Services Recommendation System based on Heterogeneous Network Analysis in Cloud Computing

Cloud computing (CC) services can offer substantial cost-effective global operational and relationship benefits if the cooperation between logistics and CC services are resilient. Potential vulnerabilities to cooperation of CC and logistics service providers can occur with respect to vital factors such as security and trust. Extant studies have demonstrated CC benefits as well as few challenges associated with CC services application. However, no extant study has examined the inter-organisational benefits based on cooperative resilience between CC and logistics service providers in terms of both capability and trust vulnerability factors. This study examines the cooperative resilience of logistics and CC service providers based on innovation diffusion theory within a supply chain risk assessment framework. Using structural equation modelling techniques, we investigate the relationship between the vulnerability factor (trust), capability factor (security) and collaboration benefits (relationship and operational) offered by CC service providers based on 236 Chinese logistics service firms’ perceptions of CC adoption. The results indicate Chinese logistics companies perceive security impediments as a major factor affecting cooperative resilience between logistics service and CC service providers.

Many complex systems derived from nature and society consist of multiple types of entities and heterogeneous interactions, which can be effectively modeled as heterogeneous information network (HIN). Structural analysis of heterogeneous networks is of great significance by leveraging the rich semantic information of objects and links in the heterogeneous networks. And, clustering heterogeneous networks aims to group vertices into classes, which sheds light on revealing the structure–function relations of the underlying systems. The current algorithms independently perform the feature extraction and clustering, which are criticized for not fully characterizing the structure of clusters. In this study, we propose a learning model by joint <underline>G</underline>raph <underline>E</underline>mbedding and <underline>N</underline>onnegative <underline>M</underline>atrix <underline>F</underline>actorization (aka GEjNMF ), where feature extraction and clustering are simultaneously learned by exploiting the graph embedding and latent structure of networks. We formulate the objective function of GEjNMF and transform the heterogeneous network clustering problem into a constrained optimization problem, which is effectively solved by l 0 -norm optimization. The advantage of GEjNMF is that features are selected under the guidance of clustering, which improves the performance and saves the running time of algorithms at the same time. The experimental results on three benchmark heterogeneous networks demonstrate that GEjNMF achieves the best performance with the least running time compared with the best state-of-the-art methods. Furthermore, the proposed algorithm is robust across heterogeneous networks from various fields. The proposed model and method provide an effective alternative for heterogeneous network clustering.

Moving data and applications to the cloud implies shifting their control from cloud consumers to the cloud service provider (CSP) indefinitely. Hence, the security and privacy (Saamp;P) of the consumers' assets becomes an important issue. Assessing and comparing potential cloud computing (CC) services, poses an issue for CC adopters to choose Saamp;P options that are sufficient and robust at the same time. In this paper, we describe CC adopters' Saamp;P concerns. We identify a set of attributes that reflect various aspects of cloud Saamp;P in an attempt to alleviate those concerns. We classify the attributes based on their tangibility and their relevance to Saamp;P threats. Every attribute in our list is represented by a set of considerations (i.e. polar questions) to assess its quality. We present a tool that embodies our set of attributes. We aim to enable consumers to assess and compare CC services, in terms of Saamp;P, so that they can make well-educated choices. CSPs can also make use of these attributes to offer better cloud Saamp;P.

Cloud computing is the use of computer hardware or virtual advances to shape a shared infrastructure that provides a user with many online value-added services. Understanding cloud services is important when choosing a suitable business arrangement that balances the required capabilities with reduced hardware costs, configuration and maintenance. The three prevailing service models in cloud computing are software, infrastructure and platform. Infrastructure as a service (IaaS) is the utilization of servers to provide utility services, for example, services for clients. Platform as a service (PaaS) suppliers give admittance to APIs that allow supporters of applications without installing or designing the advancement condition. Software as a service (SaaS) provides subscribed clients with access to software located on the cloud and not on the client's device. Mobile devices are becoming the main players in the service today. Classic client and server-based mobile models cannot meet the developing needs of mobile clients for a variety of good services. The cloud allows mobiles to download complex mobile apps that are not feasible on mobile devices alone. Cloud computing has overcome many challenges to prove its many advantages for organizations, such as the association between heterogeneous networks and working gatherings. Nevertheless challenges remain, and security, privacy and cloud integrity are crucial. Future models should enhance the characteristics of the current model without endangering it. This chapter reviews security threats to the cloud and provides an overview of mobile cloud computing services and key performance models.

