On Improving Efficiency of Data-Intensive Applications in Geo-Distributed Environments

Distributed systems are pervasively demanded and adopted in nowadays for processing data-intensive workloads since they greatly accelerate large-scale data processing with scalable parallelism and improved data locality. Traditional distributed systems initially targeted computing clusters but have since evolved to data centers with multiple clusters. These systems are mostly built on top of homogeneous, tightly integrated resources connected in high-speed local-area networks (LANs), and typically require data to be ingested to a central data center for processing. Today, with enormous volumes of data continuously generated from geographically distributed locations, direct adoption of such systems is prohibitively inefficient due to the limited system scalability and high cost for centralizing the geo-distributed data over the wide-area networks (WANs). More commonly, it becomes a trend to build geo-distributed systems wherein data processing jobs are performed on top of geo-distributed, heterogeneous resources in proximity to the data at vastly distributed geo-locations. However, critical challenges and mechanisms for efficient execution of data-intensive applications in such geo-distributed environments are unclear by far. The goal of this dissertation is to identify such challenges and mechanisms, by extensively using the research principles and methodology of conventional distributed systems to investigate the geo-distributed environment, and by developing new techniques to tackle these challenges and run data-intensive applications with efficiency at scale. The contributions of this dissertation are threefold. Firstly, the dissertation shows that the high level of resource heterogeneity exhibited in the geo-distributed environment undermines the scalability of geo-distributed systems. Virtualization-based resource abstraction mechanisms have been introduced to abstract the hardware, network, and OS resources throughout the system, to mitigate the underlying resource heterogeneity and enhance the system scalability. Secondly, the dissertation reveals the overwhelming performance and monetary cost incurred by indulgent data sharing over the WANs in geo-distributed systems. Network optimization approaches, including linear- programming-based global optimization, greedy bin-packing heuristics, and TCP enhancement, are developed to optimize the network resource utilization and circumvent unnecessary expenses imposed on data sharing in WANs. Lastly, the dissertation highlights the importance of data locality for data-intensive applications running in the geo-distributed environment. Novel data caching and locality-aware scheduling techniques are devised to improve the data locality.Doctor of Philosoph

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

Service Abstractions for Scalable Deep Learning Inference at the Edge

Deep learning driven intelligent edge has already become a reality, where millions of mobile, wearable, and IoT devices analyze real-time data and transform those into actionable insights on-device. Typical approaches for optimizing deep learning inference mostly focus on accelerating the execution of individual inference tasks, without considering the contextual correlation unique to edge environments and the statistical nature of learning-based computation. Specifically, they treat inference workloads as individual black boxes and apply canonical system optimization techniques, developed over the last few decades, to handle them as yet another type of computation-intensive applications. As a result, deep learning inference on edge devices still face the ever increasing challenges of customization to edge device heterogeneity, fuzzy computation redundancy between inference tasks, and end-to-end deployment at scale. In this thesis, we propose the first framework that automates and scales the end-to-end process of deploying efficient deep learning inference from the cloud to heterogeneous edge devices. The framework consists of a series of service abstractions that handle DNN model tailoring, model indexing and query, and computation reuse for runtime inference respectively. Together, these services bridge the gap between deep learning training and inference, eliminate computation redundancy during inference execution, and further lower the barrier for deep learning algorithm and system co-optimization. To build efficient and scalable services, we take a unique algorithmic approach of harnessing the semantic correlation between the learning-based computation. Rather than viewing individual tasks as isolated black boxes, we optimize them collectively in a white box approach, proposing primitives to formulate the semantics of the deep learning workloads, algorithms to assess their hidden correlation (in terms of the input data, the neural network models, and the deployment trials) and merge common processing steps to minimize redundancy

High performance Monte Carlo computation for finance risk data analysis

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Finance risk management has been playing an increasingly important role in the finance sector, to analyse finance data and to prevent any potential crisis. It has been widely recognised that Value at Risk (VaR) is an effective method for finance risk management and evaluation. This thesis conducts a comprehensive review on a number of VaR methods and discusses in depth their strengths and limitations. Among these VaR methods, Monte Carlo simulation and analysis has proven to be the most accurate VaR method in finance risk evaluation due to its strong modelling capabilities. However, one major challenge in Monte Carlo analysis is its high computing complexity of O(n²). To speed up the computation in Monte Carlo analysis, this thesis parallelises Monte Carlo using the MapReduce model, which has become a major software programming model in support of data intensive applications. MapReduce consists of two functions - Map and Reduce. The Map function segments a large data set into small data chunks and distribute these data chunks among a number of computers for processing in parallel with a Mapper processing a data chunk on a computing node. The Reduce function collects the results generated by these Map nodes (Mappers) and generates an output. The parallel Monte Carlo is evaluated initially in a small scale MapReduce experimental environment, and subsequently evaluated in a large scale simulation environment. Both experimental and simulation results show that the MapReduce based parallel Monte Carlo is greatly faster than the sequential Monte Carlo in computation, and the accuracy level is maintained as well. In data intensive applications, moving huge volumes of data among the computing nodes could incur high overhead in communication. To address this issue, this thesis further considers data locality in the MapReduce based parallel Monte Carlo, and evaluates the impacts of data locality on the performance in computation

