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Modern software is often realized as a modular combination of subsystems for, e. g.,
knowledge management, visualization, verification, or the interaction with users. As
a result, software libraries from possibly different programming languages have to
work together. Even more complex the case is if different programming paradigms
have to be combined. This type of diversification of programming languages and
paradigms in just one software application can only be mastered by mechanisms
for a seamless integration of the involved programming languages. However, the
integration of the common logic programming language Prolog and the popular
object-oriented programming language Java is complicated by various interoperability
problems which stem on the one hand from the paradigmatic gap between the
programming languages, and on the other hand, from the diversity of the available
Prolog systems.
The subject of the thesis is the investigation of novel mechanisms for the integration
of logic programming in Prolog and object–oriented programming in Java. We are
particularly interested in an object–oriented, uniform approach which is not specific
to just one Prolog system. Therefore, we have first identified several important
criteria for the seamless integration of Prolog and Java from the object–oriented
perspective. The main contribution of the thesis is a novel integration framework
called the Connector Architecture for Prolog and Java (CAPJa). The framework is
completely implemented in Java and imposes no modifications to the Java Virtual
Machine or Prolog. CAPJa provides a semi–automated mechanism for the integration
of Prolog predicates into Java. For compact, readable, and object–oriented
queries to Prolog, CAPJa exploits lambda expressions with conditional and relational
operators in Java. The communication between Java and Prolog is based
on a fully automated mapping of Java objects to Prolog terms, and vice versa. In
Java, an extensible system of gateways provides connectivity with various Prolog
system and, moreover, makes any connected Prolog system easily interchangeable,
without major adaption in Java.
This thesis contributes to several issues in the context of SDN and NFV, with an emphasis on performance and management.
The main contributions are guide lines for operators migrating to software-based networks, as well as an analytical model for the packet processing in a Linux system using the Kernel NAPI.
The progress which has been made in semiconductor chip production in recent years enables a multitude of cores on a single die. However, due to further decreasing structure sizes, fault tolerance and energy consumption will represent key challenges. Furthermore, an efficient communication infrastructure is indispensable due to the high parallelism at those systems. The predominant communication system at such highly parallel systems is a Network on Chip (NoC). The focus of this thesis is on NoCs which are based on deflection routing. In this context, contributions are made to two domains, fault tolerance and dimensioning of the optimal link width. Both aspects are essential for the application of reliable, energy efficient, and deflection routing based NoCs.
It is expected that future semiconductor systems have to cope with high fault probabilities. The inherently given high connectivity of most NoC topologies can be exploited to tolerate the breakdown of links and other components. In this thesis, a fault-tolerant router architecture has been developed, which stands out for the deployed interconnection architecture and the method to overcome complex fault situations. The presented simulation results show, all data packets arrive at their destination, even at high fault probabilities. In contrast to routing table based architectures, the hardware costs of the herein presented architecture are lower and, in particular, independent of the number of components in the network.
Besides fault tolerance, hardware costs and energy efficiency are of great importance. The utilized link width has a decisive influence on these aspects. In particular, at deflection routing based NoCs, over- and under-sizing of the link width leads to unnecessary high hardware costs and bad performance, respectively. In the second part of this thesis, the optimal link width at deflection routing based NoCs is investigated. Additionally, a method to reduce the link width is introduced. Simulation and synthesis results show, the herein presented method allows a significant reduction of hardware costs at comparable performance.
Nowadays, data centers are becoming increasingly dynamic due to the common adoption of virtualization technologies. Systems can scale their capacity on demand by growing and shrinking their resources dynamically based on the current load. However, the complexity and performance of modern data centers is influenced not only by the software architecture, middleware, and computing resources, but also by network virtualization, network protocols, network services, and configuration. The field of network virtualization is not as mature as server virtualization and there are multiple competing approaches and technologies. Performance modeling and prediction techniques provide a powerful tool to analyze the performance of modern data centers. However, given the wide variety of network virtualization approaches, no common approach exists for modeling and evaluating the performance of virtualized networks.
The performance community has proposed multiple formalisms and models for evaluating the performance of infrastructures based on different network virtualization technologies. The existing performance models can be divided into two main categories: coarse-grained analytical models and highly-detailed simulation models. Analytical performance models are normally defined at a high level of abstraction and thus they abstract many details of the real network and therefore have limited predictive power. On the other hand, simulation models are normally focused on a selected networking technology and take into account many specific performance influencing factors, resulting in detailed models that are tightly bound to a given technology, infrastructure setup, or to a given protocol stack.
