A Configurable Transport Layer for CAF

The message-driven nature of actors lays a foundation for developing scalable and distributed software. While the actor itself has been thoroughly modeled, the message passing layer lacks a common definition. Properties and guarantees of message exchange often shift with implementations and contexts. This adds complexity to the development process, limits portability, and removes transparency from distributed actor systems. In this work, we examine actor communication, focusing on the implementation and runtime costs of reliable and ordered delivery. Both guarantees are often based on TCP for remote messaging, which mixes network transport with the semantics of messaging. However, the choice of transport may follow different constraints and is often governed by deployment. As a first step towards re-architecting actor-to-actor communication, we decouple the messaging guarantees from the transport protocol. We validate our approach by redesigning the network stack of the C++ Actor Framework (CAF) so that it allows to combine an arbitrary transport protocol with additional functions for remote messaging. An evaluation quantifies the cost of composability and the impact of individual layers on the entire stack

Reliable Actors with Retry Orchestration

Enterprise cloud developers have to build applications that are resilient to failures and interruptions. We advocate for, formalize, implement, and evaluate a simple, albeit effective, fault-tolerant programming model for the cloud based on actors, reliable message delivery, and retry orchestration. Our model guarantees that (1) failed actor invocations are retried until success, (2) in a distributed chain of invocations only the last one may be retried, (3) pending synchronous invocations with a failed caller are automatically cancelled. These guarantees make it possible to productively develop fault-tolerant distributed applications ranging from classic problems of concurrency theory to complex enterprise applications. Built as a service mesh, our runtime system can interface application components written in any programming language and scale with the application. We measure overhead relative to reliable message queues. Using an application inspired by a typical enterprise scenario, we assess fault tolerance and the impact of fault recovery on application performance.Comment: 14 pages, 6 figure

A PROGRAMMING FRAMEWORK TO EASE DEVELOPMENT OF TIGHTLY-COUPLED CLOUD APPLICATIONS

Cloud application development is currently for professionals only. To make the cloud more accessible, cloud applications should ideally be easy to develop so that virtually anyone can develop their own cloud applications. However, they are difficult to develop, because they are essentially distributed systems, where the concurrent operations may take place, and reasoning about the behavior of concurrent operations to ensure correctness is not trivial. Additionally, programmers must consider failure handling, scalability, consistency, modularity, elasticity

Stateful Entities: Object-oriented Cloud Applications as Distributed Dataflows

Programming stateful cloud applications remains a very painful experience. Instead of focusing on the business logic, programmers spend most of their time dealing with distributed systems considerations, with the most important being consistency, load balancing, failure management, recovery, and scalability. At the same time, we witness an unprecedented adoption of modern dataflow systems such as Apache Flink, Google Dataflow, and Timely Dataflow. These systems are now performant and fault-tolerant, and they offer excellent state management primitives. With this line of work, we aim at investigating the opportunities and limits of compiling general-purpose programs into stateful dataflows. Given a set of easy-to-follow code conventions, programmers can author stateful entities, a programming abstraction embedded in Python. We present a compiler pipeline named StateFlow, to analyze the abstract syntax tree of a Python application and rewrite it into an intermediate representation based on stateful dataflow graphs. StateFlow compiles that intermediate representation to a target execution system: Apache Flink and Beam, AWS Lambda, Flink's Statefun, and Cloudburst. Through an experimental evaluation, we demonstrate that the code generated by StateFlow incurs minimal overhead. While developing and deploying our prototype, we came to observe important limitations of current dataflow systems in executing cloud applications at scale

A Review of Platforms for the Development of Agent Systems

Agent-based computing is an active field of research with the goal of building autonomous software of hardware entities. This task is often facilitated by the use of dedicated, specialized frameworks. For almost thirty years, many such agent platforms have been developed. Meanwhile, some of them have been abandoned, others continue their development and new platforms are released. This paper presents a up-to-date review of the existing agent platforms and also a historical perspective of this domain. It aims to serve as a reference point for people interested in developing agent systems. This work details the main characteristics of the included agent platforms, together with links to specific projects where they have been used. It distinguishes between the active platforms and those no longer under development or with unclear status. It also classifies the agent platforms as general purpose ones, free or commercial, and specialized ones, which can be used for particular types of applications.Comment: 40 pages, 2 figures, 9 tables, 83 reference

Engineering a Highly Scalable Object-aware Process Management Engine Using Distributed Microservices

Scalability of information systems has been a research topic for many years and is as relevant as ever with the dramatic increases in digitization of business processes and data. This also applies to process-aware information systems, most of which are currently incapable of scaling horizontally, i.e., over multiple servers. This paper presents the design science artifact that resulted from engineering a highly scalable process management system relying on the object-aware process man-agement paradigm. The latter allows for distributed process execution by conceptually encapsulating process logic and data into multiple in-teracting objects that may be processed concurrently. These objects, in turn, are represented by individual microservices at run-time, which can be hosted transparently across entire server clusters. We present mea-surement data that evaluates the scalability of the artifact on a compute cluster, demonstrating that the current prototypical implementation of the run-time engine can handle very large numbers of users and process instances concurrently in single-case mechanism experiments with large amounts of simulated user input. Finally, the development of scalable process execution engines will further the continued maturation of the data-centric business process management field