The Importance of Proper Measurement for a Cloud Security Assurance Model

Winner of a best paper award.

Information Security in the Cloud: Should We be Using a Different Approach?

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LocLess: Do You Really Care Where Your Cloud Files Are?

Physical location of data in cloud storage is a problem that gains a lot of attention not only from the actual cloud providers but also from the end users' who lately raise many concerns regarding the privacy of their data. It is a common practice that cloud service providers create replicate users' data across multiple physical locations. However, moving data in different countries means that basically the access rights are transferred based on the local laws of the corresponding country. In other words, when a cloud service provider stores users' data in a different country then the transferred data is subject to the data protection laws of the country where the servers are located. In this paper, we propose LocLess, a protocol which is based on a symmetric searchable encryption scheme for protecting users' data from unauthorized access even if the data is transferred to different locations. The idea behind LocLess is that "Once data is placed on the cloud in an unencrypted form or encrypted with a key that is known to the cloud service provider, data privacy becomes an illusion". Hence, the proposed solution is solely based on encrypting data with a key that is only known to the data owner.

A Framework for Security Transparency in Cloud Computing

Individuals and corporate users are persistently considering cloud adoption due to its significant benefits compared to traditional computing environments. The data and applications in the cloud are stored in an environment that is separated, managed and maintained externally to the organisation. Therefore, it is essential for cloud providers to demonstrate and implement adequate security practices to protect the data and processes put under their stewardship. Security transparency in the cloud is likely to become the core theme that underpins the systematic disclosure of security designs and practices that enhance customer confidence in using cloud service and deployment models. In this paper, we present a framework that enables a detailed analysis of security transparency for cloud based systems. In particular, we consider security transparency from three different levels of abstraction, i.e., conceptual, organisation and technical levels, and identify the relevant concepts within these levels. This allows us to provide an elaboration of the essential concepts at the core of transparency and analyse the means for implementing them from a technical perspective. Finally, an example from a real world migration context is given to provide a solid discussion on the applicability of the proposed framework.

A Provenance-Aware Policy Language (cProvl) and a Data Traceability Model (cProv) for the Cloud

Provenance plays a pivotal in tracing the origin of something and determining how and why something had occurred. With the emergence of the cloud and the benefits it encompasses, there has been a rapid proliferation of services being adopted by commercial and government sectors. However, trust and security concerns for such services are on an unprecedented scale. Currently, these services expose very little internal working to their customers; this can cause accountability and compliance issues especially in the event of a fault or error, customers and providers are left to point finger at each other. Provenance-based traceability provides a mean to address part of this problem by being able to capture and query events occurred in the past to understand how and why it took place. However, due to the complexity of the cloud infrastructure, the current provenance models lack the expressibility required to describe the inner-working of a cloud service. For a complete solution, a provenance-aware policy language is also required for operators and users to define policies for compliance purpose. The current policy standards do not cater for such requirement. To address these issues, in this paper we propose a provenance (traceability) model cProv, and a provenance-aware policy language (cProvl) to capture traceability data, and express policies for validating against the model. For implementation, we have extended the XACML3.0 architecture to support provenance, and provided a translator that converts cProvl policy and request into XACML type.

Continuous Quality Assurance and Optimisation in Cloud-Based Virtual Enterprises

Part 18: Optimization in Collaborative Networks

Orchestrating New Markets Using Cloud Services

Part 5: Service Orientation in Collaborative Networks

An energy-aware QoS enhanced method for service computing across clouds and data centers

QoS plays a key role in evaluating a service or a service composition plan across clouds and data centers. Currently, the energy cost of a service's execution is not covered by the QoS framework, and a service's price is often fixed during its execution. However, energy consumption has a great contribution in determining the price of a cloud service. As a result, it is not reasonable if the price of a cloud service is calculated with a fixed energy consumption value, if part of a service's energy consumption could be saved during its execution. Taking advantage of the dynamic energy-Aware optimal technique, a QoS enhanced method for service computing is proposed, in this paper, through virtual machine (VM) scheduling. Technically, two typical QoS metrics, i.e., the price and the execution time are taken into consideration in our method. Moreover, our method consists of two dynamic optimal phases. The first optimal phase aims at dynamically benefiting a user with discount price by transparently migrating his or her task execution from a VM located at a server with high energy consumption to a low one. The second optimal phase aims at shortening task's execution time, through transparently migrating a task execution from a VM to another one located at a server with higher performance. Experimental evaluation upon large scale service computing across clouds demonstrates the validity of our method.

