Cloud-assisted body area networks: state-of-the-art and future challenges

Body area networks (BANs) are emerging as enabling technology for many human-centered application domains such as health-care, sport, fitness, wellness, ergonomics, emergency, safety, security, and sociality. A BAN, which basically consists of wireless wearable sensor nodes usually coordinated by a static or mobile device, is mainly exploited to monitor single assisted livings. Data generated by a BAN can be processed in real-time by the BAN coordinator and/or transmitted to a server-side for online/offline processing and long-term storing. A network of BANs worn by a community of people produces large amount of contextual data that require a scalable and efficient approach for elaboration and storage. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of body sensor data streams. In this paper, we motivate the introduction of Cloud-assisted BANs along with the main challenges that need to be addressed for their development and management. The current state-of-the-art is overviewed and framed according to the main requirements for effective Cloud-assisted BAN architectures. Finally, relevant open research issues in terms of efficiency, scalability, security, interoperability, prototyping, dynamic deployment and management, are discussed.

Toward ease of discovery, selection and use of clusters within a cloud

While the emergence of clouds had lead to a significant paradigm shift in business and research, cloud computing is still in its infancy. Specifically, there is no effective publication and discovery service nor are cloud services easy to use. This paper presents a new technology for offering ease of discovery, selection and use of clusters hosted within clouds. By improving these services, cloud clusters become easily accessible to all clients, software services to noncomputing human user.

Prevention of information harvesting in a cloud services environment

We consider a cloud data storage involving three entities, the cloud customer, the cloud business centre which provides services, and the cloud data storage centre. Data stored in the data storage centre comes from a variety of customers and some of these customers may compete with each other in the market place or may own data which comprises confidential information about their own clients. Cloud staff have access to data in the data storage centre which could be used to steal identities or to compromise cloud customers. In this paper, we provide an efficient method of data storage which prevents staff from accessing data which can be abused as described above. We also suggest a method of securing access to data which requires more than one staff member to access it at any given time. This ensures that, in case of a dispute, a staff member always has a witness to the fact that she accessed data.

CBF : A packet filtering method for DDoS attack defense in cloud environment

Distributed Denial-of-Service attack (DDoS) is a major threat for cloud environment. Traditional defending approaches cannot be easily applied in cloud security due to their relatively low efficiency, large storage, to name a few. In view of this challenge, a Confidence-Based Filtering method, named CBF, is investigated for cloud computing environment, in this paper. Concretely speaking, the method is deployed by two periods, i.e., non-attack period and attack period. More specially, legitimate packets are collected at non-attack period, for extracting attribute pairs to generate a nominal profile. With the nominal profile, the CBF method is promoted by calculating the score of a particular packet at attack period, to determine whether to discard it or not. At last, extensive simulations are conducted to evaluate the feasibility of the CBF method. The result shows that CBF has a high scoring speed, a small storage requirement and an acceptable filtering accuracy, making it suitable for real-time filtering in cloud environment.

Active data-centric framework for data protection in cloud environment

Cloud computing is an emerging evolutionary computing model that provides highly scalable services over highspeed Internet on a pay-as-usage model. However, cloud-based solutions still have not been widely deployed in some sensitive areas, such as banking and healthcare. The lack of widespread development is related to users’ concern that their confidential data or privacy would leak out in the cloud’s outsourced environment. To address this problem, we propose a novel active data-centric framework to ultimately improve the transparency and accountability of actual usage of the users’ data in cloud. Our data-centric framework emphasizes “active” feature which packages the raw data with active properties that enforce data usage with active defending and protection capability. To achieve the active scheme, we devise the Triggerable Data File Structure (TDFS). Moreover, we employ the zero-knowledge proof scheme to verify the request’s identification without revealing any vital information. Our experimental outcomes demonstrate the efficiency, dependability, and scalability of our framework.

Enhancement of user level controls in cloud databases

Industries in developed countries are moving quickly to ensure the rapid adoption of cloud computing. At this stage, several outstanding issues exist, particularly related to Service Level Agreements (SLAs), security and privacy. Consumers and businesses are willing to use cloud computing only if they can trust that their data will remain private and secure. Our review of research literature indicates the level of control that a user has on their data is directly correlated to the level of data privacy provided by the cloud service. We considered several privacy factors from the industry perspective, namely data loss, data storage location being unknown to the client, vendor lock-in, unauthorized secondary use of user's data for advertising, targeting secured backup and easy restoration. The level of user control in database models were identified according to the level of existence in these privacy factors. Finally, we focused on a novel logical model that might help to bring the level of user control of privacy in cloud databases into a higher level.

