We present a descriptive literature review and classification scheme for cloud computing research. This includes 205 refereed journal articles published since the inception of cloud computing research. The articles are classified based on a scheme that consists of four main categories: technological issues, business issues, domains and applications, and conceptualising cloud computing. The results show that although current research is still skewed towards technological issues, new research themes regarding social and organisational implications are emerging. This review provides a reference source and classification scheme for IS researchers interested in cloud computing, and to indicate under-researched areas as well as future directions.

/pdf/a-descriptive-literature-review-and-classification-of-cloud-2p5bofjvpi.pdf

A Descriptive Literature Review and Classification of Cloud Computing Research

Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey

Keeping abreast of trends in the articles and rapidly grasping a body of article’s key points and relationship from a holistic perspective is a new challenge in both literature research and text mining. As the important component, keywords can present the core idea of the academic article. Usually, articles on a single theme or area could share one or some same keywords, and we can analyze topological features and evolution of the articles co-keyword networks and keywords co-occurrence networks to realize the in-depth analysis of the articles. This paper seeks to integrate statistics, text mining, complex networks and visualization to analyze all of the academic articles on one given theme, complex network(s). All 5944 “complex networks” articles that were published between 1990 and 2013 and are available on the Web of Science are extracted. Based on the two-mode affiliation network theory, a new frontier of complex networks, we constructed two different networks, one taking the articles as nodes, the co-keyword relationships as edges and the quantity of co-keywords as the weight to construct articles co-keyword network, and another taking the articles’ keywords as nodes, the co-occurrence relationships as edges and the quantity of simultaneous co-occurrences as the weight to construct keyword co-occurrence network. An integrated method for analyzing the topological features and evolution of the articles co-keyword network and keywords co-occurrence networks is proposed, and we also defined a new function to measure the innovation coefficient of the articles in annual level. This paper provides a useful tool and process for successfully achieving in-depth analysis and rapid understanding of the trends and relationships of articles in a holistic perspective.

Evolutionary features of academic articles co-keyword network and keywords co-occurrence network: Based on two-mode affiliation network

Big data has been a focus of research in science, technology, economics, and social studies. Many countries have already incorporated big data research into their national strategies. This paper elaborates upon the origin, connotation, and development of big data from both a spatial and temporal perspective. It proposes that scientific big data will become a new solution in scientific research as the paradigm changes from being model-driven to data-driven. This paper defines the concept of “scientific big data” and proposes strategies for solving “big data problems”. Theoretical frameworks and data systems for Digital Earth are discussed with a clear conclusion that scientific big data is a prominent feature of Digital Earth. As an example, spatial cognition of the formation mechanism of China’s Heihe-Tengchong Line—a geo-demographic demarcation line dividing China into two parts—is discussed within the context of big data computation and analysis for Digital Earth.

Scientific big data and Digital Earth

Classification of high performance computing (HPC) systems is provided.Current HPC paradigms and industrial application suites are discussed.State of the art in HPC resource allocation is reported.Hardware and software solutions are discussed for optimized HPC systems. An efficient resource allocation is a fundamental requirement in high performance computing (HPC) systems. Many projects are dedicated to large-scale distributed computing systems that have designed and developed resource allocation mechanisms with a variety of architectures and services. In our study, through analysis, a comprehensive survey for describing resource allocation in various HPCs is reported. The aim of the work is to aggregate under a joint framework, the existing solutions for HPC to provide a thorough analysis and characteristics of the resource management and allocation strategies. Resource allocation mechanisms and strategies play a vital role towards the performance improvement of all the HPCs classifications. Therefore, a comprehensive discussion of widely used resource allocation strategies deployed in HPC environment is required, which is one of the motivations of this survey. Moreover, we have classified the HPC systems into three broad categories, namely: (a) cluster, (b) grid, and (c) cloud systems and define the characteristics of each class by extracting sets of common attributes. All of the aforementioned systems are cataloged into pure software and hybrid/hardware solutions. The system classification is used to identify approaches followed by the implementation of existing resource allocation strategies that are widely presented in the literature.

