Recent years have seen a paradigm shift from PC-centric computing to cloud computing. The advent of cloud computing has led to the emergence of various cloud services and providers. Cloud service brokers (CSBs) were introduced to serve as intermediaries between cloud service providers and cloud users who wish to select an appropriate cloud service. A CSB requires intermediation technologies with service recommendation, contract management, and cloud service usage assistance (such as evaluation) capabilities. These intermediation technologies enable CSBs to increase the quality of cloud service usage. However, currently commercially available CSBs fail to satisfy user requirements. In addition, many open research problems remain in the technologies and approaches underpinning CSB intermediation technologies. This paper proposes Cloud Service—Recommendation, Contract, and Evaluation (C-RCE), which supports CSB processes, including the management and operation of each proposed process. We implement a prototype of the proposed C-RCE process in a CSB to evaluate its performance and confirm that it is superior to existing CSBs. The proposed C-RCE process may be used as a guideline and reference model for constructing, operating, and managing actual CSBs.

The paper summarizes the computation results pursuing peak values of the Riemann zeta function. The computing method is based on the RS-Peak algorithm by which we are able to solve simultaneous Diophantine approximation problems efficiently. The computation environment was served by the SZTAKI Desktop Grid operated by the Laboratory of Parallel and Distributed Systems at the Hungarian Academy of Sciences and the ATLAS supercomputing cluster of the Eotvos Lorand University, Budapest. We present the largest Riemann zeta value known till the end of 2016.

The growing adoption of the MapReduce programming model increases the appeal of using Internet-wide computing platforms to run MapReduce applications on the Internet. However, current data distribution techniques, used in such platforms to distribute the high volumes of information which are needed to run MapReduce jobs, are naive, and therefore fail to offer an efficient approach for running MapReduce over the Internet. Thus, we propose a computing platform called freeCycles that runs MapReduce jobs over the Internet and provides two new main contributions: i) it improves data distribution, and ii) it increases intermediate data availability by replicating tasks or data through nodes in order to avoid losing intermediate data and consequently avoiding significant delays on the overall MapReduce execution time. We present the design and implementation of freeCycles, in which we use the BitTorrent protocol to distribute all data, along with an extensive set of performance results, which confirm the usefulness of the above mentioned contributions. Our system's improved data distribution and availability makes it an ideal platform for large scale MapReduce jobs.

The importance of evaluating performance of cloud systems has been increasing with the rapid growing market demands for cloud computing. However, the performance testers often have to go through the hassle of tedious manual operations when interacting with the cloud. A cloud performance evaluation framework is designed for both broad cloud support and good workload extensibility, which provides an automatic interface to monitor the capability and scalability of Infrastructure-as-a-Service cloud systems. Cloud API modules are implemented for Amazon EC2 service and OpenStack. It can achieve flexible control workflows for multiple of different workloads and user customization to test scenarios. With several built-in workloads and metric aggregation methods, a series of tests is performed on our private clouds to compare the performance and scalability from multiple aspects. A methodology is also proposed to build a cost-performance model to better understand and analyze the efficiency of different types of cloud systems. Based on the results of the experiments, the model indicates a polynomial relation between performance per instance and the overall cost.

In recent years data grids have been deployed and grown in many scientific experiments and data centers. The deployment of such environments has allowed grid users to gain access to a large number of distributed data. Data replication is a key issue in a data grid and should be applied intelligently because it reduces data access time and bandwidth consumption for each grid site. Therefore this area will be very challenging as well as providing much scope for improvement. In this paper, we introduce a new dynamic data replication algorithm named Popular File Group Replication, PFGR which is based on three assumptions: first, users in a grid site (Virtual Organization) have similar interests in files and second, they have the temporal locality of file accesses and third, all files are read-only. Based on file access history and first assumption, PFGR builds a connectivity graph for a group of dependent files in each grid site and replicates the most popular group files to the requester grid site. After that, when a user of that grid site needs some files, they are available locally. The simulation results show that our algorithm increases performance by minimizing the mean job execution time and bandwidth consumption and avoids unnecessary replication.

