Cloud Computing Infrastructure Research Articles

MRTCM: A comprehensive dataset for probabilistic risk assessment of metals and metalloids in traditional Chinese medicine

Traditional Chinese medicine (TCM) is still considered a global complementary or alternative medical system, but exogenous hazardous contaminants remain in TCM even after decocting. Besides, it is time-consuming to conduct a risk assessment of trace elements in TCMs with a non-automatic approach due to the wide variety of TCMs. Here, we present MRTCM, a cloud-computing infrastructure for automating the probabilistic risk assessment of metals and metalloids in TCM. MRTCM includes a consumption database and a pollutant database involving forty million rows of consumption data and fourteen types of TCM potentially toxic elements concentrations. The algorithm of probabilistic risk assessment was also packaged in MRTCM to assess the risks of eight elements with Monte Carlo simulation. The results demonstrated that 96.64% and 99.46% had no non-carcinogenic risk (hazard indices (HI) were < 1.0) for animal and herbal medicines consumers, respectively. After twenty years of exposure, less than 1% of the total carcinogenic risk (CRt) was > 10-4 for TCM consumers, indicating that they are at potential risk for carcinogenicity. Sensitivity analysis revealed that annual consumption and concentration were the main variables affecting the assessment results. Ultimately, a priority management list of TCMs was also generated, indicating that more attention should be paid to the non-carcinogenic risks of As, Mn, and Hg and the carcinogenic risks of As and Cr in Pheretima and Cr in Arcae Conch. In general, MRTCM could significantly enhance the efficiency of risk assessment in TCM and provide reasonable guidance for policymakers to optimize risk management.

A Survey of Virtualization Technologies: Towards a New Taxonomic Proposal

At present, there is a proliferation of virtualization technologies (VTs), which are part of the basic and underlying infrastructure of popular cloud computing. Those interested in VTs are faced with a non-unified volume of information and various approaches to modes of operation, classification structures, and the performance implications of these technologies. This makes it difficult to decide which type of VT is appropriate for a particular context. Therefore, this paper reviews the state of the art on VT taxonomic models. Methodologically, a literature review is carried out to identify VT classification models, recognizing their features and weaknesses. With this in mind, a new taxonomy of virtualization technologies is proposed, which responds to the weaknesses identified in the analyzed schemes. The new VT taxonomy combines the Abstraction Level and Virtual Machine Type approaches, providing the reader with a means to visualize VTs. In doing so, the reader can locate the level of abstraction at which each VT is developed, in addition to the type of machine projected, whether it is a complete system or an execution environment for processes. The proposed taxonomy can be used in the academic environment to facilitate teaching processes or in the business environment to facilitate decision-making when implementing VTs.

Cloud computing: A study of mechanism and cloud cryptography

Cloud Computing is the on-demand delivery of computing services, databases, storage, applications, networking, and many more through the internet. Cloud Computing technology refers to pay-as-you-go pricing. The cloud delivers a hosting environment that is immediate, flexible, scalable, secure, and available which saves enterprise money, time, and resources. As we are going to use the internet services, security is the major factor to be taken into consideration. Cloud cryptography is a way through which we can secure our data on the internet. Cryptography adds several layers of protection to avoid breaching, hacking, or getting affected by malware. So in this survey, we are going to study the mechanism of cloud computing and how the activities on the cloud can be secured over the internet.In comparison to private clouds, which can scale effortlessly and on-demand, the public cloud has a massive infrastructure. IaaS is a method of delivering cloud-computing infrastructures such as servers, storage, networks, and operating systems to customers. Customers can get on-demand access to materials here. Cloud cryptography is a type of encryption that employs a number of algorithms to turn plain text into cipher text. Because symmetric cryptography is substantially faster than asymmetric cryptography, it is ideally suited for bulk data encryption.Using a single key, this technique enables data authentication and permission. The strength of an asymmetric key in assuring the security of information supplied during transmission is significantly greater.

