Abstract
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.
Highlights
Cloud Computing is a model that enables ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction [1]
We have proposed a preliminary version of an infrastructure [11] that uses models at runtime to allow the addition or modification of non-functional requirement (NFR) to be monitored and the selection of appropriate metric operationalizations depending on the actual cloud services capabilities without interruption to the services being executed
The infrastructure integrates two main components: a monitoring configurator and a monitoring & analysis middleware, and this study focused on the monitoring configurator component by describing how the Runtime Quality Model proposed in [10] can be integrated with other models (i.e., Monitoring Requirements Model, Software as a Service (SaaS) Quality Model) to support the monitoring of cloud services
Summary
Cloud Computing is a model that enables ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction [1]. If a given non-functional requirement (NFR) expressed in the SLA needs to be changed (e.g., owing to an SLA renegotiation), this can lead to significant changes in the monitoring infrastructure Another shortcoming in current solutions is the difficulty of using low-level metrics (e.g., latency, uptime) to define high-level indicators such as performance or availability [8]. This is in line with the results of a recent industrial study that revealed that cloud services monitoring is done with crude technology, mostly MySQL querying or similar (e.g., Nagios) [9]. The design and execution of an experiment that uses the proposed evaluation method for assessing MoS@RT when performing the monitoring configuration of a cloud service in Azure.
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