As more and more multi-tenant applications emerge in clouds, people increasingly recognize the importance of multi-tenant applications in realizing cloud benefit maximization. Service Level Agreement (SLA) is advocated widely to monitor these applications for multiple tenants to meet their service quality requirements. However, so far these applications provide limited multi-tenant monitoring supports, which prevents the applications from guaranteeing tenants' interests efficiently. In this paper, we propose an aspect-oriented approach to monitor multi-tenant applications based on tenant SLAs. Our approach includes monitoring code generation and runtime management. During code generation, the approach proposes an SLA feature model for tenants to specify variable requirements. Based on the requirements the approach selects code snippets, which are implemented as templates in advance, and splices them into an monitoring aspect. During runtime, the approach prioritizes aspects to determine execution order and updates monitoring status in term of tenant. An implemented prototype is used to evaluate the approach by case studies, which demonstrate the approach effectiveness in common situations.

As multi-tenant applications spring up in clouds, more and more people advocate using Service Level Agreement (SLA) in service delivery to fit tenants' non-functional needs e.g. Response time and budget limit. However, most of the present application optimizations based on SLA focuses on virtual machine-based (VM-based) computing service, while other services such as storage and cache are often neglected. In this paper, we propose an SLA-driven application optimization for cache service to help to meet tenants' needs better and improve cost-effectiveness, which can be taken as complementary to the existing work. The proposed approach, built on top of Platform-as-a-Service (PaaS), pays attention to evicted data. It considers both tenant SLA-evaluated status and data performance when weighting the evicted data with re-cache likelihoods, and then adjusts their re-cache priorities. At the beginning of every cycle it predicts tenant status and evicted data performance for the coming cycle by Holt-Winters double exponential smoothing. Our simulation experiments demonstrate the optimization effectiveness in improving cache cost-effectiveness and satisfying tenant SLAs.

With the popularity of pads, large touch screens, notebooks and smart mobiles, it is a reasonable requirement that modelers use such devices to models. However, there are few modeling tools that take full advantage of the devices. This paper discusses a Web based UML modeling tool with touch screens, based on our full analysis of human-machine interaction modes for software modeling. The tool provides more input means, i.e. combining gesture input and traditional keyboard and mouse input, and supports cross-platforms modeling by using HTML 5.

Cloud computing encourages application to migrate into it for economic of scale, where they rent shared resources to deliver services. Service Level Agreements(SLA) plays an important role in assisting various applications providing high-quality services to end users in cloud's complex and uncertain environments. Most of the existing work tries to support application claimed quality by help cloud make decisions of computing resources allocation during runtime. In this paper, we propose an approach for applications to maintain quality requirements by runtime cache adjustment in consideration of service level objectives (SLOs) and unpredictable workload in cloud, which can be taken as a complement to the existing work. Our approach includes application SLO modeling and mapping to monitor metrics during runtime, and an algorithm to adapting caches according to runtime status and SLOs. The approach has been applied to a real-world SNS application which proves effectiveness of our approach.

Estimating the cost is important for cloud application developers to services in clouds, and becomes even important when it needs remaining a certain service level at the same time. Though currently much work has been down to predict cost and performance of cloud applications, most of them perform either before application design or after the application construction, which leads to either imprecise estimation or irreparable design fault. In this paper, we propose an approach to estimate the cost of running typical applications in Amazon Web Service (AWS) cloud during design phase. We propose an UML Activity-extended model (AeModel) to describe execution of application service and introduce an extraction algorithm to extract information contained in the AeModel automatically. We propose a cost model on AWS, which can help developers to estimate operating cost during design phase and satisfy performance needs, with an algorithm to produce suitable purchase solutions automatically. We perform case studies using a web-based business application to show effectiveness of our approach, and find that our approach can help developers lessen cost by adjusting application models.

Recently model transformations quickly become large size and complex in structure. It is necessary to apply the modeling technique when developing transformations to manage the complexity. The paper proposes VisTML - a visual modeling language for model transformations. It provides six diagrams, which cover the whole lifecycle of transformation development and enable us to specify a transformation from various views and in different phases, i.e. the goal diagram, the transformation diagram, the rule diagram, the composition diagram, the test diagram, and the deployment diagram. We also provide a tool support and a real case of VisTML to illustrate the feasibility of VisTML. Besides, a comparison of VisTML, UML, and transML is presented at last.