Cloud Data Services Research Articles

Interconnection Structures, Management and Routing Challenges in Cloud-Service Data Center Networks: A Survey

Today’s data center networks employ expensive networking equipments in associated structures that were not designed to meet the increasing requirements of the current large-scale data center services. Limitations that vary between reliability, resource utilization, and high costs are challenging. The era of cloud computing represents a promise to enable large-scale data centers. Computing platforms of such cloud service data centers consist of large number of commodity low-price servers that, with a theme of virtualization on top, can meet the performance of the expensive high-level servers at only a fraction of the price. Recently, the research in data center networks started to evolve rapidly. This opened the path for addressing many of its design and management challenges, these like scalability, reliability, bandwidth capacities, virtual machines’ migration, and cost. Bandwidth resource fragmentation limits the network agility, and leads to low utilization rates, not only for the bandwidth resources, but also for the servers that run the applications. With Traffic Engineering methods, managers of such networks can adapt for rapid changes in the network traffic among their servers, this can help to provide better resource utilization and lower costs. The market is going through exciting changes, and the need to run demanding-scale services drives the work toward cloud networks. These networks that are enabled by the notation of autonomic management, and the availability of commodity low-price network equipments. This work provides the readers with a survey that presents the management challenges, design and operational constraints of the cloud-service data center networks

Outsourcing Set Intersection Computation Based on Bloom Filter for Privacy Preservation in Multimedia Processing

With the development of cloud computing, the advantages of low cost and high computation ability meet the demands of complicated computation of multimedia processing. Outsourcing computation of cloud could enable users with limited computing resources to store and process distributed multimedia application data without installing multimedia application software in local computer terminals, but the main problem is how to protect the security of user data in untrusted public cloud services. In recent years, the privacy-preserving outsourcing computation is one of the most common methods to solve the security problems of cloud computing. However, the existing computation cannot meet the needs for the large number of nodes and the dynamic topologies. In this paper, we introduce a novel privacy-preserving outsourcing computation method which combines GM homomorphic encryption scheme and Bloom filter together to solve this problem and propose a new privacy-preserving outsourcing set intersection computation protocol. Results show that the new protocol resolves the privacy-preserving outsourcing set intersection computation problem without increasing the complexity and the false positive probability. Besides, the number of participants, the size of input secret sets, and the online time of participants are not limited.

Resource management for deadline constrained MapReduce jobs for minimising energy consumption

Cloud computing has emerged as one of the leading platforms for processing large-scale data intensive applications. Such applications are executed in large clusters and data centres which require a substantial amount of energy. Energy consumption within data centres accounts for a considerable fraction of costs and is a significant contributor to global greenhouse gas emissions. Therefore, minimising energy consumption in data centres is a critical concern for data centre operators, cluster owners, and cloud service providers. In this paper, we devise a novel energy aware MapReduce resource manager for an open system, called EAMR-RM, that can effectively perform matchmaking and scheduling of MapReduce jobs each of which is characterised by a service level agreement (SLA) for performance that includes a client specified earliest start time, execution time, and a deadline with the objective of minimising data centre energy consumption. Performance analysis demonstrates that for a range of system and workload parameters experimented with the proposed technique can effectively satisfy SLA requirements while achieving up to a 45% reduction in energy consumption compared to approaches which do not consider energy in resource management decisions.

Data security and risk assessment in cloud computing

Cloud computing has attracted more and more attention as it reduces the cost of IT infrastructure of organizations. In our country, business Cloud services, such as Alibaba Cloud, Huawei Cloud, QingCloud, UCloud and so on are gaining more and more uses, especially small or median organizations. In the cloud service scenario, the program and data are migrating into cloud, resulting the lack of trust between customers and cloud service providers. However, the recent study on Cloud computing is mainly focused on the service side, while the data security and trust have not been sufficiently studied yet. This paper investigates into the data security issues from data life cycle which includes five steps when an organization uses Cloud computing. A data management framework is given out, including not only the data classification but also the risk management framework. Concretely, the data is divided into two varieties, business and personal information. And then, four classification levels (high, medium, low, normal) according to the different extent of the potential adverse effect is introduced. With the help of classification, the administrators can identify the application or data to implement corresponding security controls. At last, the administrators conduct the risk assessment to alleviate the risk of data security. The trust between customers and cloud service providers will be strengthen through this way.

