The substantial memory and computational requirements of large language models (LLMs) hinder their deployment on individual resource-constrained devices. This paper introduces HyperCluster, a framework for fully decentralized collaborative inference over peer-to-peer wireless networks. HyperCluster presents three core innovations: (1) a ring-based pipelined inference protocol where nodes deterministically self-organize into a computational ring based on device capabilities and pass intermediate states directly between peers via QUIC-based direct transport; (2) a generalizable model sharding methodology built on top of the Hugging Face Transformers library that automatically partitions any dense LLM across heterogeneous devices according to available memory; (3) selective layer loading from safetensors files, which only loads the tensor weights required by each node’s assigned shard. We validate HyperCluster on a heterogeneous cluster of consumer-grade devices, demonstrating distributed inference of models up to 3 billion parameters with comprehensive latency and throughput analysis across one to three node configurations.
The amount of data generated each year continues to increase. Some of this data must be preserved, and this becomes a significant issue when critical data are involved. Data storage systems must protect against risks that affect both data and infrastructure (primarily the storage media). To fulfil this requirement, the system should offer both data versioning (to restore previous safe versions) and replication (to mitigate device failures). These two functions should be dimensioned based on risk analysis, which may, however, result in complex software/hardware architectures. In this paper, we propose a simple graph model that accounts for risks to both data and devices and facilitates the design of a data storage system. It is complemented by a matrix model that allows analysis of specific properties. The proposed models enable the detection of suboptimal architectures and facilitate understanding of the operations of the backup system. In addition, this study can also support future work on risk management in backup systems.
Cyber-physical systems (CPS) operate across a computing continuum of heterogeneous devices with varying support for execution formats such as containers, Wasm, and native binaries. While Kubernetes is the de facto orchestration standard, its container-centric model and operational overhead make it unsuitable for resource-constrained embedded devices in CPS deployments. We present an adaptive orchestration system that treats format heterogeneity as a first-class concern, enabling distributed deployment across heterogeneous CPS environments. The system selects and places components based on device capabilities and user-specified non-functional requirements (NFRs). Through a MAPE-K control loop, local device agents continuously monitor and report constraints to a central orchestrator. Upon detecting a constraint violation, the orchestrator directs the agents to locally adapt through execution format transformations, redeployments, or component suspensions. Evaluation on a distributed image processing pipeline of five microservices demonstrates deployment initialization in < 5 min, rapid execution format transformation in < 3 s, and a stable agent memory footprint of 30–40 MB even under active load. These results establish that dynamic, NFR-driven heterogeneous orchestration can be achieved with minimal overhead in resource-constrained CPS environments.
While the Microkernel paradigm is a cornerstone for achieving modularity and extensibility, its application in modern distributed environments is often hampered by a rigid dependency on specific execution contexts. Existing frameworks often entangle business logic with specific communication technologies, necessitating extensive refactoring when transitioning between monolithic and distributed deployments. In this work, we introduce Orbitalis, an open-source framework that extends the Microkernel paradigm to distributed systems. Orbitalis integrates component development, communication, and lifecycle management, enabling seamless execution across both local and distributed environments, enabling applications to operate seamlessly across heterogeneous nodes without requiring a priori knowledge of the deployment topology. At the heart of the framework lies Busline, an asynchronous, protocol-agnostic publish/subscribe backbone that supports dynamic component evolution, replication, and replacement. A DHCP-inspired discovery protocol enables cores and plugins to negotiate, connect, and reconfigure at runtime. Experimental evaluation quantifies the framework’s overhead across various deployment scenarios, demonstrating its practical viability.
