A Semantic Model for Interchangeable Microservices in Cloud Continuum Computing

Taherizadeh, Salman; Apostolou, Dimitris; Verginadis, Yiannis; Grobelnik, Marko; Mentzas, Gregoris

doi:10.3390/info12010040

Cited by 3 publications

(2 citation statements)

References 40 publications

(45 reference statements)

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“…As a result, a conceptual model is required to formally explain the notions of distinct cloud application concepts. It is often difficult to put this statement into practice [4]. Big data, according to Gramigna, has altered the way the financial enterprise provides clients.…”

Section: Introductionmentioning

confidence: 99%

High-Concurrency and High-Performance Application of Microservice Order System Based on Big Data

Zhou

Chen

2022

Security and Communication Networks

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The order system plays the role of the central nervous system in the integrated logistics operation. Theoretically speaking, the whole operation process of logistics service is to complete the order, the order information runs through all the links of the whole logistics, and the order processing efficiency affects the logistics process. This paper studies the high-concurrency and high-performance applications of the microservice order system based on big data, which prove that on the basis of big data technology, the performance of the microservice order system will be greatly improved. To this end, the analysis provides an overview of big data technologies and related content of microservices. It describes their application scenarios and then combines the development of the order system with a comprehensive analysis to prove its feasibility. At the same time, we compared multiple algorithms applied in microservices and found the most suitable algorithm for the order system and applied it, so that the order system can achieve high concurrency and high performance. The big data-based microservices control system proved to be useful for reasonable resource planning and for improving system performance in experiments. The final experiment found that in the microservice order system using big data, its transaction average response time was 0.01 s, and the success rate was 100%.

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Section: Introductionmentioning

confidence: 99%

High-Concurrency and High-Performance Application of Microservice Order System Based on Big Data

Zhou

Chen

2022

Security and Communication Networks

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“…In the fog layer [38], the RAMA scheduling process runs through BDPS data distribution [39], containers and delivering priority scheduled data [40] from the IoT layer toward the central cloud [41]. After the fog cluster [42,43] validates the frames [44][45][46], the derived data feed to the RDD in memory accelerators to provide efficient data deliveries across the IoT clusters [47,48] (i.e., gateways).…”

Section: Introductionmentioning

confidence: 99%

BDPS: An Efficient Spark-Based Big Data Processing Scheme for Cloud Fog-IoT Orchestration

et al. 2021

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The Internet of Things (IoT) has seen a surge in mobile devices with the market and technical expansion. IoT networks provide end-to-end connectivity while keeping minimal latency. To reduce delays, efficient data delivery schemes are required for dispersed fog-IoT network orchestrations. We use a Spark-based big data processing scheme (BDPS) to accelerate the distributed database (RDD) delay efficient technique in the fogs for a decentralized heterogeneous network architecture to reinforce suitable data allocations via IoTs. We propose BDPS based on Spark-RDD in fog-IoT overlay architecture to address the performance issues across the network orchestration. We evaluate data processing delays from fog-IoT integrated parts using a depth-first-search-based shortest path node finding configuration, which outperforms the existing shortest path algorithms in terms of algorithmic (i.e., depth-first search) efficiency, including the Bellman–Ford (BF) algorithm, Floyd–Warshall (FW) algorithm, Dijkstra algorithm (DA), and Apache Hadoop (AH) algorithm. The BDPS exhibits low latency in packet deliveries as well as low network overhead uplink activity through a map-reduced resilient data distribution mechanism, better than in BF, DA, FW, and AH. The overall BDPS scheme supports efficient data delivery across the fog-IoT orchestration, outperforming faster node execution while proving effective results, compared to DA, BF, FW and AH, respectively.

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Containerized deployment of micro-services in fog devices: a reinforcement learning-based approach

Nath

Chattopadhyay

Karmakar³

et al. 2021

J Supercomput

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A Semantic Model for Interchangeable Microservices in Cloud Continuum Computing

Cited by 3 publications

References 40 publications

High-Concurrency and High-Performance Application of Microservice Order System Based on Big Data

High-Concurrency and High-Performance Application of Microservice Order System Based on Big Data

BDPS: An Efficient Spark-Based Big Data Processing Scheme for Cloud Fog-IoT Orchestration

Containerized deployment of micro-services in fog devices: a reinforcement learning-based approach

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