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.

In support of hydrocarbon exploration and discovery using clouds

Seismic data gathered from the Hydrocarbon Exploration and Discovery Operation is essential to identify possible hydrocarbon existence in a geologically surveyed area. However, the discovery operation takes a long time to be completed and computational processing of the acquired data is often delayed. Hydrocarbon exploration may end up needlessly covering an area without any hydrocarbon traces due to lack of immediate feedback from geophysical experts. This feedback can only be given when the acquired seismic data is computationally processed, analysed and interpreted. In response, we propose a comprehensive model to facilitate Hydrocarbon Exploration and Discovery Operation using encryption, decryption, satellite transmission and clouds. The model details the logical design of Seismic Data Processing (SDP) that exploits clouds and the ability for geophysical experts to provide on-line decisions on how to progress the hydrocarbon exploration operation at a remote location. Initial feasibility assessment was carried out to support our model. The SDP, data encryption and encryption for the assessment were carried out on a private cloud. The assessment shows that the overall process of hydrocarbon exploration from data acquisition, satellite data transmission through to SDP could be executed in a short time and at low costs.

Watching clouds over country : reconsidering Australian indigenous perspectives about environmental change and climate change

This paper considers an Indigenous perspective on the rapidly transforming Australian environment and the impact of world climate change. It is largely based upon a National Climate Change Adaptation and Research Facility (NCCARF) research project, in progress, that is seeking to translate a south-eastern Australian Indigenous perspective of how climate change affects 'country'. The project involves direct community consultation and workshops, framed by a literature review and longstanding author involvements with Indigenous communities in planning, design and native title projects, and will discuss conclusions being raised. Importantly, this discourse is being formulated with peri-urban based Indigenous communities whom are well educated and deeply involved with statutory and strategic planning processes and native title debates.

Personal ICT Ensembles and Ubiquitous Information Systems Environments: Key Issues and Research Implications

Personal information and communication technologies (ICTs) have become commonplace. Today many people own, or have access to, a range of different computing and communication devices, information technologies, and services, which they incorporate into their everyday routines. Increasingly, these technologies impact the way that individuals work, socialize, and play. Workers are bringing their personal ICTs to the office, and organizations are tailoring their computing environments toward ubiquitous integration with personal ICTs. These developments are opening up new ways of working, but they also create new challenges for organizations in accommodating this “nonaffiliated” use as part of their information systems environments. In this article we propose a framework for analyzing the composition and impact of personal ICT ensembles. The framework is positioned as pre-theory that invites further development and empirical testing. We illustrate how the proposed framework could be applied to consider personal ICT use across the work/home context. Several implications stemming from the notion of a personal ICT ensemble are highlighted, including practical considerations for nonaffiliated use in organizations. We conclude with suggestions for further development of the proposed framework.

Optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers

For multiple heterogeneous multicore server processors across clouds and data centers, the aggregated performance of the cloud of clouds can be optimized by load distribution and balancing. Energy efficiency is one of the most important issues for large-scale server systems in current and future data centers. The multicore processor technology provides new levels of performance and energy efficiency. The present paper aims to develop power and performance constrained load distribution methods for cloud computing in current and future large-scale data centers. In particular, we address the problem of optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers. Our strategy is to formulate optimal power allocation and load distribution for multiple servers in a cloud of clouds as optimization problems, i.e., power constrained performance optimization and performance constrained power optimization. Our research problems in large-scale data centers are well-defined multivariable optimization problems, which explore the power-performance tradeoff by fixing one factor and minimizing the other, from the perspective of optimal load distribution. It is clear that such power and performance optimization is important for a cloud computing provider to efficiently utilize all the available resources. We model a multicore server processor as a queuing system with multiple servers. Our optimization problems are solved for two different models of core speed, where one model assumes that a core runs at zero speed when it is idle, and the other model assumes that a core runs at a constant speed. Our results in this paper provide new theoretical insights into power management and performance optimization in data centers.

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.

An algorithm for finding the minimum cost of storing and regenerating datasets in multiple clouds

The proliferation of cloud computing allows users to flexibly store, re-compute or transfer large generated datasets with multiple cloud service providers. However, due to the pay-As-you-go model, the total cost of using cloud services depends on the consumption of storage, computation and bandwidth resources which are three key factors for the cost of IaaS-based cloud resources. In order to reduce the total cost for data, given cloud service providers with different pricing models on their resources, users can flexibly choose a cloud service to store a generated dataset, or delete it and choose a cloud service to regenerate it whenever reused. However, finding the minimum cost is a complicated yet unsolved problem. In this paper, we propose a novel algorithm that can calculate the minimum cost for storing and regenerating datasets in clouds, i.e. whether datasets should be stored or deleted, and furthermore where to store or to regenerate whenever they are reused. This minimum cost also achieves the best trade-off among computation, storage and bandwidth costs in multiple clouds. Comprehensive analysis and rigid theorems guarantee the theoretical soundness of the paper, and general (random) simulations conducted with popular cloud service providers' pricing models demonstrate the excellent performance of our approach.

Personalized search over encrypted data with efficient and secure updates in mobile clouds

Mobile cloud computing has been involved as a key enabling technology to overcome the physical limitations of mobile devices towards scalable and flexible mobile services. In the mobile cloud environment, searchable encryption, which enables directly search over encrypted data, is a key technique to maintain both the privacy and usability of outsourced data in cloud. On addressing the issue, many research efforts resolve to using the searchable symmetric encryption (SSE) and searchable public-key encryption (SPE). In this paper, we improve the existing works by developing a more practical searchable encryption technique, which can support dynamic updating operations in the mobile cloud applications. Specifically, we make our efforts on taking the advantages of both SSE and SPE techniques, and propose PSU, a Personalized Search scheme over encrypted data with efficient and secure Updates in mobile cloud. By giving thorough security analysis, we demonstrate that PSU can achieve a high security level. Using extensive experiments in a realworld mobile environment, we show that PUS is more efficient compared with the existing proposals.