Dynamic OverCloud: Realizing Microservices-Based IoT-Cloud Service Composition over Multiple Clouds

Han, Jungsu; Park, Sun; Kim, Jong-Won

doi:10.3390/electronics9060969

Cited by 13 publications

(12 citation statements)

References 25 publications

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“…This work's remarkable thing is that the target environment was transited from multiple clouds to the Kubernetes cluster. Kubernetes, which is used as a de-facto standard in cloudnative computing, can provide a consistent resource pool over multiple clouds [2]. Therefore, developers entirely focus on deploying their applications without worrying about underlay clouds.…”

Section: Related Workmentioning

confidence: 99%

“…• R1. Microservices Monitoring: Cloud-native computing has changed the way we deploy our applications into the containers complying with microservices architecture since containerization enables developers to make lightweight isolation that can easily and quickly deploy their codes to realize services [2]. Basically, most monitoring solutions perform resource-layer visibility, mainly focused on physical/virtual machines.…”

Section: A Requirementsmentioning

confidence: 99%

“…C LOUD computing has been unanimously accepted as the technological solution for different problems in various sectors including businesses, healthcare, factory, farm [1]. With the growth of cloud technologies, cloud-native computing is rising as the de-facto paradigm with Microservices Architecture (MSA)-based service composition for agility and efficiency [2]. Microservices are defined as small-sized functions that may be deployed and scaled independently of each other, and they may employ different middleware stacks for their implementation [3].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Refining Microservices Placement Employing Workload Profiling Over Multiple Kubernetes Clusters

2020

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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.

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Section: Related Workmentioning

confidence: 99%

Section: A Requirementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Refining Microservices Placement Employing Workload Profiling Over Multiple Kubernetes Clusters

2020

Self Cite

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“…In order to overcome such obstacles to strawberry farming, this paper designs and develops a system capable of monitoring the strawberry cultivation environment in real time and supporting decision makers with enhanced information about harvesting timing. The proposed system is designed to collect, store and analyze strawberry environmental data and photos with the concept of IoT-Edge-AI-Cloud [3,4]. The proposed monitoring system collects 13 types of related cultivation environment data using the IoT-Edge module.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Hydroponic Strawberry Monitoring and Harvesting Timing Information Supporting System Based on Nano AI-Cloud and IoT-Edge

Park

Kim

2021

Electronics

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The strawberry market in South Korea is actually the largest market among horticultural crops. Strawberry cultivation in South Korea changed from field cultivation to facility cultivation in order to increase production. However, the decrease in production manpower due to aging is increasing the demand for the automation of strawberry cultivation. Predicting the harvest of strawberries is an important research topic, as strawberry production requires the most manpower for harvest. In addition, the growing environment has a great influence on strawberry production as hydroponic cultivation of strawberries is increasing. In this paper, we design and implement an integrated system that monitors strawberry hydroponic environmental data and determines when to harvest with the concept of IoT-Edge-AI-Cloud. The proposed monitoring system collects, stores and visualizes strawberry growing environment data. The proposed harvest decision system classifies the strawberry maturity level in images using a deep learning algorithm. The monitoring and analysis results are visualized in an integrated interface, which provides a variety of basic data for strawberry cultivation. Even if the strawberry cultivation area increases, the proposed system can be easily expanded and flexibly based on a virtualized container with the concept of IoT-Edge-AI-Cloud. The monitoring system was verified by monitoring a hydroponic strawberry environment for 4 months. In addition, the harvest decision system was verified using strawberry pictures acquired from Smart Berry Farm.

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“…Therefore, in this study, based on the successful development of several embedding techniques for words in several contexts, a novel service embedding method can be devised for realistic service clustering. In addition, because of the rapid development of IoT, and edge, and fog computing, many microservices in the environment are being created for the environment [6]- [8]. This leads to better quality service compositions, such as the acceleration of efficient mashup development [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Semantic Service Clustering With Lightweight BERT-Based Service Embedding Using Invocation Sequences

Zeng

Paik

2021

IEEE Access

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Service clustering is an efficient method for facilitating service discovery and composition. Traditional approaches based on the self-description documents for services usually utilize service signatures. In Web service composition, service clustering can also be performed by the invocation relationship between services. Therefore, based on the successful development of several embedding techniques for words in several contexts, a novel deep learning-based service embedding using invocation sequences is devised for service clustering. Moreover, many microservices are being created because of the rapid development of the Internet of Things (IoT), and edge, and fog computing. Following these developments, Web service composition based on these environments has emerged in abundance.More efficient lightweight approaches to analyze large numbers of services are necessary for service clustering. Consequently, a lightweight deep learning-based approach for the semantic clustering of service composition is presented to address these requirements. In this paper, we first propose the concept of service embedding to capture semantic information from invocation sequences. Second, we suggest using state-of-the-art neural language sequence models for service embedding and develop a corresponding lightweight Bidirectional Encoder Representations of Transformers (BERT)-based model. Next, combined with K-means clustering, the semantic clustering of service composition is evaluated. Finally, the experimental results show that the clustering process can be effectively performed by the lightweight BERT-based model.

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Dynamic OverCloud: Realizing Microservices-Based IoT-Cloud Service Composition over Multiple Clouds

Cited by 13 publications

References 25 publications

Refining Microservices Placement Employing Workload Profiling Over Multiple Kubernetes Clusters

Refining Microservices Placement Employing Workload Profiling Over Multiple Kubernetes Clusters

Design and Implementation of a Hydroponic Strawberry Monitoring and Harvesting Timing Information Supporting System Based on Nano AI-Cloud and IoT-Edge

Semantic Service Clustering With Lightweight BERT-Based Service Embedding Using Invocation Sequences

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