Acuerdo de nivel de Servicio

URI permanente para esta colección:

Artículos en la categoría Acuerdo de nivel de Servicio publicados en las Actas de las XV Jornadas de Ciencia e Ingeniería de Servicios (JCIS 2019).
Notificar un error en esta colección

Examinar

Envíos recientes

Mostrando 1 - 3 de 3
  • Artículo
    A Service Level Agreement Driven Framework to Customise Cloud Service Billing
    García, José María; Martín-Díaz, Octavio; Fernández, Pablo; Müller, Carlos; Ruiz-Cortés, Antonio. Actas de las XV Jornadas de Ciencia e Ingeniería de Servicios (JCIS 2019), 2019-09-02.
    Cloud service providers offer to their customers a variety of pricing policies, which range from the simple, yet widely used pay-as-you-go schema to complex discounted models. When executing the billing process, stakeholders have to consider usage metrics and service level objectives in order to obtain the correct billing and conform to the service level agreement in place. The more metrics, discount and compensations rules are added to the pricing schema, the more complex the billing generation results. In this paper we present a monitoring-based solution that enables the dynamically definition of both service level objectives and discount rules, so that providers can customise the billing generation process in terms of the service level agreement they offer. We validate our proposal in a real-world scenario, introducing a micro-service based software solution deployed in a Kubernetes cluster.
  • Resumen
    Automated Validation of Compensable SLAs
    Müller, Carlos; Gutiérrez-Fernández, Antonio Manuel; Fernández, Pablo; Martín-Díaz, Octavio; Resinas, Manuel; Ruiz-Cortés, Antonio. Actas de las XV Jornadas de Ciencia e Ingeniería de Servicios (JCIS 2019), 2019-09-02.
    A Service Level Agreement (SLA) regulates the provisioning of a service by defining a set of guarantees. Each guarantee sets a Service Level Objective (SLO) on some service metrics, and optionally a compensation that is applied when the SLO is unfulfilled (the compensation would be a penalty) or overfulfilled (the compensation would be a reward). For instance, Amazon is penalised with a 10% in service credits if the availability of the Elastic Cloud Computing service drops below 99.95%. Currently, there are software tools and research proposals that use the information about compensations to automate and optimise certain parts of the service management. However, they assume that compensations are well defined, which is too optimistic in some circumstances and can lead to undesirable situations. For example, an unbounded, automated penalty was discarded in 2005 by the UK Royal Mail company after causing a loss of 280 million pounds in one year and a half. In the article "Automated Validation of Compensable SLAs", published in IEEE Transactions on Services Computing (Early Access), and available at https://doi.org/10.1109/TSC.2018.2885766, we aim at answering the question "How can compensations be automatically validated?". To this end, we build on the compensable SLA model proposed in a previous work to provide a technique that leverages constraint satisfaction problem solvers to automatically validate them. We also present a materialisation of the model in iAgree, a language to specify SLAs and a tooling support that implements our whole approach. Our proposal has been evaluated by modelling and analysing the compensations of 24 SLAs of real-world scenarios including 319 guarantee terms. As a result, our technique has proven to be useful for detecting mistakes that are typically derived not only from the manual specification of SLAs in natural language, but also from the complex nature of compensation definitions. Thus, we found that nine guarantees with compensations that were not properly defined in the original SLAs specified in natural language. Specifically, five were wrongly specified by Verizon, and four were wrongly specified by the outsourcing service hiring of the regional governments of: Northwest Territories of Canada, and Andalusia in Spain. Therefore, our proposal can pave the way for using compensable SLAs in a safer and more reliable way.
  • Artículo
    A Hybrid Reliability Metric for SLA Predictive Monitoring
    Comuzzi, Marco; Márquez Chamorro, Alfonso E.; Resinas, Manuel. Actas de las XV Jornadas de Ciencia e Ingeniería de Servicios (JCIS 2019), 2019-09-02.
    Modern SLA management includes SLA prediction based on data collected during service operations. Besides overall accuracy of a prediction model, decision makers should be able to measure the reliability of individual predictions before taking important decisions, such as whether to renegotiate an SLA. Measures of reliability of individual predictions provided by machine learning techniques tend to depend strictly on the technique chosen and to neglect the features of the system generating the data used to learn a model, i.e., the service provisioning landscape in this case. In this paper, we define a hybrid measure of reliability of an individual SLA prediction for classification models, which accounts for both the reliability of the chosen prediction technique, if available, and features capturing the variability of the service provisioning scenario. The metric is evaluated empirically using SLAs and event logs of a real world case. This paper was presented in ACM Symposium on Applied Computing (SAC) in April 2019 (GGS Class 2).