Dynamic SLA management with forecasting using multi-objective optimization

Cost-efficient operation while satisfying performance and availability guarantees in Service Level Agreements (SLAs) is a challenge for Cloud Computing, as these are potentially conflicting objectives. We present a framework for SLA management based on multi-objective optimization. The framework features a forecasting model for determining the best virtual machine-to-host allocation given the need to minimize SLA violations, energy consumption and resource wasting. A comprehensive SLA management solution is proposed that uses event processing for monitoring and enables dynamic provisioning of virtual machines onto the physical infrastructure. We validated our implementation against serveral standard heuristics and were able to show that our approach is significantly better.

Adapting agricultural land management to climate change: a regional multi-objective optimization approach

In several regions of the world, climate change is expected to have severe impacts on agricultural systems. Changes in land management are one way to adapt to future climatic conditions, including land-use changes and local adjustments of agricultural practices. In previous studies, options for adaptation have mostly been explored by testing alternative scenarios. Systematic explorations of land management possibilities using optimization approaches were so far mainly restricted to studies of land and resource management under constant climatic conditions. In this study, we bridge this gap and exploit the benefits of multi-objective regional optimization for identifying optimum land management adaptations to climate change. We design a multi-objective optimization routine that integrates a generic crop model and considers two climate scenarios for 2050 in a meso-scale catchment on the Swiss Central Plateau with already limited water resources. The results indicate that adaptation will be necessary in the study area to cope with a decrease in productivity by 0–10 %, an increase in soil loss by 25–35 %, and an increase in N-leaching by 30–45 %. Adaptation options identified here exhibit conflicts between productivity and environmental goals, but compromises are possible. Necessary management changes include (i) adjustments of crop shares, i.e. increasing the proportion of early harvested winter cereals at the expense of irrigated spring crops, (ii) widespread use of reduced tillage, (iii) allocation of irrigated areas to soils with low water-retention capacity at lower elevations, and (iv) conversion of some pre-alpine grasslands to croplands.

The performance of single- and multi-proxy transfer functions (testate amoebae, bryophytes, vascular plants) for reconstructing mire surface wetness and pH

Peatlands are widely exploited archives of paleoenvironmental change. We developed and compared multiple transfer functions to infer peatland depth to the water table (DWT) and pH based on testate amoeba (percentages, or presence/absence), bryophyte presence/absence, and vascular plant presence/absence data from sub-alpine peatlands in the SE Swiss Alps in order to 1) compare the performance of single-proxy vs. multi-proxy models and 2) assess the performance of presence/absence models. Bootstrapping cross-validation showing the best performing single-proxy transfer functions for both DWT and pH were those based on bryophytes. The best performing transfer functions overall for DWT were those based on combined testate amoebae percentages, bryophytes and vascular plants; and, for pH, those based on testate amoebae and bryophytes. The comparison of DWT and pH inferred from testate amoeba percentages and presence/absence data showed similar general patterns but differences in the magnitude and timing of some shifts. These results show new directions for paleoenvironmental research, 1) suggesting that it is possible to build good-performing transfer functions using presence/absence data, although with some loss of accuracy, and 2) supporting the idea that multi-proxy inference models may improve paleoecological reconstruction. The performance of multi-proxy and single-proxy transfer functions should be further compared in paleoecological data.

SOP: Parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems

This paper presents a parallel surrogate-based global optimization method for computationally expensive objective functions that is more effective for larger numbers of processors. To reach this goal, we integrated concepts from multi-objective optimization and tabu search into, single objective, surrogate optimization. Our proposed derivative-free algorithm, called SOP, uses non-dominated sorting of points for which the expensive function has been previously evaluated. The two objectives are the expensive function value of the point and the minimum distance of the point to previously evaluated points. Based on the results of non-dominated sorting, P points from the sorted fronts are selected as centers from which many candidate points are generated by random perturbations. Based on surrogate approximation, the best candidate point is subsequently selected for expensive evaluation for each of the P centers, with simultaneous computation on P processors. Centers that previously did not generate good solutions are tabu with a given tenure. We show almost sure convergence of this algorithm under some conditions. The performance of SOP is compared with two RBF based methods. The test results show that SOP is an efficient method that can reduce time required to find a good near optimal solution. In a number of cases the efficiency of SOP is so good that SOP with 8 processors found an accurate answer in less wall-clock time than the other algorithms did with 32 processors.

