC2. Secondly, we propose an optimized Hadoop extension called MRapid, which significantly speeds up the execution of short jobs on the resource management layer. It is completely backward compatible to Hadoop, and imposes negligible overhead. Our experiments on Microsoft Azure public cloud show that MRapid can improve performance by up to 88% compared to the original Hadoop. Thirdly, we introduce an efficient 3-level sampling performance model, called Hedgehog, and focus on the relationship between resource and performance. This design is a brand new white-box model for Spark, which is more complex and challenging than Hadoop. In our tool, we employ a Java bytecode manipulation and analysis framework called ASM to reduce the profiling overhead dramatically. Fourthly, on the computing layer, we optimize the current implementation of SGD in Spark\u27s MLlib by reusing data partition for multiple times within a single iteration to find better candidate weights in a more efficient way. Whether using multiple local iterations within each partition is dynamically decided by the 68-95-99.7 rule. We also design a variant of momentum algorithm to optimize step size in every iteration. This method uses a new adaptive rule that decreases the step size whenever neighboring gradients show differing directions of significance. Experiments show that our adaptive algorithm is more efficient and can be 7 times faster compared to the original MLlib\u27s SGD. At last, on the application layer, we present a scalable and distributed geographic information system, called Dart, based on Hadoop and HBase. Dart provides a hybrid table schema to store spatial data in HBase so that the Reduce process can be omitted for operations like calculating the mean center and the median center. It employs reasonable pre-splitting and hash techniques to avoid data imbalance and hot region problems. It also supports massive spatial data analysis like K-Nearest Neighbors (KNN) and Geometric Median Distribution. In our experiments, we evaluate the performance of Dart by processing 160 GB Twitter data on an Amazon EC2 cluster. The experimental results show that Dart is very scalable and efficient

Towards Lightweight Data Integration using Multi-workflow Provenance and Data Observability

Modern large-scale scientific discovery requires multidisciplinary collaboration across diverse computing facilities, including High Performance Computing (HPC) machines and the Edge-to-Cloud continuum. Integrated data analysis plays a crucial role in scientific discovery, especially in the current AI era, by enabling Responsible AI development, FAIR, Reproducibility, and User Steering. However, the heterogeneous nature of science poses challenges such as dealing with multiple supporting tools, cross-facility environments, and efficient HPC execution. Building on data observability, adapter system design, and provenance, we propose MIDA: an approach for lightweight runtime Multi-workflow Integrated Data Analysis. MIDA defines data observability strategies and adaptability methods for various parallel systems and machine learning tools. With observability, it intercepts the dataflows in the background without requiring instrumentation while integrating domain, provenance, and telemetry data at runtime into a unified database ready for user steering queries. We conduct experiments showing end-to-end multi-workflow analysis integrating data from Dask and MLFlow in a real distributed deep learning use case for materials science that runs on multiple environments with up to 276 GPUs in parallel. We show near-zero overhead running up to 100,000 tasks on 1,680 CPU cores on the Summit supercomputer.Comment: 10 pages, 5 figures, 2 Listings, 42 references, Paper accepted at IEEE eScience'2

Adaptive sliding windows for improved estimation of data center resource utilization

Accurate prediction of data center resource utilization is required for capacity planning, job scheduling, energy saving, workload placement, and load balancing to utilize the resources efficiently. However, accurately predicting those resources is challenging due to dynamic workloads, heterogeneous infrastructures, and multi-tenant co-hosted applications. Existing prediction methods use fixed size observation windows which cannot produce accurate results because of not being adaptively adjusted to capture local trends in the most recent data. Therefore, those methods train on large fixed sliding windows using an irrelevant large number of observations yielding to inaccurate estimations or fall for inaccuracy due to degradation of estimations with short windows on quick changing trends. In this paper we propose a deep learning-based adaptive window size selection method, dynamically limiting the sliding window size to capture the trend for the latest resource utilization, then build an estimation model for each trend period. We evaluate the proposed method against multiple baseline and state-of-the-art methods, using real data-center workload data sets. The experimental evaluation shows that the proposed solution outperforms those state-of-the-art approaches and yields 16 to 54% improved prediction accuracy compared to the baseline methods.This work is partially supported by the European ResearchCouncil (ERC) under the EU Horizon 2020 programme(GA 639595), the Spanish Ministry of Economy, Industry andCompetitiveness (TIN2015-65316-P and IJCI2016-27485), theGeneralitat de Catalunya, Spain (2014-SGR-1051) and Universityof the Punjab, Pakistan. The statements made herein are solelythe responsibility of the authors.Peer ReviewedPostprint (published version

