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As cloud-native computing is becoming the de-facto paradigm in the cloud field, Microservices Architecture has attracted attention from industries and researchers for agility and efficiency. Moreover, with the popularity of the IoT in the context of edge computing, cloud-native applications that utilize geographically-distributed multiple resources are emerging. In line with this trend, there is an increasing demand for microservices placement that selectively use optimal resources. However, optimal microservices placement is a significant challenge because microservices are dynamic and complex, depending on diversified workloads. Besides, generalizing workloads' characteristics consisting of complex microservices is realistically challenging. Thus, microservices deployment with mathematically structured algorithms based on simulation is less practical. As an alternative, a microservices placement framework is required that can reflect the characteristics of workloads derived from empirical profiling. Therefore, in this research work, we propose a refinement framework for profiling-based microservices placement to identify and respond to workload characteristics in a practical way. To achieve this goal, we perform profiling experiments with selected workloads to derive delicate resource requirements. Then, we perform microservices placement with a greedy-based heuristic algorithm that considers application performance by using resource requirements derived from the profiled results. Finally, we verify the proposed concept by comparing the experimental results that use our work and those that don't.

With the expansion of cloud-leveraged Information and Communications Technology (ICT) convergence trend, cloud-native computing is starting to be the de-facto paradigm together with MSA(Microservices Architecture)-based service composition for agility and efficiency. Moreover, by bridging the Internet of Things (IoT) and cloud together, a variety of cloud applications are explosively emerging. As an example, the so-called IoT-Cloud services, which are cloud-leveraged inter-connected services with distributed IoT devices, dynamically utilize geographically-distributed multiple clouds since mobile IoT devices can selectively connect to the near-by cloud resources for low-latency and high-throughput connectivity. In comparison, most public cloud providers may cause vendor lock-in problems that limit the inter-operable service compositions. Thus, in this paper, we propose a new overlay approach to address the above limitations, denoted as Dynamic OverCloud, which is a specially-arranged razor-thin overlay layer that provides users with an inter-operable and visibility-supported environment for MSA-based IoT-Cloud service composition over the existing multiple clouds. Then, we design a software framework that dynamically builds the proposed concept. We also describe a detailed implementation of the software framework with workflows. Finally, we verify its feasibility by realizing a smart energy IoT-Cloud service with the suggested operation lifecycle.

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