Mariam Kiran

Validation and discovery from computational biology models

Simulation software is often a fundamental component in systems biology projects and provides a key aspect of the integration of experimental and analytical techniques in the search for greater understanding and prediction of biology at the systems level. It is important that the modelling and analysis software is reliable and that techniques exist for automating the analysis of the vast amounts of data which such simulation environments generate. A rigorous approach to the development of complex modelling software is needed. Such a framework is presented here together with techniques for the automated analysis of such models and a process for the automatic discovery of biological phenomena from large simulation data sets. Illustrations are taken from a major systems biology research project involving the in vitro investigation, modelling and simulation of epithelial tissue.

Analysis of Cloud Test Beds Using OpenSource Solutions

Cloud computing is increasingly attracting large attention both in academic research and in industrial initiatives. However, despite the popularity, there is a lack of research on the suitability of software tools and parameters for creating and deploying Cloud test beds. Virtualization and how to set up virtual environments can be done through software tools, which are available as open source, but there still needs to be work in terms of which tools to use and how to monitor parameters with the suitability of hardware resources available. This paper discusses the concepts of virtualization, as a practical view point, presenting an in-depth critical analysis of open source cloud implementation tools such as CloudStack, Eucalyptus, Nimbus, OpenStack, OpenNebula, OpenIoT, to name a few. This paper analyzes the various toolkits, parameters of these tools, and their usability for researchers looking to deploy their own Cloud test beds. The paper also extends further in developing an experimental case study of using OpenStack to construct and deploy a test bed using current resources available in the labs at the University of Bradford. This paper contributes to the theme of software setups and open source issues for developing Cloud test bed for deploying and constructing private Cloud test bed.

Model-based Testing in Cloud Brokerage Scenarios

In future Cloud ecosystems, brokers will mediate between service providers and consumers, playing an increased role in quality assurance, checking services for functional compliance to agreed standards, among other aspects. To date, most Software-as-a-Service (SaaS) testing has been performed manually, requiring duplicated effort at the development, certification and deployment stages of the service lifecycle. This paper presents a strategy for achieving automated testing for certification and re-certification of SaaS applications, based on the adoption of simple state-based and functional specifications. High-level test suites are generated from specifications, by algorithms that provide the necessary and sufficient coverage. The high-level tests must be grounded for each implementation technology, whether SOAP, REST or rich-client. Two examples of grounding are presented, one into SOAP for a traditional web service and the other into Selenium for a SAP HANA rich-client application. The results demonstrate good test coverage. Further work is required to fully automate the grounding.

Failover strategy for fault tolerance in cloud computing environment.

Cloud fault tolerance is an important issue in cloud computing platforms and applications. In the event of an unexpected system failure or malfunction, a robust fault‐tolerant design may allow the cloud to continue functioning correctly possibly at a reduced level instead of failing completely. To ensure high availability of critical cloud services, the application execution, and hardware performance, various fault‐tolerant techniques exist for building self‐autonomous cloud systems. In comparison with current approaches, this paper proposes a more robust and reliable architecture using optimal checkpointing strategy to ensure high system availability and reduced system task service finish time. Using pass rates and virtualized mechanisms, the proposed smart failover strategy (SFS) scheme uses components such as cloud fault manager, cloud controller, cloud load balancer, and a selection mechanism, providing fault tolerance via redundancy, optimized selection, and checkpointing. In our approach, the cloud fault manager repairs faults generated before the task time deadline is reached, blocking unrecoverable faulty nodes as well as their virtual nodes. This scheme is also able to remove temporary software faults from recoverable faulty nodes, thereby making them available for future request. We argue that the proposed SFS algorithm makes the system highly fault tolerant by considering forward and backward recovery using diverse software tools. Compared with existing approaches, preliminary experiment of the SFS algorithm indicates an increase in pass rates and a consequent decrease in failure rates, showing an overall good performance in task allocations. We present these results using experimental validation tools with comparison with other techniques, laying a foundation for a fully fault‐tolerant infrastructure as a service cloud environment. Copyright

Optimising Fault Tolerance in Real-Time Cloud Computing IaaS Environment

Fault tolerance is the ability of a system to respond swiftly to an unexpected failure. Failures in a cloud computing environment are normal rather than exceptional, but fault detection and system recovery in a real time cloud system is a crucial issue. To deal with this problem and to minimize the risk of failure, an optimal fault tolerance mechanism was introduced where fault tolerance was achieved using the combination of the Cloud Master, Compute nodes, Cloud load balancer, Selection mechanism and Cloud Fault handler. In this paper, we proposed an optimized fault tolerance approach where a model is designed to tolerate faults based on the reliability of each compute node (virtual machine) and can be replaced if the performance is not optimal. Preliminary test of our algorithm indicates that the rate of increase in pass rate exceeds the decrease in failure rate and it also considers forward and backward recovery using diverse software tools. Our results obtained are demonstrated through experimental validation thereby laying a foundation for a fully fault tolerant IaaS Cloud environment, which suggests a good performance of our model compared to current existing approaches.