CloudIDS: Cloud Intrusion Detection Model Inspired by Dendritic Cell Mechanism

Cloud Computing Security is a new era of computer technology and opens a new research area and creates a lot of opportunity of exploration. One of the new implementation in Cloud is Intrusion Detection System (IDS).There are problems with existing IDS approach in Cloud environment. Implementing traditional IDS need a lot of self-maintenance and did not scale with the customer security requirements. In addition, maintenance of traditional IDS in Cloud Computing system requires expertise and consumes more time where not each Cloud user has. A decentralized traditional IDS approach where being deployed in current Cloud Computing infrastructure will make the IDS management become complicated. Each user's IDS will not be the same in term of type and configurations and each user may have outdated signatures. Inter VM's communication also become a big concern when we implementing Cloud Computing system where communication between Clouds are not monitored and controlled by the traditional IDS. A specific IDS model for Cloud computing is required to solve these problems. In this paper, we develop a prototype of Cloud IDS inspired by Dendritic Cell mechanism. Experiment result proved that Cloud IDS was able to detect any attempt to attack the Cloud environment. The experiments show that the Cloud IDS model based on Dendritic Cell algorithm able to identify and detect novel threat that targeting Cloud environment.

A Survey of Biological Data in a Big Data Perspective.

The amount of available data is continuously growing. This phenomenon promotes a new concept, named big data. The highlight technologies related to big data are cloud computing (infrastructure) and Not Only SQL (NoSQL; data storage). In addition, for data analysis, machine learning algorithms such as decision trees, support vector machines, artificial neural networks, and clustering techniques present promising results. In a biological context, big data has many applications due to the large number of biological databases available. Some limitations of biological big data are related to the inherent features of these data, such as high degrees of complexity and heterogeneity, since biological systems provide information from an atomic level to interactions between organisms or their environment. Such characteristics make most bioinformatic-based applications difficult to build, configure, and maintain. Although the rise of big data is relatively recent, it has contributed to a better understanding of the underlying mechanisms of life. The main goal of this article is to provide a concise and reliable survey of the application of big data-related technologies in biology. As such, some fundamental concepts of information technology, including storage resources, analysis, and data sharing, are described along with their relation to biological data.

Genetically-modified Multi-objective Particle Swarm Optimization approach for high-performance computing workflow scheduling

Nowadays, scientific research, industry, and many other fields are greedy regarding computing resources. Therefore, Cloud Computing infrastructures are now attracting pervasive interest thanks to their excellent hallmarks such as scalability, high performance, reliability, and the pay-per-use strategy. The execution of these high-performant applications on such kind of computing environments in respect of optimizing many conflicting objectives brings us to a challenging issue commonly known as the multi-objective workflows scheduling on large scale distributed systems. Having this in mind, we outline in the present paper our proposed approach called Genetically-modified Multi-objective Particle Swarm Optimization (GMPSO) for scheduling application workflows on hybrid Clouds in the context of high-performance computing in an attempt to optimize Makespan and Cost. The GMPSO consists of incorporating genetic operations into the Multi-objective Particle Swarm Optimization to enhance the resulting solutions. To achieve this, we have designed a novel solution encoding that represents the task ordering, the task mapping and the resource provisioning processes of the workflow scheduling problem in hybrid Clouds. In addition, a set of particular adaptive evolutionary operators have been designed. Conducted simulations lead to significant results compared with a set of well-performed algorithms such NSGA-II, OMOPSO and SMPSO, especially, for the most-demanding workload of workflows.

