Enhancing microservices architectures using data-driven service discovery and QoS guarantees

Houmani, Zeina; Balouek-Thomert, Daniel; Caron, Eddy; Parashar, Manish

doi:10.1109/ccgrid49817.2020.00-64

Cited by 17 publications

(18 citation statements)

References 11 publications

(9 reference statements)

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“…Deploying nanoservices at non-uniform, resource constrained local IoT networks is much more complex since the service requirements are highly dynamic. Therefore, the traditional resource discovery and orchestration mechanisms used in current microservice architectures are not suitable in resource-limited, highly dynamic and decentralized local environments [11]. Hence, this paper extends our previous prototype implementation of the nanoservice architecture [10], by developing further its orchestration mechanism to fulfil the requirements of highly dynamic and decentralized operation environment.…”

Section: Introductionmentioning

confidence: 91%

Resource-Aware Dynamic Service Deployment for Local IoT Edge Computing: Healthcare Use Case

et al. 2021

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Edge computing is a novel computing paradigm moving server resources closer to enddevices. In the context of IoT, Edge computing is a centric technology for enabling reliable, context-aware and low-latency services for several application areas such as smart healthcare, smart industry and smart cities. In our previous work, we have proposed a three-tier IoT Edge architecture and a virtual decentralized service platform based on lightweight microservices, called nanoservices, running on it. Together, these proposals form a basis for virtualizing the available local computational capacity and utilizing it to provide localized resource-efficient IoT services based on the applications' need. Furthermore, locally deployed functions are resilient to access network problems and can limit the propagation of sensitive user data for improved privacy. In this paper, we propose an automatic service and resource discovery mechanism for efficient on-the-fly deployment of nanoservices on local IoT nodes. As use case, we have selected a healthcare remote monitoring scenario, which requires high service reliability and availability in a highly dynamic environment. Based on the selected use case, we propose a real-world prototype implementation of the proposed mechanism on Raspberry Pi platform. We evaluate the performance and resourceefficiency of the proposed resource matching function with two alternative deployment approaches: containerized and non-containerized deployment. The results show that the containerized deployment is more resource-efficient, while the resource discovery and matching process takes approximately 6-17 seconds, where containerization adds only 1-1.5 seconds. This can be considered a feasible price for streamlined service management, scalability, resource-efficiency and fault-tolerance. IoT, edge computing, distributed computing, virtualization, resource discovery, microservices, nanoservices. INDEX TERMS

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Section: Introductionmentioning

confidence: 91%

Resource-Aware Dynamic Service Deployment for Local IoT Edge Computing: Healthcare Use Case

et al. 2021

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“…These data are vital to providing the model with a way to monitor the symptoms of DD through the use of indicators (e.g., user facial expressions, voice tone, user emotions from wearable sensors, social network services posts (SNS) and tweets). Generally, microservices do not make intelligent decisions based on data [60]; therefore, it is necessary to implement the following two dedicated servers in the model for this scenario: data mining/machine learning (extracts features and monitors the symptoms from the sensor data and SNS) and web of objects server (applies the semantic web technologies and inference of the user situation to provide the services to the users).…”

Section: Microservice Architecture (Msa)mentioning

confidence: 99%

“…Unlike the SPIDEP-SOA variant, this platform requires applying the implementation patterns based on MSA (i.e., service instance per container, database per service and Backends for Frontends), since it is necessary to fully support a horizontal scalability of the resources. However, this entails an increase in the effort and complexity to manage all interactions between microservices, according to the size of the organization to be implemented [60]. Additionally, in this variant, we verify the identity of the user for each message sent and/or received through the signal mechanisms; therefore, a secure communication channel is established for different computer attacks (e.g., man-in-themiddle) during active sessions [104].…”