Impact of treatment characteristics on response of different breast cancer phenotypes: pooled analysis of the German neo-adjuvant chemotherapy trials

Pathological complete response (pCR) to neoadjuvant treatment correlates with outcome in breast cancer. We determined whether characteristics of neoadjuvant therapy are associated with pCR. We used multi-level models, which accounted for heterogeneity in pCR across trials and trial arms, to analyze individual patient data from 3332 women included in 7 German neoadjuvant trials with uniform protocols. PCR was associated with an increase in number of chemotherapy cycles (odds ratio [OR] 1.2 for every two additional cycles; P = 0.009), with higher cumulative anthracycline doses (OR 1.6; P = 0.002), higher cumulative taxane doses (OR 1.6; P = 0.009), and with capecitabine containing regimens (OR 1.62; P = 0.022). Association of pCR with increase in number of cycles appeared more pronounced in hormone receptor (HR)-positive tumors (OR 1.35) than in HR-negative tumors (OR 1.04; P for interaction = 0.046). Effect of anthracycline dose was particularly pronounced in HER2-negative tumors (OR 1.61), compared to HER2-positive tumors (OR 0.83; P for interaction = 0.14). Simultaneous trastuzumab treatment in HER2-positive tumors increased odds of pCR 3.2-fold (P < 0.001). No association of pCR and number of trastuzumab cycles was found (OR 1.20, P = 0.39). Dosing characteristics appear important for successful treatment of breast cancer. Longer treatment, higher cumulative doses of anthracyclines and taxanes, and the addition of capecitabine and trastuzumab are associated with better response. Tailoring according to breast cancer phenotype might be possible: longer treatment in HR-positive tumors, higher cumulative anthracycline doses for HER2-negative tumors, shorter treatment at higher cumulative doses for triple-negative tumors, and limited number of preoperative trastuzumab cycles in HER2-positive tumors.

Individual and structural factors of member commitment in sports clubs

This article analyses the conditions influencing the commitment of members of sports clubs. It focuses not only on individual characteristics of members, but also on the corresponding structural conditions of sports clubs related to the individual decision to quit or continue their membership. The influences of both the individual and context levels on the commitment of members are estimated in different multi-level models. Results of these multi-level analyses indicate that commitment of members is not just an outcome of individual characteristics such as strong commitment to the club, positively perceived communication and cooperation, satisfaction with sports clubsʼ offers, or voluntary engagement. It is also influenced by club-specific structural conditions: commitment is more probable in rural sports clubs, and clubs who explicitly support sociability, whereas success-oriented sporting goals in clubs have a destabilizing effect.

