A compiler providing incremental scalability for web applications

International audienceTo develop a web application, one needs to choose between two programming models. The monolithic one favors features improvements, while the decentralized one favors performance improvements. To avoid this choice, we compile monolithic web applications into a high-level language compliant with a distributed model

AMaχoS—Abstract Machine for Xcerpt

Web query languages promise convenient and efficient access to Web data such as XML, RDF, or Topic Maps. Xcerpt is one such Web query language with strong emphasis on novel high-level constructs for effective and convenient query authoring, particularly tailored to versatile access to data in different Web formats such as XML or RDF. However, so far it lacks an efficient implementation to supplement the convenient language features. AMaχoS is an abstract machine implementation for Xcerpt that aims at efficiency and ease of deployment. It strictly separates compilation and execution of queries: Queries are compiled once to abstract machine code that consists in (1) a code segment with instructions for evaluating each rule and (2) a hint segment that provides the abstract machine with optimization hints derived by the query compilation. This article summarizes the motivation and principles behind AMaχoS and discusses how its current architecture realizes these principles

RELEASE: A High-level Paradigm for Reliable Large-scale Server Software

Erlang is a functional language with a much-emulated model for building reliable distributed systems. This paper outlines the RELEASE project, and describes the progress in the first six months. The project aim is to scale the Erlang’s radical concurrency-oriented programming paradigm to build reliable general-purpose software, such as server-based systems, on massively parallel machines. Currently Erlang has inherently scalable computation and reliability models, but in practice scalability is constrained by aspects of the language and virtual machine. We are working at three levels to address these challenges: evolving the Erlang virtual machine so that it can work effectively on large scale multicore systems; evolving the language to Scalable Distributed (SD) Erlang; developing a scalable Erlang infrastructure to integrate multiple, heterogeneous clusters. We are also developing state of the art tools that allow programmers to understand the behaviour of massively parallel SD Erlang programs. We will demonstrate the effectiveness of the RELEASE approach using demonstrators and two large case studies on a Blue Gene

CPPC: a compiler‐assisted tool for portable checkpointing of message‐passing applications

This is the peer reviewed version of the following article: Rodríguez, G. , Martín, M. J., González, P. , Touriño, J. and Doallo, R. (2010), CPPC: a compiler‐assisted tool for portable checkpointing of message‐passing applications. Concurrency Computat.: Pract. Exper., 22: 749-766. doi:10.1002/cpe.1541, which has been published in final form at https://doi.org/10.1002/cpe.1541. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.[Abstract] With the evolution of high‐performance computing toward heterogeneous, massively parallel systems, parallel applications have developed new checkpoint and restart necessities. Whether due to a failure in the execution or to a migration of the application processes to different machines, checkpointing tools must be able to operate in heterogeneous environments. However, some of the data manipulated by a parallel application are not truly portable. Examples of these include opaque state (e.g. data structures for communications support) or diversity of interfaces for a single feature (e.g. communications, I/O). Directly manipulating the underlying ad hoc representations renders checkpointing tools unable to work on different environments. Portable checkpointers usually work around portability issues at the cost of transparency: the user must provide information such as what data need to be stored, where to store them, or where to checkpoint. CPPC (ComPiler for Portable Checkpointing) is a checkpointing tool designed to feature both portability and transparency. It is made up of a library and a compiler. The CPPC library contains routines for variable level checkpointing, using portable code and protocols. The CPPC compiler helps to achieve transparency by relieving the user from time‐consuming tasks, such as data flow and communications analyses and adding instrumentation code. This paper covers both the operation of the CPPC library and its compiler support. Experimental results using benchmarks and large‐scale real applications are included, demonstrating usability, efficiency, and portability.Miniesterio de Educación y Ciencia; TIN2007‐67537‐C03Xunta de Galicia; 2006/

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

CHERI: a research platform deconflating hardware virtualisation and protection

Contemporary CPU architectures conflate virtualization and protection, imposing virtualization-related performance, programmability, and debuggability penalties on software requiring finegrained protection. First observed in micro-kernel research, these problems are increasingly apparent in recent attempts to mitigate software vulnerabilities through application compartmentalisation. Capability Hardware Enhanced RISC Instructions (CHERI) extend RISC ISAs to support greater software compartmentalisation. CHERI’s hybrid capability model provides fine-grained compartmentalisation within address spaces while maintaining software backward compatibility, which will allow the incremental deployment of fine-grained compartmentalisation in both our most trusted and least trustworthy C-language software stacks. We have implemented a 64-bit MIPS research soft core, BERI, as well as a capability coprocessor, and begun adapting commodity software packages (FreeBSD and Chromium) to execute on the platform

Size Matters: Microservices Research and Applications

In this chapter we offer an overview of microservices providing the introductory information that a reader should know before continuing reading this book. We introduce the idea of microservices and we discuss some of the current research challenges and real-life software applications where the microservice paradigm play a key role. We have identified a set of areas where both researcher and developer can propose new ideas and technical solutions.Comment: arXiv admin note: text overlap with arXiv:1706.0735