Agent-Based High-Performance Simulation of Biological Systems on the GPU

Simulation of biological systems are computationally demanding due to the large scale reaction networks of bacterial cells. This scalability issue escalates, in particular, when bacterial colonies, formed by many individual cells, are simulated. Agent-based modelling environments on parallel architectures, such as the FLAME (Flexible Large-scale Modelling Environment) framework, are good candidates to simulate such systems, but due to the complex nature of cellular systems more advance technology is needed. In this paper, we utilise FLAME GPU, extending FLAME with a high performance graphics processing unit, to simulate a pulse generator, a typical multicellular synthetic biology system. This system is specified using a membrane computing model. We also illustrate the performance improvement of FLAME GPU over FLAME.

A Methodology for Cloud Security Risks Management

Cloud computing is an extremely attractive model for both the users and the providers of Cloud-based infrastructure, who have their own business angle for using and providing these services. However, as with many business ventures, as the use of Cloud environments grow, the risks and the threats associated with a successful use of the model also increase. Although, the Cloud paradigm is an evolution of grid systems, Clouds have particular threats specific to virtualized and multi-tenant environments, which need to be managed with proper methodologies to ensure that the entire ecosystem is secure. Security consists of three main aspects—availability, integrity and confidentiality—and each of these needs to be considered to make sure that the complete ecosystem is secure. This chapter presents a comprehensive discussion of the concerns associated with the Cloud security depicting the best practices currently used in the industry. This chapter presents an in-depth analysis of these issues with an innovative holistic approach on how to manage and assess security risks for different kinds of Cloud ecosystems which allows documentation as well as design tools which can be in place to monitor security at both deployment and operation phases. The proposed risk methodology approach allows better management and mitigation of security threats when they occur during the service lifecycle of any kind of Cloud ecosystem and Cloud services provision.

Testing Software Services in Cloud Ecosystems

Testing in the Cloud is far more challenging than testing individual software services. A multitude of factors affect testing, including variations across platforms and infrastructure. Architectural issues include differences between private, public Clouds, multi-Clouds and Cloud-bursting. Platform issues include cross-vendor incompatibility, and diverse locales of service deployment and consumption. Software issues include integration with third-party services, the desire to validate competing service offerings to similar standards and need to re-validate services at different stages of service lifecycle. A complete approach to testing whole Cloud ecosystems should involve all relevant stakeholders, such as service provider, consumer and broker. When testing Clouds, the methodologies used should not hinder the advantages Cloud usage brings to the users or programmers and more importantly be simple and cost effective. However, these testing methodologies differ according to the various kinds of Cloud ecosystems and the different user perspectives of the actors involved such as the end-user, the infrastructures, or the different software i.e. web services. This paper also studies the state-of-the-art in Cloud testing where most research focuses predominantly on web services, functional testing and quality-of-service, usually being considered separately. The authors suggest a framework, Quality-as-a-Service QaaS which integrates quality issues such as functional behaviour and performance monitoring with lifecycle governance and security of the service. This paper maps out the themes in the contemporary research literature and links them with the service lifecycle process for validating future Cloud services. Along the way, the authors identify important research questions that the future Cloud service testing agenda should seek to address.

Agent-based modelling as a service on amazon EC2: opportunities and challenges

Modelling and Simulation is heavily influenced by availability of computational power and resources, to successfully complete simulation tasks. In this paper, we investigate deploying the FLAME framework, the only supercomputing framework that automatically produces parallelisable code on different parallel hardware architectures, on cloud infrastructures. The framework focuses on agent-based modelling (ABM) technique which has presented various challenges in the high-performance computing fields and how these reflect in Cloud environments. Computationally these simulations are extremely complex to program with interconnected software, using massive amount of computational power and architectural challenges. High-performance computing grids have provided solutions to some of these issues, but are still not capable enough to solve most of the issues faced by the modelers. This paper discusses the computational problems of executing these simulations and open challenges. Presenting ABM-as-a-service with a possible framework on how this can be implemented with a platform as a service backend. Computational problems such as memory, processing and time are discussed highlighting the issues for enabling these services for non-computing scientists.

Threat methodology for securing scalable video in the Cloud

Scalable video using Cloud Computing is a potential solution for the distribution of media content to a large number of users. This may occur over a heterogeneous network connected to devices with different capabilities and diverse set of users. Although some of the problems are well known and understood in information and network security, there is still a need to improve the existing solutions to produce a solution that is both adequately secure and efficient in highly distributed and scalable environments. In this paper, we describe such improvements using a cloud computing scenario where video content is made available through a cloud platform. When put on an Infrastructure as a Service (IaaS) cloud video content should then be viewable by different consumers using different levels of bandwidth and security requirements depending on their identity. This requires a mechanism through which a cloud service could be authenticated and encrypted by end users. This paper describes the novel solution of securing scalable video in the cloud discussing the various threats for video distribution and how these can be made more secure in terms of confidentiality, availability and integrity, particularly threw source authentication and encryption.