A Sensor-Actuator Model for Data Center Optimization

Cloud data centers commonly use virtualization technologies to provision compute capacity with a level of indirection between virtual machines and physical resources. In this paper we explore the use of that level of indirection as a means for autonomic data center configuration optimization and propose a sensor-actuator model to capture optimization-relevant relationships between data center events, monitored metrics (sensors data), and management actions (actuators). The model characterizes a wide spectrum of actions to help identify the suitability of different actions in specific situations, and outlines what (and how often) data needs to be monitored to capture, classify, and respond to events that affect the performance of data center operations

Adaptation-Aware Architecture Modeling and Analysis of Energy Efficiency for Software Systems

This thesis presents an approach for the design time analysis of energy efficiency for static and self-adaptive software systems. The quality characteristics of a software system, such as performance and operating costs, strongly depend upon its architecture. Software architecture is a high-level view on software artifacts that reflects essential quality characteristics of a system under design. Design decisions made on an architectural level have a decisive impact on the quality of a system. Revising architectural design decisions late into development requires significant effort. Architectural analyses allow software architects to reason about the impact of design decisions on quality, based on an architectural description of the system. An essential quality goal is the reduction of cost while maintaining other quality goals. Power consumption accounts for a significant part of the Total Cost of Ownership (TCO) of data centers. In 2010, data centers contributed 1.3% of the world-wide power consumption. However, reasoning on the energy efficiency of software systems is excluded from the systematic analysis of software architectures at design time. Energy efficiency can only be evaluated once the system is deployed and operational. One approach to reduce power consumption or cost is the introduction of self-adaptivity to a software system. Self-adaptive software systems execute adaptations to provision costly resources dependent on user load. The execution of reconfigurations can increase energy efficiency and reduce cost. If performed improperly, however, the additional resources required to execute a reconfiguration may exceed their positive effect. Existing architecture-level energy analysis approaches offer limited accuracy or only consider a limited set of system features, e.g., the used communication style. Predictive approaches from the embedded systems and Cloud Computing domain operate on an abstraction that is not suited for architectural analysis. The execution of adaptations can consume additional resources. The additional consumption can reduce performance and energy efficiency. Design time quality analyses for self-adaptive software systems ignore this transient effect of adaptations. This thesis makes the following contributions to enable the systematic consideration of energy efficiency in the architectural design of self-adaptive software systems: First, it presents a modeling language that captures power consumption characteristics on an architectural abstraction level. Second, it introduces an energy efficiency analysis approach that uses instances of our power consumption modeling language in combination with existing performance analyses for architecture models. The developed analysis supports reasoning on energy efficiency for static and self-adaptive software systems. Third, to ease the specification of power consumption characteristics, we provide a method for extracting power models for server environments. The method encompasses an automated profiling of servers based on a set of restrictions defined by the user. A model training framework extracts a set of power models specified in our modeling language from the resulting profile. The method ranks the trained power models based on their predicted accuracy. Lastly, this thesis introduces a systematic modeling and analysis approach for considering transient effects in design time quality analyses. The approach explicitly models inter-dependencies between reconfigurations, performance and power consumption. We provide a formalization of the execution semantics of the model. Additionally, we discuss how our approach can be integrated with existing quality analyses of self-adaptive software systems. We validated the accuracy, applicability, and appropriateness of our approach in a variety of case studies. The first two case studies investigated the accuracy and appropriateness of our modeling and analysis approach. The first study evaluated the impact of design decisions on the energy efficiency of a media hosting application. The energy consumption predictions achieved an absolute error lower than 5.5% across different user loads. Our approach predicted the relative impact of the design decision on energy efficiency with an error of less than 18.94%. The second case study used two variants of the Spring-based community case study system PetClinic. The case study complements the accuracy and appropriateness evaluation of our modeling and analysis approach. We were able to predict the energy consumption of both variants with an absolute error of no more than 2.38%. In contrast to the first case study, we derived all models automatically, using our power model extraction framework, as well as an extraction framework for performance models. The third case study applied our model-based prediction to evaluate the effect of different self-adaptation algorithms on energy efficiency. It involved scientific workloads executed in a virtualized environment. Our approach predicted the energy consumption with an error below 7.1%, even though we used coarse grained measurement data of low accuracy to train the input models. The fourth case study evaluated the appropriateness and accuracy of the automated model extraction method using a set of Big Data and enterprise workloads. Our method produced power models with prediction errors below 5.9%. A secondary study evaluated the accuracy of extracted power models for different Virtual Machine (VM) migration scenarios. The results of the fifth case study showed that our approach for modeling transient effects improved the prediction accuracy for a horizontally scaling application. Leveraging the improved accuracy, we were able to identify design deficiencies of the application that otherwise would have remained unnoticed