Design and implementation of aquaculture resource planning using underwater sensor wireless network

Aquaculture is a globally fast-growing food sector, and its economic significance is increasing consistently. Currently, site selection for aquaculture is based on remote sensing and Geographic Information Systems (GIS), which provide infrastructure information. The information regarding environmental factors such as water properties are measured manually. There is a requirement for building a database on water properties at various river sites that are favourable for aquaculture. Underwater wireless sensor network (WSN) systems are being developed to measure various water parameters for aquaculture monitoring, and the mobility of sensor is considered for node deployment to increase the accuracy ofmeasuredwater parameters. The present system integrates WSN nodes to extract the data and connect to cloud data server. A centralized database is created to store the information/data, which is analysed using clustering k-means algorithm. The proposed work presents an aquaculture resource planning (ARP), a SaaS (Software-as-a-Service) application; the users can access information using the application “Aquaward” and analyse various river sites that can be used for aquaculture development

Design of Intelligent Manufacturing Big Data Cloud Service Platform

With the coming of the intelligent manufacturing, the technology and application of industrial big data will be popular in the future. The productivity, competitiveness and innovation of the manufacturing industries will be improved through the integrated innovation of big data technology and industries. Besides, products, production process, management, services, new form and new models will be more intellectualized. They will support the transformation and upgrading of manufacturing industry and the construction of an open, shared and collaborative ecological environment for intelligent manufacturing industry.

Bi-level programming optimization method for cloud manufacturing service composition based on harmony search

Mobile computing diffluence has become critical technology to break out inherent processing capacity and memory limitation of mobile terminal equipment. Existing mobile cloud computing solution is to gain relatively little delay by visiting adjacent cloud service data center with abundant resources through mobile equipment, but it will increase network energy consumption. To solve the problem, research wireless-optical network model firstly and design new multi-objective model to consider network delay and energy consumption simultaneously, and because magnitude order of 2 indexes is different, single object optimization method is not applicable. So multi-objective water droplet algorithm is utilized to gain its second-best solution, and to improve performance of multi-objective water droplet algorithm simultaneously, improve population diversity of multi-objective intelligent water droplet algorithm by referring to NSGA II density design method and designing quick sorting method on the basis of individual density. Simulation result shows that performance of multi-objective intelligent water droplet algorithm is improved obviously through density design and multi-objective optimization to mobile cloud service of wireless-optical network is realized.

IoT Based Agriculture as a Cloud and Big Data Service

Cloud computing has transpired as a new model for managing and delivering applications as services efficiently. Convergence of cloud computing with technologies such as wireless sensor networking, Internet of Things (IoT) and Big Data analytics offers new applications' of cloud services. This paper proposes a cloud-based autonomic information system for delivering Agriculture-as-a-Service (AaaS) through the use of cloud and big data technologies. The proposed system gathers information from various users through preconfigured devices and IoT sensors and processes it in cloud using big data analytics and provides the required information to users automatically. The performance of the proposed system has been evaluated in Cloud environment and experimental results show that the proposed system offers better service and the Quality of Service (QoS) is also better in terms of QoS parameters.

2.5D FPGA-HBM Integration Challenges

Abstract FPGA partitioning and high density integration using interposer based 2.5D stacked silicon interconnect technology (SSIT) has been the pioneering work at Xilinx for several years enabling advanced applications in high performance computing, networking, hyper scale data center and cloud services etc. With the insatiable demand for acceleration workloads, FPGAs need to be coupled with in package memory to enable higher bandwidth, lower power and smaller form factor architecture. 3D stacking based high bandwidth memory (HBM) has paved the way to realize such applications providing 10X higher bandwidth, 4X lower power vs DDR4. This paper provides an overview of unique challenges involved with 2.5D FPGA-HBM SSIT integration from design, process/package development, test and reliability point of view

An Extensive Study on Data Anonymization Algorithms Based on K-Anonymity

For business and research oriented works engaging Data Analysis and Cloud services needing qualitative data, many organizations release huge microdata. It excludes an individual’s explicit identity marks like name, address and comprises of specific information like DOB, Pin-code, sex, marital status, which can be combined with other public data to recognize a person. This implication attack can be manipulated to acquire any sensitive information from social network platform, thereby putting the privacy of a person in grave danger. To prevent such attacks by modifying microdata, K-anonymization is used. With potentially increasing data, the effective method to anonymize it stands challenging. After series of trails and systematic comparison, in this paper, we propose three best algorithms along with its efficiency and effectiveness. Studies help researchers to identify the relationship between the values of k, degree of anonymization, choosing a quasi-identifier and focus on execution time.

