Dependability modeling framework : a test procedure for high availability in cloud operating systems

This paper describes a framework on how to test High Availability capabilities of cloud architectures, particularly OpenStack. The “Dependability Modeling Framework” which consists of a modelling of the system parts, user interactions and dependencies between them will form the basis for this test. The test procedure consists of simulating random shutdown of system components, polling the availability of user interactions and measuring the impact of outages and expected downtime. Outage impacts and downtime are used to rate the underlying system architecture. The test procedure is applied on a single node OpenStack installation in order to show validity of the test concept

Elastic scaling of cloud application performance based on Western Electric rules by injection of aspect-oriented code

The main benefit of cloud computing lies in the elasticity of virtual resources that are provided to end users. Cloud users do not have to pay fixed hardware costs and are charged for consumption of computing resources only. While this might be an improvement for software developers who use the cloud, application users and consumers might rather be interested in paying for application performance than resource consumption. However there is little effort on providing elasticity based on performance goals instead of resource consumption. In this paper an autoscaling method is presented which aims at providing increased application performance as it is demanded by consumers. Elastic scaling is based on “statistical process monitoring and control” and “Western Electric rules”. By demonstrating the architecture of the autoscaling method and providing performance measurements gathered in an OpenStack cloud environment, we show how the injection of aspect-oriented code into cloud applications allows for improving application performance by automatically adapting the underlying virtual environment to pre-defined performance goals. The effectiveness of the autoscaling method is verified by an experiment with a test program which shows that the program executes in only half of the time which is required if no autoscaling capabilities are provided

Hera Object Storage : a seamless, automated multi-tiering solution on top of OpenStack Swift

Over the last couple of decades, the demand for storage in the Cloud has grown exponentially. Distributed Cloud storage and object storage for the increasing share of unstructured data, are in high focus in both academic and industrial research activities. At the same time, efficient storage and the corresponding costs are often contrasting parameters raising a trade-off problem for any proposed solution. To this aim, classifying the data in terms of access probability became a hot topic. This paper introduces Hera Object Storage, a storage system built on top of OpenStack Swift that aims at selecting the most appropriate storage tier for any object to be stored. The goal of the multi-tiering storage we propose is to be automated and seamless, guaranteeing the required storage performance at the lowest possible cost. The paper discusses the design challenges, the proposed algorithmic solutions to the scope and, based on a prototype implementation it presents a basic proof-of-concept validation

Monitoring resilience in a rook-managed containerized cloud storage system

Distributed cloud storage solutions are currently gaining high momentum in industry and academia. The enterprise data volume growth and the recent tendency to move as much as possible data to the cloud is strongly stimulating the storage market growth. In this context, and as a main requirement for cloud native applications, it is of utmost importance to guarantee resilience of the deployed applications and the infrastructure. Indeed, with failures frequently occurring, a storage system should quickly recover to guarantee service availability. In this paper, we focus on containerized cloud storage, proposing a resilience monitoring solution for the recently developed Rook storage operator. While, Rook brings storage systems into a cloud-native container platform, in this paper we design an additional module to monitor and evaluate the resilience of the Rook-based system. Our proposed module is validated in a production environment, with software components generating a constant load and a controlled removal of system elements to evaluate the self-healing capability of the storage system. Failure recovery time revealed to be 41 and 142 seconds on average for a 32GB and a 215GB object storage device respectively

Stealth databases : ensuring user-controlled queries in untrusted cloud environments

Sensitive data is increasingly being hosted online in ubiquitous cloud storage services. Recent advances in multi-cloud service integration through provider multiplexing and data dispersion have alleviated most of the associated risks for hosting files which are retrieved by users for further processing. However, for structured data managed in databases, many issues remain, including the need to perform operations directly on the remote data to avoid costly transfers. In this paper, we motivate the need for distributed stealth databases which combine properties from structure-preserving dispersed file storage for capacity-saving increased availability with emerging work on structure-preserving encryption for on-demand increased confidentiality with controllable performance degradation. We contribute an analysis of operators executing in map-reduce or map-carry-reduce phases and derive performance statistics. Our prototype, StealthDB, demonstrates that for typical amounts of personal structured data, stealth databases are a convincing concept for taming untrusted and unsafe cloud environments

Self-managing cloud-native applications : design, implementation and experience

Running applications in the cloud efficiently requires much more than deploying software in virtual machines. Cloud applications have to be continuously managed: (1) to adjust their resources to the incoming load and (2) to face transient failures replicating and restarting components to provide resiliency on unreliable infrastructure. Continuous management monitors application and infrastructural metrics to provide automated and responsive reactions to failures (health management) and changing environmental conditions (auto-scaling) minimizing human intervention. In the current practice, management functionalities are provided as infrastructural or third party services. In both cases they are external to the application deployment. We claim that this approach has intrinsic limits, namely that separating management functionalities from the application prevents them from naturally scaling with the application and requires additional management code and human intervention. Moreover, using infrastructure provider services for management functionalities results in vendor lock-in effectively preventing cloud applications to adapt and run on the most effective cloud for the job. In this paper we discuss the main characteristics of cloud native applications, propose a novel architecture that enables scalable and resilient self-managing applications in the cloud, and relate on our experience in porting a legacy application to the cloud applying cloud-native principles

Experimental evaluation of the cloud-native application design

Cloud-Native Applications (CNA) are designed to run on top of cloud computing infrastructure services with inherent support for self-management, scalability and resilience across clustered units of application logic. Their systematic design is promising especially for recent hybrid virtual machine and container environments for which no dominant application development model exists. In this paper, we present a case study on a business application running as CNA and demonstrate the advantages of the design experimentally. We also present Dynamite, an application auto-scaler designed for containerised CNA. Our experiments on a Vagrant host, on a private OpenStack installation and on a public Amazon EC2 testbed show that CNA require little additional engineering

Attitude Determination from Single-Antenna Carrier-Phase Measurements

A model of carrier phase measurement (as carried out by a satellite navigation receiver) is formulated based on electromagnetic theory. The model shows that the phase of the open-circuit voltage induced in the receiver antenna with respect to a local oscillator (in the receiver) depends on the relative orientation of the receiving and transmitting antennas. The model shows that using a {\it single} receiving antenna, and making carrier phase measurements to seven satellites, the 3-axis attitude of a user platform (in addition to its position and time) can be computed relative to an initial point. This measurement model can also be used to create high-fidelity satellite signal simulators that take into account the effect of platform rotation as well as translation.Comment: 12 pages, and one figure. Published in J. Appl. Phys. vol. 91, No. 7, April 1, 200