Towards the Development of a Cloud Computing Intrusion Detection Framework Using an Ensemble Hybrid Feature Selection Approach

Attacks on cloud computing (CC) services and infrastructure have raised concerns about the efficacy of data protection mechanisms in this environment. The framework developed in this study (CCAID: cloud computing, attack, and intrusion detection) aims to improve the performance of intrusion detection systems (IDS) operating in CC environments. It deploys a proposed new hybrid ensemble feature selection (FS) method. The ensemble includes FS algorithms of three different types (filter, wrapper, and embedded algorithms). The selected features used to train the ML (machine learning) model of the intrusion detection component comprised a binary detection engine for the identification of malicious/attack packets and a multiclassification detection engine for the identification of the type of attack. Both detection engines deploy ensemble classifiers. Experiments were carried out using the NSL KDD dataset. The binary model achieved a classification accuracy of 99.55% with a very low false alarm rate of 0.45%. The classification accuracy of the multiclassification model was also high (98.92%). These results compare very favourably with the results reported in the literature and indicate the feasibility of the framework implementation.

Fault-Tolerant Secure Routing of B H n -Based Data Center Networks

As the core infrastructure of cloud computing, a large scale of the data center networks (DCNs), which consist of millions of servers with high capacity, suffer from node failure such that the reliability is deteriorated. Malicious group could inevitably compromise the quality and reliability of data; thus, how to ensure the security routing of data is an urgent practical problem. As models for large-scale DCNs, it is worth mentioning the balanced hypercube, which is well-known for its strong connectivity, regularity, and a smaller diameter. Each of which makes a balanced hypercube a trustworthy model to deal with data traffic and provides a certain degree of fault-tolerance as well. In this paper, we use the balanced hypercube as a model for the data center networks and design a reliable safety level by referring to different safety levels of related subgraph. This subgraph contains the source and destination nodes, and the shortest feasible paths are located so that the reliable transmission is achieved. Then, we get that the length of fault-tolerant safety routing of data center networks based on balanced hypercube is always no greater than the Hamming distance plus two. Experiment shows that our fault-tolerant security routing scheme is more effective in the same reliable network environment of DCNs.

Cloud-based systems of open science in educational institutions

In the modern educational and research university environment there are new models of organization of investigations, as well as educational and scientific activities, based on innovative technological solutions and pedagogical approaches to open science. Issues of setting up cloud-based open science tools and services for users, forming open science systems within the educational and scientific environment of the educational institution in order to maximize the pedagogical potential of their use, improving learning and research outcomes, and improving the organization of research and educational activities are the subject of the article. Conceptual aspects of formation and development of high-tech learning and research environment of educational institution with the use of cloud-oriented systems of open science, as well as organization of access to electronic resources and services to support investigative activities based on cloud computing and research infrastructures are outlined.

Doughnutie: An efficient and low‐latency cloud data center network architecture

SummaryData center network provides the supporting infrastructure for cloud computing. Torus interconnection network is widely employed in the cloud data center networks. However, its long network diameter and average path length (APL) are prohibitive factors for supporting the delay‐sensitive applications. With the aim of meeting latency, network capacity, fault‐tolerance, scalability, and energy‐efficiency requirements of cloud data center networks, we propose a new data center network architecture, named Doughnutie. Doughnutie is built by adding a number of flyway connections to the base Torus network to ameliorate its performance. Doughnutie does not follow a single principle for establishing all of its flyways and consequently, each part of the network can profit from all local, faraway, and neither‐local‐nor‐faraway types of connection. Doughnutie decreases both network diameter and APL, which in turn leads to higher communication efficiency and lower network latency. Moreover, an exclusively designed loop‐free load‐balanced adaptive routing algorithm, named Doughnuter, is proposed. Employing the Doughnuter routing algorithm results in more efficient utilization of all the Doughnutie network resources. Theoretical analysis and simulation results also prove the superiorities of Doughnutie in throughput, load balancing, fault tolerance, and energy‐efficiency.

