Katinka Wolter

Analysis of Restart Mechanisms in Software Systems

Restarts or retries are a common phenomenon in computing systems, for instance, in preventive maintenance, software rejuvenation, or when a failure is suspected. Typically, one sets a time-out to trigger the restart. We analyze and optimize time-out strategies for scenarios in which the expected required remaining time of a task is not always decreasing with the time invested in it. Examples of such tasks include the download of Web pages, randomized algorithms, distributed queries, and jobs subject to network or other failures. Assuming the independence of the completion time of successive tries, we derive computationally attractive expressions for the moments of the completion time, as well as for the probability that a task is able to meet a deadline. These expressions facilitate efficient algorithms to compute optimal restart strategies and are promising candidates for pragmatic online optimization of restart timers

Tradeoff between performance improvement and energy saving in mobile cloud offloading systems

Cloud computation offloading is a promising method that sending heavy computation to resourceful servers on cloud and then receiving the results from them. In this paper, we study the offloading techniques and further explore the tradeoff between shortening execution time and extending battery life of mobile devices. A novel adaptive offloading scheme is proposed and analyzed based on the tradeoff analysis. And it can be realized thanks to the elasticity of cloud computing that the resources can be bought on demand. We have tried to find a server on cloud with a critical value of speedup F for a specified mobile device. When satisfying the requirement such as performance improvement by the system, it is worth sacrificing large F when taking economic factor into consideration.

A measurement study of the interplay between application level restart and transport protocol

Restart is an application-level mechanism to speed up the completion of tasks that are subject to failures or unpredictable delays. In this paper we investigate if restart can be beneficial for Internet applications. For that reason we conduct and analyze a measurement study for restart applied to HTTP GET over TCP. Since application-level restart and TCP time-out mechanisms may interfere, we discuss in detail the relation between restart and transport protocol. The analysis shows that restart may especially be beneficial in the TCP set-up phase, in essence tuning TCP time-out values for the application at hand. In addition, we discuss the design of and experimentation with a proxy-based restart tool that includes a statistical oracle module to automatically adapt and optimize the restart time.

Methods of cloud-path selection for offloading in mobile cloud computing systems

Recently, there emerge a variety of clouds in sky and thus, several similar cloud services (from different cloud venders) can be provided to a mobile end device. The goal of cloud-path selection is to find an optimal cloud among a certain class of clouds that provide the same service, in order to carry out the offloaded computation tasks. It is easy to choose the optimal cloud to save execution time incurred by offloading to cloud when considering only one factor. However, there are many criteria such as speed, bandwidth, price, security and availability that need to be considered when making final decisions. In this paper, a multiple criteria decision analysis approach based on the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS) in a fuzzy environment is proposed to decide which cloud is the most suitable one for offloading. The AHP is used to determine the weights of the criteria for cloud-path selection, while fuzzy TOPSIS is to obtain the final ranking of alternative clouds. The numerical analysis is performed to evaluate the model.

Resilience Assessment and Evaluation of Computing Systems

Cloud Computing is a novel paradigm for providing data center resources as on demand services in a pay-as-you-go manner. It promises significant cost savings by making it possible to consolidate workloads and share infrastructure resources among multiple applications resulting in higher costand energy-efficiency. However, these benefits come at the cost of increased system complexity and dynamicity posing new challenges in providing service dependability and resilience for applications running in a Cloud environment. At the same time, the virtualization of physical resources, inherent in Cloud Computing, provides new opportunities for novel dependability and quality-of-service management techniques that can potentially improve system resilience. In this chapter, we first discuss in detail the challenges and opportunities introduced by the Cloud Computing paradigm. We then provide a review of the state-of-the-art on dependability and resilience management in Cloud environments, and conclude with an overview of emerging research directions.The resilience of computing systems includes their dependability as well as their fault tolerance and security. It defines the ability of a computing system to perform properly in the presence of various kinds of disturbances and to recover from any service degradation. These properties are immensely important in a world where many aspects of our daily life depend on the correct, reliable and secure operation of often large-scale distributed computing systems. Wolter and her co-editors grouped the 20 chapters from leading researchers into seven parts: an introduction and motivating examples, modeling techniques, model-driven prediction, measurement and metrics, testing techniques, case studies, and conclusions. The core is formed by 12 technical papers, which are framed by motivating real-world examples and case studies, thus illustrating the necessity and the application of the presented methods. While the technical chapters are independent of each other and can be read in any order, the reader will benefit more from the case studies if he or she reads them together with the related techniques. The papers combine topics like modeling, benchmarking, testing, performance evaluation, and dependability, and aim at academic and industrial researchers in these areas as well as graduate students and lecturers in related fields. In this volume, they will find a comprehensive overview of the state of the art in a field of continuously growing practical importance.