Volunteering in sport clubs

Volunteers are the most important resource for non-profit sport clubs seeking to bolster their viability (e.g. sporting programs). Although many people do voluntary work in sport clubs, stable voluntary engagement can no longer be granted. This difficulty is confirmed by existing research across various European countries. From a club management point of view, a detailed understanding of how to attract volunteers and retain them in the long term is becoming a high priority. The purpose of this study is (1) to analyse the influence of individual characteristics and corresponding organisational conditions on volunteering in sports clubs as well as (2) to examine the decision-making processes in relation to implement effective strategies for recruiting volunteers. For the first perspective a multi-level framework for the investigation of the factors of voluntary engagement in sports clubs is developed. The individual and context factors are estimated in different multi-level models based on a sample of n = 1,434 sport club members from 36 sport clubs in Switzerland. Results indicate that volunteering is not just an outcome of individual characteristics such as lower workloads, higher income, children belonging to the sport club, longer club memberships, or a strong commitment to the club. It is also influenced by club-specific structural conditions; volunteering is more probable in rural sports clubs whereas growth-oriented goals in clubs have a destabilising effect. Concerning decision-making processes an in-depth analysis of recruitment practices for volunteers was conducted in nine selected sport clubs (case study design) based on the garbage can model. Results show that the decision-making processes are generally characterised by a reactive approach in which dominant actors try to handle personnel problems of recruitment in the administration and sport domains through routine formal committee work and informal networks. In addition, it proved possible to develop a typology that deliver an overview of different decision-making practices in terms of the specific interplay of the relevant components of process control (top-down vs. bottom-up) and problem processing (situational vs. systematic). Based on the findings some recommendations for volunteer management in sport clubs are worked out.

Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations

Multi-objective optimization algorithms aim at finding Pareto-optimal solutions. Recovering Pareto fronts or Pareto sets from a limited number of function evaluations are challenging problems. A popular approach in the case of expensive-to-evaluate functions is to appeal to metamodels. Kriging has been shown efficient as a base for sequential multi-objective optimization, notably through infill sampling criteria balancing exploitation and exploration such as the Expected Hypervolume Improvement. Here we consider Kriging metamodels not only for selecting new points, but as a tool for estimating the whole Pareto front and quantifying how much uncertainty remains on it at any stage of Kriging-based multi-objective optimization algorithms. Our approach relies on the Gaussian process interpretation of Kriging, and bases upon conditional simulations. Using concepts from random set theory, we propose to adapt the Vorob’ev expectation and deviation to capture the variability of the set of non-dominated points. Numerical experiments illustrate the potential of the proposed workflow, and it is shown on examples how Gaussian process simulations and the estimated Vorob’ev deviation can be used to monitor the ability of Kriging-based multi-objective optimization algorithms to accurately learn the Pareto front.

Does the (structural) context matter? Analysing individual and structural factors of members’ commitment to sports clubs

Introduction: Over the last decades, Swiss sports clubs have lost their "monopoly" in the market for sports-related services and increasingly are in competition with other sports providers. For many sport clubs long-term membership cannot be seen as a matter of course. Current research on sports clubs in Switzerland – as well as for other European countries – confirms the increasing difficulties in achieving long-term member commitment. Looking at recent findings of the Swiss sport clubs report (Lamprecht, Fischer & Stamm, 2012), it can be noted, that a decrease in memberships does not equally affect all clubs. There are sports clubs – because of their specific situational and structural conditions – that have few problems with member fluctuation, while other clubs show considerable declines in membership. Therefore, a clear understanding of individual and structural factors that trigger and sustain member commitment would help sports clubs to tackle this problem more effectively. This situation poses the question: What are the individual and structural determinants that influence the tendency to continue or to quit the membership? Methods: Existing research has extensively investigated the drivers of members’ commitment at an individual level. As commitment of members usually occurs within an organizational context, the characteristics of the organisation should be also considered. However, this context has been largely neglected in current research. This presentation addresses both the individual characteristics of members and the corresponding structural conditions of sports clubs resulting in a multi-level framework for the investigation of the factors of members’ commitment in sports clubs. The multilevel analysis grant a adequate handling of hierarchically structured data (e.g., Hox, 2002). The influences of both the individual and context level on the stability of memberships are estimated in multi-level models based on a sample of n = 1,434 sport club members from 36 sports clubs. Results: Results of these multi-level analyses indicate that commitment of members is not just an outcome of individual characteristics, such as strong identification with the club, positively perceived communication and cooperation, satisfaction with sports clubs’ offers, or voluntary engagement. It is also influenced by club-specific structural conditions: stable memberships are more probable in rural sports clubs, and in clubs that explicitly support sociability, whereas sporting-success oriented goals in clubs have a destabilizing effect. Discussion/Conclusion: The proposed multi-level framework and the multi-level analysis can open new perspectives for research concerning commitment of members to sports clubs and other topics and problems of sport organisation research, especially in assisting to understand individual behavior within organizational contexts. References: Hox, J. J. (2002). Multilevel analysis: Techniques and applications. Mahwah: Lawrence Erlbaum. Lamprecht, M., Fischer, A., & Stamm, H.-P. (2012). Die Schweizer Sportvereine – Strukturen, Leistungen, Herausforderungen. Zurich: Seismo.