Adaptation-Aware Architecture Modeling and Analysis of Energy Efficiency for Software Systems

This work presents an approach for the architecture analysis of energy efficiency for static and self-adaptive software systems. It introduces a modeling language that captures consumption characteristics on an architectural level. The outlined analysis predicts the energy efficiency of systems described with this language. Lastly, this work introduces an approach for considering transient effects in design time architecture analyses

D5.1: Accelerator Deployment Models

In this deliverable, we explore this question by studying accelerator deployment models. Under accelerator, we understand for example application-specific GPUs or specially programmed FPGAs. A deployment specifies types, amount, and connectivity of accelerators in a datacenter. With these definitions in mind, we created a theoretical model of the datacenter, its components, expected workloads, and finally, it is possible deployments. We have developed VineSim, a software simulator of a datacenter, based on the aforementioned theoretical modeling. VineSim takes as inputs a workload and a deployment description and outputs performance metrics of interest, such as job latency and resource utilization. In VineSim, one can configure several parameters, including how tasks are allocated to nodes, and estimations of how fast they execute on different accelerators. VineSim can be used to explore how different deployments respond to different kinds of workloads, thus allowing one to determine how to best compose a datacenter based on particular workload, performance, or budgeting requirements

Application of an Artificial Neural Network as a Third-Party Database Auditing System

Data auditing is a fundamental challenge for organizations who deal with large databases. Databases are frequently targeted by attacks that grow in quantity and sophistication every day, and one-third of which are coming from users inside the organizations. Database auditing plays a vital role in protecting against these attacks. Native features in data base auditing systems monitor and capture activities and incidents that occur within a database and notify the database administrator. However, the cost of administration and performance overhead in the software must be considered. As opposed to using native auditing tools, the better solution for having a more secure database is to utilize third-party products. The primary goal of this thesis is to utilize an efficient and optimized deep learning approach to detect suspicious behaviors within a database by calculating the amount of risk that each user poses for the system. This will be accomplished by using an Artificial Neural Network as an enhanced feature of analyzer component of a database auditing system. This ANN will work as a third-party product for the database auditing system. The model has been validated in order to have a low bias and low variance. Moreover, parameter tuning technique has been utilized to find the best parameters that would result in the highest accuracy for the model

The Cloud-to-Thing Continuum

The Internet of Things offers massive societal and economic opportunities while at the same time significant challenges, not least the delivery and management of the technical infrastructure underpinning it, the deluge of data generated from it, ensuring privacy and security, and capturing value from it. This Open Access Pivot explores these challenges, presenting the state of the art and future directions for research but also frameworks for making sense of this complex area. This book provides a variety of perspectives on how technology innovations such as fog, edge and dew computing, 5G networks, and distributed intelligence are making us rethink conventional cloud computing to support the Internet of Things. Much of this book focuses on technical aspects of the Internet of Things, however, clear methodologies for mapping the business value of the Internet of Things are still missing. We provide a value mapping framework for the Internet of Things to address this gap. While there is much hype about the Internet of Things, we have yet to reach the tipping point. As such, this book provides a timely entrée for higher education educators, researchers and students, industry and policy makers on the technologies that promise to reshape how society interacts and operates

Efficiently Conducting Quality-of-Service Analyses by Templating Architectural Knowledge

Previously, software architects were unable to effectively and efficiently apply reusable knowledge (e.g., architectural styles and patterns) to architectural analyses. This work tackles this problem with a novel method to create and apply templates for reusable knowledge. These templates capture reusable knowledge formally and can efficiently be integrated in architectural analyses