Source-linked AI summary
Skywing: A Platform for Decentralized Mathematical Computing in Unreliable Environments
Alyson Fox, Colin Ponce, Annika Mauro, Wayne Mitchell, Sarah Osborn, Tom Benson, Shayna Kapadia
TL;DR
Skywing addresses decentralized mathematical computing when centralized coordination, reliable communication, and global synchronization cannot be assumed. It provides abstractions and a runtime for diverse decentralized algorithms, with demonstrations spanning consensus, optimization, numerical linear algebra, and resilience scenarios.
Problem
Decentralized devices increasingly require mathematical computation despite unreliable communication and the inability to assume centralized coordination or global synchronization.
Method
Skywing uses agents, processors, and iterations to separate algorithmic update rules from distributed execution while supporting asynchronous, publish-subscribe, and composable workflows.
Results
Skywing’s runtime supports several classes of decentralized algorithms and resilience experiments involving delayed communication and adversarial data corruption.
Takeaways & Limitations
Skywing serves as both a deployment framework for decentralized applications and a research platform for developing resilient mathematical algorithms.
Takeaways & Limitations
Skywing does not by itself provide Byzantine fault tolerance or protection against malicious agents.
Abstract
from arXiv · showhide
Emerging edge, autonomous, and cyber-physical systems increasingly require mathematical computation across heterogeneous devices connected by unreliable communication networks. Traditional high-performance computing and distributed data-processing frameworks provide powerful abstractions for managed environments but are less suited to decentralized settings where centralized coordination, reliable communication, and global synchronization cannot be assumed. This paper presents Skywing, an open-source platform for decentralized mathematical computing in unreliable environments. Its programming model consists of three abstractions: agents represent participants in a decentralized computation, processors encapsulate algorithm-specific update rules, and iterations manage distributed execution. Skywing supports asynchronous operation, publish-subscribe communication, managed message handling, and the composition of independent algorithms into complex decentralized workflows. We demonstrate Skywing using representative algorithms from consensus, optimization, and numerical linear algebra. Experiments on the native Skywing runtime include Push Sum and Max Consensus, a composed monitoring and control workflow, resilient Push Sum under delayed communication, and resilient asynchronous Jacobi under malevolent data corruption. These demonstrations show that Skywing supports diverse decentralized algorithms while separating mathematical logic from communication and execution infrastructure. Skywing serves as both a deployment framework for decentralized applications and a research platform for developing resilient mathematical algorithms.
1 Introduction
Skywing addresses decentralized mathematical computing across heterogeneous devices and unreliable networks by providing a reusable, coordinator-free platform. Its abstractions separate algorithmic updates from communication and execution while supporting asynchronous execution and composed workflows.
- Motivation: Unreliable communication, heterogeneous device rates, disconnections, stale or corrupted data, and unpredictable agents complicate decentralized mathematical computation.These conditions challenge algorithms designed for reliable, synchronized, relatively homogeneous systems.
- Motivation: Existing high-performance and distributed data-processing frameworks assume managed coordination, centralized orchestration, or batch execution, leaving a practical infrastructure gap.Decentralized methods also are often delivered as specialized systems or one-off prototypes rather than reusable platforms.
- Skywing platform: Skywing provides a reusable, open-source platform for decentralized mathematical computing without requiring a central coordinator.It targets unreliable, heterogeneous, coordinator-free systems where synchronization is expensive and failures are expected.
- Programming model: Agents represent participants, Processors encapsulate mathematical update rules, and Iterations combine them into asynchronously executing distributed tasks.Processors communicate through publish-subscribe tags, separating named data channels from explicit communication logic.
- Demonstrations: The same runtime supports consensus, optimization, numerical linear algebra, composed workflows, delayed communication, and adversarial data corruption.The demonstrations position Skywing as both a deployment framework and a research platform for decentralized applications and resilient algorithms.
2 Related Work and Positioning
Skywing occupies a different design space from managed distributed platforms and algorithm-specific research prototypes. It provides reusable abstractions for asynchronous, decentralized, resilient computation and for composing independent algorithms into larger workflows.
- Distributed computing platforms: HPC frameworks are effective in managed, relatively homogeneous environments with reliable communication and coordinated synchronization, but do not directly provide decentralized continuous computation across unreliable devices.Skywing complements rather than replaces these frameworks by targeting a different operating setting.
- Distributed computing platforms: Batch-oriented platforms such as MapReduce, Hadoop, and Spark are less natural for iterative numerical algorithms whose state evolves continuously through local neighbor communication.Skywing instead treats each participant as an autonomous computational agent with continuously executing processors.
- Algorithmic foundations: Prior asynchronous, consensus, optimization, and resilient numerical methods provide algorithmic foundations, but much prior work focuses on particular algorithms rather than reusable infrastructure.Skywing provides common execution conditions for implementing and evaluating such methods.
- Resilience: Skywing separates runtime-managed communication and asynchronous coordination from resilience mechanisms encapsulated within Processor implementations.This lets researchers compare resilience strategies without modifying the underlying distributed infrastructure.
- Composition and positioning: Its reusable abstractions compose independent algorithms into larger workflows, turning decentralized algorithms into computational building blocks rather than standalone programs.This supports applications combining aggregation, optimization, control, estimation, planning, and decision-making.
3 Programming Model
Skywing separates decentralized algorithm logic from communication and execution through Agent, Processor, and Iteration abstractions. Its asynchronous, publish-subscribe runtime supports independent agents, concurrent algorithms, composition, and diverse mathematical workloads.
