Control Strategies for Improving Cloud Service Robustness

This thesis addresses challenges in increasing the robustness of cloud-deployed applications and services to unexpected events and dynamic workloads. Without precautions, hardware failures and unpredictable large traffic variations can quickly degrade the performance of an application due to mismatch between provisioned resources and capacity needs. Similarly, disasters, such as power outages and fire, are unexpected events on larger scale that threatens the integrity of the underlying infrastructure on which an application is deployed.First, the self-adaptive software concept of brownout is extended to replicated cloud applications. By monitoring the performance of each application replica, brownout is able to counteract temporary overload situations by reducing the computational complexity of jobs entering the system. To avoid existing load balancers interfering with the brownout functionality, brownout-aware load balancers are introduced. Simulation experiments show that the proposed load balancers outperform existing load balancers in providing a high quality of service to as many end users as possible. Experiments in a testbed environment further show how a replicated brownout-enabled application is able to maintain high performance during overloads as compared to its non-brownout equivalent.Next, a feedback controller for cloud autoscaling is introduced. Using a novel way of modeling the dynamics of typical cloud application, a mechanism similar to the classical Smith predictor to compensate for delays in reconfiguring resource provisioning is presented. Simulation experiments show that the feedback controller is able to achieve faster control of the response times of a cloud application as compared to a threshold-based controller.Finally, a solution for handling the trade-off between performance and disaster tolerance for geo-replicated cloud applications is introduced. An automated mechanism for differentiating application traffic and replication traffic, and dynamically managing their bandwidth allocations using an MPC controller is presented and evaluated in simulation. Comparisons with commonly used static approaches reveal that the proposed solution in overload situations provides increased flexibility in managing the trade-off between performance and data consistency

Utility-based Allocation of Resources to Virtual Machines in Cloud Computing

In recent years, cloud computing has gained a wide spread use as a new computing model that offers elastic resources on demand, in a pay-as-you-go fashion. One important goal of a cloud provider is dynamic allocation of Virtual Machines (VMs) according to workload changes in order to keep application performance to Service Level Agreement (SLA) levels, while reducing resource costs. The problem is to find an adequate trade-off between the two conflicting objectives of application performance and resource costs. In this dissertation, resource allocation solutions for this trade-off are proposed by expressing application performance and resource costs in a utility function. The proposed solutions allocate VM resources at the global data center level and at the local physical machine level by optimizing the utility function. The utility function, given as the difference between performance and costs, represents the profit of the cloud provider and offers the possibility to capture in a flexible and natural way the performance-cost trade-off. For global level resource allocation, a two-tier resource management solution is developed. In the first tier, local node controllers are located that dynamically allocate resource shares to VMs, so to maximize a local node utility function. In the second tier, there is a global controller that makes VM live migration decisions in order to maximize a global utility function. Experimental results show that optimizing the global utility function by changing the number of physical nodes according to workload maintains the performance at acceptable levels while reducing costs. To allocate multiple resources at the local physical machine level, a solution based on feed-back control theory and utility function optimization is proposed. This dynamically allocates shares to multiple resources of VMs such as CPU, memory, disk and network I/O bandwidth. In addressing the complex non-linearities that exist in shared virtualized infrastructures between VM performance and resource allocations, a solution is proposed that allocates VM resources to optimize a utility function based on application performance and power modelling. An Artificial Neural Network (ANN) is used to build an on- line model of the relationships between VM resource allocations and application performance, and another one between VM resource allocations and physical machine power. To cope with large utility optimization times in the case of an increased number of VMs, a distributed resource manager is proposed. It consists of several ANNs, each responsible for modelling and resource allocation of one VM, while exchanging information with other ANNs for coordinating resource allocations. Experiments, in simulated and realistic environments, show that the distributed ANN resource manager achieves better performance-power trade-offs than a centralized version and a distributed non-coordinated resource manager. To deal with the difficulty of building an accurate online application model and long model adaptation time, a solution that offers model-free resource management based on fuzzy control is proposed. It optimizes a utility function based on a hill-climbing search heuristic implemented as fuzzy rules. To cope with long utility optimization time in the case of an increased number of VMs, a multi-agent fuzzy controller is developed where each agent, in parallel with others, optimizes its own local utility function. The fuzzy control approach eliminates the need to build a model beforehand and provides a robust solution even for noisy measurements. Experimental results show that the multi-agent fuzzy controller performs better in terms of utility value than a centralized fuzzy control version and a state-of-the-art adaptive optimal control approach, especially for an increased number of VMs. Finally, to address some of the problems of reactive VM resource allocation approaches, a proactive resource allocation solution is proposed. This approach decides on VM resource allocations based on resource demand prediction, using a machine learning technique called Support Vector Machine (SVM). To deal with interdependencies between VMs of the same multi-tier application, cross- correlation demand prediction of multiple resource usage time series of all VMs of the multi-tier application is applied. As experiments show, this results in improved prediction accuracy and application performance

