The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author

Sensor web geoprocessing on the grid

Recent standardisation initiatives in the fields of grid computing and geospatial sensor middleware provide an exciting opportunity for the composition of large scale geospatial monitoring and prediction systems from existing components. Sensor middleware standards are paving the way for the emerging sensor web which is envisioned to make millions of geospatial sensors and their data publicly accessible by providing discovery, task and query functionality over the internet. In a similar fashion, concurrent development is taking place in the field of grid computing whereby the virtualisation of computational and data storage resources using middleware abstraction provides a framework to share computing resources. Sensor web and grid computing share a common vision of world-wide connectivity and in their current form they are both realised using web services as the underlying technological framework. The integration of sensor web and grid computing middleware using open standards is expected to facilitate interoperability and scalability in near real-time geoprocessing systems. The aim of this thesis is to develop an appropriate conceptual and practical framework in which open standards in grid computing, sensor web and geospatial web services can be combined as a technological basis for the monitoring and prediction of geospatial phenomena in the earth systems domain, to facilitate real-time decision support. The primary topic of interest is how real-time sensor data can be processed on a grid computing architecture. This is addressed by creating a simple typology of real-time geoprocessing operations with respect to grid computing architectures. A geoprocessing system exemplar of each geoprocessing operation in the typology is implemented using contemporary tools and techniques which provides a basis from which to validate the standards frameworks and highlight issues of scalability and interoperability. It was found that it is possible to combine standardised web services from each of these aforementioned domains despite issues of interoperability resulting from differences in web service style and security between specifications. A novel integration method for the continuous processing of a sensor observation stream is suggested in which a perpetual processing job is submitted as a single continuous compute job. Although this method was found to be successful two key challenges remain; a mechanism for consistently scheduling real-time jobs within an acceptable time-frame must be devised and the tradeoff between efficient grid resource utilisation and processing latency must be balanced. The lack of actual implementations of distributed geoprocessing systems built using sensor web and grid computing has hindered the development of standards, tools and frameworks in this area. This work provides a contribution to the small number of existing implementations in this field by identifying potential workflow bottlenecks in such systems and gaps in the existing specifications. Furthermore it sets out a typology of real-time geoprocessing operations that are anticipated to facilitate the development of real-time geoprocessing software.EThOS - Electronic Theses Online ServiceEngineering and Physical Sciences Research Council (EPSRC) : School of Civil Engineering & Geosciences, Newcastle UniversityGBUnited Kingdo

Geometric Approaches to Big Data Modeling and Performance Prediction

Big Data frameworks (e.g., Spark) have many configuration parameters, such as memory size, CPU allocation, and the number of nodes (parallelism). Regular users and even expert administrators struggle to understand the relationship between different parameter configurations and the overall performance of the system. In this work, we address this challenge by proposing a performance prediction framework to build performance models with varied configurable parameters on Spark. We take inspiration from the field of Computational Geometry to construct a d-dimensional mesh using Delaunay Triangulation over a selected set of features. From this mesh, we predict execution time for unknown feature configurations. To minimize the time and resources spent in building a model, we propose an adaptive sampling technique to allow us to collect as few training points as required. Our evaluation on a cluster of computers using several workloads shows that our prediction error is lower than the state-of-art methods while having fewer samples to train

Parallel Execution of ATL Transformation Rules

International audienceIndustrial environments that make use of Model-Driven Engineering (MDE) are starting to see the appearance of very large models, made by millions of elements. Such models are produced automatically (e.g., by reverse engineering complex systems) or manually by a large number of users (e.g., from social networks). The success of MDE in these application scenarios strongly depends on the scalability of model manipulation tools. While parallelization is one of the traditional ways of making computation systems scalable, developing parallel model transformations in a general-purpose language is a complex and error-prone task. In this paper we show that rule-based languages like ATL have strong parallelization properties. Transformations can be developed without taking into account concurrency concerns, and a transformation engine can automatically parallelize execution. We describe the implementation of a parallel transformation engine for the current version of the ATL language and experimentally evaluate the consequent gain in scalability

Ray: A Distributed Execution Engine for the Machine Learning Ecosystem

In recent years, growing data volumes and more sophisticated computational procedures have greatly increased the demand for computational power. Machine learning and artificial intelligence applications, for example, are notorious for their computational requirements. At the same time, Moores law is ending and processor speeds are stalling. As a result, distributed computing has become ubiquitous. While the cloud makes distributed hardware infrastructure widely accessible and therefore offers the potential of horizontal scale, developing these distributed algorithms and applications remains surprisingly hard. This is due to the inherent complexity of concurrent algorithms, the engineering challenges that arise when communicating between many machines, the requirements like fault tolerance and straggler mitigation that arise at large scale and the lack of a general-purpose distributed execution engine that can support a wide variety of applications.In this thesis, we study the requirements for a general-purpose distributed computation model and present a solution that is easy to use yet expressive and resilient to faults. At its core our model takes familiar concepts from serial programming, namely functions and classes, and generalizes them to the distributed world, therefore unifying stateless and stateful distributed computation. This model not only supports many machine learning workloads like training or serving, but is also a good t for cross-cutting machine learning applications like reinforcement learning and data processing applications like streaming or graph processing. We implement this computational model as an open-source system called Ray, which matches or exceeds the performance of specialized systems in many application domains, while also offering horizontally scalability and strong fault tolerance properties

