Service Level Agreements (SLA) represent the contract which captures the agreed upon guarantees between a service provider and its customers. The specifications of existing service level agreements (SLA) for cloud services are not designed to flexibly handle even relatively straightforward performance and technical requirements of consumer applications. In this article, we present a novel approach for SLA-based management of cloud-hosted databases from the consumer perspective. We present an end-to-end framework for consumer-centric SLA management of cloud-hosted databases. The framework facilitates adaptive and dynamic provisioning of the database tier of the software applications based on application-defined policies for satisfying their own SLA performance requirements, avoiding the cost of any SLA violation and controlling the monetary cost of the allocated computing resources. In this framework, the SLA of the consumer applications are declaratively defined in terms of goals which are subjected to a number of constraints that are specific to the application requirements. The framework continuously monitors the application-defined SLA and automatically triggers the execution of necessary corrective actions (scaling out/in the database tier) when required. The framework is database platform-agnostic, uses virtualization-based database replication mechanisms, and requires zero source code changes of the cloud-hosted software applications. The experimental results demonstrate the effectiveness of our SLA-based framework in providing the consumer applications with the required flexibility for achieving their SLA requirements.

We present an end-to-end framework for consumer-centric SLA management of virtualized database servers. The framework facilitates adaptive and dynamic provisioning of the database tier of the software applications based on application-defined policies for satisfying their own SLA performance requirements, avoiding the cost of any SLA violation and controlling the monetary cost of the allocated computing resources. In this framework, the SLA of the consumer applications are declaratively defined in terms of goals which are subjected to a number of constraints that are specific to the application requirements. The framework continuously monitors the application-defined SLA and automatically triggers the execution of necessary corrective actions (scaling out/in the database tier) when required. The framework is database platform-agnostic, uses virtualization-based database replication mechanisms and requires zero source code changes of the cloud-hosted application.

Data replication is a well-known strategy to achieve the availability, scalability and performance improvement goals in the data management world. However, the cost of maintaining several database replicas always strongly consistent is very high. The CAP theorem shows that a shared-data system can choose at most two out of three properties: consistency, availability, and tolerance to partitions. In practice, most of the cloud-based data management systems tend to overcome the difficulties of distributed replication by relaxing the consistency guarantees of the system. In particular, they implement various forms of weaker consistency models such as eventual consistency. This solution is accepted by many new Web 2.0 applications (e.g. social networks) which could be more tolerant with a wider window of data staleness (replication delay).However, unfortunately, there are no generic application-independent and consumer-centric mechanisms by which software applications can specify and manage to what extent inconsistencies can be tolerated. We introduce an adaptive framework for database replication at the middleware layer of cloud environments. The framework provides flexible mechanisms to enable software applications of keeping several database replicas (that can be hosted in different data centers) with different levels of service level agreements (SLA) for their data freshness. The experimental evaluation demonstrates the effectiveness of our framework in providing the software applications with the required flexibility to achieve and optimize their requirements in terms of overall system throughput, data freshness and invested monetary cost.

In this paper, we present the design and the architecture of the CloudDB AutoAdmin system which aims to fill the existing gaps between the provided cloud database services and the requirements of the consumer applications. In particular, it focuses on facilitating the job of the cloud database consumers in implementing database applications as distributed, scalable, and elastic services with a minimum effort on the side of the application developer and a limited footprint in the application code.

There is an emergence of Cloud application platforms such as Microsoft's Azure, Google's App Engine and Amazon's EC2/SimpleDB/S3. Startups and Enterprise alike, lured by the promise of `infinite scalability', `ease of development', `low infrastructure setup cost' are increasingly using these Cloud service building blocks to develop and deploy their web based applications. However, the precise nature of these Cloud platforms and the resultant Cloud application runtime behavior is still largely an unknown. Given the black box nature of these platforms, and the novel programming and data models of Cloud, there is a dearth of tools and techniques for enabling the rigorously evaluation of Cloud platforms at runtime. This paper introduces the CARE (Cloud Architecture Runtime Evaluation) approach, a framework for evaluating Cloud application development and runtime platforms. CARE implements a unified interface with WSDL and REST in order to evaluate different Cloud platforms for Cloud application hosting servers and Cloud databases. With the unified interface, we are able to perform selective high stress and low stress evaluations corresponding to desired test scenarios. Result shows the effectiveness of CARE in the evaluation of Cloud variations in terms of scalability, availability and responsiveness, across both compute and storage capabilities. Thus placing CARE as an important tool in the path of Cloud computing research.