Django Armstrong

Risk driven Smart Home resource management using cloud services

In order to fully exploit the concept of Smart Home, challenges associated with multiple device management in consumer facing applications have to be addressed. Specific to this is the management of resource usage in the home via the improved utilization of devices, this is achieved by integration with the wider environment they operate in. The traditional model of the isolated device no longer applies, the future home will be connected with services provided by third parties ranging from supermarkets to domestic appliance manufacturers. In order to achieve this risk based integrated device management and contextualization is explored in this paper based on the cloud computing model. We produce an architecture and evaluate risk models to assist in this management of devices from a security, privacy and resource management perspective. We later propose an expansion on the risk based approach to wider data sharing between the home and external services using the key indicators of TREC (Trust, Risk, Eco-efficiency and Cost). The paper contributes to Smart Home research by defining how Cloud service management principles of risk and contextualization for virtual machines can produce solutions to emerging challenges facing a new generation of Smart Home devices.

Brokering of risk-aware service level agreements in grids

Service level agreements (SLAs) are facilitators for widening the commercial uptake of Grid technology. They provide explicit statements of expectation and obligation between service consumers and providers. However, without the ability to assess the probability that an SLA might fail, commercial uptake will be restricted, since neither party will be willing to agree. Therefore, risk assessment mechanisms are critical to increase confidence in Grid technology usage within the commercial sector. This paper presents an SLA brokering mechanism with risk assessment support, which evaluates the probability of SLA failure. WS‐Agreement and risk metrics are used to facilitate SLA creation between service consumers and providers within a typical Grid resource usage scenario. An evaluation is conducted to examine risk models, the performance of the brokers implementation as well as a comparison of its capabilities against similar SLA‐based solutions from the literature. Copyright

Towards a Service Lifecycle Based Methodology for Risk Assessment in Cloud Computing

The principles of risk management have been introduced in grid computing to help document and anticipate certain risks and manage them to ensure job executions are successful. Clouds are more complex environments with further concerns like risk, trust, eco-efficiency, green, security or cost. In this paper we present ongoing research work to analyze and address the risk factor in clouds with the aim of optimizing cloud services. The main contribution of this work is the presentation of a methodology for performing risk assessment in cloud environments including the target use cases, risk identification, mitigation and monitoring. Together with the corresponding mitigation strategies, the methodology provides technological assurance that will lead to a high confidence of Cloud service consumers on one side, and a cost effective and reliable productivity of cloud Service/Infrastructure Providers on the other side. The design of the risk assessment framework and its software toolkit implementation are part of the research and development work of the OPTIMIS (Optimized Infrastructure Services) project whose objective is to enable an open and dependable Cloud Service Ecosystem that delivers IT services that are adaptable, reliable, auditable and sustainable both ecologically and economically. The paper presents some preliminary results on the risk assessment of a Service/Infrastructure Provider at the cloud service deployment stage.

Performance Issues in Clouds

As a technology, cloud computing has become an IT buzzword for the past few years. Cloud computing has often been used with synonymous terms such as software as a service, platform as a service, and infrastructure as a service (IaaS). Cloud computing has the potential to advance research discoveries by making data and computing resources readily available at an unprecedented economy of scale and with tremendous scalability. This paper discusses the importance of QoS and Iaas performance in cloud computing. The results of a quantitative evaluation are presented into the performance overheads of propagating virtual machine (VM) images to physical resources, at the Iaas layer and then accessing the images, via a Hypervisors virtual block I/O device. Two virtual infrastructure managers are evaluated: Nimbus and OpenNebula, alongside two VM managers: XEN and KVM. Nimbus is found to outperform OpenNebula, while XEN outperforms KVM in the majority of cases. Conclusions are drawn from the results on the suitability of these technologies for data-intensive applications and applications requiring highly dynamic resource sets, where making an uninformed decision on what technology to use could prevent an application reaching its full potential, once deployed onto a cloud.

Runtime virtual machine recontextualization for clouds

We introduce and define the concept of recontextualization for cloud applications by extending contextualization, i.e. the dynamic configuration of virtual machines (VM) upon initialization, with autonomous updates during runtime. Recontextualization allows VM images and instances to be dynamically re-configured without restarts or downtime, and the concept is applicable to all aspects of configuring a VM from virtual hardware to multi-tier software stacks. Moreover, we propose a runtime cloud recontextualization mechanism based on virtual device management that enables recontextualization without the need to customize the guest VM. We illustrate our concept and validate our mechanism via a use case demonstration: the reconfiguration of a cross-cloud migratable monitoring service in a dynamic cloud environment. We discuss the details of the interoperable recontextualization mechanism, its architecture and demonstrate a proof of concept implementation. A performance evaluation illustrates the feasibility of the approach and shows that the recontextualization mechanism performs adequately with an overhead of 18% of the total migration time.