<p>The vehicular ad hoc network (VANET) has emerged as a heterogeneous network with no fixed infrastructure. In VANET, vehicles are the mobile nodes that communicate with one another and with the infrastructure. One of the major challenges in VANET is to allocate resources efficiently due to environmental characteristics such as highly mobile vehicles, latency-sensitive and short connection times. The conventional resource management mechanisms are not very effective in such an environment. The data-driven Artificial Intelligence (AI) based techniques are a very efficient alternative to traditional statistical techniques. This project proposes an AI-empowered task offloading and computing resource allocation model which can dynamically organize the computing resources in VANET. The model is divided into two layers. First, the task offloading layer, where the Random Forest (RF) algorithm is used to determine whether the vehicle’s computing tasks should be offloaded to the cloud computing (CC) server or mobile edge computing (MEC) server or to be processed locally (in-vehicle computing). Second, the resource allocation layer, where the deep deterministic policy gradient (DDPG) algorithm is used to determine the computing platform again when the task is determined to be offloaded to either MEC servers or the cloud servers. To evaluate the performance of the RF classifier, we applied the model to a real-world driving trajectory dataset then compared the results with a different set of machine learning algorithms namely, K-nearest neighbour (KNN), Multilayer Perceptron (MLP) and Support Vector Machine (SVM). The results show the RF model outperformed other models in classification accuracy score of 99.83% for task offloading decision, where the KNN, MLP and SVM achieved 98%, 94.81% and 90.94%, respectively. Moreover, the DDPG based resource allocation scheme converges within 150 episodes. Thus, the proposed model can find the optimal offload decision and the computational resource allocation simultaneously and achieves a high delay to quality-of-service (QoS) satisfaction ratio.</p>

Cloud Computing (CC) is a paradigm shift with the potential of transforming the information and communication technology (ICT) industry. ICT is an integral part of all businesses, and CC helps in lightening the burden of massive ICT investments. Thus, it is essential to understand the factors that influence CC adoption. The purpose of this study is to identify and rank critical factors for CC adoption in the Indian context. The research has adopted mixed methodology (qualitative and quantitative). Experts from 13 organizations (including 8 cloud service providers (CSPs) and 5 cloud service users (CSUs)) are interviewed. Content analysis is used to confirm existing factors and identify new factors in the Indian context. Qualitative analysis has identified four new factors, namely, ‘time to market’, ‘futuristic’, ‘resistance to change’ and ‘financial losses’ (arising from security and resource availability). The quantitative part of the research used analytic hierarchy process (AHP) and fuzzy analytic hierarchy process (FAHP) techniques to rank the factors. The analysis revealed ‘IT service cost’ and ‘time to market’ as the most critical factor through FAHP and AHP respectively. However, ‘organization size’ is reported the least critical for CC adoption through both the methods. The results also highlight divergence among the criticality of CC adoption factors using the two techniques. The study is very useful for CSPs and key decision-makers to understand the critical adoption factors and judiciously strategize for CC service promotions. It will also help users to wisely decide while making CC adoption decision.

Job scheduling system problem is a core and challenging issue in Cloud Computing. How to use Cloud computing resources efficiently and gain the maximum profits with job scheduling. Job scheduling system is one of the Cloud computing service providers’ ultimate goals. In this paper, firstly, by analysis the differentiated QoS requirements of Cloud computing resources users’ jobs, we build the corresponding non-pre-emptive priority M/G/1 queuing model for the jobs.Then, considering Cloud computing service providers’ destination which is to gain the maximum profits by offering Cloud computing resources, we built the system cost function for this queuing model. After that, based on the queuing model and system cost function, considering the goals of both the Cloud Computing service users and providers, we gave the corresponding strategy and algorithm to get the approximate optimistic value of service for each job in the corresponding no-pre-emptive priority M/G/1 queuing model. Finally, we also provide corresponding simulations and numeral results.Analysis and number results show that our approach for job scheduling system can not only guarantee the QoS requirements of the users’ jobs, but also can make the maximum profits for the Cloud computing service providers.While the economic case for cloud computing is compelling, the security challenges it poses are equally striking. In this work we strive to frame the full space of cloud-computing security issues, attempting to separate justified concerns from possible over-reactions. We examine contemporary and historical perspectives from industry, academia, government, and “black hats”. We argue that few cloud computing security issues are fundamentally new or fundamentally intractable; often what appears “new” is so only relative to “traditional”computing of the past several years. Looking back further to the time-sharing era, many of these problems already received attention. On the other hand, we argue that two facets are to some degree new and fundamental to cloud computing: the complexities of multi-party trust considerations, and the ensuing need for mutual audit ability.

Cloud computing is a promising computing model that enables convenient and on-demand network access to a shared pool of configurable computing resources. The first offered cloud service is moving data into the cloud: data owners let cloud service providers host their data on cloud servers and data consumers can access the data from the cloud servers. This new paradigm of data storage service also introduces new security challenges, because data owners and data servers have different identities and different business interests. Therefore, an independent auditing service is required to make sure that the data is correctly hosted in the Cloud. In this paper, we investigate this kind of problem and give an extensive survey of storage auditing methods in the literature. First, we give a set of requirements of the auditing protocol for data storage in cloud computing. Then, we introduce some existing auditing schemes and analyze them in terms of security and performance. Finally, some challenging issues are introduced in the design of efficient auditing protocol for data storage in cloud computing.