Multi-Epoch Matrix Factorization Mechanisms for Private Machine Learning

We introduce new differentially private (DP) mechanisms for gradient-based machine learning (ML) with multiple passes (epochs) over a dataset, substantially improving the achievable privacy-utility-computation tradeoffs. We formalize the problem of DP mechanisms for adaptive streams with multiple participations and introduce a non-trivial extension of online matrix factorization DP mechanisms to our setting. This includes establishing the necessary theory for sensitivity calculations and efficient computation of optimal matrices. For some applications like $>\!\! 10,000$ SGD steps, applying these optimal techniques becomes computationally expensive. We thus design an efficient Fourier-transform-based mechanism with only a minor utility loss. Extensive empirical evaluation on both example-level DP for image classification and user-level DP for language modeling demonstrate substantial improvements over all previous methods, including the widely-used DP-SGD . Though our primary application is to ML, our main DP results are applicable to arbitrary linear queries and hence may have much broader applicability.Comment: 9 pages main-text, 3 figures. 40 pages with 13 figures tota

Data center's telemetry reduction and prediction through modeling techniques

Nowadays, Cloud Computing is widely used to host and deliver services over the Internet. The architecture of clouds is complex due to its heterogeneous nature of hardware and is hosted in large scale data centers. To effectively and efficiently manage such complex infrastructure, constant monitoring is needed. This monitoring generates large amounts of telemetry data streams (e.g. hardware utilization metrics) which are used for multiple purposes including problem detection, resource management, workload characterization, resource utilization prediction, capacity planning, and job scheduling. These telemetry streams require costly bandwidth utilization and storage space particularly at medium-long term for large data centers. Moreover, accurate future estimation of these telemetry streams is a challenging task due to multi-tenant co-hosted applications and dynamic workloads. The inaccurate estimation leads to either under or over-provisioning of data center resources. In this Ph.D. thesis, we propose to improve the prediction accuracy and reduce the bandwidth utilization and storage space requirement with the help of modeling and prediction methods from machine learning. Most of the existing methods are based on a single model which often does not appropriately estimate different workload scenarios. Moreover, these prediction methods use a fixed size of observation windows which cannot produce accurate results because these are not adaptively adjusted to capture the local trends in the recent data. Therefore, the estimation method trains on fixed sliding windows use an irrelevant large number of observations which yields inaccurate estimations. In summary, we C1) efficiently reduce bandwidth and storage for telemetry data through real-time modeling using Markov chain model. C2) propose a novel method to adaptively and automatically identify the most appropriate model to accurately estimate data center resources utilization. C3) propose a deep learning-based adaptive window size selection method which dynamically limits the sliding window size to capture the local trend in the latest resource utilization for building estimation model.Hoy en día, Cloud Computing se usa ampliamente para alojar y prestar servicios a través de Internet. La arquitectura de las nubes es compleja debido a su naturaleza heterogénea del hardware y está alojada en centros de datos a gran escala. Para administrar de manera efectiva y eficiente dicha infraestructura compleja, se necesita un monitoreo constante. Este monitoreo genera grandes cantidades de flujos de datos de telemetría (por ejemplo, métricas de utilización de hardware) que se utilizan para múltiples propósitos, incluyendo detección de problemas, gestión de recursos, caracterización de carga de trabajo, predicción de utilización de recursos, planificación de capacidad y programación de trabajos. Estas transmisiones de telemetría requieren una utilización costosa del ancho de banda y espacio de almacenamiento, particularmente a mediano y largo plazo para grandes centros de datos. Además, la estimación futura precisa de estas transmisiones de telemetría es una tarea difícil debido a las aplicaciones cohospedadas de múltiples inquilinos y las cargas de trabajo dinámicas. La estimación inexacta conduce a un suministro insuficiente o excesivo de los recursos del centro de datos. En este Ph.D. En la tesis, proponemos mejorar la precisión de la predicción y reducir la utilización del ancho de banda y los requisitos de espacio de almacenamiento con la ayuda de métodos de modelado y predicción del aprendizaje automático. La mayoría de los métodos existentes se basan en un modelo único que a menudo no estima adecuadamente diferentes escenarios de carga de trabajo. Además, estos métodos de predicción utilizan un tamaño fijo de ventanas de observación que no pueden producir resultados precisos porque no se ajustan adaptativamente para capturar las tendencias locales en los datos recientes. Por lo tanto, el método de estimación entrena en ventanas corredizas fijas utiliza un gran número de observaciones irrelevantes que produce estimaciones inexactas. En resumen, C1) reducimos eficientemente el ancho de banda y el almacenamiento de datos de telemetría a través del modelado en tiempo real utilizando el modelo de cadena de Markov. C2) proponer un método novedoso para identificar de forma adaptativa y automática el modelo más apropiado para estimar con precisión la utilización de los recursos del centro de datos. C3) proponer un método de selección de tamaño de ventana adaptativo basado en el aprendizaje profundo que limita dinámicamente el tamaño de ventana deslizante para capturar la tendencia local en la última utilización de recursos para el modelo de estimación de construcción.Postprint (published version