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Volare mobile context-aware adaptation for the Cloud

As the explosive growth in the proliferation and use of mobile devices accelerates, more web service providers move their premises on the Cloud under the Software as a Service (SaaS) service model. Mobile environments present new challenges that Service Discovery methods developed for non-mobile environments cannot address. The requirements a mobile client device will have from internet services may change, even at runtime, due to variable context, which may include hardware resources, environmental variables (like network availability) and user preferences. Binding to a discovered service having QoS levels different from the ones imposed by current context and policy requirements may lead to low application performance, excessive consumption of mobile resources such as battery life and service disruption, especially for long lasting foreground applications like media-streaming, navigation etc. This thesis presents the Volare approach for performing parameter adaptation for service requests to Cloud services, in SaaS architecture. For this purpose, we introduce an adaptive mobile middleware solution that performs context-aware QoS parameter adaptation. When service discovery is initiated, the middleware calculates the optimal service requests QoS levels under the current context, policy requirements and goals and adapts the service request accordingly. At runtime, it can trigger dynamic service rediscovery following significant context changes, to ensure optimal binding. The adaptation logic is built through the characteristics of the declarative domain-specific Volare Adaptation Policy Specification Language (APSL). Key characteristics of this approach include two-level policy support (providing both device specific and application specific adaptation), integration of a User Preferences Model and high behavioral (parameter adaptation) variability, by allowing multiple weighted adaptation rules to influence each QoS variable. The Volare approach supports unanticipated quantitative long term performance goals (LTPGs) with finite horizons. A use case and a proof-of-concept implementation have been developed on cloud service discovery through a cloud service provider, as well as an appropriate case study, which demonstrates significant savings in battery consumption, provider data usage and monetary cost, compared to unadapted QoS service bindings, while consistently avoiding service disruptions caused by QoS levels that the device cannot support. In addition, adaptation policies using the Volare approach tend to increase in size, in a mostly linear fashion, instead of the combinatorial increase of more conventional situation-action approaches.