Dynamic Optimization of SLA-Based Services Scaling Rules

Current advanced cloud infrastructure management solutions allow scheduling actions for dynamically changing the number of running virtual machines (VMs). This approach, however, does not guarantee that the scheduled number of VMs will properly handle the actual user generated workload, especially if the user utilization patterns will change. We propose using a dynamically generated scaling model for the VMs containing the services of the distributed applications, which is able to react to the variations in the number of application users. We answer the following question: How to dynamically decide how many services of each type are needed in order to handle a larger workload within the same time constraints? We describe a mechanism for dynamically composing the SLAs for controlling the scaling of distributed services by combining data analysis mechanisms with application benchmarking using multiple VM configurations. Based on processing of multiple application benchmarks generated data sets we discover a set of service monitoring metrics able to predict critical Service Level Agreement (SLA) parameters. By combining this set of predictor metrics with a heuristic for selecting the appropriate scaling-out paths for the services of distributed applications, we show how SLA scaling rules can be inferred and then used for controlling the runtime scale-in and scale-out of distributed services. We validate our architecture and models by performing scaling experiments with a distributed application representative for the enterprise class of information systems. We show how dynamically generated SLAs can be successfully used for controlling the management of distributed services scaling.

Dynamic Activity-Related Incentives for Physical Activity.

The present studies adopted the theoretical framework of activity- and purpose-related incentives (Rheinberg, 2008) to explain the maintenance of physical activity. We hypothesized that activity-related incentives (e.g., “fun”) increase more than purpose-related incentives (e.g., “health”) between the initiation and maintenance phase of physical activity. Additionally, change in activity-related incentives was hypothesized to be a better predictor of maintenance of physical activity than change in purpose-related incentives. Two correlative field studies with rehabilitation patients (Study 1) and Nordic Walkers (Study 2) were conducted to test the hypotheses. Participants’ incentives of physical activity were measured at the beginning of exercising and two weeks (Study 1; T2) and three months (Study 2; T2) later. At T2, participants were asked for their current physical activity. Both studies showed a greater change of activity-related incentives than purpose-related incentives. Furthermore, change in activity-related incentives was more predictive of the maintenance of physical activity than change in purpose-related incentives. The results showed the important role of activity-related incentives in maintenance of physical activity. The theoretical contribution to physical activity maintenance research and practical implications for health promotion programs were discussed.

Combination of MRI and dynamic FET PET for initial glioma grading

AIM MRI and PET with 18F-fluoro-ethyl-tyrosine (FET) have been increasingly used to evaluate patients with gliomas. Our purpose was to assess the additive value of MR spectroscopy (MRS), diffusion imaging and dynamic FET-PET for glioma grading. PATIENTS, METHODS 38 patients (42 ± 15 aged, F/M: 0.46) with untreated histologically proven brain gliomas were included. All underwent conventional MRI, MRS, diffusion sequences, and FET-PET within 3±4 weeks. Performances of tumour FET time-activity-curve, early-to-middle SUVmax ratio, choline / creatine ratio and ADC histogram distribution pattern for gliomas grading were assessed, as compared to histology. Combination of these parameters and respective odds were also evaluated. RESULTS Tumour time-activity-curve reached the best accuracy (67%) when taken alone to distinguish between low and high-grade gliomas, followed by ADC histogram analysis (65%). Combination of time-activity-curve and ADC histogram analysis improved the sensitivity from 67% to 86% and the specificity from 63-67% to 100% (p < 0.008). On multivariate logistic regression analysis, negative slope of the tumour FET time-activity-curve however remains the best predictor of high-grade glioma (odds 7.6, SE 6.8, p = 0.022). CONCLUSION Combination of dynamic FET-PET and diffusion MRI reached good performance for gliomas grading. The use of FET-PET/MR may be highly relevant in the initial assessment of primary brain tumours.