Ensuring performance and provider profit through data replication in cloud systems

Cloud computing is a relatively recent computing paradigm that is often the answer for dealing with large amounts of data. Tenants expect the cloud providers to keep supplying an agreed upon quality of service, while cloud providers aim to increase profits as it is a key ingredient of any economic enterprise. In this paper, we propose a data replication strategy for cloud systems that satisfies the response time objective for executing queries while simultaneously enables the provider to return a profit from each execution. The proposed strategy estimates the response time of the queries and performs data replication in a way that the execution of any particular query is still estimated to be profitable for the provider. We show with simulations that how the proposed strategy fulfills these two criteria.     

Enabling Big Geoscience Data Analytics with a Cloud-Based,MapReduce-Enabled and Service-Oriented Workflow Framework

Geoscience observations and model simulations are generating vast amounts of multi-dimensional data. Effectively analyzing these data are essential for geoscience studies. However, the tasks are challenging for geoscientists because processing the massive amount of data is both computing and data intensive in that data analytics requires complex procedures and multiple tools. To tackle these challenges, a scientific workflow framework is proposed for big geoscience data analytics. In this framework techniques are proposed by leveraging cloud computing, MapReduce, and Service Oriented Architecture (SOA). Specifically, HBase is adopted for storing and managing big geoscience data across distributed computers. MapReduce-based algorithm framework is developed to support parallel processing of geoscience data. And service-oriented workflow architecture is built for supporting on-demand complex data analytics in the cloud environment. A proof-of-concept prototype tests the performance of the framework. Results show that this innovative framework significantly improves the efficiency of big geoscience data analytics by reducing the data processing time as well as simplifying data analytical procedures for geoscientists.     

Operational cost-aware resource provisioning for continuous write applications in cloud-of-clouds

The emergence of cloud computing has made it become an attractive solution for large-scale data processing and storage applications. Cloud infrastructures provide users a remote access to powerful computing capacity, large storage space and high network bandwidth to deploy various applications. With the support of cloud computing, many large-scale applications have been migrated to cloud infrastructures instead of running on in-house local servers. Among these applications, continuous write applications (CWAs) such as online surveillance systems, can significantly benefit due to the flexibility and advantages of cloud computing. However, with specific characteristics such as continuous data writing and processing, and high level demand of data availability, cloud service providers prefer to use sophisticated models for provisioning resources to meet CWAs’ demands while minimizing the operational cost of the infrastructure. In this paper, we present a novel architecture of multiple cloud service providers (CSPs) or commonly referred to as Cloud-of-Clouds. Based on this architecture, we propose two operational cost-aware algorithms for provisioning cloud resources for CWAs, namely neighboring optimal resource provisioning algorithm and global optimal resource provisioning algorithm, in order to minimize the operational cost and thereby maximizing the revenue of CSPs. We validate the proposed algorithms through comprehensive simulations. The two proposed algorithms are compared against each other to assess their effectiveness, and with a commonly used and practically viable round-robin approach. The results demonstrate that NORPA and GORPA outperform the conventional round-robin algorithm by reducing the operational cost by up to 28 and 57 %, respectively. The low complexity of the proposed cost-aware algorithms allows us to apply it to a realistic Cloud-of-Clouds environment in industry as well as academia.     

Efficient cloud service ranking based on uncertain user requirements

In a cloud computing environment, there are many providers offering various services of different quality attributes. Selecting a cloud service that meets user requirements from such a large number of cloud services is a complex and time-consuming process. At the same time, user requirements are sometimes described as uncertain (sets or intervals), something which should be taken into account while selecting cloud services. This paper proposes an efficient method for ranking cloud services while accounting for uncertain user requirements. For this purpose, a requirement interval is defined to fulfill uncertain user requirements. Since there are a large number of cloud services, the services falling outside the requirement interval are filtered out. Finally, the analytic hierarchy process is employed for ranking. The results evaluate the proposed method in terms of optimality of ranking, scalability, and sensitivity analyses. According to the test results, the proposed method outperforms the previous methods.

