Fault Tolerant Central Saga Orchestrator in RESTful Architecture

Malyuga, Konstantin; Perl, Olga; Slapoguzov, Alexandr; Perl, Ivan

doi:10.23919/fruct48808.2020.9087389

Cited by 9 publications

(4 citation statements)

References 7 publications

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“…4. In conclusion, to guarantee the versatility and robustness of the architecture, three aspects are important to take into account: (i) scalability, microservices can be scaled individually when running a heavy workload just by replicating them on several containers without and not replicating the others underutilized (maximize the performance with minimal cost) [19], [74]; (ii) communication in a microservice, is important establishing simple communication protocol like http, httprest "request/response" (synchronous protocol) or mqtt "publish/subscribe" (asynchronous protocol), depending on needs [67]; (iii) fine-grained microservices, it is fundamental to decompose each service focused on a specific function and of limited influence, according to the established requirements [75]; otherwise, this architecture suffers from a high level of abstraction and coordination among the teams [67], [76].…”

Section: Discussionmentioning

confidence: 99%

Telemonitoring System for Infectious Disease Prediction in Elderly People Based on a Novel Microservice Architecture

et al. 2020

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This article describes the design, development and implementation of a set of microservices based on an architecture that enables detection and assisted clinical diagnosis within the field of infectious diseases of elderly patients, via a telemonitoring system. The proposed system is designed to continuously update a medical database fed with vital signs from biosensor kits applied by nurses to elderly people on a daily basis. The database is hosted in the cloud and is managed by a flexible microservices software architecture. The computational paradigms of the edge and the cloud were used in the implementation of a hybrid cloud architecture in order to support versatile high-performance applications under the microservices pattern for the pre-diagnosis of infectious diseases in elderly patients. The results of an analysis of the usability of the equipment, the performance of the architecture and the service concept show that the proposed e-health system is feasible and innovative. The system components are also selected to give a cost-effective implementation for people living in disadvantaged areas. The proposed e-health system is also suitable for distributed computing, big data and NoSQL structures, thus allowing the immediate application of machine learning and AI algorithms to discover knowledge patterns from the overall population.

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Section: Discussionmentioning

confidence: 99%

Telemonitoring System for Infectious Disease Prediction in Elderly People Based on a Novel Microservice Architecture

et al. 2020

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“…2) Central orchestrator approach [9] where services are unaware of each other but post the events to a central orchestrator which takes care of firing the relevant actions in the destination services. This can be seen in Fig.…”

Section: Although This Technique Work For Limited Use Cases It Has the Following Challengesmentioning

confidence: 99%

Impacts of Decomposition Techniques on Performance and Latency of Microservices

Rudrabhatla¹

2020

IJACSA

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Micro service architecture (MSA) has undoubtedly become the most popular modern-day architecture, often used in conjunction with the rapidly advancing public cloud platforms to reap the best benefits of salability, elasticity and agility. Though MSA is highly advantageous and comes with a huge set of benefits, it has its own set of challenges. To achieve the separation of concerns and optimal performance, defining the boundaries for the services clearly and their underlying persistent stores is quintessential. But logically segregating the services is a major challenge faced while designing the MSA. Some of the guiding principles like Single responsibility principle (SRP) and common closure principle (CCP) are put in place to drive the design and separation of microservices. With the use of these techniques the service layer can be designed either by (i) Building the services related to a business subdomain and packaging them as a microservice; (ii) or Defining the entity relationship model and then building the services based on the business capabilities which are grouped together as a microservice; (iii) or understanding the big picture of the application scope and combining both the strategies to achieve the best of both worlds. This paper explains these decomposition approaches in detail by comparing them with the real-world use cases and explains which pattern is suitable under which circumstances and at the same time examines the impacts of these approaches on the performance and latency using a research project.

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“…Cluster can be called a group of similar objects, and clustering is a process of making similar sets out of raw data, which helps in segregation of unknown data easily. The parameters involved should be used cautiously as incompatible use of parameters of clustering like, Number of Clusters (k-means) and Density Limit, may lead to situations like improper density shape of clusters, ambiguity in finding centroid and the noise [5][6][7]. Mainly The improved semi supervised K mean clustering is used for the greedy iteration to find the K mean clustering is presented in [8].…”

Section: Introductionmentioning

confidence: 99%

Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

Karthik¹,

Bhadoria²,

Lee³

et al. 2022

Computers, Materials &Amp; Continua

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Data is always a crucial issue of concern especially during its prediction and computation in digital revolution. This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication. It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data. The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means. The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from data. The proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown variables. This scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic approach. It also demonstrates the generative function for Kalman-filer based prediction model and its observations. This paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration (CPE) for Python.

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Fault Tolerant Central Saga Orchestrator in RESTful Architecture

Cited by 9 publications

References 7 publications

Telemonitoring System for Infectious Disease Prediction in Elderly People Based on a Novel Microservice Architecture

Telemonitoring System for Infectious Disease Prediction in Elderly People Based on a Novel Microservice Architecture

Impacts of Decomposition Techniques on Performance and Latency of Microservices

Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

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