Contribution à la convergence d'infrastructure entre le calcul haute performance et le traitement de données à large échelle

The amount of produced data, either in the scientific community or the commercialworld, is constantly growing. The field of Big Data has emerged to handle largeamounts of data on distributed computing infrastructures. High-Performance Computing (HPC) infrastructures are traditionally used for the execution of computeintensive workloads. However, the HPC community is also facing an increasingneed to process large amounts of data derived from high definition sensors andlarge physics apparati. The convergence of the two fields -HPC and Big Data- iscurrently taking place. In fact, the HPC community already uses Big Data tools,which are not always integrated correctly, especially at the level of the file systemand the Resource and Job Management System (RJMS).In order to understand how we can leverage HPC clusters for Big Data usage, andwhat are the challenges for the HPC infrastructures, we have studied multipleaspects of the convergence: We initially provide a survey on the software provisioning methods, with a focus on data-intensive applications. We contribute a newRJMS collaboration technique called BeBiDa which is based on 50 lines of codewhereas similar solutions use at least 1000 times more. We evaluate this mechanism on real conditions and in simulated environment with our simulator Batsim.Furthermore, we provide extensions to Batsim to support I/O, and showcase thedevelopments of a generic file system model along with a Big Data applicationmodel. This allows us to complement BeBiDa real conditions experiments withsimulations while enabling us to study file system dimensioning and trade-offs.All the experiments and analysis of this work have been done with reproducibilityin mind. Based on this experience, we propose to integrate the developmentworkflow and data analysis in the reproducibility mindset, and give feedback onour experiences with a list of best practices.RésuméLa quantité de données produites, que ce soit dans la communauté scientifiqueou commerciale, est en croissance constante. Le domaine du Big Data a émergéface au traitement de grandes quantités de données sur les infrastructures informatiques distribuées. Les infrastructures de calcul haute performance (HPC) sont traditionnellement utilisées pour l’exécution de charges de travail intensives en calcul. Cependant, la communauté HPC fait également face à un nombre croissant debesoin de traitement de grandes quantités de données dérivées de capteurs hautedéfinition et de grands appareils physique. La convergence des deux domaines-HPC et Big Data- est en cours. En fait, la communauté HPC utilise déjà des outilsBig Data, qui ne sont pas toujours correctement intégrés, en particulier au niveaudu système de fichiers ainsi que du système de gestion des ressources (RJMS).Afin de comprendre comment nous pouvons tirer parti des clusters HPC pourl’utilisation du Big Data, et quels sont les défis pour les infrastructures HPC, nousavons étudié plusieurs aspects de la convergence: nous avons d’abord proposé uneétude sur les méthodes de provisionnement logiciel, en mettant l’accent sur lesapplications utilisant beaucoup de données. Nous contribuons a l’état de l’art avecune nouvelle technique de collaboration entre RJMS appelée BeBiDa basée sur 50lignes de code alors que des solutions similaires en utilisent au moins 1000 fois plus.Nous évaluons ce mécanisme en conditions réelles et en environnement simuléavec notre simulateur Batsim. En outre, nous fournissons des extensions à Batsimpour prendre en charge les entrées/sorties et présentons le développements d’unmodèle de système de fichiers générique accompagné d’un modèle d’applicationBig Data. Cela nous permet de compléter les expériences en conditions réellesde BeBiDa en simulation tout en étudiant le dimensionnement et les différentscompromis autours des systèmes de fichiers.Toutes les expériences et analyses de ce travail ont été effectuées avec la reproductibilité à l’esprit. Sur la base de cette expérience, nous proposons d’intégrerle flux de travail du développement et de l’analyse des données dans l’esprit dela reproductibilité, et de donner un retour sur nos expériences avec une liste debonnes pratiques

Improving efficiency and resilience in large-scale computing systems through analytics and data-driven management

