Service Level Agreements-Driven Management of Distributed Applications in Cloud Computing Environments

Cloud Computing enables provisioning and distribution of highly scalable services in a reliable, on-demand and sustainable manner. However, objectives of managing enterprise distributed applications in cloud environments under Service Level Agreement (SLA) constraints lead to challenges for maintaining optimal resource control. Furthermore, conflicting objectives in management of cloud infrastructure and distributed applications might lead to violations of SLAs and inefficient use of hardware and software resources. This dissertation focusses on how SLAs can be used as an input to the cloud management system, increasing the efficiency of allocating resources, as well as that of infrastructure scaling. First, we present an extended SLA semantic model for modelling complex service-dependencies in distributed applications, and for enabling automated cloud infrastructure management operations. Second, we describe a multi-objective VM allocation algorithm for optimised resource allocation in infrastructure clouds. Third, we describe a method of discovering relations between the performance indicators of services belonging to distributed applications and then using these relations for building scaling rules that a CMS can use for automated management of VMs. Fourth, we introduce two novel VM-scaling algorithms, which optimally scale systems composed of VMs, based on given SLA performance constraints. All presented research works were implemented and tested using enterprise distributed applications.

Service level agreements-driven management of distributed applications in cloud computing environments

Advancements in cloud computing have enabled the proliferation of distributed applications, which require management and control of multiple services. However, without an efficient mechanism for scaling services in response to changing workload conditions, such as number of connected users, application performance might suffer, leading to violations of Service Level Agreements (SLA) and possible inefficient use of hardware resources. Combining dynamic application requirements with the increased use of virtualised computing resources creates a challenging resource Management context for application and cloud-infrastructure owners. In such complex environments, business entities use SLAs as a means for specifying quantitative and qualitative requirements of services. There are several challenges in running distributed enterprise applications in cloud environments, ranging from the instantiation of service VMs in the correct order using an adequate quantity of computing resources, to adapting the number of running services in response to varying external loads, such as number of users. The application owner is interested in finding the optimum amount of computing and network resources to use for ensuring that the performance requirements of all her/his applications are met. She/he is also interested in appropriately scaling the distributed services so that application performance guarantees are maintained even under dynamic workload conditions. Similarly, the infrastructure Providers are interested in optimally provisioning the virtual resources onto the available physical infrastructure so that her/his operational costs are minimized, while maximizing the performance of tenants’ applications. Motivated by the complexities associated with the management and scaling of distributed applications, while satisfying multiple objectives (related to both consumers and providers of cloud resources), this thesis proposes a cloud resource management platform able to dynamically provision and coordinate the various lifecycle actions on both virtual and physical cloud resources using semantically enriched SLAs. The system focuses on dynamic sizing (scaling) of virtual infrastructures composed of virtual machines (VM) bounded application services. We describe several algorithms for adapting the number of VMs allocated to the distributed application in response to changing workload conditions, based on SLA-defined performance guarantees. We also present a framework for dynamic composition of scaling rules for distributed service, which used benchmark-generated application Monitoring traces. We show how these scaling rules can be combined and included into semantic SLAs for controlling allocation of services. We also provide a detailed description of the multi-objective infrastructure resource allocation problem and various approaches to satisfying this problem. We present a resource management system based on a genetic algorithm, which performs allocation of virtual resources, while considering the optimization of multiple criteria. We prove that our approach significantly outperforms reactive VM-scaling algorithms as well as heuristic-based VM-allocation approaches.

A novel cell compatible impingement system to study in vitro drug absorption from dry powder aerosol formulations

A modified Astra type multistage liquid impinger (MSLI) with integrated bronchial cell monolayers was used to study deposition and subsequent drug absorption on in vitro models of the human airway epithelial barrier. Inverted cell culture of Calu-3 cells on the bottom side of cell culture filter inserts was integrated into a compendial MSLI. Upside down cultivation did not impair the barrier function, morphology and viability of Calu-3 cells. Size selective deposition with subsequent absorption was studied for three different commercially available dry powder formulations of salbutamol sulphate and budesonide. After deposition without size separation the absorption rates from the aerosol formulations differed but correlated with the size of the carrier lactose particles. However, after deposition in the MSLI, simulating relevant impaction and causing the separation of small drug crystals from the carrier lactose, the absorption rates of the three formulations were identical, confirming the bioequivalence of the three formulations.

CompAS: A new approach to commonality and variability analysis with applications in computer assisted orthopaedic surgery

In rapidly evolving domains such as Computer Assisted Orthopaedic Surgery (CAOS) emphasis is often put first on innovation and new functionality, rather than in developing the common infrastructure needed to support integration and reuse of these innovations. In fact, developing such an infrastructure is often considered to be a high-risk venture given the volatility of such a domain. We present CompAS, a method that exploits the very evolution of innovations in the domain to carry out the necessary quantitative and qualitative commonality and variability analysis, especially in the case of scarce system documentation. We show how our technique applies to the CAOS domain by using conference proceedings as a key source of information about the evolution of features in CAOS systems over a period of several years. We detect and classify evolution patterns to determine functional commonality and variability. We also identify non-functional requirements to help capture domain variability. We have validated our approach by evaluating the degree to which representative test systems can be covered by the common and variable features produced by our analysis.

State-of-the-art aortic imaging: Part II - applications in transcatheter aortic valve replacement and endovascular aortic aneurysm repair.

