Flávio R. C. Sousa

Elasticity in cloud computing: a survey

Cloud computing is now a well-consolidated paradigm for on-demand services provisioning on a pay-as-you-go model. Elasticity, one of the major benefits required for this computing model, is the ability to add and remove resources “on the fly” to handle the load variation. Although many works in literature have surveyed cloud computing and its features, there is a lack of a detailed analysis about elasticity for the cloud. As an attempt to fill this gap, we propose this survey on cloud computing elasticity based on an adaptation of a classic systematic review. We address different aspects of elasticity, such as definitions, metrics and tools for measuring, evaluation of the elasticity, and existing solutions. Finally, we present some open issues and future directions. To the best of our knowledge, this is the first study on cloud computing elasticity using a systematic review approach.

Towards Elastic Multi-Tenant Database Replication with Quality of Service

Database systems serving cloud platforms must handle a large number of applications or tenants. Multi-tenancy database has been prevalent for hosting multiple tenants within a single DBMS while enabling effective resource sharing. Providing such performance goals is challenging for providers as they must balance the performance that they can deliver to tenants and the operating costs. In such database systems, a key functionality for service providers is database replication, which is useful for availability, performance, elasticity, and quality of service. This paper presents RepliC, an approach to database replication in the cloud with quality of service, elasticity, and support tomulti-tenancy. In order to evaluate RepliC, some experiments that measure the quality of service and elasticity are presented. Our experiment results confirm that RepliC ensures the quality of service with small SLA violations, while using resources efficiently.

A Petri net-based decision-making framework for assessing cloud services adoption

Cloud services are widely used nowadays, especially in Infrastructure as a service (IaaS), with vendors offering several purchasing options and expanding the range of services offered on almost a daily basis. Cost reduction is a major factor promoting the adoption of cloud services among enterprises. However, qualitative factors need to be evaluated as well, thus rendering the decision regarding the adoption of cloud services among enterprises a non-trivial task for Information Technology (IT) managers. In this paper, we propose a place/transition or Petri net-based multi-criteria decision-making (MCDM) framework to assess a cloud service in comparison with a similar on-premises service. The framework helps IT managers choose between two such options, and can be used for any type of cloud service: Infrastructure as a Service (IaaS), Platform as a service (PaaS), Software as a service (SaaS), etc. Because its low cost is among the most important reasons for adopting cloud services, we also propose a Petri net to model cost savings using the spot instances purchasing option in public clouds. Through simulation of several scenarios, we conclude that spot instances present a very interesting cost-saving option in the auto-scaling process, even for simple business applications using few servers. A Petri net-based multi-criteria decision making framework to assess a cloud service against a similar on-premises service.Our framework helps IT managers choose between two such options, and can be used for any type of cloud service.We also propose a Petri net to model cost savings using the spot instances purchasing option in public clouds.Simulations showed that spot instances present a promising cost-saving option in the auto-scaling process, even for simple business applications using few servers.

Scale-Space Filtering for Workload Analysis and Forecast

Dynamic resource provisioning poses a major challenge for infrastructure providers because it is necessary to both forecast resource consumption and react to recent surges on demand for maintaining a tradeoff between quality of service and cost. However, approaches to workload analysis and forecast are affected due to noise in observed data, specially in forecast models. Moreover, most studies do not consider different prediction horizons may be necessary in order to take action before an SLA violation occurs. This paper presents an approach based in the scale-space theory to assist the dynamic resource provisioning. This theory is capable of eliminating the presence of irrelevant information from a signal that can potentially induce wrong or late decision making. In order to evaluate our approach, some experiments are presented considering both reactive and proactive strategies. The results confirm that our approach improves the workload analysis and forecast.

A distributed concurrency control mechanism for XML data

XML has become a widely used standard for data exchange among applications. Consequently, a large amount of data is distributed on the Web and stored in different persistence models. DBMSs provide concurrency control techniques to manage such data. However, the structure of XML data makes the application of these techniques difficult. Regarding distributed environments, there are few papers available and they all have limitations. This paper introduces DTX, a mechanism for distributed concurrency control for XML data. In order to evaluate DTX, experiments that measure its performance are presented.

Assessing cloud computing SaaS adoption for enterprise applications using a Petri net MCDM framework

Cloud computing has become a very popular technology for IT infrastructure (infrastructure as a service - IaaS), with big players offering attractive pricing schemes. Software vendors are interested in offering products in the cloud as services. However, in order to evaluate software as an offer of service (SaaS) for a given enterprise application, we need to assess some qualitative factors other than just price-security and trust being the most relevant factors. In this work, we propose a Petri net multi-criteria decision making (MCDM) framework to evaluate SaaS as opposed to an on-premises alternative. The proposed framework can help IT managers in deciding between the two options, as well as being able to help software vendors in their efforts to establish SaaS pricing.

A new approach to replication of XML data

XML has become a widely used standard for data exchange in several application domains. In order to manage data in this format, Native XML Databases (NXDBs) are being proposed and implemented. Even though, currently there are a number of available NXDBs, few of them provide replication mechanisms. This paper presents RepliX, a mechanism for replication of XML data based on group communication. With the purpose of validating RepliX, experiments were conducted to measure its performance.

Machine Learning Approach for Cloud NoSQL Databases Performance Modeling

Cloud computing is a successful, emerging paradigm that supports on-demand services with pay-as-you-go model. With the exponential growth of data, NoSQL databases have been used to manage data in the cloud. In these newly emerging settings, mechanisms to guarantee Quality of Service heavily relies on performance predictability, i.e., the ability to estimate the impact of concurrent query execution on the performance of individual queries in a continuously evolving workload. This paper presents a performance modeling approach for NoSQL databases in terms of performance metrics which is capable of capturing the non-linear effects caused by concurrency and distribution aspects. Experimental results confirm that our performance modeling can accurately predict mean response time measurements under a wide range of workload configurations.

A Distributed Concurrency Control Mechanism for XML Data

XML has become a widely used standard for data exchange among applications. Consequently, a large amount of data is distributed on the Web and stored in different persistence models. DBMSs provide concurrency control techniques to manage such data. However, the structure of XML data makes the application of these techniques difficult. Regarding distributed environments, there are few papers available and they all have limitations. This paper introduces DTX, a mechanism for distributed concurrency control for XML data. In order to evaluate DTX, experiments that measure its performance are presented.

Elastic provisioning for cloud databases with uncertainty management

Cloud computing is a compelling, emerging paradigm that supports on-demand services with pay-as-you-go model. It is fundamental for cloud providers to allocate resource quantities suitable to ensure performance while reducing the operational costs related to both overprovisioning and penalties for SLA violations. Performance of cloud services may be unstable because of the environment. Most automatic provisioning techniques lack the capacity to handle the uncertainty of service performance. In this work, we investigate uncertainty management for cloud database elasticity from a probabilistic, performance-driven standpoint, and propose ProDBC, a novel approach for elastic provisioning. ProDBC uses a separate network for profiling in order to build a probabilistic model describing the relationship between workload, resource quantities and the subsequent performance. This model is embedded into a cost function in which the trade-off between infrastructure cost, SLA violation rate and the confidence level (uncertainty) is controlled intuitively. Experimental results obtained with the OLTP Database Benchmark showed provisioning actions taken by ProDBC provides elasticity while limiting the number of SLA violations.