     

A resource-sharing model based on a repeated game in fog computing

With the rapid development of cloud computing techniques, the number of users is undergoing exponential growth. It is difficult for traditional data centers to perform many tasks in real time because of the limited bandwidth of resources. The concept of fog computing is proposed to support traditional cloud computing and to provide cloud services. In fog computing, the resource pool is composed of sporadic distributed resources that are more flexible and movable than a traditional data center. In this paper, we propose a fog computing structure and present a crowd-funding algorithm to integrate spare resources in the network. Furthermore, to encourage more resource owners to share their resources with the resource pool and to supervise the resource supporters as they actively perform their tasks, we propose an incentive mechanism in our algorithm. Simulation results show that our proposed incentive mechanism can effectively reduce the SLA violation rate and accelerate the completion of tasks.     

Energy aware resource allocation of cloud data center: review and open issues

The demand for cloud computing is increasing dramatically due to the high computational requirements of business, social, web and scientific applications. Nowadays, applications and services are hosted on the cloud in order to reduce the costs of hardware, software and maintenance. To satisfy this high demand, the number of large-scale data centers has increased, which consumes a high volume of electrical power, has a negative impact on the environment, and comes with high operational costs. In this paper, we discuss many ongoing or implemented energy aware resource allocation techniques for cloud environments. We also present a comprehensive review on the different energy aware resource allocation and selection algorithms for virtual machines in the cloud. Finally, we come up with further research issues and challenges for future cloud environments.     

Biomedical cloud computing with Amazon Web Services

In this overview to biomedical computing in the cloud, we discussed two primary ways to use the cloud (a single instance or cluster), provided a detailed example using NGS mapping, and highlighted the associated costs. While many users new to the cloud may assume that entry is as straightforward as uploading an application and selecting an instance type and storage options, we illustrated that there is substantial up-front effort required before an application can make full use of the cloud's vast resources. Our intention was to provide a set of best practices and to illustrate how those apply to a typical application pipeline for biomedical informatics, but also general enough for extrapolation to other types of computational problems. Our mapping example was intended to illustrate how to develop a scalable project and not to compare and contrast alignment algorithms for read mapping and genome assembly. Indeed, with a newer aligner such as Bowtie, it is possible to map the entire African genome using one m2.2xlarge instance in 48 hours for a total cost of approximately $48 in computation time. In our example, we were not concerned with data transfer rates, which are heavily influenced by the amount of available bandwidth, connection latency, and network availability. When transferring large amounts of data to the cloud, bandwidth limitations can be a major bottleneck, and in some cases it is more efficient to simply mail a storage device containing the data to AWS (http://aws.amazon.com/importexport/). More information about cloud computing, detailed cost analysis, and security can be found in references.     

Trust management on user behavioral patterns for a mobile cloud computing

With the development of ubiquitous computing technology, users are using mobile devices which are for producing and accessing information. Due to the limited computing capability and storage, however, mobile cloud computing technology are emerging research issues in the architecture, design, and implementation. This paper proposes the trust management approach by analyzing user behavioral patterns for reliable mobile cloud computing. For this, we suggest a method to quantify a one-dimensional trusting relation based on the analysis of telephone call data from mobile devices. After that, we integrate inter-user trust relationship in mobile cloud environment. As a result, trustworthiness of data in data production, management, overall application, is enhanced.     

A strengthening plan for enterprise information security based on cloud computing

Cloud computing environment came about in order to effectively manage and use enormous amount of data that have become available with the development of the Internet. Cloud computing service is widely used not only to manage the users’ IT resources, but also to use enterprise IT resources in an effective manner. Various security threats have occurred while using cloud computing and plans for reaction are much needed, since they will eventually elevate to security threats to enterprise information. Plans to strengthen the security of enterprise information by using cloud security will be proposed in this research. These cloud computing security measures must be supported by the governmental policies. Publications on guidelines to information protection will raise awareness among the users and service providers. System of reaction must be created in order to constantly monitor and to promptly respond to any security accident. Therefore, both technical countermeasures and governmental policy must be supported at the same time. Cloud computing service is expanding more than ever, thus active research on cloud computing security is expected.     