Existing models are inflexible, that means, they provide a single solution method without providing means for the user to influence the solution accuracy and solution overhead. To allow for flexibility in the performance prediction, the user is required to build multiple different performance models obtaining multiple performance predictions. Each performance prediction may then have different focus, different performance metrics, prediction accuracy, and solving time.
The goal of this thesis is to develop a modeling approach that does not require the user to have experience in any of the applied performance modeling formalisms. The approach offers the flexibility in the modeling and analysis by balancing between: (a) generic character and low overhead of coarse-grained analytical models, and (b) the more detailed simulation models with higher prediction accuracy.
The contributions of this thesis intersect with technologies and research areas, such as: software engineering, model-driven software development, domain-specific modeling, performance modeling and prediction, networking and data center networks, network virtualization, Software-Defined Networking (SDN), Network Function Virtualization (NFV). The main contributions of this thesis compose the Descartes Network Infrastructure (DNI) approach and include:
• Novel modeling abstractions for virtualized network infrastructures. This includes two meta-models that define modeling languages for modeling data center network performance. The DNI and miniDNI meta-models provide means for representing network infrastructures at two different abstraction levels. Regardless of which variant of the DNI meta-model is used, the modeling language provides generic modeling elements allowing to describe the majority of existing and future network technologies, while at the same time abstracting factors that have low influence on the overall performance. I focus on SDN and NFV as examples of modern virtualization technologies.
• Network deployment meta-model—an interface between DNI and other meta- models that allows to define mapping between DNI and other descriptive models. The integration with other domain-specific models allows capturing behaviors that are not reflected in the DNI model, for example, software bottlenecks, server virtualization, and middleware overheads.
• Flexible model solving with model transformations. The transformations enable solving a DNI model by transforming it into a predictive model. The model transformations vary in size and complexity depending on the amount of data abstracted in the transformation process and provided to the solver. In this thesis, I contribute six transformations that transform DNI models into various predictive models based on the following modeling formalisms: (a) OMNeT++ simulation, (b) Queueing Petri Nets (QPNs), (c) Layered Queueing Networks (LQNs). For each of these formalisms, multiple predictive models are generated (e.g., models with different level of detail): (a) two for OMNeT++, (b) two for QPNs, (c) two for LQNs. Some predictive models can be solved using multiple alternative solvers resulting in up to ten different automated solving methods for a single DNI model.
• A model extraction method that supports the modeler in the modeling process by automatically prefilling the DNI model with the network traffic data. The contributed traffic profile abstraction and optimization method provides a trade-off by balancing between the size and the level of detail of the extracted profiles.
• A method for selecting feasible solving methods for a DNI model. The method proposes a set of solvers based on trade-off analysis characterizing each transformation with respect to various parameters such as its specific limitations, expected prediction accuracy, expected run-time, required resources in terms of CPU and memory consumption, and scalability.
• An evaluation of the approach in the context of two realistic systems. I evaluate the approach with focus on such factors like: prediction of network capacity and interface throughput, applicability, flexibility in trading-off between prediction accuracy and solving time. Despite not focusing on the maximization of the prediction accuracy, I demonstrate that in the majority of cases, the prediction error is low—up to 20% for uncalibrated models and up to 10% for calibrated models depending on the solving technique.
In summary, this thesis presents the first approach to flexible run-time performance prediction in data center networks, including network based on SDN. It provides ability to flexibly balance between performance prediction accuracy and solving overhead. The approach provides the following key benefits:
• It is possible to predict the impact of changes in the data center network on the performance. The changes include: changes in network topology, hardware configuration, traffic load, and applications deployment.
• DNI can successfully model and predict the performance of multiple different of network infrastructures including proactive SDN scenarios.
• The prediction process is flexible, that is, it provides balance between the granularity of the predictive models and the solving time. The decreased prediction accuracy is usually rewarded with savings of the solving time and consumption of resources required for solving.
• The users are enabled to conduct performance analysis using multiple different prediction methods without requiring the expertise and experience in each of the modeling formalisms.