Cluster-based fitting of phase-type distributions to empirical data

We present a clustering-based fitting approach for phase-type distributions that is particularly suited to capture common characteristics of empirical data sets. The distributions fitted by this approach are especially useful in efficient simulation approaches. We describe the Hyper-* tool, which implements the algorithm and offers a user-friendly interface to efficient phase-type fitting. We provide a comparison of cluster-based fitting with segmentation-based approaches and other algorithms and show that clustering provides good results for typical empirical data sets.

Analysis of service availability for time-triggered rejuvenation policies

In this paper we investigate the effect of three time-triggered system rejuvenation policies on service availability using a queuing model. The model is formulated as an extended stochastic Petri net using a variety of distributions for times between state changes. We define a metric for steady-state service availability and derive how it can be estimated from the models in a hybrid approach combining simulation and analytical reasoning. We further analyze time-to-failure of systems with rejuvenation. Experiments show that the optimal rejuvenation interval as well as the achievable service availability improvement depend significantly on system utilization. The experiments also show that service availability can deviate significantly from steady-state system availability. For low utilization all rejuvenation policies perform well. For medium utilization, one policy is significantly inferior to the other two, while for high utilization, no rejuvenation should be performed at all.

A survey of experimental evaluation in indoor localization research

During the last decade, research in indoor localization and navigation has focused on techniques, protocols, and algorithms. The first International Conference on Indoor Positioning and Indoor Navigation (IPIN) was held in 2010. Since then, this annual conference showed the progress of research and technology. The variations of evaluation methods are significant in this field: they range from none, to extensive simulations, and real-world experiments under non-lab conditions. We look at the articles published in the proceedings of IPIN by IEEE Xplore from 2010 to 2014, and analyze the development of evaluation methods. We categorized 183 randomly selected papers, in respect to five different aspects. Namely: (1) the underlying system/technology in use, (2) the evaluation method for the proposed technique, (3) the method of ground truth data gathering, (4) the applied metrics, and (5) whether the authors establish a baseline for their work.

Mobile Healthcare Systems with Multi-cloud Offloading

The fast growth of cloud computing has attracted more companies to migrate their in-house IT applications into cloud and it also occurs in the medical field. A mobile healthcare system with cloud offloading is considered in this paper and it can be divided into two stages: sensor network and cloud offloading. In the first stage, information collected by body sensors should be transmitted to a remote mobile device. In order to save energy, an energy-efficient transmission scheme called cooperative multi-input multi-output (MIMO) is constructed for the data transfer when allowing individual sensor nodes to cooperate with each other. In the second stage, two offloading schemes called self-reliant multi-cloud offloading system and multi-cloud offloading system are proposed and further analyzed based on serve topology and optimal graph partition. The former provides stability but with high communication cost, while the latter reduces communication cost but is less stable. Both schemes can be applied to other scenarios in which we would like to perform offloading on multiple servers.

Optimal restart times for moments of completion time

Restart is an application-level technique that speeds up jobs with highly variable completion times. The authors present an efficient iterative algorithm to determine the restart strategy that minimises higher moments of completion time, when the total number of restarts is finite. They demonstrate its computational efficiency in comparison with alternative algorithms. They also discuss fast approximations to determine close to optimal restart times for limiting cases.