Stream management within the CloudMiner

Nowadays cloud computing has become a major trend that enterprises and research organizations are pursuing with increasing zest. A potentially important application area for clouds is data analytics. In our previous publication, we introduced a novel cloud infrastructure, the CloudMiner, which facilitates data mining on massive scientific data. By providing a cloud platform which hosts data mining cloud services following the Software as a Service (SaaS) paradigm, CloudMiner offers the capability for realizing cloud-based data mining tasks upon traditional distributed databases and other dataset types. However, little attention has been paid to the issue of data stream management on the cloud so far. We have noticed the fact that some features of the cloud meet very well the requirements of data stream management. Consequently, we developed an innovative software framework, called the StreamMiner, which is introduced in this paper. It serves as an extension to the CloudMiner for facilitating, in particular, real-world data stream management and analysis using cloud services. In addition, we also introduce our tentative implementation of the framework. Finally, we present and discuss the first experimental performance results achieved with the first StreamMiner prototype.

EST-PAC HPC - a web portal for high-throughput EST annotation and protein sequence prediction

Expressed Sequence Tags (ESTs) are short DNA sequences generated by sequencing the transcribed cDNAs coming from a gene expression. They can provide significant functional, structural and evolutionary information and thus are a primary resource for gene discovery. EST annotation basically refers to the analysis of unknown ESTs that can be performed by database similarity search for possible identities and database search for functional prediction of translation products. Such kind of annotation typically consists of a series of repetitive tasks which should be automated, and be customizable and amenable to using distributed computing resources. Furthermore, processing of EST data should be done efficiently using a high performance computing platform. In this paper, we describe an EST annotator, EST-PACHPC, which has been developed for harnessing HPC resources potentially from Grid and Cloud systems for high throughput EST annotations. The performance analysis of EST-PACHPC has shown that it provides substantial performance gain in EST annotation.

Novel applications and research problems for sensor-clouds

Recent developments in sensor networks and cloud computing saw the emergence of a new platform called sensor-clouds. While the proposition of such a platform is to virtualise the management of physical sensor devices, we foresee novel applications being created based on a new class of social sensors. Social sensors are effectively a human-device combination that sends torrents of data as a result of social interactions. The data generated appear in different formats such as photographs, videos, or short texts, etc. Unlike other sensor devices, social sensors operate on the control of individuals via their mobile devices like smart phones, tablets or laptops. Further, they do not generate data at a constant rate or format like other sensors do. Instead, data from social sensors are spurious and varied, often in response to social events, or a news announcement of interests to the public. This collective presence of social data creates opportunities for novel applications never experienced before. This paper discusses three such applications utilising social sensors within a sensor-cloud environment. Consequently, the associated research problems are also presented.

Current trends in product lifecycle management

Efficient and effective Product Lifecycle Management, as an evolution and enhancement of Product Data Management, is of strategic importance for virtually any company. Hence, it is crucial for companies to analyze and evaluate recent trends in information technology (IT) and their implications on Product Lifecycle Management. In this paper, the results of an interdisciplinary study conducted by Siemens AG, a major international technologies firm, and two universities are presented. The study identifies four current trends in IT and then evaluates their potential implications on Product Lifecycle Management. Finally, the IT trends are ranked according to their short and medium term effects on Product Lifecycle Management.

Execution of compute intensive applications on hybrid clouds (case study with mpiBLAST)

Cloud computing is the most recent realisation of computing as a utility. Recently, fields with substantial computational requirements, e.g., biology, are turning to clouds for cheap, on-demand provisioning of resources. Of interest to this paper is the execution of compute intensive applications on hybrid clouds. If application requirements exceed private cloud resource capacity, clients require scaling down their applications. The outcome of this research is Web technology realising a new form of cloud called HPC Hybrid Deakin (H2D) Cloud -- an experimental hybrid cloud capable of utilising both local and remote computational services for single large embarrassingly parallel applications.

