Dynamic QoS optimization architecture for cloud-based DDDAS

Cloud computing urges the need for novel on-demand approaches, where the Quality of Service (QoS) requirements of cloud-based services can dynamically and adaptively evolve at runtime as Service Level Agreement (SLA) and environment changes. Given the unpredictable, dynamic and on-demand nature of the cloud, it would be unrealistic to assume that optimal QoS can be achieved at design time. As a result, there is an increasing need for dynamic and self- adaptive QoS optimization solutions to respond to dynamic changes in SLA and the environment. In this context, we posit that the challenge of self-adaptive QoS optimization encompasses two dynamics, which are related to QoS sensitivity and conflicting objectives at runtime. We propose novel design of a dynamic data-driven architecture for optimizing QoS influenced by those dynamics. The architecture leverages on DDDAS primitives by employing distributed simulations and symbiotic feedback loops, to dynamically adapt decision making metaheuristics, which optimizes for QoS tradeoffs in cloud-based systems. We use a scenario to exemplify and evaluate the approach

Big Data Analytics for QoS Prediction Through Probabilistic Model Checking

As competitiveness increases, being able to guaranting QoS of delivered services is key for business success. It is thus of paramount importance the ability to continuously monitor the workflow providing a service and to timely recognize breaches in the agreed QoS level. The ideal condition would be the possibility to anticipate, thus predict, a breach and operate to avoid it, or at least to mitigate its effects. In this paper we propose a model checking based approach to predict QoS of a formally described process. The continous model checking is enabled by the usage of a parametrized model of the monitored system, where the actual value of parameters is continuously evaluated and updated by means of big data tools. The paper also describes a prototype implementation of the approach and shows its usage in a case study.Comment: EDCC-2014, BIG4CIP-2014, Big Data Analytics, QoS Prediction, Model Checking, SLA compliance monitorin

Symbiotic and sensitivity-aware architecture for globally-optimal benefit in self-adaptive cloud

Due to the uncertain and dynamic demand for Quality of Service (QoS) in cloud-based systems, engineering self-adaptivity in cloud architectures require novel approaches to support on-demand elasticity. The architecture should dynamically select an elastic strategy, which optimizes the global benefit for QoS and cost objectives for all cloud-based services. The architecture shall also provide mechanisms for reaching the strategy with minimal overhead. However, the challenge in the cloud is that the nature of objectives (e.g., throughput and the required cost) and QoS interference could cause overlapping sensitivity amongst intra-and inter-services objectives, which leads to objective-dependency (i.e., conflicted or harmonic) during optimization. In this paper, we propose a symbiotic and sensitivity-aware architecture for optimizing global-benefit with reduced overhead in the cloud. The architecture dynamically partitions QoS and cost objectives into sensitivity independent regions, where the local optimums are achieved. In addition, the architecture realizes the concept of symbiotic feedback loop, which is a bio-directional self-adaptive action that not only allows to dynamically monitor and adapt the managed services by scaling to their demand, but also to adaptively consolidate the managing system by re-partitioning the regions based on symptoms. We implement the architecture as a prototype extending on decentralized MAPE loop by introducing an Adaptor component. We then experimentally analyze and evaluate our architecture using hypothetical scenarios. The results reveal that our symbiotic and sensitivity-aware architecture is able to produce even better global benefit and smaller overhead in contrast to other non sensitivity-aware architectures

Advancing Intra-operative Precision: Dynamic Data-Driven Non-Rigid Registration for Enhanced Brain Tumor Resection in Image-Guided Neurosurgery

During neurosurgery, medical images of the brain are used to locate tumors and critical structures, but brain tissue shifts make pre-operative images unreliable for accurate removal of tumors. Intra-operative imaging can track these deformations but is not a substitute for pre-operative data. To address this, we use Dynamic Data-Driven Non-Rigid Registration (NRR), a complex and time-consuming image processing operation that adjusts the pre-operative image data to account for intra-operative brain shift. Our review explores a specific NRR method for registering brain MRI during image-guided neurosurgery and examines various strategies for improving the accuracy and speed of the NRR method. We demonstrate that our implementation enables NRR results to be delivered within clinical time constraints while leveraging Distributed Computing and Machine Learning to enhance registration accuracy by identifying optimal parameters for the NRR method. Additionally, we highlight challenges associated with its use in the operating room

Self-aware software architecture style and patterns for cloud-based applications

Modern cloud-reliant software systems are faced with the problem of cloud service providers violating their Service Level Agreement (SLA) claims. Given the large pool of cloud providers and their instability, cloud applications are expected to cope with these dynamics autonomously. This thesis investigates an approach for designing self-adaptive cloud architectures using a systematic methodology that guides the architect while designing cloud applications. The approach termed $Self-aware$ $Architecture$ $Pattern$ promotes fine-grained representation of architectural concerns to aid design-time analysis of risks and trade-offs. To support the coordination and control of architectural components in decentralised self-aware cloud applications, we propose a $Reputation-aware$ $posted$ $offer$ $market$ $coordination$ $mechanism$. The mechanism builds on the classic posted offer market mechanism and extends it to track behaviour of unreliable cloud services. The self-aware cloud architecture and its reputation-aware coordination mechanism are quantitatively evaluated within the context of an Online Shopping application using synthetic and realistic workload datasets under various configurations (failure, scale, resilience levels etc.). Additionally, we qualitatively evaluated our self-aware approach against two classic self-adaptive architecture styles using independent experts' judgment, to unveil its strengths and weaknesses relative to these styles

On the cloud deployment of a session abstraction for service/data aggregation

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaThe global cyber-infrastructure comprehends a growing number of resources, spanning over several abstraction layers. These resources, which can include wireless sensor devices or mobile networks, share common requirements such as richer inter-connection capabilities and increasing data consumption demands. Additionally, the service model is now widely spread, supporting the development and execution of distributed applications. In this context, new challenges are emerging around the “big data” topic. These challenges include service access optimizations, such as data-access context sharing, more efficient data filtering/ aggregation mechanisms, and adaptable service access models that can respond to context changes. The service access characteristics can be aggregated to capture specific interaction models. Moreover, ubiquitous service access is a growing requirement, particularly regarding mobile clients such as tablets and smartphones. The Session concept aggregates the service access characteristics, creating specific interaction models, which can then be re-used in similar contexts. Existing Session abstraction implementations also allow dynamic reconfigurations of these interaction models, so that the model can adapt to context changes, based on service, client or underlying communication medium variables. Cloud computing on the other hand, provides ubiquitous access, along with large data persistence and processing services. This thesis proposes a Session abstraction implementation, deployed on a Cloud platform, in the form of a middleware. This middleware captures rich/dynamic interaction models between users with similar interests, and provides a generic mechanism for interacting with datasources based on multiple protocols. Such an abstraction contextualizes service/users interactions, can be reused by other users in similar contexts. This Session implementation also permits data persistence by saving all data in transit in a Cloud-based repository, The aforementioned middleware delivers richer datasource-access interaction models, dynamic reconfigurations, and allows the integration of heterogenous datasources. The solution also provides ubiquitous access, allowing client connections from standard Web browsers or Android based mobile devices