Ontology as a Service (OaaS): extracting and replacing sub-ontologies on the cloud

Cloud computing is founded by the concept of service computing, where everything is a service—computing services are now utilities. There are various known services in cloud computing. At the moment, there are Software as a Service (SaaS), Platform as a Service (PaaS), Hardware/Infrastructure as a Service (HaaS/IaaS), and Database as a Service (DaaS). In this paper, we propose Ontology as a Service (OaaS), which is an ontology tailoring process service in the cloud. In particular, we focus on sub-ontology extraction and replacement on the cloud. We use the Maximum Extraction method to facilitate this. UMLS meta-thesaurus ontology is used as a walk-through case study to illustrate our proposed method.     

vcluster: a framework for auto scalable virtual cluster system in heterogeneous clouds

Cloud computing is an emerging technology and is being widely considered for resource utilization in various research areas. One of the main advantages of cloud computing is its flexibility in computing resource allocations. Many computing cycles can be ready in very short time and can be smoothly reallocated between tasks. Because of this, there are many private companies entering the new business of reselling their idle computing cycles. Research institutes have also started building their own cloud systems for their various research purposes. In this paper, we introduce a framework for virtual cluster system called vcluster which is capable of utilizing computing resources from heterogeneous clouds and provides a uniform view in computing resource management. vcluster is an IaaS (Infrastructure as a Service) based cloud resource management system. It distributes batch jobs to multiple clouds depending on the status of queue and system pool. The main design philosophy behind vcluster is cloud and batch system agnostic and it is achieved through plugins. This feature mitigates the complexity of integrating heterogeneous clouds. In the pilot system development, we use FermiCloud and Amazon EC2, which are a private and a public cloud system, respectively. In this paper, we also discuss the features and functionalities that must be considered in virtual cluster systems.     

Laserfarm – A high-throughput workflow for generating geospatial data products of ecosystem structure from airborne laser scanning point clouds

Quantifying ecosystem structure is of key importance for ecology, conservation, restoration, and biodiversity monitoring because the diversity, geographic distribution and abundance of animals, plants and other organisms is tightly linked to the physical structure of vegetation and associated microclimates. Light Detection And Ranging (LiDAR) — an active remote sensing technique — can provide detailed and high resolution information on ecosystem structure because the laser pulse emitted from the sensor and its subsequent return signal from the vegetation (leaves, branches, stems) delivers three-dimensional point clouds from which metrics of vegetation structure (e.g. ecosystem height, cover, and structural complexity) can be derived. However, processing 3D LiDAR point clouds into geospatial data products of ecosystem structure remains challenging across broad spatial extents due to the large volume of national or regional point cloud datasets (typically multiple terabytes consisting of hundreds of billions of points). Here, we present a high-throughput workflow called ‘Laserfarm’ enabling the efficient, scalable and distributed processing of multi-terabyte LiDAR point clouds from national and regional airborne laser scanning (ALS) surveys into geospatial data products of ecosystem structure. Laserfarm is a free and open-source, end-to-end workflow which contains modular pipelines for the re-tiling, normalization, feature extraction and rasterization of point cloud information from ALS and other LiDAR surveys. The workflow is designed with horizontal scalability and can be deployed with distributed computing on different infrastructures, e.g. a cluster of virtual machines. We demonstrate the Laserfarm workflow by processing a country-wide multi-terabyte ALS dataset of the Netherlands (covering ∼34,000 km² with ∼700 billion points and ∼ 16 TB uncompressed LiDAR point clouds) into 25 raster layers at 10 m resolution capturing ecosystem height, cover and structural complexity at a national extent. The Laserfarm workflow, implemented in Python and available as Jupyter Notebooks, is applicable to other LiDAR datasets and enables users to execute automated pipelines for generating consistent and reproducible geospatial data products of ecosystems structure from massive amounts of LiDAR point clouds on distributed computing infrastructures, including cloud computing environments. We provide information on workflow performance (including total CPU times, total wall-time estimates and average CPU times for single files and LiDAR metrics) and discuss how the Laserfarm workflow can be scaled to other LiDAR datasets and computing environments, including remote cloud infrastructures. The Laserfarm workflow allows a broad user community to process massive amounts of LiDAR point clouds for mapping vegetation structure, e.g. for applications in ecology, biodiversity monitoring and ecosystem restoration.     