Towards designing privacy-preserving signature-based IDS as a service : a study and practice

With the advent of Cloud Computing, IDS as a service (IDSaaS) has been proposed as an alternative to protect a network (e.g., financial organization) from a wide range of network attacks by offloading the expensive operations such as the process of signature matching to the cloud. The IDSaaS can be roughly classified into two types: signature-based detection and anomaly-based detection. During the packet inspection, no party wants to disclose their own data especially sensitive information to others, even to the cloud provider, for privacy concerns. However, current solutions of IDSaaS have not much discussed this issue. In this work, focus on the signature-based IDSaaS, we begin by designing a promising privacy-preserving intrusion detection mechanism, the main feature of which is that the process of signature matching does not reveal any specific content of network packets by means of a fingerprint-based comparison. We further conduct a study to evaluate this mechanism under a cloud scenario and identify several open problems and issues for designing such a privacy-preserving mechanism for IDSaaS in a practical environment.

Supporting decision making : the BI sweet spot

In this paper, we propose the concept: the BI Sweet Spot. The BI Sweet Spot ecosystem includes mobile computing, cloud computing and Big Data. We provide an overview for each of the key components and explain how these three components support the BI Sweet Spot. We also discuss best practices for managing these essential components. This study is the first-of-its-kind work in the BI research that considers the inter-relationships and the combined effect of mobile, cloud and Big Data.

Analysis of insiders attack mitigation strategies

Insider threat has become a serious information security issues within organizations. In this paper, we analyze the problem of insider threats with emphases on the Cloud computing platform. Security is one of the major anxieties when planning to adopt the Cloud. This paper will contribute towards the conception of mitigation strategies that can be relied on to solve the malicious insider threats. While Cloud computing relieves organizations from the burden of the data management and storage costs, security in general and the malicious insider threats in particular is the main concern in cloud environments. We will analyses the existing mitigation strategies to reduce malicious insiders threats in Cloud computing.

Secure outsourced attribute-based signatures

Attribute-based signature (ABS) enables users to sign messages over attributes without revealing any information other than the fact that they have attested to the messages. However, heavy computational cost is required during signing in existing work of ABS, which grows linearly with the size of the predicate formula. As a result, this presents a significant challenge for resource-constrained devices (such as mobile devices or RFID tags) to perform such heavy computations independently. Aiming at tackling the challenge above, we first propose and formalize a new paradigm called Outsourced ABS, i.e., OABS, in which the computational overhead at user side is greatly reduced through outsourcing intensive computations to an untrusted signing-cloud service provider (S-CSP). Furthermore, we apply this novel paradigm to existing ABS schemes to reduce the complexity. As a result, we present two concrete OABS schemes: i) in the first OABS scheme, the number of exponentiations involving in signing is reduced from O(d) to O(1) (nearly three), where d is the upper bound of threshold value defined in the predicate; ii) our second scheme is built on Herranz et al.'s construction with constant-size signatures. The number of exponentiations in signing is reduced from O(d2) to O(d) and the communication overhead is O(1). Security analysis demonstrates that both OABS schemes are secure in terms of the unforgeability and attribute-signer privacy definitions specified in the proposed security model. Finally, to allow for high efficiency and flexibility, we discuss extensions of OABS and show how to achieve accountability as well.

Changing media landscapes: visual networking

 Combines theory, key issues for debate and an exploration of legacy and digital media industries to provide a holistic approach to communication and media.Activities, study questions and further reading/weblinks at the end of each chapter to help students put theory into context and further their understanding of key concepts.It covers the latest trends emerging from the deregulation of many media industries and then outlines future scenarios for a globally competitive digital media environment.Explores the contemporary intersections between social media, legacy media and communications with other studies in history, statistics, privacy and surveillance, public policy, media law and economics. The nature of media forms and industries is changing rapidly and constantly. As such, Changing Media Landscapes explores the concept of visual networking to describe the ways multiple media devices are used now for a variety of tasks. Visual networking extends the ability to engage in human communication particularly in today's context where most of our daily activities and routines are carried out with the help of various forms of communication technologies. It explores the changing media landscape through contemporary and developing latest trends, issues and developments including multicasting, cloud computing, privacy and social networking. It combines theory, key issues for debate and an exploration of legacy and digital media industries to provide a holistic approach to communication and media.