An Elasticity Framework for Distributed Message Queuing Telemetry Transport Brokers

An Elasticity Framework for Distributed Message Queuing Telemetry Transport Brokers

Cloud Simulation and Virtualization for Testing of Critical Energy Infrastructure Components

Cloud computing and cloud infrastructure are beginning to be applied in order to ensure effective functioning of management system of Critical Energy Infrastructure. This is improved informativeness of Supervisory Control and Data Acquisition system by the application of additional Wide-area Measurement and Control Systems (WAMCSs), since most of all processing and transition operations of data information streams of the WAMCSs may be performed by using on clouds. In light of these circumstances, it is importantly to get extensive modeling results considering wide area range of input data and latency time between critical energy infrastructure components and cloud infrastructure components. However, researchers were not, yet, had possibility to receive more adequate results based only mathematical modeling process. In this case, simulation models considering the cost time are more preferable than mathematical models. In the spirit development of this conception, authors are proposing to employ the simulation model based on virtual technology and Matlab Simulink toolkit in order to get modeling results for more important on-line mode of functioning of the WAMCSs.

A semantic malware detection model based on the GMDH neural networks

There are several approaches for preventing mobile devices from malware intrusion, but most of them suffer from the insufficient accuracy required for detecting Trojan malware. A combination of semantic and machine learning techniques can be effective in preventing intrusions. In this paper, we have used a hierarchical semantic approach to convert numerical and string data to meaningful values, Subgraph Semantic Homomorphism Coefficient (SSHC) to select optimal features, and Group Method of Data Handling (GMDH) deep neural network (DNN) algorithm to detect malware via a cloud-computing infrastructure. To evaluate our model, Android Trojan Dataset has been used. After evaluation, the accuracy reached 99.91%, which was improved by about 5.25% compared to StormDroid, Drebin, and KuafuDet models. Also, the accuracy was improved by about 10.4% and 31.9% compared to machine learning based approaches of Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), in the state-of-the-art KuafuDet model, respectively.

A Multiparameter Analytical Model of the Physical Infrastructure of a Cloud-Based System

The large popularity of services offered by cloud computing (CC) requires constant expansion of its physical infrastructure. At the same time, CC operators apply various mechanisms that enable the existing physical resources to be optimally used. Useful tools in the design and optimization process are analytical and simulation models. They allow information about the operation of CC to be obtained without the need to make changes to the physical infrastructure. This article presents and discusses a multiservice, multiparameter model of a CC physical infrastructure that provides services of the infrastructure-as-a-service (IaaS) type. In the proposed model, the following four elements are used to specify network settings and describe the demands necessary to activate a virtual machine: the number of processors, the capacity of RAM, the HDD capacity, and the required network bitrate. Such an approach gives the opportunity to describe accurately the use of resources in real servers. To verify and validate the proposed model, we developed and implemented a simulator of the physical infrastructure of CC. The results of the simulations confirm the validity of all the theoretical assumptions adopted in the model. The proposed model can also serve as a tool, in the form of an appropriate application, for determining the resources that are necessary to service calls at the required loss level.

The GaaS Paradigm: How Fortnite’s Cloud Computing Infrastructure and Tencent Partnership May Be Ethically Compromised

The GaaS Paradigm: How Fortnite’s Cloud Computing Infrastructure and Tencent Partnership May Be Ethically Compromised

Security Analysis of Storage, Virtualization, Infrastructure and Standardization in Cloud Computing

Nowadays the cloud computing grows in high rate with the help of recent technological advancement. It provides the on demand and low cost services to the customer. Cloud computing distributes the services to various location based on the demand. The services can cross various layers and location which leads the security problem. The existing security system provides reliable solutions which are not enough to secure the resource and services from unauthorized access. Three types of security is available in the cloud namely Infrastructure security, Platform security and application security. The proposed analysis gives the complete information about these security and its issues. Data security management process has various steps for securing the data around the cloud network with reliable access. Virtualization level security is analyzed with different parameter based on the number issues present in the cloud. Authentication types are also identified and analyzed over security level. Various types of cloud framework and vulnerabilities are assessed in order to achieve better security.

