Esiur preserves object identity across nodes and performs incremental state synchronization without application-level coordination, enabling runtime type definition (TypeDef) discovery and efficient transmission of strongly typed and heterogeneous data without external schemas.
Abstract
Modern distributed applications operate across heterogeneous cloud, edge, and cyber-physical environments where maintaining object identity, synchronizing state, and enabling real-time interaction remain challenging. Existing communication frameworks are mainly message-centric and treat application state as serialized payloads, which increases overhead when objects are continuously observed, incrementally updated, or shared across multiple nodes. This paper presents Esiur, a resource-centric distributed runtime that provides reference-based interaction through network-transparent resources exposing properties, events, and functions. Esiur preserves object identity across nodes and performs incremental state synchronization without application-level coordination. It defines a unified binary protocol based on the Type Representation Unit (TRU) and Transfer Data Unit (TDU), enabling runtime type definition (TypeDef) discovery and efficient transmission of strongly typed and heterogeneous data without external schemas. The framework is evaluated through serialization benchmarks, protocol-overhead measurements, ordered-notification analysis, annotation-guided invocation, subscriber and attachment scalability tests, and cross-language reattachment recovery between C# and TypeScript implementations. Results show a 36.4%–68.7% reduction in serialization size relative to JavaScript Object Notation (JSON), statistically bounded notification latency, successful runtime TypeDef-based invocation, final-state convergence after transient wide-area network (WAN) disconnection, and stable behavior under concurrent subscriber and attachment loads.
Enterprise software systems increasingly operate across distributed cloud infrastructures where contractual entities, regulatory constraints, and high-volume transactional data must coexist within dynamic, multi-version environments. Traditional monolithic architectures and CRUD-centered data models are insufficient for managing cross-contract replication, version lineage, and synchronized deployment across heterogeneous cloud regions. In such contexts, replication is not merely a data-copying operation but a structural architectural discipline that governs identity, traceability, temporal consistency, and compliance integrity. This article proposes a unified microservices-based architectural framework for designing replicable enterprise systems in which contract identity, version lineage, and cross-environment synchronization are treated as first-class architectural primitives. The study reframes replication as an infrastructural concern embedded within service boundaries, event streams, and immutable version graphs rather than as an afterthought of persistence layers. By integrating domain-driven decomposition, event-driven communication patterns, and distributed cloud deployment strategies, the proposed framework enables differential replication, full-contract cloning, and version-safe synchronization across multi-region infrastructures. The framework further incorporates governance mechanisms, auditability models, and compliance-aware replication logic to ensure that system evolution remains traceable and legally aligned in regulated domains such as finance, healthcare, and large-scale contract management platforms. Through conceptual modeling and architectural validation scenarios, this study demonstrates how enterprise replication can be transformed from an operational risk factor into a strategic design capability. By positioning replication as an architectural discipline rather than a technical utility, this research contributes to a structured design model for scalable, resilient, and compliant enterprise systems operating across distributed cloud environments.
Sefa Teyek· International Journal of Res...· 0 citations
Edge AI is evolving from isolated inference toward long-running services that coordinate model pipelines, data streams, state, and accelerators near users and physical environments. Cloud-native and edge-native platforms offer useful foundations, but their primary control objects–containers, nodes, links, and enrolled sites–generally do not expose the model, data, state, quality, and participation semantics required by these services. This paper presents eAI+, a vision for an edgeAI-native service platform built around three first-class control objects: AI service graphs, dynamic edge resource fabrics, and participant contracts. eAI+ aims to preserve service quality under latency, privacy, reliability, cost, and participation constraints through three coordinated mechanisms. Runtime would select safe execution adaptations based on current workload, environment, and contract signals. Deployment would map service-graph components and prepared fallbacks to heterogeneous resources. PolyLink is the participant-contract module for plug-and-play resource onboarding; it would register contributors, verified resource offers, capabilities, and participation terms. Once a resource is onboarded, it would become available to Deployment for placing eligible service-graph components under the registered contract, while PolyLink would maintain metering, reputation, rewards, and exit events. Migration would transfer only continuity-critical state or control when mobility, overload, policy changes, or contributor lifecycle events invalidate the current placement. This framing treats edge AI as a coordinated service-platform problem across models, data, state, resources, and contracts.