A data as a product model for future consumption of big stream data in clouds

Data is becoming the world’s new natural resourceand big data use grows quickly. The trend of computingtechnology is that everything is merged into the Internet and‘big data’ are integrated to comprise completeinformation for collective intelligence. With the increasingsize of big data, refining big data themselves to reduce data sizewhile keeping critical data (or useful information) is a newapproach direction. In this paper, we provide a novel dataconsumption model, which separates the consumption of datafrom the raw data, and thus enable cloud computing for bigdata applications. We define a new Data-as-a-Product (DaaP)concept; a data product is a small sized summary of theoriginal data and can directly answer users’ queries. Thus, weseparate the mining of big data into two classes of processingmodules: the refine modules to change raw big data into smallsizeddata products, and application-oriented mining modulesto discover desired knowledge further for applications fromwell-defined data products. Our practices of mining big streamdata, including medical sensor stream data, streams of textdata and trajectory data, demonstrated the efficiency andprecision of our DaaP model for answering users’ queries

Enabling efficient and secure outsourcing of large matrix multiplications

With the growing popularity of cloud computing, outsourced computing has attracted much research effort recently. A computationally weak client is capable of delegating its heavy computing tasks, such as large matrix multiplications, to the cloud server. Critical requirements for such tasks include the need to guarantee the unforgeability of computing results and the preservation of the privacy of clients. On one hand, the result computed by the cloud server needs to be verified since the cloud server cannot be fully honest. On the other hand, as the data involved in computing may contain some sensitive information of the client, the data should not be identified by the cloud server. In this paper, we address these above issues by developing an Efficient and Secure Outsourcing scheme for Large Matrix Multiplication, named ESO- LMM. Security analysis demonstrates that ESO-LMM achieves the security requirements in terms of unforgeability of proof and privacy protection of outsourced data. Furthermore, performance evaluation indicates that ESO-LMM is much more efficient compared with the existing works in terms of computation, communication and storage overhead.

Competing in the clouds: a strategic challenge for ITSP Ltd

By 2010, cloud computing had become established as a new model of IT provisioning for service providers. New market players and businesses emerged, threatening the business models of established market players. This teaching case explores the challenges arising through the impact of the new cloud computing technology on an established, multinational IT service provider called ITSP. Should the incumbent vendors adopt cloud computing offerings? And, if so, what form should those offerings take? The teaching case focuses on the strategic dimensions of technological developments, their threats and opportunities. It requires strategic decision making and forecasting under high uncertainty. The critical question is whether cloud computing is a disruptive technology or simply an alternative channel to supply computing resources over the Internet. The case challenges students to assess this new technology and plan ITSP’s responses.

Enabling efficient publicly verifiable outsourcing computation for matrix multiplication

Outsourcing heavy computational tasks to remote cloud server, which accordingly significantly reduce the computational burden at the end hosts, represents an effective and practical approach towards extensive and scalable mobile applications and has drawn increasing attention in recent years. However, due to the limited processing power of the end hosts yet the keen privacy concerns on the outsourced data, it is vital to ensure both the efficiency and security of the outsourcing computation in the cloud computing. In this paper, we address the issue by developing a publicly verifiable outsourcing computation proposal. In particular, considering a large amount of applications of matrix multiplication in large datasets and image processing, we propose a publicly verifiable outsourcing computation scheme for matrix multiplication in the amortized model. Security analysis demonstrates that the proposed scheme is provable secure by blinding input and output in a simple way. By comparing the developed scheme with existing proposals, we show that our proposal is more efficient in terms of functionality, as well as the computation, communication and storage overhead.