A Reinforcement Learning Based Data Storage and Traffic Management in Information-Centric Data Center Networks

Data Center Networks (DCN), a core infrastructure of cloud computing, place heavy demands on efficient storage and management of massive data. The data storage scheme, which decides how to assign data to nodes for storage, has a significant impact on the performance of the data center. However, most of the existing solutions focus on where to store the data (i.e., the selection of storage node) but have not considered how to store them (i.e., the traffic management such as routing and transmission rate adjustment). By leveraging the Information-Centric Networks (ICN) architecture, this paper tackles the data storage and traffic management issue in Information-Centric Data Center Networks (ICDCN) based on Reinforcement Learning (RL) method, since RL has been developed as a promising solution to address dynamic network issues. We present a global optimization of joint traffic management and data storage and then solve it by the distributed multi-agent Q-learning. In ICDCN, the data is routed based on the data’s name, which achieves better routing scalability by decoupling the data and its physical location. Compared with IP’s stateless forwarding plane, the stateful forwarding information maintained at every node supports adaptively routing and hop-by-hop traffic control by using the Q-learning method. We evaluate our proposal on an NS-3-based simulator, and the results show that the proposed scheme can effectively reduce transmission time and increase throughput while achieving load-balanced among servers.

Advanced Data Storage Security System for Public Cloud

Cloud is a collective term for a large number of developments and possibilities. Various data can be stored by the large amount of people onto the cloud storage facility without any bound of limitations as it provides tremendous space. Open systems like Android (Google Apps) still face many day- to-day security threats or attacks. With recent demand, cloud computing has raised security concerns for both service providers and consumers. Major issues like data transfer over wireless network across the globe have to be protected from unauthorized usage over the cloud as altered data can lead to great loss. In this regard, data auditing along with integrity, dynamic capabilities, and privacy preserving, and plays as an important role for preventing data from various cloud attacks which is considered in this work. The work also includes efficient auditor which plays a crucial role in securing the cloud environment. This paper presents a review on the cloud computing concepts and security issues inherent within the context of cloud computing and cloud infrastructure.

Resource Allocation Algorithm of Cloud Computing Infrastructure Services based on Continuous Optimization Algorithm

The cloud environment is extremely complex, dynamic and changeable. How to use cloud services efficiently to create economic benefits for enterprises and even society is a matter that most scholars and manufacturers pay close attention to at present. Therefore, the resource allocation of cloud computing becomes the focus of research. The purpose of this paper is to further study the resource allocation algorithm of cloud computing infrastructure services. Firstly, the IAAs mode in three main application modes of cloud computing is introduced in detail. Based on the understanding of optimization theory, the resource allocation algorithm of cloud computing infrastructure services with continuous optimization algorithm is established. The experimental results show that the number of iterations of the three algorithms is generally on the rise with the increase of the number of task nodes. In general, the number of iterations of ant colony algorithm is less than that of genetic algorithm, and the performance of continuous optimization algorithm is better than that of both, which shows that it can promote the overall efficiency of the algorithm.

Workload Based Geo-Distributed Data Center Planning in Fast Developing Economies

Data centers (DCs), as the core infrastructure of cloud computing, are experiencing explosive growth worldwide. The planning of geo-distributed DCs has to take into account geographically differentiated costs, so that the overall expenditure could be minimized. In fast developing economies, the planning of DCs is further complicated by the fact that demands are quickly evolving, both in the type of services, and in geographical distributions. This paper is dedicated to the planning of DCs in fast developing economies. Instead of modeling demands by aggregated number of servers, we introduce a workload-based model to better capture the quickly changing nature of demand composition. The variability and dynamics of computational demand are modeled by dynamic compositions of workflows of different types. We provide approaches to solve the planning problem. Finally, we apply the model to China by using real-life data, and show how factors like economic growth, population migration and latency constraint may affect DC planning. Our research suggests that factors endemic in fast developing economies can significantly influence the overall cost and performance. For instance, by considering these factors, one can save 5.8% in cost in a five-year planning period, and 5% population migration may lead to 30ms increase in latency.