A Service-Defined approach for orchestrating cloud/edge services in a distributed fashion, where each application can define its own orchestration strategy by means of declarative statements, which are parsed into a Service-Defined Orchestrator (SDO).
The advent of 6G networks and the rapid growth of Internet of Things (IoT) are revolutionizing the telecommunication sector, integrating edge-cloud systems with vast amounts of data from IoT and AI techniques. These advancements make these systems essential in managing and delivering a multitude of services, offered by smart cities and industrial domains, being just a few among the many possible application scenarios. Specifically, this shift introduces complexities in orchestrating services and managing available resources while addressing challenges such as reducing latency, growing bandwidth, ensuring trust, and integrating different technologies. In this sense, this review explores the recent approaches of the state of the art focused on service orchestration in IoT edge-cloud environments, concentrating on architectures and methodologies that enable resource allocation and service management. Furthermore, it examines platforms for orchestration development, highlighting their characteristics and contributions. Finally, emerging concerns such as network topology and trust, which previous surveys have often overlooked, are discussed together with the most relevant research directions.
Cloud-native architecture has been the new paradigm in enterprise application development that facilitates organization to reach unprecedented levels of scalability, agility, reliability, and operational efficiency. The dynamic business requirements, short delivery cycles of software solutions and the necessity of globally distributed services are mounting an increasing challenge on traditional monolithic systems. The concepts of microservices architecture, containerization, DevOps culture, and continuous delivery pipelines are the main principles in cloud-native systems that circumvent the limitations. This paper will be a detailed study of cloud-native architecture and its strategic role in a digital transformation process of businesses. Scalability models, distributed resource management, service orchestration, elasticity patterns and resilience techniques applied by cloud-native platforms are further detailed in the abstract. We address the transformation of enterprise application infrastructure out of on-premise legacy resource setting into service-based cloud environments properly configured to scale horizontally. The paper also investigates that the container orchestration systems such as Kubernetes make deployment, scaling, and failover operations to be declaratively automated. The purpose of service mesh, API-oriented architecture, event-based systems, policy-oriented autoscaling, and infrastructure-as-code (IaC) are examined to show how the architectural resilience and operational administration are accomplished. An approach to assess the maturity of cloud-native systems is presented based on the performance benchmarking, lifecycle automation, security compliance, and cost optimization indicators as part of a methodological framework. Also, the paper presents experimental evaluations of the response time, throughput, service resiliency, and infrastructure utilization in both traditional and cloud-native deployments. Findings indicate the application availability, frequency of deployment and scalability efficiency are very high. Lastly, such challenges as state management, data consistency, complexities in migrating, observability, and operational complexity are also addressed. The innovations that are emphasized by the best practices and future-oriented thinking include serverlesscomputing, auto-scaling that is run with AI, workloads based on WebAssembly, and automated cloud operations. All in all, the paper points to the idea that cloud-native architectures are not something the enterprises can afford to ignore on their quest towards maintaining competitive viability in the rapidly changing digital economy.
Kanya Mohammed, Naree Thongchai· International Journal of Mod...· 0 citations
With the advent of the 6G era and the evolution of distributed systems, edge computing has become a pivotal architecture for deploying latency-sensitive, resource-efficient applications. In particular, the microservice architecture, characterized by modular and loosely coupled components, has gained significant traction for building scalable and maintainable applications at the network edge. However, deploying microservice-based applications in heterogeneous and geographically distributed Multi-Cluster Edge Computing (MCEC) environments presents critical challenges, especially in achieving efficient and scalable resource management. Although existing research has explored resource allocation and container scaling for microservice-based systems, most prior works consider container efficiency in isolation or within single-cluster or cloud-centric environments, without jointly addressing container-level efficiency, inter-cluster task offloading, and resource allocation in MCEC scenarios. To address this gap, we propose RACCOON, a request-offloading cascaded resource allocation algorithm tailored for microservice-oriented deployments in MCEC settings. RACCOON aims to minimize user-perceived service latency while optimizing overall resource utilization. Complementing this, we introduce RASCAL, a reinforcement learning (RL)-based container scaling mechanism that dynamically adjusts resource provisioning at the container level to further enhance system performance. Experimental evaluation shows that our approach consistently outperforms methods that address only resource allocation, only task offloading, or only container scaling, by jointly optimizing these dimensions to reduce end-to-end user-perceived latency and computational overhead.
This paper presents a comprehensive study of edge-cloud orchestration strategies tailored for scalable industrial automation systems, and reveals that intelligent orchestration can significantly enhance operational efficiency, system scalability, and responsiveness in industrial settings.
A. Reza· International Journal of Mac...· 0 citations
The convergence of microservice architectures and serverless computing promises an elastic and cost-efficient model for modern cloud applications that often span multiple geo-distributed regions. However, prevailing serverless orchestrators that prioritize resource utilization or simple cold-start mitigation often prove suboptimal concerning SLO compliance and cost-efficiency in this emerging use case. In this paper, we present Krysha, an adaptive orchestration framework that jointly optimizes function scheduling and resource allocation for geo-distributed serverless microservices. Krysha employs a novel bi-level scheduling strategy: global-level early-binding to regions for fast function dispersion, coupled with regional-level late-binding to compute nodes for optimized resource use and cost. Moreover, Krysha achieves fine-grained resource allocation by decoupling CPU and memory provisioning and applying in-place vertical scaling on individual function instances. These capabilities are guided by a comprehensive cost model and practical online optimization techniques. Our extensive evaluation shows that Krysha can achieve up to 74.7% cost savings in scaled deployments compared to state-of-the-art alternatives while maintaining SLO requirements.
Yuqiu Zhang, Hans-Arno Jacobsen· IEEE International Symposium...· 0 citations
Cloud-edge computing environments are evolving rapidly, requiring orchestration mechanisms that may automatically construct and manage complex multi-step workflows with little human intervention. We introduce a framework for the agentic AI and how it should be able to orchestrate an autonomous end-to-end workload of cloud-edge enterprise infrastructures in general. The proposed framework relies on large language model (LLM)-driven agents capable of dynamic task decomposition, real-time decision-making, and self-correcting execution pipelines to manage heterogeneous workloads. Through the incorporation of multi-agent coordination protocols, context-aware scheduling algorithms, and feedback-driven optimization loops, the system facilitates seamless task delegation throughout edge nodes and cloud backend systems while managing latency, resource allocation, and compliance constraints. Experimental evaluations show up to percentage improvements in workflow completion rates, resource utilization, and fault tolerance over traditional static-command Rule-based orchestration approaches. Additionally, the framework features explainability modules and audit trails to promote transparency and accountability in autonomous operations. The results provide evidence that agentic AI architectures can serve as a scalable, resilient and intelligent control mechanism for next generation enterprise workflow management across hybrid cloud-edge settings. This has laid a foundation and is to our best of knowledge, the first systematic pioneers work that lays down a roadmap for production-grade autonomous orchestration deployed in analytics and enterprise domains.
Shiza Arshad, Anusha Joodala, A. Agade et al.· 2026 International Conferenc...· 0 citations