Design of Cold Chain Traceability System Based on Internet of Things

A cold chain traceability system controlled via the Internet of Things technology was developed to investigate the details of the cold chain logistics process. This system includes the logistics environment detection part and the cloud data service part. By the cold chain traceability system, it is able to store the ambient temperature data, humidity data and geographic location data in the cold chain logistics process locally and transmit them through the GPRS to the cloud data service platform. The cold chain traceability system can not only provide managers with a convenient way of monitoring but also bring more intuitively obtain real-time and historical information.

A Study of Development and Formation of Personal Information Trust Service in Japan

This paper examines a Personal Information Trust Service as a conceptual model of a Personal Data Service to unlock personal data from collection to use without privacy invasion. Personal Data Service is a service to let an individual store, manage, and deploy their key personal data in a highly secure and structured way. First, the authors explore an accounting cloud service with two-sided markets structure and freemium business model and discuss success factors of its diffusion. Second, they illustrate the duality of structure situated between the business model of the accounting cloud service and Personal Data Service. Third, they examine a conceptual model of Personal Information Trust Service as one of Personal Data Service. At last, the authors discuss strategies for diffusion of the conceptual model and examine introduction to a smart city in Japan.

Service Scheme for Data Cloud Services

Service Scheme for Data Cloud Services

VIRTUAL IN REAL. INTERACTIVE SOLUTIONS FOR LEARNING AND COMMUNICATION IN THE NATIONAL ARCHAEOLOGICAL MUSEUM OF MARCHE

Abstract. Today the ICTs are favourable additions to museum exhibitions. This work aims to realize an innovative system of digital exploitation of artefacts in the National Archaeological Museum of Marche (MANaM), in order to create a shared museum that will improve the knowledge of cultural contents through the paradigm "learning by interacting" and “edutainment”. The main novelty is the implementation of stand-alone multimedia installations for digital artefacts that combine real and virtual scenarios in order to enrich the experience, the knowledge and the multi-sensory perception. A Digital Library (DL) is created using Close Range Photogrammetry (CRP) techniques applied to 21 archaeological artefacts belonging to different categories. Enriched with other data (texts, images, multimedia), all 3D models flow into the cloud data server from which are recalled in the individual exhibitions. In particular, we have chosen three types of technological solutions: VISUAL, TACTILE, SPATIAL. All the solutions take into account the possibility of group interaction, allowing the participation of the interaction to an appropriate number of users. Sharing the experience enables greater involvement, generating communicative effectiveness much higher than it would get from a lonely visit. From the “Museum Visitors Behaviour Analysis” we obtain a survey about users’ needs and efficiency of the interactive solutions. The main result of this work is the educational impact in terms of increase in visitors, specially students, learning increase of historical and cultural content, greater user involvement during the visit to the museum.

The intelligent industry of the future: A survey on emerging trends, research challenges and opportunities in Industry 4.0

Strongly rooted in the Internet of Things and Cyber-Physical Systems-enabled manufacturing, disruptive paradigms like the Factory of the Future and Industry 4.0 envision knowledge-intensive industrial intelligent environments where smart personalized products are created through smart processes and procedures. The 4th industrial revolution will be based on Cyber-Physical Systems that will monitor, analyze and automate business processes, transforming production and logistic processes into smart factory environments where big data capabilities, cloud services and smart predictive decision support tools are used to increase productivity and efficiency. This survey provides insights into the latest developments in these domains, and identifies relevant research challenges and opportunities to shape the future of intelligent manufacturing environments.

