Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

https://link.springer.com/content/pdf/10.1057%2Fjors.1996.159.pdf

Modeling and Analysis of Stochastic Systems

The development of autonomous vehicles for urban driving has seen rapid progress in the past 30 years. This paper provides a summary of the current state of the art in autonomous driving in urban environments, based primarily on the experiences of the authors in the 2007 DARPA Urban Challenge (DUC). The paper briefly summarizes the approaches that different teams used in the DUC, with the goal of describing some of the challenges that the teams faced in driving in urban environments. The paper also highlights the long-term research challenges that must be overcome in order to enable autonomous driving and points to opportunities for new technologies to be applied in improving vehicle safety, exploiting intelligent road infrastructure and enabling robotic vehicles operating in human environments.

Autonomous driving in urban environments: approaches, lessons and challenges

This paper summarizes the state of the art in connected vehicles—from the need for vehicle data and applications thereof, to enabling technologies, challenges, and identified opportunities. Connectivity is increasing around the world and its expansion to vehicles is no exception. With improvements in connectivity, sensing, and computation, the future will see vehicles used as development platforms capable of generating rich data, acting based on inference, and effecting great change in transportation, the human-vehicle dynamic, the environment, and the economy. Connected vehicle technologies have already been used to improve fleet safety and efficiency, with emerging technologies additionally allowing data to be used to inform aspects of vehicle design, ownership, and use. While the demand for connected vehicles and its enabling technology has progressed significantly in recent years, there remain challenges to connected and collaborative vehicle application deployment before the full potential of connected cars may be realized. From extensibility and scalability to privacy and security, this paper informs the reader about key enabling technologies, opportunities, and challenges in the connected vehicle landscape.

/pdf/a-survey-of-the-connected-vehicle-landscape-architectures-yn3vya93jr.pdf

A Survey of the Connected Vehicle Landscape—Architectures, Enabling Technologies, Applications, and Development Areas

Architecture and evaluation of a unified V2V and V2I communication system based on cellular networks

Serverless computing is an emerging paradigm that greatly simplifies the usage of cloud resources and suits well to many tasks. Most notably, Function-as-a-Service (FaaS) enables programmers to develop cloud applications as individual functions that can run and scale independently. Yet, due to the disaggregation of storage and compute resources in FaaS, applications that require fine-grained support for mutable state and synchronization, such as machine learning and scientific computing, are hard to build. In this work, we present Crucial, a system to program highly-concurrent stateful applications with serverless architectures. Its programming model keeps the simplicity of FaaS and allows to port effortlessly multi-threaded algorithms to this new environment. Crucial is built upon the key insight that FaaS resembles to concurrent programming at the scale of a data center. As a consequence, a distributed shared memory layer is the right answer to the need for fine-grained state management and coordination in serverless. We validate our system with the help of micro-benchmarks and various applications. In particular, we implement two common machine learning algorithms: k-means clustering and logistic regression. For both cases, Crucial obtains superior or comparable performance to an equivalent Spark cluster.

On the FaaS Track: Building Stateful Distributed Applications with Serverless Architectures

Since the appearance of Amazon Lambda in 2014, all major cloud providers have embraced the "Function as a Service" (FaaS) model, because of its enormous potential for a wide variety of applications. As expected (and also desired), the competition is fierce in the serverless world, and includes aspects such as the run-time support for the orchestration of serverless functions. In this regard, the three major production services are currently Amazon Step Functions (December 2016), Azure Durable Functions (June 2017), and IBM Composer (October 2017), still young and experimental projects with a long way ahead. In this article, we will compare and analyze these three serverless orchestration systems under a common evaluation framework. We will study their architectures, programming and billing models, and their effective support for parallel execution, among others. Through a series of experiments, we will also evaluate the run-time overhead of the different infrastructures for different types of workflows.

Comparison of FaaS Orchestration Systems

Unexpectedly, the rise of serverless computing has also collaterally started the "democratization" of massive-scale data parallelism. This new trend heralded by PyWren pursues to enable untrained users to execute single-machine code in the cloud at massive scale through platforms like AWS Lambda. Inspired by this vision, this industry paper presents IBM-PyWren, which continues the pioneering work begun by PyWren in this field. It must be noted that IBM-PyWren is not, however, just a mere reimplementation of PyWren's API atop IBM Cloud Functions. Rather, it is must be viewed as an advanced extension of PyWren to run broader MapReduce jobs. We describe the design, innovative features (API extensions, data discovering & partitioning, composability, etc.) and performance of IBM-PyWren, along with the challenges encountered during its implementation.

https://dl.acm.org/doi/pdf/10.1145/3284028.3284029

Serverless Data Analytics in the IBM Cloud

Personal Cloud services, such as Dropbox or Box, have been widely adopted by users. Unfortunately, very little is known about the internal operation and general characteristics of Personal Clouds since they are proprietary services.In this paper, we focus on understanding the nature of Personal Clouds by presenting the internal structure and a measurement study of UbuntuOne (U1). We first detail the U$1$ architecture, core components involved in the U1 metadata service hosted in the datacenter of Canonical, as well as the interactions of U$1$ with Amazon S3 to outsource data storage. To our knowledge, this is the first research work to describe the internals of a large-scale Personal Cloud.Second, by means of tracing the U$1$ servers, we provide an extensive analysis of its back-end activity for one month. Our analysis includes the study of the storage workload, the user behavior and the performance of the U1 metadata store. Moreover, based on our analysis, we suggest improvements to U1 that can also benefit similar Personal Cloud systems.Finally, we contribute our dataset to the community, which is the first to contain the back-end activity of a large-scale Personal Cloud. We believe that our dataset provides unique opportunities for extending research in the field.

/pdf/dissecting-ubuntuone-autopsy-of-a-global-scale-personal-51gu6hgzur.pdf

Marc Sanchez-Artigas

Papers

Architecture and evaluation of a unified V2V and V2I communication system based on cellular networks

On the FaaS Track: Building Stateful Distributed Applications with Serverless Architectures

Comparison of FaaS Orchestration Systems

Serverless Data Analytics in the IBM Cloud

Dissecting UbuntuOne: Autopsy of a Global-scale Personal Cloud Back-end