Cloud omnibus system for evaluating cloud market services

Rapid expansion in numbers of cloud service providers (CSPs) has resulted in the introduction of various brokering services and cloud marketplaces designed to help customers to assess and choose CSPs. The problem of evaluating marketplaces and also separate sub-offerings supplied by cloud service providers (CSPs) has become vitally important because cloud marketplaces can now incorporate sub-offerings from different CSPs into a combined package tailored for each customer. In this paper, we introduce a new concept which we refer to as a cloud omnibus system (COS) and which we define as a cloud marketing system evaluating whole cloud marketplaces, CSPs, brokers and separate service sub-offerings from CSPs (sometimes combined into packages tailored for customers). COS is based on trust and management of trust and is beyond the state-of-the-art in providing a formal trust measuring mechanism for customers by introducing three types of trust which we refer to as ‘direct’, ‘relative’ and ‘transparent’.

Automating performance and energy consumption analysis for cloud applications

In cloud environments, IT solutions are delivered to users via shared infrastructure, enabling cloud service providers to deploy applications as services according to user QoS (Quality of Service) requirements. One consequence of this cloud model is the huge amount of energy consumption and significant carbon footprints caused by large cloud infrastructures. A key and common objective of cloud service providers is thus to develop cloud application deployment and management solutions with minimum energy consumption while guaranteeing performance and other QoS specified in Service Level Agreements (SLAs). However, finding the best deployment configuration that maximises energy efficiency while guaranteeing system performance is an extremely challenging task, which requires the evaluation of system performance and energy consumption under various workloads and deployment configurations. In order to simplify this process we have developed Stress Cloud, an automatic performance and energy consumption analysis tool for cloud applications in real-world cloud environments. Stress Cloud supports the modelling of realistic cloud application workloads, the automatic generation of load tests, and the profiling of system performance and energy consumption. We demonstrate the utility of Stress Cloud by analysing the performance and energy consumption of a cloud application under a broad range of different deployment configurations.

Building cloud-based healthcare data mining services

The linkage between healthcare service and cloud computing techniques has drawn much attention lately. Up to the present, most works focus on IT system migration and the management of distributed healthcare data rather than taking advantage of information hidden in the data. In this paper, we propose to explore healthcare data via cloud-based healthcare data mining services. Specifically, we propose a cloud-based healthcare data mining framework for healthcare data mining service development. Under such framework, we further develop a cloud-based healthcare data mining service to predict patients future length of stay in hospital.

Cloud computing adoption by SMEs in Australia.

This study aims to examine the important factors that influence SMEs’ adoption of cloud computing technology. The results showing that SMEs were influenced by factors related to advantaging their organizational capability rather than risk-related factors. The findings are useful to SMEs owners, Cloud service providers and government in establishing Cloud computing adoption strategies for SMEs.

Combating cyber attacks in cloud computing using machine learning techniques.

An extensive investigative survey on Cloud Computing with the main focus on gaps that is slowing down Cloud adoption as well as reviewing the threat remediation challenges. Some experimentally supported thoughts on novel approaches to address some of the widely discussed cyber-attack types using machine learning techniques. The thoughts have been constructed in such a way so that Cloud customers can detect the cyber-attacks in their VM without much help from Cloud service provider

A data dependency based strategy for intermediate data storage in scientific cloud workflow systems

Many scientific workflows are data intensive where large volumes of intermediate data are generated during their execution. Some valuable intermediate data need to be stored for sharing or reuse. Traditionally, they are selectively stored according to the system storage capacity, determined manually. As doing science in the cloud has become popular nowadays, more intermediate data can be stored in scientific cloud workflows based on a pay-for-use model. In this paper, we build an intermediate data dependency graph (IDG) from the data provenance in scientific workflows. With the IDG, deleted intermediate data can be regenerated, and as such we develop a novel intermediate data storage strategy that can reduce the cost of scientific cloud workflow systems by automatically storing appropriate intermediate data sets with one cloud service provider. The strategy has significant research merits, i.e. it achieves a cost-effective trade-off of computation cost and storage cost and is not strongly impacted by the forecasting inaccuracy of data sets' usages. Meanwhile, the strategy also takes the users' tolerance of data accessing delay into consideration. We utilize Amazon's cost model and apply the strategy to general random as well as specific astrophysics pulsar searching scientific workflows for evaluation. The results show that our strategy can reduce the overall cost of scientific cloud workflow execution significantly.

Situation awareness application

This thesis describes the design and implementation of a situation awareness application. The application gathers data from sensors including accelerometers for monitoring earthquakes, carbon monoxide sensors for monitoring fires, radiation detectors, and dust sensors. The application also gathers Internet data sources including data about traffic congestion on daily commute routes, information about hazards, news relevant to the user of the application, and weather. The application sends the data to a Cloud computing service which aggregates data streams from multiple sites and detects anomalies. Information from the Cloud service is then displayed by the application on a tablet, computer monitor, or television screen. The situation awareness application enables almost all members of a community to remain aware of critical changes in their environments.