International audienceTo program parallel systems efficiently and easily, a wide range of programming models have been proposed, each with different choices concerning synchronization and communication between parallel entities. Among them, the actor model is based on loosely coupled parallel entities that communicate by means of asynchronous messages and mailboxes. Some actor languages provide a strong integration with object-oriented concepts; these are often called active object languages. This paper reviews four major actor and active object languages and compares them according to carefully chosen dimensions that cover central aspects of the programming paradigms and their implementation
Abstract-The ever-growing trend of deploying applications over the Internet has resulted in increasingly tougher constraints and requirements. Data management systems are a major concern when it comes to scalability, flexibility and reliability due to being implemented in a distributed way. In this paper we present a Distributed Data Aggregation Service relying on a storage system designed to meet these demands, namely BlobSeer. The primary goal is to serve as a repository backend for complex analysis and automatic mining of scientific data (like bibtex entries). Several requirements, derived from this objective, match BlobSeer's features: versioning used for lock-free access to data and different granularity of read / write operations. We proposed a model to perform the correct translation between BlobSeer's unstructured view of data and the user's structured view. We implemented a client providing a formal description for the data retrieval queries and a specification for a search API. A benchmark tool relying on a performance model of BlobSeer, will be used to automatically determine the best BlobSeer deployment configuration for a specific data aggregation pattern.
Abstract. In this paper we present an API to support modeling applications with Actors based on the paradigm of the Abstract Behavioural Specification (ABS) language. With the introduction of JAVA 8, we expose this API through a JAVA library to allow for a high-level actorbased methodology for programming distributed systems which supports the programming to interfaces discipline. We validate this solution through a case study where we obtain significant performance improvements as well as illustrating the ease with which simple high and low-level optimizations can be obtained by examining topologies and communication within an application. Using this API we show it is much easier to observe drawbacks of shared data-structures and communications methods in the design phase of a distributed application and apply the necessary corrections in order to obtain better results.
SUMMARYCloud environments have become a standard method for enterprises to offer their applications by means of web-services, data management systems or simply renting out computing resources. In our previous work we presented how we can use a modeling language together with the new features of JAVA 8 to overcome certain drawbacks of data structures and synchronization mechanisms in parallel applications. We extend this solution into a design pattern that allows application-specific optimizations in a distributed setting. We validate this integration using our previous case study of the Prime Sieve of Eratosthenes and illustrate the performance improvements in terms of speed-up and memory consumption.
In this paper we introduce a new programming model of multi-threaded actors which feature the parallel processing of their messages. In this model an actor consists of a group of active objects which share a message queue. We provide a formal operational semantics, and a description of a Java-based implementation for the basic programming abstractions describing multi-threaded actors. Finally, we evaluate our proposal by means of an example application.Comment: In Proceedings ICE 2016, arXiv:1608.0313
Distributed systems and applications require large amounts of resources in terms of memory and computing power and are becoming a standard for large businesses and enterprises [12] within and outside the domain of Computer Science. A very important topic for distributed applications is Big Data management and more specifically the generation of large-scale social networks graphs where the number of nodes reaches very large numbers. Analysis of such networks is of importance in many areas, e.g., data mining, network sciences, physics, and social sciences [3]. The need for efficient and scalable methods of network generation is frequently mentioned in the literature [8], particularly for the preferential attachment process [1,13,14]. Barabasi-Albert model, which is based on preferential attachment (PA) [4], is one of the most commonly used models to produce artificial networks, because of its explanatory power, conceptual simplicity, and interesting mathematical properties [13]. Nevertheless the large number of nodes in such graphs may not fit in the memory on one machine. The need for efficient solutions which provide scalability also requires more computational resources as well as implementation considerations. As such, distribution and synchronization are two main challenges. In this chapter, we investigate as a case study a distributed solution for PA-based graph generation which avoids low level synchronization management, thanks to the notion of cooperative scheduling and futures.
The usage of Cloud Serviced has increased rapidly in the last years. Data management systems, behind any Cloud Service, are a major concern when it comes to scalability, flexibility and reliability due to being implemented in a distributed way. A Distributed Data Aggregation Service relying on a storage system meets these demands and serves as a repository back-end for complex analysis and automatic mining of any type of data. In this paper we continue our previous work on data management in Cloud storage. We present a formal approach to express retrieval and aggregation rules with a compact, yet powerful tool called Rule Markup Language. Our extended solution proposes a standard form to schemes and uses the tool to match the rules to the XML form of the structured data in order to obtain the unstructured entries from BlobSeer data storage system. This allows the Distributed Data Aggregation Service (DDAS) to bypass several steps when processing a retrieval request. Our new architecture is more loosely-coupled with a separate module, the new tool, used for transforming the XML entries to standard XML files which represent the final result. We model the dynamic behavior of the system using this new standard to ensure a simpler and efficient representation of the operations performed by the client while maintaining the constraints imposed by a distributed system running in the Cloud. Furthermore we prove that this method correctly performs the translation between the storage model's unstructured view of data and the client's structured objects.
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