Efficient and Privacy-Preserving Polygons Spatial Query Framework for Location-Based Services

With the pervasiveness of mobile devices and the development of wireless communication technique, location-based services (LBSs) have made our life more convenient, and the polygons spatial query, which can provide more flexible LBS, has attracted considerable interest recently. However, the flourish of polygons spatial query still faces many challenges including the query information privacy. In this paper, we present an efficient and privacy-preserving polygons spatial query framework for location-based services, called Polaris. With Polaris, the LBS provider (LP) outsources the encrypted LBS data to cloud server (CS), and the registered user can query any polygon range to get accurate LBS results without divulging his/her query information to the LP and CS. Specifically, an efficient special polygons spatial query (SPSQ) algorithm over ciphertext is constructed, based on an improved homomorphic encryption technology over composite order group. With SPSQ, Polaris can search outsourced encrypted LBS data in CS by the encrypted request, and respond the encrypted polygons spatial query results accurately. Detailed security analysis shows that the proposed Polaris can resist various known security threats. In addition, performance evaluations via implementing Polaris on smartphone and workstation with real LBS dataset demonstrate Polaris' effectiveness in term of real environment.

Science in the cloud (SIC): A use case in MRI connectomics.

Modern technologies are enabling scientists to collect extraordinary amounts of complex and sophisticated data across a huge range of scales like never before. With this onslaught of data, we can allow the focal point to shift from data collection to data analysis. Unfortunately, lack of standardized sharing mechanisms and practices often make reproducing or extending scientific results very difficult. With the creation of data organization structures and tools that drastically improve code portability, we now have the opportunity to design such a framework for communicating extensible scientific discoveries. Our proposed solution leverages these existing technologies and standards, and provides an accessible and extensible model for reproducible research, called ‘science in the cloud’ (SIC). Exploiting scientific containers, cloud computing, and cloud data services, we show the capability to compute in the cloud and run a web service that enables intimate interaction with the tools and data presented. We hope this model will inspire the community to produce reproducible and, importantly, extensible results that will enable us to collectively accelerate the rate at which scientific breakthroughs are discovered, replicated, and extended.

An improved recommendation algorithm for big data cloud service based on the trust in sociology

Personal recommendation technology is becoming a useful and popular solution to solve the problem of information overload with the popularity of big data cloud services. But most recommendation algorithms pay too much attention to the similarity to focus on the social trust between users. So this paper focus on the research of hybrid Recommendation algorithm for big data based on the optimization combining with the similarity and trust in sociology. In this paper, we introduced some user trust models including trust path model and loop trust model, and then we took these models into the calculation of mixed weighting. The experiment results show that the recommendation algorithm considering the trust models has the higher accuracy than the traditional recommendation algorithm, and we have a 2% increase in both MEA (Mean Absolute Error) and RMSE (Root Mean Square Error).

SecureMed: Secure Medical Computation Using GPU-Accelerated Homomorphic Encryption Scheme.

Sharing the medical records of individuals among healthcare providers and researchers around the world can accelerate advances in medical research. While the idea seems increasingly practical due to cloud data services, maintaining patient privacy is of paramount importance. Standard encryption algorithms help protect sensitive data from outside attackers but they cannot be used to compute on this sensitive data while being encrypted. Homomorphic Encryption presents a very useful tool that can compute on encrypted data without the need to decrypt it. In this paper, we describe an optimized NTRU-based implementation of the GSW homomorphic encryption scheme. Our results show a factor of 58 × improvement in CPU performance compared to other recent work on encrypted medical data under the same security settings. Our system is built to be easily portable to GPUs resulting in an additional speedup of up to a factor of 104 × (and 410 ×) to offer an overall speedup of 6085 × (and 24011 ×) using a single GPU (or four GPUs), respectively.

Design and application of cloud platform based soil moisture monitoring system at the farm level

The cloud platform based soil moisture monitoring system at the farm level is mainly used for remote monitoring, storage and analysis of agricultural ecological factors and their videos in real time. The system is composed of solar powered soil moisture collector, video collector, self-made farm-level map, GPS location, cloud data service center, web-based client and mobile-based client. These servers and devices have realized several functions such as intelligent multi-block data acquisition, solar power supply, unattended operation all year round, and satellite upload in image/data format. Also, data is independently managed and checked in the cloud data service center and the clients, respectively. We conducted an in-situ test on the performance of the system inQingfeng Farm,HeilongjiangProvince. The test result shows that the system can be installed regardless of locations and surrounding environments, and can acquire dynamic and real-time information of soil moisture and crop growing conditions, thus providing scientific basis for improving soil water utilization rate.