A Taxonomy of Quality Metrics for Cloud Services

Guerron, Ximena; Abrahão, Silvia; Insfrán, Emilio; Fernández-Diego, Marta; González-Ladrón-de-Guevara, Fernando

doi:10.1109/access.2020.3009079

Cited by 13 publications

(10 citation statements)

References 61 publications

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“…Resource utilization measures the level of use/percentage of resources (CPU and memory), response time represents the speed of time required from sending requests to receiving responses. In contrast, throughput transfer measures the number of requests successful within a definite time interval [17]. Server access utilized both single and multiple accesses.…”

Section: Testing Scenariomentioning

confidence: 99%

“…The performance analysis of the IaaS cloud involved CPU, memory [11], and response time [12] by adding the availability of cloud service [16] and throughput transfer parameters. This research referred to the taxonomy metrics metamodel for cloud services [17]. Furthermore, the results of this research included an IaaS prototype that was tested using 4 test parameters.…”

Section: Introductionmentioning

confidence: 99%

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Peer to peer (P2P) and cloud computing on infrastructure as a service (IaaS) performance analysis

Ernawati¹,

Febiansyah²

2022

J.INFOTEL

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The resources of information technology and the availability of services on non-cloud network systems are limited. This constitutes problems for companies, especially in the efficient management of information technology. The high investment in infrastructure procurement is an obstacle in building centralized systems, including the adoption of cloud computing through Infrastructure as a Service (IaaS), as an elective solution. This research aims to analyze the performance of cloud servers on IaaS services using the parameters of cloud service availability, resource utilization, and throughput transfer which were implemented in companies engaged in the toll road concession sector. Furthermore, the results are expected to be a reference in supporting company decisions/policies related to cloud system adoption. The methodology involved the Network Development Life Cycle (NDLC), a system constituted by 6 (six) stages of management, namely user, proxy server, database, web service, monitoring service, and Remote Desktop Protocol (RDP). The results of cloud service availability indicate that the cloud system provides service availability (system interface, broad network access, and resource pooling). Furthermore, cloud systems have a significant performance on resource utilization (CPU) and throughput transfer parameters, while non-cloud systems only excel in response time and resource utilization (Memory) parameters. The overall result analysis based on this research scenario showed that the cloud system provides services according to user needs and has a better speed in data transmission, but has shortcomings in response time.

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Section: Testing Scenariomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Peer to peer (P2P) and cloud computing on infrastructure as a service (IaaS) performance analysis

Ernawati¹,

Febiansyah²

2022

J.INFOTEL

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“…The monitoring infrastructure has two main components: the Monitoring Configurator and the Monitoring & Analysis Middleware. The Monitoring Configurator uses the Monitoring Requirements Model and the SaaS Quality Model (i.e., an ISO/IEC 25010-compliant quality model for cloud services built from a systematic literature review [30]) to configure the monitoring of services and obtain the Runtime Quality Model. The Monitoring & Analysis Middleware uses the Runtime Quality Model and relies on two engines: the Measurement Engine, which gathers raw data from services and applies the monitoring instructions, and the Analysis Engine, which compares the expected values with the monitored values and generates the SLA violations report.…”

Section: Monitoring Infrastructurementioning

confidence: 99%

Empirical Evaluation of a Method for Monitoring Cloud Services Based on Models at Runtime

et al. 2021

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Cloud computing is being adopted by commercial and governmental organizations driven by the need to reduce the operational cost of their information technology resources and search for a scalable and flexible way to provide and release their software services. In this computing model, the Quality of Services (QoS) is agreed between service providers and their customers through Service Level Agreements (SLA). There is thus a need for systematic approaches with which to assess the quality of cloud services and their compliance with the SLA. In previous work, we introduced a generic method for Monitoring cloud Services using models at RunTime (MoS@RT), which allows the monitoring requirements or the metric operationalizations of these requirements to be changed at runtime without the modification of the underlying infrastructure. In this paper, we present the design of a monitoring infrastructure that supports the proposed method with its instantiation to a specific platform and reports the results of an experiment carried out to evaluate the perceived efficacy of 58 undergraduate students when using the infrastructure to configure the monitoring of cloud services deployed on the Microsoft Azure platform. The results show that the participants perceived MoS@RT to be easy to use, useful, and they also expressed their intention to use the method in the future. Although further experiments must be carried out to strengthen these results, MoS@RT has proved to be a promising monitoring method for cloud services.INDEX TERMS Cloud computing, models@runtime, quality of service (QoS), services monitoring, software as a service (SaaS).

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“…Before proceeding with the exposition of the architectural model and all the parts composing it, it is necessary to provide some definitions that will allow a better understanding of all the elements that make up the model and what is the relationship among them [55].…”

Section: Glossarymentioning

confidence: 99%

“…However, the power of the tool derives precisely from the versatility and the lack of a standard definition of the concepts themselves, which allows the more experienced to make a more accurate evaluation, adding new and more specific attributes inherent to the scope of application treated. The proposed model, in fact, is aimed at all service stakeholders, i.e., those who need to measure and use the proposed metrics [55]:…”

Section: Coknoweme: Architectural and Analytical Modelmentioning

confidence: 99%

CoKnowEMe: An Edge Evaluation Scheme for QoS of IoMT Microservices in 6G Scenario

et al. 2021

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The forthcoming 6G will attempt to rewrite the communication networks’ perspective focusing on a shift in paradigm in the way technologies and services are conceived, integrated and used. In this viewpoint, the Internet of Medical Things (IoMT) represents a merger of medical devices and health applications that are connected through networks, introducing an important change in managing the disease, treatments and diagnosis, reducing costs and faults. In 6G, the edge intelligence moves the innovative abilities from the central cloud to the edge and jointly with the complex systems approach will enable the development of a new category of lightweight applications as microservices. It requires edge intelligence also for the service evaluation in order to introduce the same degree of adaptability. We propose a new evaluation model, called CoKnowEMe (context knowledge evaluation model), by introducing an architectural and analytical scheme, modeled following a complex and dynamical approach, consisting of three inter-operable level and different networked attributes, to quantify the quality of IoMT microservices depending on a changeable context of use. We conduct simulations to display and quantify the structural complex properties and performance statistical estimators. We select and classify suitable attributes through a further detailed procedure in a supplementary information document.

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A Taxonomy of Quality Metrics for Cloud Services

Cited by 13 publications

References 61 publications

Peer to peer (P2P) and cloud computing on infrastructure as a service (IaaS) performance analysis

Peer to peer (P2P) and cloud computing on infrastructure as a service (IaaS) performance analysis

Empirical Evaluation of a Method for Monitoring Cloud Services Based on Models at Runtime

CoKnowEMe: An Edge Evaluation Scheme for QoS of IoMT Microservices in 6G Scenario

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