The components of the DNI approach can be also applied to scenarios that are not considered in this thesis. The approach is generalizable and applicable for the following examples: (a) networks outside of data centers may be analyzed with DNI as long as the background traffic profile is known; (b) uncalibrated DNI models may serve as a basis for design-time performance analysis; (c) the method for extracting and compacting of traffic profiles may be used for other, non-network workloads as well.
While teleoperation of technical highly sophisticated systems has already been a wide field of research, especially for space and robotics applications, the automation industry has not yet benefited from its results. Besides the established fields of application, also production lines with industrial robots and the surrounding plant components are in need of being remotely accessible. This is especially critical for maintenance or if an unexpected problem cannot be solved by the local specialists.
Special machine manufacturers, especially robotics companies, sell their technology worldwide. Some factories, for example in emerging economies, lack qualified personnel for repair and maintenance tasks. When a severe failure occurs, an expert of the manufacturer needs to fly there, which leads to long down times of the machine or even the whole production line. With the development of data networks, a huge part of those travels can be omitted, if appropriate teleoperation equipment is provided.
This thesis describes the development of a telemaintenance system, which was established in an active production line for research purposes. The customer production site of Braun in Marktheidenfeld, a factory which belongs to Procter & Gamble, consists of a six-axis cartesian industrial robot by KUKA Industries, a two-component injection molding system and an assembly unit. The plant produces plastic parts for electric toothbrushes.
In the research projects "MainTelRob" and "Bayern.digital", during which this plant was utilised, the Zentrum für Telematik e.V. (ZfT) and its project partners develop novel technical approaches and procedures for modern telemaintenance. The term "telemaintenance" hereby refers to the integration of computer science and communication technologies into the maintenance strategy. It is particularly interesting for high-grade capital-intensive goods like industrial robots. Typical telemaintenance tasks are for example the analysis of a robot failure or difficult repair operations. The service department of KUKA Industries is responsible for the worldwide distributed customers who own more than one robot. Currently such tasks are offered via phone support and service staff which travels abroad. They want to expand their service activities on telemaintenance and struggle with the high demands of teleoperation especially regarding security infrastructure. In addition, the facility in Marktheidenfeld has to keep up with the high international standards of Procter & Gamble and wants to minimize machine downtimes. Like 71.6 % of all German companies, P&G sees a huge potential for early information on their production system, but complains about the insufficient quality and the lack of currentness of data.
The main research focus of this work lies on the human machine interface for all human tasks in a telemaintenance setup. This thesis provides own work in the use of a mobile device in context of maintenance, describes new tools on asynchronous remote analysis and puts all parts together in an integrated telemaintenance infrastructure. With the help of Augmented Reality, the user performance and satisfaction could be raised. A special regard is put upon the situation awareness of the remote expert realized by different camera viewpoints. In detail the work consists of:
- Support of maintenance tasks with a mobile device
- Development and evaluation of a context-aware inspection tool
- Comparison of a new touch-based mobile robot programming device to the former teach pendant
- Study on Augmented Reality support for repair tasks with a mobile device
- Condition monitoring for a specific plant with industrial robot
- Human computer interaction for remote analysis of a single plant cycle
- A big data analysis tool for a multitude of cycles and similar plants
- 3D process visualization for a specific plant cycle with additional virtual information
- Network architecture in hardware, software and network infrastructure
- Mobile device computer supported collaborative work for telemaintenance
- Motor exchange telemaintenance example in running production environment
- Augmented reality supported remote plant visualization for better situation awareness
3D point clouds are a de facto standard for 3D documentation and modelling. The advances in laser scanning technology broadens the usability and access to 3D measurement systems. 3D point clouds are used in many disciplines such as robotics, 3D modelling, archeology and surveying. Scanners are able to acquire up to a million of points per second to represent the environment with a dense point cloud. This represents the captured environment with a very high degree of detail. The combination of laser scanning technology with photography adds color information to the point clouds. Thus the environment is represented more realistically. Full 3D models of environments, without any occlusion, require multiple scans. Merging point clouds is a challenging process. This thesis presents methods for point cloud registration based on the panorama images generated from the scans. Image representation of point clouds introduces 2D image processing methods to 3D point clouds. Several projection methods for the generation of panorama maps of point clouds are presented in this thesis. Additionally, methods for point cloud reduction and compression based on the panorama maps are proposed. Due to the large amounts of data generated from the 3D measurement systems these methods are necessary to improve the point cloud processing, transmission and archiving. This thesis introduces point cloud processing methods as a novel framework for the digitisation of archeological excavations. The framework replaces the conventional documentation methods for excavation sites. It employs point clouds for the generation of the digital documentation of an excavation with the help of an archeologist on-site. The 3D point cloud is used not only for data representation but also for analysis and knowledge generation. Finally, this thesis presents an autonomous indoor mobile mapping system. The mapping system focuses on the sensor placement planning method. Capturing a complete environment requires several scans. The sensor placement planning method solves for the minimum required scans to digitise large environments. Combining this method with a navigation system on a mobile robot platform enables it to acquire data fully autonomously. This thesis introduces a novel hole detection method for point clouds to detect obscured parts of a captured environment. The sensor placement planning method selects the next scan position with the most coverage of the obscured environment. This reduces the required number of scans. The navigation system on the robot platform consist of path planning, path following and obstacle avoidance. This guarantees the safe navigation of the mobile robot platform between the scan positions. The sensor placement planning method is designed as a stand alone process that could be used with a mobile robot platform for autonomous mapping of an environment or as an assistant tool for the surveyor on scanning projects.