Efficacy of motivational enhancement therapy on alcohol use disorders in patients with chronic hepatitis C: a randomized controlled trial

AIMS: To determine the efficacy of motivational enhancement therapy (MET) on alcohol use in patients with the hepatitis C virus (HCV) and an alcohol use disorder (AUD). DESIGN: Randomized, single-blind, controlled trial comparing MET to a control education condition with 6-month follow-up. SETTING: Patients were recruited from hepatitis clinics at the Minneapolis, Minnesota and Portland, Oregon Veterans Affairs Health Care Systems, USA. PARTICIPANTS AND INTERVENTION: Patients with HCV, an AUD and continued alcohol use (n = 139) were randomized to receive either MET (n = 70) or a control education condition (n = 69) over 3 months. MEASUREMENTS: Data were self-reported percentage of days abstinent from alcohol and number of standard alcohol drinks per week 6 months after randomization. FINDINGS: At baseline, subjects in MET had 34.98% days abstinent, which increased to 73.15% at 6 months compared to 34.63 and 59.49% for the control condition. Multi-level models examined changes in alcohol consumption between MET and control groups. Results showed a significant increase in percentage of days abstinent overall (F(1120.4)  = 28.04, P < 0.001) and a significant group × time effect (F(1119.9)  = 5.23, P = 0.024) with the MET group showing a greater increase in percentage of days abstinent at 6 months compared with the education control condition. There were no significant differences between groups for drinks per week. The effect size of the MET intervention was moderate (0.45) for percentage of days abstinent. CONCLUSION: Motivational enhancement therapy (MET) appears to increase the percentage of days abstinent in patients with chronic hepatitis C, alcohol use disorders and ongoing alcohol use. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

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.

Guidelines for WB3 Part III: Stakeholder workshop 2: Selection and decision on prevention and mitigation strategies to be implemented

These guidelines were developed in the context of working block 3 of the DESIRE project. They address the facilitators in the 18 DESIRE study sites and support them in conducting stakeholder workshops aiming at the selection and decision on mitigation strategies to be implemented in the study site context. The decision-making process is supported by a multi-objective decision support system (MODSS) Software called 'Facilitator'.

Economic analysis of soil conservation: Case studies from the highlands of Amhara Region, Ethiopia

Soil degradation is widespread in the Ethiopian Highlands. Its negative impacts on soil productivity contribute to the extreme poverty of the rural population. Soil conservation is propagated as a means of reducing soil erosion, however, it is a costly investment for small-scale farming households. The present study is an attempt to show whether or not selected mechanical Soil and Water Conservation (SWC) technologies are profitable from a farmer’s point of view. A financial Cost-Benefit Analysis (CBA) is carried out to assess whether or not the considered SWC technologies are profitable from a farmer’s point of view. The CBA is supplemented by an evaluation of aspects from the economic and institutional environment. Whether or not soil conservation is profitable from a farmer’s point of view depends on a broad range of factors from the ecological, economic, political, institutional and socio-cultural sphere and also depends on the technology and the prevailing farming system. Because these factors are closely interlinked, it is often not sufficient to change or influence one to make SWC profitable. Several recommendations are formulated with regard to improving the profitability of SWC investments from a farmer’s point of view. Because the reasons for unsustainable resource use are manifold and highly interlinked, only a multi-stakeholder, multi-level and multi-objective approach is likely to offer solutions that address the underlying problems adequately.

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.