Microsoft and Amazon : a comparison of approaches to cloud security

Research has shown that data security has always been an important aspect of quality of service for data service providers; but cloud computing poses new and challenging security threats. The most common security concerns for users of cloud storage are data confidentiality, integrity and availability. Microsoft has considered these concerns and responded with the Azure virtual private storage based on Searchable Encryption. Amazon has also responded to these security issues with its Amazon Web Services. In this paper, we investigate and compare in depth the features of Microsoft Azure and Amazon Web Services deemed to provide security with a particular focus on confidentiality, integrity and availability of data.

Multi-cloud application execution and data management

Recently, fields with substantial computing requirementshave turned to cloud computing for economical, scalable, and on-demandprovisioning of required execution environments. However, current cloudofferings focus on providing individual servers while tasks such as applicationdistribution and data preparation are left to cloud users. This article presents anew form of cloud called HPC Hybrid Deakin (H2D) cloud; an experimentalhybrid cloud capable of utilising both local and remote computational servicesfor large embarrassingly parallel applications. As well as supporting execution,H2D also provides a new service, called DataVault, that provides transparentdata management services so all cloud-hosted clusters have required datasetsbefore commencing execution.

Exposing HPC and sequential applications as services through the development and deployment of a SaaS cloud

Cloud and service computing has started to change the way research in science, in particular biology and medicine, is being carried out. Researchers that have taken advantage of this technology (making use of public and private cloud compute resources) can process large amounts of data (big data) and speed up discovery. However, this requires researchers to acquire a solid knowledge and skills in the development of sequential and high performance computing (HPC), and cloud development and deployment background. In response a technology exposing HPC applications as services through the development and deployment of a SaaS cloud, and its proof of concept in the form of implementation of a cloud environment, Uncinus, has been developed and implemented to allow researchers easy access to cloud computing resources. The new technology offers and Uncinus supports the development of applications as services and the sharing of compute resources to speed up applications' execution. Users access these cloud resources and services through web interfaces. Using the Uncinus platform, a bio-informatics workflow was executed on a private (HPC) cloud, server and public cloud (Amazon EC2) resources, performance results showing a 3 fold improvement compared to local resources' performance. Biology and medicine specialists with no programming and application deployment on clouds background could run the case study applications with ease.

Security and privacy concerns for Australian SMEs cloud adoption: empirical study of metropolitan vs regional SMEs

New national infrastructure initiatives such as the National Broadband Network (NBN) allow small and medium-sized enterprises (SMEs) in Australia to have greater access to cost effective Cloud computing. However, the ability of Cloud computing to store data remotely and share services in a dynamic environment brings with it security and privacy concerns. Evaluating these concerns is critical to address the Cloud computing underutilisation issue and leverage the benefits of costly NBN investment. This paper examines the influence of privacy and security factors on Cloud adoption by Australian SMEs in metropolitan and regional area. Data were collected from 150 Australian SMEs (specifically, 79 metropolitan SMEs and 71 regional SMEs) and structural equation modelling was used for the analysis. The findings reveal that privacy and security factors do not significantly influence the decision-making of Australian SMEs in the adoption of Cloud computing. Moreover, the results indicate that Cloud computing adoption is not influenced by the geographical location (i.e., metropolitan or regional location) of the SMEs. The findings extend the current understanding of Cloud computing adoption by Australian SMEs. The results will be useful to SMEs, Cloud service providers and policy makers devising Cloud security and privacy policies.

Cloud Integrator: uma plataforma para composição de serviços em ambientes de computação em nuvem

With the advance of the Cloud Computing paradigm, a single service offered by a cloud platform may not be enough to meet all the application requirements. To fulfill such requirements, it may be necessary, instead of a single service, a composition of services that aggregates services provided by different cloud platforms. In order to generate aggregated value for the user, this composition of services provided by several Cloud Computing platforms requires a solution in terms of platforms integration, which encompasses the manipulation of a wide number of noninteroperable APIs and protocols from different platform vendors. In this scenario, this work presents Cloud Integrator, a middleware platform for composing services provided by different Cloud Computing platforms. Besides providing an environment that facilitates the development and execution of applications that use such services, Cloud Integrator works as a mediator by providing mechanisms for building applications through composition and selection of semantic Web services that take into account metadata about the services, such as QoS (Quality of Service), prices, etc. Moreover, the proposed middleware platform provides an adaptation mechanism that can be triggered in case of failure or quality degradation of one or more services used by the running application in order to ensure its quality and availability. In this work, through a case study that consists of an application that use services provided by different cloud platforms, Cloud Integrator is evaluated in terms of the efficiency of the performed service composition, selection and adaptation processes, as well as the potential of using this middleware in heterogeneous computational clouds scenarios