One Trillion Edges: Graph Processing at Facebook-Scale

ABSTRACT Analyzing large graphs provides valuable insights for social networking and web companies in content ranking and recommendations. While numerous graph processing systems have been developed and evaluated on available benchmark graphs of up to 6.6B edges, they often face significant difficulties in scaling to much larger graphs. Industry graphs can be two orders of magnitude larger -hundreds of billions or up to one trillion edges. In addition to scalability challenges, real world applications often require much more complex graph processing workflows than previously evaluated. In this paper, we describe the usability, performance, and scalability improvements we made to Apache Giraph, an open-source graph processing system, in order to use it on Facebook-scale graphs of up to one trillion edges. We also describe several key extensions to the original Pregel model that make it possible to develop a broader range of production graph applications and workflows as well as improve code reuse. Finally, we report on real-world operations as well as performance characteristics of several large-scale production applications

PiCo: A Domain-Specific Language for Data Analytics Pipelines

In the world of Big Data analytics, there is a series of tools aiming at simplifying programming applications to be executed on clusters. Although each tool claims to provide better programming, data and execution models—for which only informal (and often confusing) semantics is generally provided—all share a common under- lying model, namely, the Dataflow model. Using this model as a starting point, it is possible to categorize and analyze almost all aspects about Big Data analytics tools from a high level perspective. This analysis can be considered as a first step toward a formal model to be exploited in the design of a (new) framework for Big Data analytics. By putting clear separations between all levels of abstraction (i.e., from the runtime to the user API), it is easier for a programmer or software designer to avoid mixing low level with high level aspects, as we are often used to see in state-of-the-art Big Data analytics frameworks. From the user-level perspective, we think that a clearer and simple semantics is preferable, together with a strong separation of concerns. For this reason, we use the Dataflow model as a starting point to build a programming environment with a simplified programming model implemented as a Domain-Specific Language, that is on top of a stack of layers that build a prototypical framework for Big Data analytics. The contribution of this thesis is twofold: first, we show that the proposed model is (at least) as general as existing batch and streaming frameworks (e.g., Spark, Flink, Storm, Google Dataflow), thus making it easier to understand high-level data-processing applications written in such frameworks. As result of this analysis, we provide a layered model that can represent tools and applications following the Dataflow paradigm and we show how the analyzed tools fit in each level. Second, we propose a programming environment based on such layered model in the form of a Domain-Specific Language (DSL) for processing data collections, called PiCo (Pipeline Composition). The main entity of this programming model is the Pipeline, basically a DAG-composition of processing elements. This model is intended to give the user an unique interface for both stream and batch processing, hiding completely data management and focusing only on operations, which are represented by Pipeline stages. Our DSL will be built on top of the FastFlow library, exploiting both shared and distributed parallelism, and implemented in C++11/14 with the aim of porting C++ into the Big Data world

MACRM: A Multi-agent Cluster Resource Management System

The falling cost of cluster computing has significantly increased its use in the last decade. As a result, the number of users, the size of clusters, and the diversity of jobs that are submitted to clusters have grown. These changes lead to a quest for redesigning of clusters' resource management systems. The growth in the number of users and increase in the size of clusters require a more scalable approach to resource management. Moreover, ever-increasing use of clusters for carrying out a diverse range of computations demands fault-tolerant and highly available cluster management systems. Last, but not the least, serving highly parallel and interactive jobs in a cluster with hundreds of nodes, requires high throughput scheduling with a very short service time. This research presents MACRM, a multi-agent cluster resource management system. MACRM is an adaptive distributed/centralized resource management system which addresses the requirements of scalability, fault-tolerance, high availability, and high throughput scheduling. It breaks up resource management responsibilities and delegates it to different agents to be scalable in various aspects. Also, modularity in MACRM's design increases fault-tolerance because components are replicable and recoverable. Furthermore, MACRM has a very short service time in different loads. It can maintain an average service time of less than 15ms by adaptively switching between centralized and distributed decision making based on a cluster's load. Comparing MACRM with representative centralized and distributed systems (YARN [67] and Sparrow [52]) shows several advantages. We show that MACRM scales better when the number of resources, users, or jobs increase in a cluster. As well, MACRM has faster and less expensive failure recovery mechanisms compared with the two other systems. And finally, our experiments show that MACRM's average service time beats the other systems, particularly in high loads