Jiannong Cao, Zhiyuan Hu, Mingjin Zhang et al.· International Symposium on S...· 0 citations
Global-scale software delivery increasingly relies on distributed localization pipelines to manage multilingual content across diverse deployment environments. While Continuous Integration and Delivery (CI/CD) practices have matured, existing approaches struggle to deliver scalable and reproducible orchestration under unpredictable, high-volume workloads. VM-based infrastructures often suffer from limited elasticity and high operational overhead, especially when integrating ML components such as Neural Machine Translation (NMT), which increases complexity in resource allocation and coordination of concurrent tasks. This paper introduces a serverless CI/CD framework that reimagines localization orchestration through cloud-native, event-driven design. The framework's core innovation lies in its fine-grained function scheduling, enabling parallel execution of translation workflows with dynamic scaling and adaptive resource utilization. Stateless serverless functions eliminate idletime overhead and support deterministic reproducibility through modular encapsulation and predictable invocation. A hybrid translation model integrates NMT with Translation Memory (TM), balancing adaptive fluency with domain-specific precision while maximizing concurrency across both orchestration and translation layers. Preliminary evaluation on Microsoft Azure demonstrates the framework's operational advantages. It achieves sub-second cold-start latency and scales to $\mathbf{1, 0 0 0}$ parallel instances within 15 seconds under specific consumption plans. The framework processes a $\mathbf{1 2 0}$-word batch $\mathbf{6. 4} \times$ faster (0.25 s vs. 1.6 s) and increases per-instance throughput by 72% (43 vs. 25 strings/min), supporting a peak capacity of 43,000 strings/min. These results validate the framework's ability to meet demanding localization workloads efficiently and at scale. By shifting focus from model-level optimization to orchestration-layer efficiency, this work contributes a scalable and extensible paradigm for ML-enabled localization. It demonstrates practical applicability by delivering a resilient, cloud-native solution that enhances scalability and efficiency in real-world multilingual software localization workflows.
Neeraj Kumar Sharma, Pranav Dilip Mate, Sandeep Kanchan Pandit et al.· Annual International Compute...· 0 citations
This work presents SemanticXR, the first device-cloud system for real-time, open-vocabulary semantic mapping and querying under XR power, bandwidth, and memory constraints, and key insight is to elevate semantically identifiable objects to first-class units of system design, governing how the system communicates, executes, and manages memory across the device and the server.
Rahul Singh, Devdeep Ray, Connor Smith et al.· 0 citations
WebAssembly (WASM) is emerging as a lightweight alternative to containers for Function-as-a-Service (FaaS) across the edge-cloud continuum. However, existing WASM-based serverless platforms are tightly coupled to specific execution engines and predominantly designed for stateless workloads. This clashes with the heterogeneity of edge deployments, which demand support for stateful applications under diverse hardware and workload constraints. We introduce WASP, a configurable framework that brings stateful serverless execution to the edge-cloud continuum. By abandoning monolithic architectures in favor of strictly decoupled, pluggable components, WASP lets system administrators swap the WASM runtime and the datastore to fit available resources and application requirements, without altering application code. Configurable lifecycle and caching policies further enable fine-tuning for diverse non-functional requirements. Our experimental evaluation demonstrates that WASP introduces negligible runtime overhead and, by swapping runtimes, datastores, and policies, exposes radically different memory and latency profiles, confirming its adaptability to the heterogeneous constraints of the edge-cloud continuum.
Matteo Cenzato, Dario d'Abate, Arianna Dragoni et al.· 0 citations
As computing resources in cloud environments become increasingly abundant, executing complex scientific workflows on large-scale cloud infrastructure has become a standard practice. However, communication-intensive workflows face two fundamental bottlenecks. First, the lack of physical topology awareness often forces high-frequency interacting microservices to be placed on geographically distant nodes, which generates excessive cross-node communication overhead, leads to network load imbalance, and increases latency. Second, the prohibitive online computation time of conventional iterative scheduling algorithms further degrades response speed, making them unsuitable for real-time scenarios. To address these bottlenecks, this paper proposes AMSche, a framework for microservice deployment and task scheduling that is aware of both position and topology. The framework comprises two core mechanisms. The first mechanism, position-aware service deployment, colocates high-frequency interacting services on the same physical node based on communication affinity, thereby compressing cross-node communication overhead at the physical level. The second mechanism, topology-aware task scheduling, leverages online topology feature similarity mapping to instantly reuse historical scheduling plans, achieving scheduling decisions at the millisecond level. Extensive experiments on real-world scientific workflow datasets demonstrate that AMSche achieves an average improvement of 16.19% to 39.15% over existing baseline methods in comprehensive metrics including response time, total communication volume, and network load balance.
Hao Wei, Hailiang Chen, Jia-Nan Sun et al.· Fall Joint Computer Conferen...· 0 citations