A key challenge of designing distributed software systems is maintaining data consistency. We can define data consistency and data isolation guarantees --e.g. serializability-- in terms of schedules of atomic reads and writes, but this excludes schedules that would be semantically consistent. Others use manually provided information on "non-conflicting operations" to define guarantees that work for more applications allowing more parallel schedules. To be safe, an engineer might avoid marking operations as non-conflicting, with detrimental effects to efficiency. To be fast, they might mark more non-conflicting operations than is strictly safe. Our goal is to help engineers by automatically deriving commutative operations (using their respective contracts) such that more parallel schedules with global consistency are possible. We define a new general consistency and isolation guarantee named "Return-Value Serializability" to check consistency claims automatically, and we present distributed event processing algorithms that make use of the same "Contract-based Commutativity" information. We validated both the definitions and the algorithms using model-checking with TLA+. Previous work provided evidence that local coordination avoidance such as applied here has a significant positive effect on the performance of distributed transaction systems. Client-centric return-value commutativity promises to hit a sweet spot in design trade-offs for business applications, such as payment systems, that must scale-out while their operations are not embarrassingly parallel and consistency guarantees are of the highest priority. It can also provide design feedback, indicating that some operations will simply not scale together even before a line of code has been written.
This paper proposes an adaptation of session types to provide behavioural information about public functions in Elixir modules. We formalise typechecking rules for the main constructs of the language. This allows us to statically determine whether a function implementation observes its session endpoint specification. Based on this type system, we then construct a tool that automates typechecking for Elixir modules.
Programs written using Communicating Event-Loops (CEL) concurrency model do not suffer from low-level data races by design but are not exempt from other concurrency bugs, such as behavioral deadlocks and message order violations. When programmers need to find the root cause of a bug, they typically ask questions about the application's behavior. However, current debugging tools are mostly operational, offering features at the source code level like breakpoints and watchpoints. Consequently, understanding the program behavior when debugging can take a lot of time for developers since questions on behaviors need to be mapped into operations in the debugger. Inspired by interrogative debugging, this paper proposes an interactive debugging approach for actor-based programs that enable developers to reason about the program output by selecting questions from a set of predefined questions about the code and the program's execution. We present the design of the questions and answers, and we describe a prototype implementation in Apgar, an online debugger for actor-based programs written in SOMns. We define questions based on key concepts of the actor model: actors, turns, messages, and promises. The debugger then computes the answers by analyzing a recorded trace of events about the program execution.
The steady growth of data volume produced as continuous streams makes paramount the development of software capable of providing timely results to the users. The Actor Model (AM) offers a high-level of abstraction suited for developing scalable message-passing applications. It allows the application developer to focus on the application logic moving the burden of implementing fast and reliable inter-Actors message-exchange to the implementation framework. In this paper, we focus on evaluating the model in high data rate streaming applications targeting scale-up servers. Our approach leverages Parallel Patterns (PP) abstractions to model streaming computations and introduces optimizations that otherwise could be challenging to implement without violating the Actor Model's semantics. The experimental analysis demonstrates that the new implementation skeletons we propose for our PPs can bring significant performance boosts (more than 2X) in high data rate streaming applications implemented in CAF.
The paper introduces an Agent-Oriented Programming (AOP) framework based on the Belief-Desire-Intention (BDI) model of agency. The novelty of this framework is in relying on the Actor model, instantiating each intentional agent as an autonomous micro-system run by actors. The working hypothesis behind this choice is that defining the agents via actors results in a more fine-grained modular architecture and that the execution of agent-oriented programs is enhanced (in scalability as well as in performance) by relying on robust implementations of Actor models such as Akka. The framework is benchmarked and analyzed quantitatively and qualitatively against three other AOP frameworks: Jason, ASTRA and Sarl.