Enhancing service capability with multiple finite capacity server queues in cloud data centers

Cloud computing took a step forward in the efficient use of hardware through virtualization technology. And as a result, cloud brings evident benefits for both users and providers. While users can acquire computational resources on-demand elastically, cloud vendors can also utilize maximally the investment costs for data centers infrastructure. In the Internet era, the number of appliances and services migrated to cloud environment increases exponentially. This leads to the expansion of data centers, which become bigger and bigger. Not just that these data centers must have the architecture with a high elasticity in order to serve the huge upsurge of tasks and balance the energy consumption. Although in recent times, many research works have dealt with finite capacity for single job queue in data centers, the multiple finite-capacity queues architecture receives less attention. In reality, the multiple queues architecture is widely used in large data centers. In this paper, we propose a novel three-state model for cloud servers. The model is deployed in both single and multiple finite capacity queues. We also bring forward several strategies to control multiple queues at the same time. This approach allows to reduce service waiting time for jobs and managing elastically the service capability for the whole system. We use CloudSim to simulate the cloud environment and to carry out the experiments in order to demonstrate the operability and effectiveness of the proposed method and strategies. The power consumption is also evaluated to provide insights into the system performance in respect of performance-energy trade-off.     

Genetic research and consent: On the crossroads of human and data research

This paper explores the legal and ethical concept of human subject research in order to determine whether genetic research with already available biosamples and data falls within this concept. Although the ethical concept seems to have evolved to recognize research based on data as human research, from a supranational legal perspective this form of research is not considered human subject research. Thus human subject research regulations do not apply and therefore do not invoke the requirement of obtaining consent prior to using an individual’s biosample or genetic data in research. Furthermore, it remains ambiguous in both the legal and ethical realm whether the use of biosamples or genetic data without additional links to the individual would invoke the same safeguards as research involving additional or specific identifiers. Seeing that research based on already available biosamples and genetic data is not governed by rules concerning human subject research, the second part of the paper analyses whether any consent requirements apply for the further use of already available bio‐samples or genetic data in research. Whereas further use of biosamples is subject to considerably lax consent requirements under Article 22 of the Oviedo Convention, under the General Data Protection Regulation further use of genetic data might not be subject to a prior consent requirement at all, unless it is stipulated in national laws. When it comes to clinical trials, however, sponsors will have the possibility under Article 28(2) of Regulation 536/2014 to obtain open consent for further use of data in any kind of future research.     

Proxy based seamless connection management method in mobile cloud computing

Nowadays, complex smartphone applications are developed that support gaming, navigation, video editing, augmented reality, and speech recognition which require considerable computational power and battery lifetime. The cloud computing provides a brand new opportunity for the development of mobile applications. Mobile Hosts (MHs) are provided with data storage and processing services on a cloud computing platform rather than on the MHs. To provide seamless connection and reliable cloud service, we are focused on communication. When the connection to cloud server is increased explosively, each MH connection quality has to be declined. It causes several problems: network delay, retransmission, and so on. In this paper, we propose proxy based architecture to improve link performance for each MH in mobile cloud computing. By proposed proxy, the MH need not keep connection of the cloud server because it just connected one of proxy in the same subnet. And we propose the optimal access network discovery algorithm to optimize bandwidth usage. When the MH changes its point of attachment, proposed discovery algorithm helps to connect the optimal access network for cloud service. By experiment result and analysis, the proposed connection management method has better performance than the 802.11 access method.     

Software Architecture for Processing Clusters Based on I2O

Mainstream computing equipment and the advent of affordable multi-Gigabit communication technology permit us to address data acquisition and processing problems with clusters of COTS machinery. Such networks typically contain heterogeneous platforms, real-time partitions and even custom devices. Vital overall system requirements are high efficiency and flexibility. In preceding projects we experienced the difficulties to meet both requirements at once. Intelligent I/O (I₂O) is an industry specification that defines a uniform messaging format and execution environment for hardware and operating system independent device drivers in systems with processor based communication equipment. Mapping this concept to a distributed computing environment and encapsulating the details of the specification into an application-programming framework allow us to provide architectural support for (i) efficient and (ii) extensible cluster operation. This paper portrays our view of applying I₂O to high-performance clusters. We demonstrate the feasibility of this approach and report on the efficiency of our XDAQ software framework for distributed data acquisition systems.     

Survey on granularity clustering

With the rapid development of uncertain artificial intelligent and the arrival of big data era, conventional clustering analysis and granular computing fail to satisfy the requirements of intelligent information processing in this new case. There is the essential relationship between granular computing and clustering analysis, so some researchers try to combine granular computing with clustering analysis. In the idea of granularity, the researchers expand the researches in clustering analysis and look for the best clustering results with the help of the basic theories and methods of granular computing. Granularity clustering method which is proposed and studied has attracted more and more attention. This paper firstly summarizes the background of granularity clustering and the intrinsic connection between granular computing and clustering analysis, and then mainly reviews the research status and various methods of granularity clustering. Finally, we analyze existing problem and propose further research.     

