Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAIOCJ: A Choreographic Framework for Safe Adaptive Distributed Applications
291
0
0.0
(
0
)
Added by
Saverio Giallorenzo
Publication date
2014
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
We present AIOCJ, a framework for programming distributed adaptive applications. Applications are programmed using AIOC, a choreographic language suited for expressing patterns of interaction from a global point of view. AIOC allows the programmer to specify which parts of the application can be adapted. Adaptation takes place at runtime by means of rules, which can change during the execution to tackle possibly unforeseen adaptation needs. AIOCJ relies on a solid theory that ensures applications to be deadlock-free by construction also after adaptation. We describe the architecture of AIOCJ, the design of the AIOC language, and an empirical validation of the framework.
rate research
Read More
Programming distributed applications free from communication deadlocks and races is complex. Preserving these properties when applications are updated at runtime is even harder. We present DIOC, a language for programming distributed applications that are free from deadlocks and races by construction. A DIOC program describes a whole distributed application as a unique entity (choreography). DIOC allows the programmer to specify which parts of the application can be updated. At runtime, these parts may be replaced by new DIOC fragments from outside the application. DIOC programs are compiled, generating code for each site, in a lower-level language called DPOC. We formalise both DIOC and DPOC semantics as labelled transition systems and prove the correctness of the compilation as a trace equivalence result. As corollaries, DPOC applications are free from communication deadlocks and races, even in presence of runtime updates.
Programming systems incorporating aspects of functional programming, e.g., higher-order functions, are becoming increasingly popular for large-scale distributed programming. New frameworks such as Apache Spark leverage functional techniques to provide high-level, declarative APIs for in-memory data analytics, often outperforming traditional big data frameworks like Hadoop MapReduce. However, widely-used programming models remain rather ad-hoc; aspects such as implementation trade-offs, static typing, and semantics are not yet well-understood. We present a new asynchronous programming model that has at its core several principles facilitating functional processing of distributed data. The emphasis of our model is on simplicity, performance, and expressiveness. The primary means of communication is by passing functions (closures) to distributed, immutable data. To ensure safe and efficient distribution of closures, our model leverages both syntactic and type-based restrictions. We report on a prototype implementation in Scala. Finally, we present preliminary experimental results evaluating the performance impact of a static, type-based optimization of serialization.
Adaptive model predictive control (MPC) robustly ensures safety while reducing uncertainty during operation. In this paper, a distributed version is proposed to deal with network systems featuring multiple agents and limited communication. To solve the problem in a distributed manner, structure is imposed on the control design ingredients without sacrificing performance. Decentralized and distributed adaptation schemes that allow for a reduction of the uncertainty online compatibly with the network topology are also proposed. The algorithm ensures robust constraint satisfaction, recursive feasibility and finite gain $ell_2$ stability, and yields lower closed-loop cost compared to robust distributed MPC in simulations.
Motion planning is an extremely well-studied problem in the robotics community, yet existing work largely falls into one of two categories: computationally efficient but with few if any safety guarantees, or able to give stronger guarantees but at high computational cost. This work builds on a recent development called FaSTrack in which a slow offline computation provides a modular safety guarantee for a faster online planner. We introduce the notion of meta-planning in which a refined offline computation enables safe switching between different online planners. This provides autonomous systems with the ability to adapt motion plans to a priori unknown environments in real-time as sensor measurements detect new obstacles, and the flexibility to maneuver differently in the presence of obstacles than they would in free space, all while maintaining a strict safety guarantee. We demonstrate the meta-planning algorithm both in simulation and in hardware using a small Crazyflie 2.0 quadrotor.
Blockchains host smart contracts for crowdfunding, tokens, and many other purposes. Vulnerabilities in contracts are often discovered, leading to the loss of large quantities of money. Psamathe is a new language we are designing around a new flow abstraction, reducing asset bugs and making contracts more concise than in existing languages. We present an overview of Psamathe, including a partial formalization. We also discuss several example contracts in Psamathe, and compare the Psamathe examples to the same contracts written in Solidity.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