Applications running in large-scale computing systems such as high performance computing (HPC) or cloud data centers are essential to many aspects of modern society, from weather forecasting to financial services. As the number and size of data centers increase with the growing computing demand, scalable and efficient management becomes crucial. However, data center management is a challenging task due to the complex interactions between applications, middleware, and hardware layers such as processors, network, and cooling units. This thesis claims that to improve robustness and efficiency of large-scale computing systems, significantly higher levels of automated support than what is available in today's systems are needed, and this automation should leverage the data continuously collected from various system layers. Towards this claim, we propose novel methodologies to automatically diagnose the root causes of performance and configuration problems and to improve efficiency through data-driven system management. We first propose a framework to diagnose software and hardware anomalies that cause undesired performance variations in large-scale computing systems. We show that by training machine learning models on resource usage and performance data collected from servers, our approach successfully diagnoses 98% of the injected anomalies at runtime in real-world HPC clusters with negligible computational overhead. We then introduce an analytics framework to address another major source of performance anomalies in cloud data centers: software misconfigurations. Our framework discovers and extracts configuration information from cloud instances such as containers or virtual machines. This is the first framework to provide comprehensive visibility into software configurations in multi-tenant cloud platforms, enabling systematic analysis for validating the correctness of software configurations. This thesis also contributes to the design of robust and efficient system management methods that leverage continuously monitored resource usage data. To improve performance under power constraints, we propose a workload- and cooling-aware power budgeting algorithm that distributes the available power among servers and cooling units in a data center, achieving up to 21% improvement in throughput per Watt compared to the state-of-the-art. Additionally, we design a network- and communication-aware HPC workload placement policy that reduces communication overhead by up to 30% in terms of hop-bytes compared to existing policies.2019-07-02T00:00:00

Collaborative Edge Computing in Mobile Internet of Things

The proliferation of Internet-of-Things (IoT) devices has opened a plethora of opportunities for smart networking, connected applications and data driven intelligence. The large distribution of IoT devices within a finite geographical area and the pervasiveness of wireless networking present an opportunity for such devices to collaborate. Centralized decision systems have so far dominated the field, but they are starting to lose relevance in the wake of heterogeneity of the device pool. This thesis is driven by three key hypothesis: (i) In solving complex problems, it is possible to harness unused compute capabilities of the device pool instead of always relying on centralized infrastructures; (ii) When possible, collaborating with neighbors to identify security threats scales well in large environments; (iii) Given the abundance of data from a large pool of devices with possible privacy constraints, collaborative learning drives scalable intelligence. This dissertation defines three frameworks for these hypotheses; collaborative computing, collaborative security and collaborative privacy intelligence. The first framework, Opportunistic collaboration among IoT devices for workload execution, profiles applications and matches resource grants to requests using blockchain to put excess capacity at the edge to good use. The evaluation results show app execution latency comparable to the centralized edge and an outstanding resource utilization at the edge. The second framework, Integrity Threat Identification for Distributed IoT, uses a new spatio-temporal algorithm, based on Local Outlier Factor (LOF) uniquely using mean and variance collaboratively across spatial and temporal dimensions to identify potential threats. Evaluation results on real world underground sensor dataset (Thoreau) show good accuracy and efficiency. The third frame- work, Collaborative Privacy Intelligence, aims to understand privacy invasion by reverse engineering a user’s privacy model using sensors data, and score the level of intrusion for various dimensions of privacy. By having sensors track activities, and learning rule books from the collective insights, we are able to predict ones privacy attributes and states, with reasonable accuracy. As the Edge gains more prominence with computation moving closer to the data source, the above frameworks will drive key solutions and research in areas of Edge federation and collaboration

Bridging the Gap between Application and Solid-State-Drives

Data storage is one of the important and often critical parts of the computing system in terms of performance, cost, reliability, and energy. Numerous new memory technologies, such as NAND flash, phase change memory (PCM), magnetic RAM (STT-RAM) and Memristor, have emerged recently. Many of them have already entered the production system. Traditional storage optimization and caching algorithms are far from optimal because storage I/Os do not show simple locality. To provide optimal storage we need accurate predictions of I/O behavior. However, the workloads are increasingly dynamic and diverse, making the long and short time I/O prediction challenge. Because of the evolution of the storage technologies and the increasing diversity of workloads, the storage software is becoming more and more complex. For example, Flash Translation Layer (FTL) is added for NAND-flash based Solid State Disks (NAND-SSDs). However, it introduces overhead such as address translation delay and garbage collection costs. There are many recent studies aim to address the overhead. Unfortunately, there is no one-size-fits-all solution due to the variety of workloads. Despite rapidly evolving in storage technologies, the increasing heterogeneity and diversity in machines and workloads coupled with the continued data explosion exacerbate the gap between computing and storage speeds. In this dissertation, we improve the data storage performance from both top-down and bottom-up approach. First, we will investigate exposing the storage level parallelism so that applications can avoid I/O contentions and workloads skew when scheduling the jobs. Second, we will study how architecture aware task scheduling can improve the performance of the application when PCM based NVRAM are equipped. Third, we will develop an I/O correlation aware flash translation layer for NAND-flash based Solid State Disks. Fourth, we will build a DRAM-based correlation aware FTL emulator and study the performance in various filesystems