/pdf/a-survey-on-resource-allocation-in-high-performance-m3oiot8yrv.pdf

A survey on resource allocation in high performance distributed computing systems

Recently, the computational requirements for large scale data-intensive analysis of scientific data have grown significantly. In High Energy Physics (HEP) for example, the Large Hadron Collider (LHC) produced 13 petabytes of data in 2010. This huge amount of data are processed on more than 140 computing centers distributed across 34 countries. The MapReduce paradigm has emerged as a highly successful programming model for large-scale data-intensive computing applications. However, current MapReduce implementations are developed to operate on single cluster environments and cannot be leveraged for large-scale distributed data processing across multiple clusters. On the other hand, workflow systems are used for distributed data processing across data centers. It has been reported that the workflow paradigm has some limitations for distributed data processing, such as reliability and efficiency. In this paper, we present the design and implementation of GHadoop, a MapReduce framework that aims to enable large-scale distributed computing across multiple clusters. G-Hadoop uses the Gfarm file system as an underlying file system and executes MapReduce tasks across distributed clusters. Experiments of the G-Hadoop framework on distributed clusters show encouraging results.

MapReduce across Distributed Clusters for Data-intensive Applications

Cloud computing introduces a novel computing paradigm that provisions on-demand computational capacity as a service. Increasing numbers of users are migrating their applications to the computing Clouds to remove or reduce the costs on resource investment and management. However, individual Cloud platforms, either private or public, provide their own interfaces for customers to interact with the underlying infrastructures. This situation undoubtedly burdens the users who compute on different Clouds. In this work, we developed a generic interface that allows the user to access the diverse Clouds in a unified way. It provides utilities for requesting and invoking the services. It offers graphical views to show the Cloud information that is visible to the users. The interface also combines different Clouds into a single platform enabling inter-Cloud communications.

An Intuitive Framework for Accessing Computing Clouds

With modern techniques that allow billions of transistors on a chip, microprocessor design is going to a world of multicore. A cluster of multicores will be commonly used as an efficient computational platform for high performance computing in the near future. Correspondingly, the resource providers, who share their computing elements and storage with customers, will also upgrade their machines to the multicore architecture. At the same time, the dominating resource sharing platforms, in the form of compute Grids, are facing a grand challenge from Cloud computing. With an easy interface, high-level services, and on-demand provision of compute resources, compute Clouds are attracting more users to join their realm. Upcoming Cloud environments will be primarily built on top of multicore technologies. However, to fully exploit the computational capacity and the other advantages of multicores, a lot of novel techniques must be proposed and investigated. This paper serves as an overview for multicore technologies in the context of  Cloud computing. This paper firstly  introduces multicore technologies  and the Cloud computing concept. Then it investigates  various open research issues in the context of multicore Cloud computing, for example, how to efficiently employ multicore techniques for Cloud computing, how to achieve the high performance of multicore for high performance applications in the Cloud environments.  Several examples and discussion are presented.

Multicores in Cloud Computing: Research Challenges for Applications

Cloud computing has promoted the widespread use of virtualized machines. A question arises: How does virtualization influence the performance of running applications? The answer must be a common interest of application dev elopers and users. This paper describes the results of our performance evaluation on a virtualized multicore ma chine. We tested a set of benchmark applications and detected some general features that should be considered when running applications on a virtualized multicore machine. We also studied the application execution behavior using profiling tools. We found the reason for unexpectedly poor performance of an OpenMP application in a virtualized setting and optimized the program. The optimization resulted in a significant performance gain.

A Performance Study of Virtual Machines on Multicore Architectures

This work developed a workflow engine that enables the execution of workflows on existing Cloud platfor ms. The workflow engine automatically delivers the computation of each individual task to the selected Cloud and transfers the input/output data across different platforms. Additionally, it predicts the execution time and payment of the tasks, helping users select the best Cloud services with respect to the performance vs. cost tradeoff. Keywords-Cloud Computing, Workflow Management System, Grid Computing

A Workflow Engine for Computing Clouds

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