Section: Spidep Msa Variant: Implementation Of the Platformmentioning

confidence: 99%

“…After deployment of the K8 environments, the use of scripts with random variables developed in Apache JMeter (5.2) is required to measure and contrast the performance among the 50 virtual users (simulating the terminals of the biometric sensors) and the SPIDEP platforms; however, to avoid altering the results, it is necessary to use dedicated servers to perform these tests. Therefore, we have selected the BlazeMeter servers (US East [Virginia, Google]) to generate a heavy workload towards the platforms, according to small or medium instances of medical telemonitoring environments and their specifications (approximately 200,000 queries every 20 min) [7,21,53,60,93]; however, these platforms have the capacity of automatic scaling that will determine the distribution of computer resources according to demand (for these tests they were disabled, since we had medium instances), as shown in Figure 3.…”

Section: Experiments Settingsmentioning

confidence: 99%

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Evaluating Service-Oriented and Microservice Architecture Patterns to Deploy eHealth Applications in Cloud Computing Environment

et al. 2021

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This article proposes a new framework for a Cloud-based eHealth platform concept focused on Cloud computing environments, since current and emerging approaches using digital clinical history increasingly demonstrate their potential in maintaining the quality of the benefits in medical care services, especially in computer-assisted clinical diagnosis within the field of infectious diseases and due to the worsening of chronic pathologies. Our objective is to evaluate and contrast the performance of the architectural patterns most commonly used for developing eHealth applications (i.e., service-oriented architecture (SOA) and microservices architecture (MSA)), using as reference the quantitative values obtained from the various performance tests and their ability to adapt to the required software attribute (i.e., versatile high-performance). Therefore, it was necessary to modify our platform to fit two architectural variants. As a follow-up to this activity, corresponding tests were performed that showed that the MSA variant functions better in terms of performance and response time compared to the SOA variant; however, it consumed significantly more bandwidth than SOA, and scalability in SOA is generally not possible or requires significant effort to be achieved. We conclude that the implementation of SOA and MSA depends on the nature and needs of organizations (e.g., performance or interoperability).

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“…It consists of discovering available microservices depending on the data characteristics such as resolution, type, and format. Details about this mechanism are presented by Z. Houmani et al in [32]. In the proposed system, the discovery assigns data sources to pipelines supporting their data resolution.…”

Section: Data-driven Microservices Discoverymentioning

confidence: 99%

Enabling microservices management for Deep Learning applications across the Edge-Cloud Continuum

Houmani

Balouek-Thomert

Caron

et al. 2021

2021 IEEE 33rd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

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Deep Learning has shifted the focus of traditional batch workflows to data-driven feature engineering on streaming data. In particular, the execution of Deep Learning workflows presents expectations of near-real-time results with user-defined acceptable accuracy. Meeting the objectives of such applications across heterogeneous resources located at the edge of the network, the core, and in-between requires managing trade-offs between the accuracy and the urgency of the results. However, current data analysis rarely manages the entire Deep Learning pipeline along the data path, making it complex for developers to implement strategies in realworld deployments. Driven by an object detection use case, this paper presents an architecture for time-critical Deep Learning workflows by providing a data-driven scheduling approach to distribute the pipeline across Edge to Cloud resources. Furthermore, it adopts a data management strategy that reduces the resolution of incoming data when potential trade-off optimizations are available. We illustrate the system's viability through a performance evaluation of the object detection use case on the Grid'5000 testbed. We demonstrate that in a multi-user scenario, with a standard frame rate of 25 frames per second, the system speed-up data analysis up to 54.4% compared to a Cloud-only-based scenario with an analysis accuracy higher than a fixed threshold.

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Enhancing microservices architectures using data-driven service discovery and QoS guarantees

Cited by 17 publications

References 11 publications

Resource-Aware Dynamic Service Deployment for Local IoT Edge Computing: Healthcare Use Case

Resource-Aware Dynamic Service Deployment for Local IoT Edge Computing: Healthcare Use Case

Evaluating Service-Oriented and Microservice Architecture Patterns to Deploy eHealth Applications in Cloud Computing Environment

Enabling microservices management for Deep Learning applications across the Edge-Cloud Continuum

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