The security of software systems can be threatened by many internal and external threats, including data leakages due to timing channels. Even if developers manage to avoid security threats in the source code or bytecode during development and testing, new threats can arise as the compiler generates machine codes from representations at the binary code level during execution on the processor or due to operating system specifics. Current approaches either do not allow the neutralization of timing channels to be achieved comprehensively with a sufficient degree of security or require an unjustifiable amount of time and/or resources. Herein, a method is demonstrated for the protected execution of software based on a secure virtual execution environment (VEE) that combines the results from dynamic and static analyses to find timing channels through the application of code transformations. This solution complements other available techniques to prevent timing channels from being exploited. This approach helps control the appearance and neutralization of timing channels via just-in-time code modifications during all stages of program development and usage. This work demonstrates the identification of threats using timing channels as an example. The approach presented herein can be expanded to the neutralization of other types of threats.

This paper describes our proposal for Quality of Service (QoS) for Financial Modeling and Prediction as a Service (FMPaaS), since a majority of papers does not focus on SaaS level. We focus on two factors for delivering successful QoS, which are performance and accuracy for FMPaaS. The design process, theories and models behind the FMPaaS service have been explained. To support our FMPaaS service, two APIs have been developed to improve on performance and accuracy. Two major experiments have been illustrated and results show that each API processing can be completed in 2.12 seconds and 100,000 simulations can be completed in an acceptable period of time. Accuracy tests have been performed while using Facebook as an example. Three points of comparisons between actual and predicted prices have been undertaken. Results support accuracy since results are between 93.72 % and 99.63 % for Facebook. Three case studies have been used and results can support the accuracy and validity of the high level of accuracy offered by FMPaaS.

Researchers can perform large-scale analyses on diverse computing and data infrastructures such as NGIs (National Grid Infrastructures), XSEDE (Extreme Science and Engineering Discovery Environment) and PRACE (Partnership for Advanced Computing in Europe). Some are national like NGIs and XSEDE, some are international like PRACE and all of them require a more or less restrictive application process to get access to resources. Science gateways integrating diverse infrastructures provide the possibility to re-use methods independent of such underlying infrastructures and thus potentially deliver the technical prerequisite for creating reproducible science. To achieve this goal, science gateways have to be integrated seamlessly with security mechanisms and job, data as well as workflow management of the targeted resources. This paper gives an overview on general findings for porting science gateways as well as the challenges faced for porting the German MoSGrid science gateway (Molecular Simulation Grid) to exploit XSEDE and PRACE infrastructures.

Big data technologies and a range of Government open data initiatives provide the basis for discovering new insights into cities; how they are planned, how they managed and the day-to-day challenges they face in health, transport and changing population profiles. The Australian Urban Research Infrastructure Network (AURIN – www.aurin.org.au ) project is one example of such a big data initiative that is currently running across Australia. AURIN provides a single gateway providing online (live) programmatic access to over 2000 data sets from over 70 major and typically definitive data-driven organizations across federal and State government, across industry and across academia. However whilst open (public) data is useful to bring data-driven intelligence to cities, more often than not, it is the data that is not-publicly accessible that is essential to understand city challenges and needs. Such sensitive (unit-level) data has unique requirements on access and usage to meet the privacy and confidentiality demands of the associated organizations. In this paper we highlight a novel geo-privacy supporting solution implemented as part of the AURIN project that provides seamless and secure access to individual (unit-level) data from the Department of Health in Victoria. We illustrate this solution across a range of typical city challenges in localized contexts around Melbourne. We show how unit level data can be combined with other data in a privacy-protecting manner. Unlike other secure data access and usage solutions that have been developed/deployed, the AURIN solution allows any researcher to access and use the data in a manner that meets all of the associated privacy and confidentiality concerns, without obliging them to obtain ethical approval or any other hurdles that are normally put in place on access to and use of sensitive data. This provides a paradigm shift in secure access to sensitive data with geospatial content.