The thesis focuses on Quality of Experience (QoE) of HTTP adaptive video streaming (HAS) and traffic management in access networks to improve the QoE of HAS. First, the QoE impact of adaptation parameters and time on layer was investigated with subjective crowdsourcing studies. The results were used to compute a QoE-optimal adaptation strategy for given video and network conditions. This allows video service providers to develop and benchmark improved adaptation logics for HAS. Furthermore, the thesis investigated concepts to monitor video QoE on application and network layer, which can be used by network providers in the QoE-aware traffic management cycle. Moreover, an analytic and simulative performance evaluation of QoE-aware traffic management on a bottleneck link was conducted. Finally, the thesis investigated socially-aware traffic management for HAS via Wi-Fi offloading of mobile HAS flows. A model for the distribution of public Wi-Fi hotspots and a platform for socially-aware traffic management on private home routers was presented. A simulative performance evaluation investigated the impact of Wi-Fi offloading on the QoE and energy consumption of mobile HAS.
Content Delivery Networks (CDNs) are networks that distribute content in the Internet. CDNs are increasingly responsible for the largest share of traffic in the Internet. CDNs distribute popular content to caches in many geographical areas to save bandwidth by avoiding unnecessary multihop retransmission. By bringing the content geographically closer to the user, CDNs also reduce the latency of the services.
Besides end users and content providers, which require high availability of high quality content, CDN providers and Internet Service Providers (ISPs) are interested in an efficient operation of CDNs. In order to ensure an efficient replication of the content, CDN providers have a network of (globally) distributed interconnected datacenters at different points of presence (PoPs). ISPs aim to provide reliable and high speed Internet access. They try to keep the load on the network low and to reduce cost for connectivity with other ISPs.
The increasing number of mobile devices such as smart phones and tablets, high definition video content and high resolution displays result in a continuous growth in mobile traffic. This growth in mobile traffic is further accelerated by newly emerging services, such as mobile live streaming and broadcasting services. The steep increase in mobile traffic is expected to reach by 2018 roughly 60% of total network traffic, the majority of which will be video. To handle the growth in mobile networks, the next generation of 5G mobile networks is designed to have higher access rates and an increased densification of the network infrastructure. With the explosion of access rates and number of base stations the backhaul of wireless networks will become congested.
To reduce the load on the backhaul, the research community suggests installing local caches in gateway routers between the wireless network and the Internet, in base stations of different sizes, and in end-user devices. The local deployment of caches allows keeping the traffic within the ISPs network. The caches are organized in a hierarchy, where caches in the lowest tier are requested first. The request is forwarded to the next tier, if the requested object is not found. Appropriate evaluation methods are required to optimally dimension the caches dependent on the traffic characteristics and the available resources. Additionally methods are necessary that allow performance evaluation of backhaul bandwidth aggregation systems, which further reduce the load on the backhaul.
This thesis analyses CDNs utilizing locally available resources and develops the following evaluations and optimization approaches: Characterization of CDNs and distribution of resources in the Internet, analysis and optimization of hierarchical caching systems with bandwidth constraints and performance evaluation of bandwidth aggregation systems.
The field of genetics faces a lot of challenges and opportunities in both research and diagnostics due to the rise of next generation sequencing (NGS), a technology that allows to sequence DNA increasingly fast and cheap.