Transcatheter aortic valve replacement (TAVR) as well as thoracic and abdominal endovascular aortic repair (TEVAR and EVAR) rely on accurate pre- and postprocedural imaging. This review article discusses the application of imaging, including preprocedural assessment and measurements as well as postprocedural imaging of complications. Furthermore, the exciting perspective of computational fluid dynamics (CFD) based on cross-sectional imaging is presented. TAVR is a minimally invasive alternative for treatment of aortic valve stenosis in patients with high age and multiple comorbidities who cannot undergo traditional open surgical repair. Given the lack of direct visualization during the procedure, pre- and peri-procedural imaging forms an essential part of the intervention. Computed tomography angiography (CTA) is the imaging modality of choice for preprocedural planning. Routine postprocedural follow-up is performed by echocardiography to confirm treatment success and detect complications. EVAR and TEVAR are minimally invasive alternatives to open surgical repair of aortic pathologies. CTA constitutes the preferred imaging modality for both preoperative planning and postoperative follow-up including detection of endoleaks. Magnetic resonance imaging is an excellent alternative to CT for postoperative follow-up, and is especially beneficial for younger patients given the lack of radiation. Ultrasound is applied in screening and postoperative follow-up of abdominal aortic aneurysms, but cross-sectional imaging is required once abnormalities are detected. Contrast-enhanced ultrasound may be as sensitive as CTA in detecting endoleaks.

MR Spectroscopy and Spectroscopic Imaging for Evaluation of Skeletal Muscle Metabolism: Basics and Applications in Metabolic Diseases

Magnetic resonance spectroscopy (MRS) and spectroscopic imaging (MRSI) provide metabolic information on the musculoskeletal system, thus helping to understand the biochemical and pathophysiological nature of numerous diseases. In particular, MRS has been used to study the energy metabolism of muscular tissue since the very beginning of magnetic resonance examinations in humans when small-bore magnets for studies of the limbs became available. Even more than in other organs, the observation of non-proton-nuclei was important in muscle tissue. Spatial localization was less demanding in these studies, however, high temporal resolution was necessary to follow metabolism during exercise and recovery. The observation of high-energy phosphates during and after the application of workload gives insight into oxidative phosphorylation, a process that takes place in the mitochondria and characterizes impaired mitochondrial function. New applications in insulin-resistant patients followed the development of volume-selective 1H-MRS in whole-body magnets. Nowadays, multinuclear MRS and MRSI of the musculoskeletal system provide several windows to vital biochemical pathways noninvasively. It is shown how MRS and MRSI have been used in numerous diseases to characterize an involvement of the muscular metabolism.

DW-MRI of the urogenital tract: applications in oncology

Diffusion-weighted magnetic resonance imaging (DW-MRI) appears to hold promise as a non-invasive imaging modality in the detection of early microstructural and functional changes of different organs. DW-MRI is an imaging technique with a high sensitivity for the detection of a large variety of diseases in the urogenital tract. In kidneys, DW-MRI has shown promise for the characterization of solid lesions. Also in focal T1 hyperintense lesions DW-MRI was able to differentiate hemorrhagic cysts from tumours according to the lower apparent diffusion coefficient (ADC) values reported for renal cell carcinomas. Promising results were also published for the detection of prostate cancer. DW-MRI applied in addition to conventional T2-weighted imaging has been found to improve tumour detection. On a 3 T magnetic resonance unit ADC values were reported to be lower for tumours compared with the normal-appearing peripheral zone. The combined approach of T2-weighted imaging and DW-MRI also showed promising results for the detection of recurrent tumour in patients after radiation therapy. DW-MRI may improve the performance of conventional T2-weighted and contrast-enhanced MRI in the preoperative work-up of bladder cancer, as it may help in distinguishing superficial from muscle invasive bladder cancer, which is critical for patient management. Another challenging application of DW-MRI in the urogenital tract is the detection of pelvic lymph node metastases. As the ADC is generally reduced in malignant tumours and increased under inflammatory conditions, reduced ADC values were expected in patients with lymph node metastases.

Diffusion-weighted MRI in head and neck radiology: applications in oncology

Extracranial application of diffusion-weighted magnetic resonance imaging (MRI) has gained increasing importance in recent years. As a result of technical advances, this new non-invasive functional technique has also been applied in head and neck radiology for several clinical indications. In cancer imaging, diffusion-weighted MRI can be performed for tumour detection and characterization, monitoring of treatment response as well as the differentiation of recurrence and post-therapeutic changes after radiotherapy. Even for lymph node staging promising results have been reported recently. This review article provides overview of potential applications of diffusion-weighted MRI in head and neck with the main focus on its applications in oncology.

Stem cell-based therapeutic applications in retinal degenerative diseases

Retinal degenerative diseases that target photoreceptors or the adjacent retinal pigment epithelium (RPE) affect millions of people worldwide. Retinal degeneration (RD) is found in many different forms of retinal diseases including retinitis pigmentosa (RP), age-related macular degeneration (AMD), diabetic retinopathy, cataracts, and glaucoma. Effective treatment for retinal degeneration has been widely investigated. Gene-replacement therapy has been shown to improve visual function in inherited retinal disease. However, this treatment was less effective with advanced disease. Stem cell-based therapy is being pursued as a potential alternative approach in the treatment of retinal degenerative diseases. In this review, we will focus on stem cell-based therapies in the pipeline and summarize progress in treatment of retinal degenerative disease.