Towards a Contextualization Solution for Cloud Platform Services

We propose a cloud contextualization mechanism which operates in two stages, contextualization of VM images prior to service deployment (PaaS level) and self-contextualization of VM instances created from the image (IaaS level). The contextualization tools are implemented as part of the OPTIMIS Toolkit, a set of software components for simplified management of cloud services and infrastructures. We present the architecture of our contextualization tools and the feasibility of our contextualization mechanism is demonstrated in a three-tier web application scenario. Preliminary performance results suggest acceptable performance and scalability

A Risk Assessment Framework for Cloud Computing

Cloud service providers offer access to their resources through formal service level agreements (SLA), and need well-balanced infrastructures so that they can maximise the quality of service (QoS) they offer and minimise the number of SLA violations. This paper focuses on a specific aspect of risk assessment as applied in cloud computing: methods within a framework that can be used by cloud service providers and service consumers to assess risk during service deployment and operation. It describes the various stages in the service lifecycle whereas risk assessment takes place, and the corresponding risk models that have been designed and implemented. The impact of risk on architectural components, with special emphasis on holistic management support at service operation, is also described. The risk assessor is shown to be effective through the experimental evaluation of the implementation, and is already integrated in a cloud computing toolkit.

Legal issues in clouds: towards a risk inventory.

Cloud computing technologies have reached a high level of development, yet a number of obstacles still exist that must be overcome before widespread commercial adoption can become a reality. In a cloud environment, end users requesting services and cloud providers negotiate service-level agreements (SLAs) that provide explicit statements of all expectations and obligations of the participants. If cloud computing is to experience widespread commercial adoption, then incorporating risk assessment techniques is essential during SLA negotiation and service operation. This article focuses on the legal issues surrounding risk assessment in cloud computing. Specifically, it analyses risk regarding data protection and security, and presents the requirements of an inherent risk inventory. The usefulness of such a risk inventory is described in the context of the OPTIMIS project.

Contextualization: dynamic configuration of virtual machines

New VM instances are created from static templates that contain the basic configuration of the VM to achieve elasticity with regards to capacity. Instance specific settings can be injected into the VM during the deployment phase through means of contextualization. So far this is limited to a single data source and data remains static throughout the lifecycle of the VM.We present a layered approach to contextualization that supports different classes of contextualization data available from several sources. The settings are made available to the VM through virtual devices. Inside each VM data from different classes are layered on top of each other to create a unified file hierarchy.Context data can be modified during runtime by updating the contents of the virtual devices, making our approach the first contextualization approach to natively support recontextualization. Recontextualization enables runtime reconfiguration of an executing service and can act as a trigger and key enabler of self-* techniques. This trigger provides a service with a mechanism to adapt or optimize itself in response to a changing environment. The runtime reconfiguration using recontextualization and its potential gains are illustrated in an example with a distributed file system, demonstrating the feasibility of our approach.

Towards an Energy-Aware Cloud Architecture for Smart Grids

Energy consumption in Cloud computing is a significant issue in regards to aspects such as the cost of energy, cooling in the data center and the environmental impact of cloud data centers. Smart grids offers the prospect of dynamic costs for a data center’s energy usage. These dynamic costs can be passed on to Cloud users providing incentives for users to moderate their load while also ensuring the Cloud providers are insulated from fluctuations in the cost of energy. The first step towards this is an architecture that focuses on energy monitoring and usage prediction. We provide such an architecture at both the PaaS and IaaS layers, resulting in energy metrics for applications, VMs and physical hosts, which is key to enabling active demand in cloud data centers. This architecture is demonstrated through our initial results utilising a generic use case, providing energy consumption information at the PaaS and IaaS layers. Such monitoring and prediction provides the groundwork for providers passing on energy consumption costs to end users. It is envisaged that the resulting varying price associated with energy consumption can help motivate the formation of methods and tools to support software developers aiming to optimise energy efficiency and minimise the carbon footprint of Cloud applications.