Service-oriented models for audiovisual content storage

What are the important topics to understand if involved with storage services to hold digital audiovisual content? This report takes a look at how content is created and moves into and out of storage; the storage service value networks and architectures found now and expected in the future; what sort of data transfer is expected to and from an audiovisual archive; what transfer protocols to use; and a summary of security and interface issues

Term-driven E-Commerce

Die Arbeit nimmt sich der textuellen Dimension des E-Commerce an. Grundlegende Hypothese ist die textuelle Gebundenheit von Information und Transaktion im Bereich des elektronischen Handels. Überall dort, wo Produkte und Dienstleistungen angeboten, nachgefragt, wahrgenommen und bewertet werden, kommen natürlichsprachige Ausdrücke zum Einsatz. Daraus resultiert ist zum einen, wie bedeutsam es ist, die Varianz textueller Beschreibungen im E-Commerce zu erfassen, zum anderen können die umfangreichen textuellen Ressourcen, die bei E-Commerce-Interaktionen anfallen, im Hinblick auf ein besseres Verständnis natürlicher Sprache herangezogen werden

The legal regime for anti-cyberlaundering

Doctor Legum - LLDAlong with its inumerable wonders, the advent of the internet has brought with it very bad vices. The notion of convenience, which comes with the use of the internet, can be attributed to criminals who wish to disguise the proceeds of their ill-derived funds, or what is better known as cyberlaundering. Cyberlaundering is a phenomenon that seems negligible on face value, but, to the contrary, has very dire effects, especially on national economies, which are in no way trifling.This study describes the problem of cyberlaundering, pointing out the various legal issues pertaining to it. Given that cyberlaundering is a comparatively new crime, which is not yet conceptualized legally, criminal justice authorities find it hard to detect, investigate and prosecute cyberlaundering. An adequate legal regime against cyberlaundering is currently non-existent, as there is presently no concise international or national legal framework in place to contain the problem. Whilst the chief focus of the thesis is to devise a legal framework to combat cyberlaundering, considerable attention is also devoted to the tension that arises between public and private interests, amongst several other legal issues that come to play along the way. This is a debate that necessarily arises when legislatures resort to more radical anti-cyberlaundering laws. The study advocates a middle ground, which leads to the desired end of curbing the exponential growth of cyberlaundering, at the very least

Human-robot interaction for telemanipulation by small unmanned aerial systems

This dissertation investigated the human-robot interaction (HRI) for the Mission Specialist role in a telemanipulating unmanned aerial system (UAS). The emergence of commercial unmanned aerial vehicle (UAV) platforms transformed the civil and environmental engineering industries through applications such as surveying, remote infrastructure inspection, and construction monitoring, which normally use UAVs for visual inspection only. Recent developments, however, suggest that performing physical interactions in dynamic environments will be important tasks for future UAS, particularly in applications such as environmental sampling and infrastructure testing. In all domains, the availability of a Mission Specialist to monitor the interaction and intervene when necessary is essential for successful deployments. Additionally, manual operation is the default mode for safety reasons; therefore, understanding Mission Specialist HRI is important for all small telemanipulating UAS in civil engineering, regardless of system autonomy and application. A 5 subject exploratory study and a 36 subject experimental study were conducted to evaluate variations of a dedicated, mobile Mission Specialist interface for aerial telemanipulation from a small UAV. The Shared Roles Model was used to model the UAS human-robot team, and the Mission Specialist and Pilot roles were informed by the current state of practice for manipulating UAVs. Three interface camera view designs were tested using a within-subjects design, which included an egocentric view (perspective from the manipulator), exocentric view (perspective from the UAV), and mixed egocentric-exocentric view. The experimental trials required Mission Specialist participants to complete a series of tasks with physical, visual, and verbal requirements. Results from these studies found that subjects who preferred the exocentric condition performed tasks 50% faster when using their preferred interface; however, interface preferences did not affect performance for participants who preferred the mixed condition. This result led to a second finding that participants who preferred the exocentric condition were distracted by the egocentric view during the mixed condition, likely caused by cognitive tunneling, and the data suggest tradeoffs between performance improvements and attentional costs when adding information in the form of multiple views to the Mission Specialist interface. Additionally, based on this empirical evaluation of multiple camera views, the exocentric view was recommended for use in a dedicated Mission Specialist telemanipulation interface. Contributions of this thesis include: i) conducting the first focused HRI study of aerial telemanipulation, ii) development of an evaluative model for telemanipulation performance, iii) creation of new recommendations for aerial telemanipulation interfacing, and iv) contribution of code, hardware designs, and system architectures to the open-source UAV community. The evaluative model provides a detailed framework, a complement to the abstraction of the Shared Roles Model, that can be used to measure the effects of changes in the system, environment, operators, and interfacing factors on performance. The practical contributions of this work will expedite the use of manipulating UAV technologies by scientists, researchers, and stakeholders, particularly those in civil engineering, who will directly benefit from improved manipulating UAV performance

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words