NGS is not only used to analyze DNA, but also RNA, which is a very similar molecule also present in the cell, in both cases producing large amounts of data.
The big amount of data raises both infrastructure and usability problems, as powerful computing infrastructures are required and there are many manual steps in the data analysis which are complicated to execute.
Both of those problems limit the use of NGS in the clinic and research, by producing a bottleneck both computationally and in terms of manpower, as for many analyses geneticists lack the required computing skills.
Over the course of this thesis we investigated how computer science can help to improve this situation to reduce the complexity of this type of analysis.
We looked at how to make the analysis more accessible to increase the number of people that can perform OMICS data analysis (OMICS groups various genomics data-sources).
To approach this problem, we developed a graphical NGS data analysis pipeline aimed at a diagnostics environment while still being useful in research in close collaboration with the Human Genetics Department at the University of Würzburg.
The pipeline has been used in various research papers on covering subjects, including works with direct author participation in genomics, transcriptomics as well as epigenomics.
To further validate the graphical pipeline, a user survey was carried out which confirmed that it lowers the complexity of OMICS data analysis.
We also studied how the data analysis can be improved in terms of computing infrastructure by improving the performance of certain analysis steps.
We did this both in terms of speed improvements on a single computer (with notably variant calling being faster by up to 18 times), as well as with distributed computing to better use an existing infrastructure.
The improvements were integrated into the previously described graphical pipeline, which itself also was focused on low resource usage.
As a major contribution and to help with future development of parallel and distributed applications, for the usage in genetics or otherwise, we also looked at how to make it easier to develop such applications.
Based on the parallel object programming model (POP), we created a Java language extension called POP-Java, which allows for easy and transparent distribution of objects.
Through this development, we brought the POP model to the cloud, Hadoop clusters and present a new collaborative distributed computing model called FriendComputing.
The advances made in the different domains of this thesis have been published in various works specified in this document.
Multimodal interfaces (MMIs) are a promising human-computer interaction paradigm.
They are feasible for a wide rang of environments, yet they are especially suited if interactions are spatially and temporally grounded with an environment in which the user is (physically) situated.
Real-time interactive systems (RISs) are technical realizations for situated interaction environments, originating from application areas like virtual reality, mixed reality, human-robot interaction, and computer games.
RISs include various dedicated processing-, simulation-, and rendering subsystems which collectively maintain a real-time simulation of a coherent application state.
They thus fulfil the complex functional requirements of their application areas. Two contradicting principles determine the architecture of RISs: coupling and cohesion.
On the one hand, RIS subsystems commonly use specific data structures for multiple purposes to guarantee performance and rely on close semantic and temporal coupling between each other to maintain consistency.
This coupling is exacerbated if the integration of artificial intelligence (AI) methods is necessary, such as for realizing MMIs.
On the other hand, software qualities like reusability and modifiability call for a decoupling of subsystems and architectural elements with single well-defined purposes, i.e., high cohesion.
Systems predominantly favour performance and consistency over reusability and modifiability to handle this contradiction.
They thus accept low maintainability in general and hindered scientific progress in the long-term.
This thesis presents six semantics-based techniques that extend the established entity-component system (ECS) pattern and pose a solution to this contradiction without sacrificing maintainability: semantic grounding, a semantic entity-component state, grounded actions, semantic queries, code from semantics, and decoupling by semantics.
The extension solves the ECS pattern's runtime type deficit, improves component granularity, facilitates access to entity properties outside a subsystem's component association, incorporates a concept to semantically describe behavior as complement to the state representation, and enables compatibility even between RISs.
The presented reference implementation Simulator X validates the feasibility of the six techniques and may be (re)used by other researchers due to its availability under an open-source licence.
It includes a repertoire of common multimodal input processing steps that showcase the particular adequacy of the six techniques for such processing.
The repertoire adds up to the integrated multimodal processing framework miPro, making Simulator X a RIS platform with explicit MMI support.
The six semantics-based techniques as well as the reference implementation are validated by four expert reviews, multiple proof of concept prototypes, and two explorative studies.
Informal insights gathered throughout the design and development supplement this assessment in the form of lessons learned meant to aid future development in the area.