Software quality in the clouds: a cloud-based solution

Cloud computing, an on-demand computation model that consists of large data-centers (Clouds) managed by cloud providers, offers storage and computation needs for cloud users based on service level agreements (SLAs). Services in cloud computing are offered at relatively low cost. The model, therefore, forms a great target for many applications, such as startup businesses and e-commerce applications. The area of cloud computing has grown rapidly in the last few years; yet, it still faces some obstacles. For example, there is a lack of mechanisms that guarantee for cloud users the quality that they are actually getting, compared to the quality of service that is specified in SLAs. Another example is the concern of security, privacy and trust, since users lose control over their data and programs once they are sent to cloud providers. In this paper, we introduce a new architecture that aids the design and implementation of attestation services. The services monitor cloud-based applications to ensure software quality, such as security, privacy, trust and usability of cloud-based applications. Our approach is a user-centric approach through which users have more control on their own data/applications. Further, the proposed approach is a cloud-based approach where the powers of the clouds are utilized. Simulation results show that many services can be designed based on our architecture, with limited performance overhead.     

A Service Brokering and Recommendation Mechanism for Better Selecting Cloud Services

Cloud computing is becoming the new generation computing infrastructure, and many cloud vendors provide different types of cloud services. How to choose the best cloud services for specific applications is very challenging. Addressing this challenge requires balancing multiple factors, such as business demands, technologies, policies and preferences in addition to the computing requirements. This paper recommends a mechanism for selecting the best public cloud service at the levels of Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). A systematic framework and associated workflow include cloud service filtration, solution generation, evaluation, and selection of public cloud services. Specifically, we propose the following: a hierarchical information model for integrating heterogeneous cloud information from different providers and a corresponding cloud information collecting mechanism; a cloud service classification model for categorizing and filtering cloud services and an application requirement schema for providing rules for creating application-specific configuration solutions; and a preference-aware solution evaluation mode for evaluating and recommending solutions according to the preferences of application providers. To test the proposed framework and methodologies, a cloud service advisory tool prototype was developed after which relevant experiments were conducted. The results show that the proposed system collects/updates/records the cloud information from multiple mainstream public cloud services in real-time, generates feasible cloud configuration solutions according to user specifications and acceptable cost predication, assesses solutions from multiple aspects (e.g., computing capability, potential cost and Service Level Agreement, SLA) and offers rational recommendations based on user preferences and practical cloud provisioning; and visually presents and compares solutions through an interactive web Graphical User Interface (GUI).     

Fault tolerance and QoS scheduling using CAN in mobile social cloud computing

The performance of mobile devices including smart phones and laptops is steadily rising as prices plummet sharply. So, mobile devices are changing from being a mere interface for requesting services to becoming computing resources for providing and sharing services due to immeasurably improved performance. With the increasing number of mobile device users, the utilization rate of SNS (Social Networking Service) is also soaring. Applying SNS to the existing computing environment enables members of social network to share computing services without further authentication. To use mobile device as a computing resource, temporary network disconnection caused by user mobility and various HW/SW faults causing service disruption should be considered. Also these issues must be resolved to support mobile users and to provide user requirements for services. Accordingly, we propose fault tolerance and QoS (Quality of Services) scheduling using CAN (Content Addressable Network) in Mobile Social Cloud Computing (MSCC). MSCC is a computing environment that integrates social network-based cloud computing and mobile devices. In the computing environment, a mobile user can, through mobile devices, become a member of a social network through real world relationships. Essentially, members of a social network share cloud service or data with other members without further authentication by using their mobile device. We use CAN as the underlying MSCC to logically manage the locations of mobile devices. Fault tolerance and QoS scheduling consists of four sub-scheduling algorithms: malicious-user filtering, cloud service delivery, QoS provisioning, and replication and load-balancing. Under the proposed scheduling, a mobile device is used as a resource for providing cloud services, faults caused from user mobility or other reasons are tolerated and user requirements for QoS are considered. We simulate scheduling both with and without CAN. The simulation results show that our proposed scheduling algorithm enhances cloud service execution time, finish time and reliability and reduces the cloud service error rate.