- Core design: The Skywing runtime separates communication, message delivery, and distributed execution from algorithm-specific behavior encapsulated within processors.Applications interact with runtime infrastructure through the programming model rather than implementing communication mechanisms directly.
- Core abstractions: An Agent represents a participant and local execution environment, while a collective denotes the group of agents participating in one shared computation.Agents maintain local state and communicate with configured neighbors.
- Core abstractions: A Processor maintains local state, consumes subscribed neighbor information, applies the algorithm-specific update rule, and publishes results under named tags.The same abstraction supports consensus, optimization, iterative linear solvers, estimation, and control.
- Core abstractions: An Iteration combines an agent and processor into an independently executing distributed task that publishes data, collects subscriptions, invokes updates, and evaluates stopping criteria.Applications can query intermediate results, update processor inputs, or launch additional computations while iterations continue.
- Message passing: Publish-subscribe tags let processors exchange information while the runtime manages transmission, reception, delivery, and network connections.Applications configure neighbor relationships instead of explicitly opening sockets or sending and receiving messages.
- Asynchronous execution: Asynchronous execution allows agents to progress independently and consume the most recent available neighbor data without mandatory global synchronization.This avoids blocking behavior when communication delays, heterogeneous devices, and intermittent failures make synchronization restrictive or costly.
- Composition and scope: Independent processors can be coordinated into larger workflows, enabling concurrent algorithms and reuse of shared communication and execution infrastructure.Representative implementations span consensus, optimization, and numerical linear algebra while using the same abstractions.
- Representative workloads: Implementations across consensus, optimization, and numerical linear algebra use the same Agent, Processor, and Iteration abstractions despite differing computational patterns, state representations, and communication requirements.The demonstrations therefore cover substantially different computational patterns within one programming model.
4 Case Studies and Numerical Demonstrations
The demonstrations exercise Skywing’s programming model across consensus, composed workflows, and resilient computation under unreliable conditions. They show that diverse algorithms and modular stages can share a runtime while remaining distinct from competitive algorithm benchmarks.
- Evaluation scope: The evaluation demonstrates decentralized execution, processor composition, and algorithmic resilience under unreliable conditions.Experiments run on the native runtime and are intended as platform demonstrations rather than benchmarks of individual algorithms.
- Consensus: The Agent, Processor, and Iteration abstractions support different consensus update rules within the same runtime infrastructure.Push Sum uses weighted state exchange, whereas Max Consensus propagates the largest observed value through the communication graph.
- Consensus: Push Sum estimates converge toward the network average while consensus error decreases toward zero.The implementation uses eight agents in a bidirectional ring, with trajectories and consensus error averaged across five trials.
- Consensus: Max Consensus propagates the largest known value directly, while damping only the reported estimates makes convergence dynamics visible.The experiment uses eight agents in a bidirectional line; the right panel reports mean reported-estimate error across five trials.
- Composable distributed workflows: A three-stage workflow combines distributed aggregation, local control, and decision processing without modifying processor implementations after input measurements change.The aggregation estimates a network average, control generates a negative signal when the estimate exceeds the target, and decision applies safety constraints.
- Resilience: Under delayed communication and malevolent data corruption, resilient implementations continue reducing error while standard implementations degrade.These experiments reuse the same communication and execution infrastructure, enabling algorithm-specific resilience mechanisms to be evaluated in controlled conditions.
- Discussion: Skywing supports implementing, executing, composing, and evaluating diverse decentralized mathematical methods as both a deployment framework and research platform.The paper states that the demonstrations should be interpreted as platform demonstrations rather than evidence that particular algorithms outperform alternatives.
5 Conclusion
Skywing is an open-source platform for decentralized mathematical computing that prioritizes decentralization, asynchronous execution, and algorithm composability over tightly coupled HPC assumptions. Its abstractions and runtime support reusable implementation, composition, and evaluation of diverse decentralized methods under unreliable operating conditions, while current scope includes explicit deployment-time neighbors and cooperative communication assumptions.
- Programming model: Skywing separates algorithm logic from distributed systems concerns through Agent, Processor, and Iteration abstractions.Agents provide local execution, processors encapsulate update rules, and iterations manage distributed task execution.
- Modularity and composition: Separating communication, computation, and execution improves modularity, portability, and code reuse while enabling algorithms to be evaluated within a shared framework.Independent processors can be coordinated into larger workflows while remaining modular components.
- Demonstrated capabilities: The same runtime supports consensus algorithms, composed workflows, and iterative linear solvers across diverse decentralized computational patterns.Demonstrations include Push Sum, Max Consensus, a monitoring and control workflow, resilient Push Sum, and asynchronous Jacobi.
- Tradeoffs: Skywing emphasizes abstraction and composability at the cost of potentially lower communication efficiency than specialized implementations.Publish-subscribe adds indirection relative to explicit message passing, and Python may limit performance for some workloads.
- Scope and positioning: The platform targets decentralized systems where synchronization and reliability cannot be assumed, rather than replacing highly optimized HPC software.Its design responds to heterogeneous devices, unreliable communication, and settings where centralized coordination may be impractical or impossible.
- Current limitations: Current implementation limits support for highly dynamic networks and does not itself provide Byzantine fault tolerance or protection against malicious agents.Neighbor relationships are configured explicitly at deployment, while the communication layer assumes cooperative participants.