Multitier Modules

Multitier programming languages address the complexity of developing distributed systems abstracting over low level implementation details such as data representation, serialization and network protocols. Since the functionalities of different peers can be defined in the same compilation unit, multitier languages do not force developers to modularize software along network boundaries. Unfortunately, combining the code for all tiers into the same compilation unit poses a scalability challenge or forces developers to resort to traditional modularization abstractions that are agnostic to the multitier nature of the language. In this paper, we address this issue with a module system for multitier languages. Our module system supports encapsulating each (cross-peer) functionality and defining it over abstract peer types. As a result, we disentangle modularization and distribution and we enable the definition of a distributed system as a composition of multitier modules, each representing a subsystem. Our case studies on distributed algorithms, distributed data structures, as well as on the Apache Flink task distribution system, show that multitier modules allow the definition of reusable (abstract) patterns of interaction in distributed software and enable separating the modularization and distribution concerns, properly separating functionalities in distributed systems

On the Execution of Ambients

Successfully harnessing multi-threaded programming has recently received renewed attention. The GHz war of the last years has been replaced with a parallelism war, each manufacturer seeking to produce CPUs supporting a greater number of threads in parallel execution. The Ambient calculus offers a simple yet powerful means to model communication, distributed computation and mobility. However, given its first class support for concurrency, we sought to investigate the utility of the Ambient calculus for practical programming purposes. Although too low-level to be considered as a general-purpose programming language itself, the Ambient calculus is nevertheless a suitable virtual machine for the execution of mobile and distributed higher-level languages. We present the Glint Virtual Machine: an interpreter for the Safe Boxed Ambient calculus. The GlintVM provides an effective platform for mobile, distributed and parallel computation and should ease some of the difficulties of writing compilers for languages that can exploit the new thread-parallel architectures

A catalog of stream processing optimizations

Cataloged from PDF version of article.Various research communities have independently arrived at stream processing as a programming model for efficient and parallel computing. These communities include digital signal processing, databases, operating systems, and complex event processing. Since each community faces applications with challenging performance requirements, each of them has developed some of the same optimizations, but often with conflicting terminology and unstated assumptions. This article presents a survey of optimizations for stream processing. It is aimed both at users who need to understand and guide the system's optimizer and at implementers who need to make engineering tradeoffs. To consolidate terminology, this article is organized as a catalog, in a style similar to catalogs of design patterns or refactorings. To make assumptions explicit and help understand tradeoffs, each optimization is presented with its safety constraints (when does it preserve correctness?) and a profitability experiment (when does it improve performance?). We hope that this survey will help future streaming system builders to stand on the shoulders of giants from not just their own community. © 2014 ACM

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

Investigation of fast initialization of spacecraft bubble memory systems

Bubble domain technology offers significant improvement in reliability and functionality for spacecraft onboard memory applications. In considering potential memory systems organizations, minimization of power in high capacity bubble memory systems necessitates the activation of only the desired portions of the memory. In power strobing arbitrary memory segments, a capability of fast turn on is required. Bubble device architectures, which provide redundant loop coding in the bubble devices, limit the initialization speed. Alternate initialization techniques are investigated to overcome this design limitation. An initialization technique using a small amount of external storage is demonstrated