Enterprise applications in virtualized data centers are often subject to time-varying workloads, i.e., the load intensity and request mix change over time, due to seasonal patterns and trends, or unpredictable bursts in user requests. Varying workloads result in frequently changing resource demands to the underlying hardware infrastructure. Virtualization technologies enable sharing and on-demand allocation of hardware resources between multiple applications. In this context, the resource allocations to virtualized applications should be continuously adapted in an elastic fashion, so that "at each point in time the available resources match the current demand as closely as possible" (Herbst el al., 2013). Autonomic approaches to resource management promise significant increases in resource efficiency while avoiding violations of performance and availability requirements during peak workloads.
Traditional approaches for autonomic resource management use threshold-based rules (e.g., Amazon EC2) that execute pre-defined reconfiguration actions when a metric reaches a certain threshold (e.g., high resource utilization or load imbalance). However, many business-critical applications are subject to Service-Level-Objectives defined on an application performance metric (e.g., response time or throughput). To determine thresholds so that the end-to-end application SLO is fulfilled poses a major challenge due to the complex relationship between the resource allocation to an application and the application performance. Furthermore, threshold-based approaches are inherently prone to an oscillating behavior resulting in unnecessary reconfigurations.
In order to overcome the deficiencies of threshold-based
approaches and enable a fully automated approach to dynamically control the resource allocations of virtualized applications, model-based approaches are required that can predict the impact of a reconfiguration on the application performance in advance. However, existing model-based approaches are severely limited in their learning capabilities. They either require complete performance models of the application as input, or use a pre-identified model structure and only learn certain model parameters from empirical data at run-time. The former requires high manual efforts and deep system knowledge to create the performance models. The latter does not provide the flexibility to capture the specifics of complex and heterogeneous system architectures.
This thesis presents a self-aware approach to the resource management in virtualized data centers. In this context, self-aware means that it automatically learns performance models of the application and the virtualized infrastructure and reasons based on these models to autonomically adapt the resource allocations in accordance with given application SLOs. Learning a performance model requires the extraction of the model structure representing the system architecture as well as the estimation of model parameters, such as resource demands. The estimation of resource demands is a key challenge as they cannot be observed directly in most systems.
The major scientific contributions of this thesis are:
- A reference architecture for online model learning in virtualized systems. Our reference architecture is based on a set of model extraction agents. Each agent focuses on specific tasks to automatically create and update model skeletons capturing its local knowledge of the system and collaborates with other agents to extract the structural parts of a global performance model of the system. We define different agent roles in the reference architecture and propose a model-based collaboration mechanism for the agents. The agents may be bundled within virtual appliances and may be tailored to include knowledge about the software stack deployed in a specific virtual appliance.
- An online method for the statistical estimation of resource demands. For a given request processed by an application, the resource time consumed for a specified resource within the system (e.g., CPU or I/O device), referred to as resource demand, is the total average time the resource is busy processing the request. A request could be any unit of work (e.g., web page request, database transaction, batch job) processed by the system. We provide a systematization of existing statistical approaches to resource demand estimation and conduct an extensive experimental comparison to evaluate the accuracy of these approaches. We propose a novel method to automatically select estimation approaches and demonstrate that it increases the robustness and accuracy of the estimated resource demands significantly.
- Model-based controllers for autonomic vertical scaling of virtualized applications. We design two controllers based on online model-based reasoning techniques in order to vertically scale applications at run-time in accordance with application SLOs. The controllers exploit the knowledge from the automatically extracted performance models when determining necessary reconfigurations. The first controller adds and removes virtual CPUs to an application depending on the current demand. It uses a layered performance model to also consider the physical resource contention when determining the required resources. The second controller adapts the resource allocations proactively to ensure the availability of the application during workload peaks and avoid reconfiguration during phases of high workload.
We demonstrate the applicability of our approach in current virtualized environments and show its effectiveness leading to significant increases in resource efficiency and improvements of the application performance and availability under time-varying workloads. The evaluation of our approach is based on two case studies representative of widely used enterprise applications in virtualized data centers. In our case studies, we were able to reduce the amount of required CPU resources by up to 23% and the number of reconfigurations by up to 95% compared to a rule-based approach while ensuring full compliance with application SLO. Furthermore, using workload forecasting techniques we were able to schedule expensive reconfigurations (e.g., changes to the memory size) during phases of load load and thus were able to reduce their impact on application availability by over 80% while significantly improving application performance compared to a reactive controller. The methods and techniques for resource demand estimation and vertical application scaling were developed and evaluated in close collaboration with VMware and Google.