With the maturity of virtualization technology, the advantages of cloud computing have been gradually apparent with low cost, improvement of resource utilization, energy conservation of service consolidation and convenience of using. Cloud manufacturing provides a huge amount of services and there are kinds of user requirements and devices which are complicated and changeable. So it is necessary that the users quickly develop personalized products to meet their needs. With the popularity of smart phones and the development of 3G technology, the amount of information grows dramatically fast, which makes it a trend to extract information that users are interested in from the world of information, but traditional recommendation methods cant meet the need of people. Starting from information resource requirements of users in the cloud environment, this article discusses the application of cloud computing in personalized information services, analyzes the necessity and feasibility of the cloud computing technology applying to personalized services, proposes the personalized service idea based on cloud computing, and builds a model to introduce cloud computing and cloud service idea to the management and service, hoping to find the integrating point of the cloud computing technology applications in the personalized field.

 Abstract—Cloud computing is an emerging technology that is still unclear to many security problems. Ensuring the security ofstored data in cloud servers is one of the most challenging issues in such environments. Accordingly, this schema presents a Hybrid Encryption algorithm that is based on RSA Small-e and Efficient RSA (HE-RSA) for improving the reliability in cloud computing environments. The main aim of this project is to use the cryptography concepts in cloud computing communications and to increase the security of encrypted data in cloud servers with the least consumption of time and cost at the both of encryption and decryption processes.In the proposed model, the number of key generation exponents has been increased in comparison to the original RSA. Moreover,a dual encryption process has been applied in this algorithm to prevent common attacks against RSA algorithm. In addition, this schema also presents the comparison between HE-RSA and original RSA in terms of security and performance. For this purpose, Key Generation, Encryption and Decryption Time in Original RSA and HE-RSA have been compared according to the different size of exponents. Moreover, some of the common attacks against RSA algorithm have been analysed to detect the resistance of the proposed algorithm against possible attacks. Finally, the effects of using HE-RSA on cloud computing environments have been reviewed in more depth and details.

Background: The integration of mobile computing and cloud computing has the potential to revolutionize healthcare delivery by providing ubiquitous access to resources and enabling new service development and delivery models. While cloud computing has been extensively studied in fields such as genomics and molecular medicine, its application in healthcare beyond these domains remains relatively unexplored. This scoping review aims to identify the current state and emerging research topics in cloud computing in healthcare outside of the traditional “OMICS context.” Methods: A comprehensive search was conducted in MEDLINE in July 2013 and December 2014 using keywords related to cloud computing and cloud-based services. The identified journal and conference articles were independently categorized and summarized by two researchers, who subsequently consolidated their findings. Results: A total of 102 publications were analyzed, revealing six main topics in cloud computing in healthcare: telemedicine/teleconsultation, medical imaging, public health and patient self-management, hospital management and information systems, therapy, and secondary use of data. Common features utilized in these applications included broad network access for data sharing and access, as well as rapid elasticity to meet computing demands. While some articles highlighted the cost-effectiveness of pay-per-use cloud services, only 14 articles reported successful implementations, with many publications focusing on conceptual or prototypic projects. Additionally, several articles equated cloud computing with internet-/web-based data sharing, failing to illustrate the unique benefits of the cloud computing paradigm. Conclusions: Although the integration of mobile computing and cloud computing in healthcare is gaining attention, successful implementations in the field are still limited. Many papers use the term “cloud” interchangeably with “virtual machines” or “web-based” without clearly demonstrating the advantages of the cloud paradigm. Data safety and security concerns associated with involving external cloud partners remain significant barriers to adoption in the healthcare domain. As of now, cloud computing is primarily favored for its individual features, such as elasticity, pay-per-use models, and broad network access, rather than as a comprehensive cloud computing paradigm.

In this paper, we present a management system for distributed software development in cloud computing environments, called DDMan. Cloud computing environments provide more flexibility than conventional computing environments. In particular, the platform as a service (PaaS) in cloud computing environments provides benefits for users in a variety of aspects, such as application design, development testing, software deployment, team collaboration, web service integration, database integration, and scalability. Although the process of distributed software development evolves and changes by a lot in the past decade, there are still some user needs remaining unsatisfied. For managing distributed software development in cloud computing systems, we present the DDMan system, which extends the WebSD model of distributed software development management. In DDMan, the interactions among the roles in the software development are precisely defined. In addition to the conventional web-based interface, the DDMan system also supports the interface for the Amazon cloud environment, which is an approach to a fully automatic service of distributed software development management in cloud computing environments.

Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms. Keywords: Cloud computing, Task scheduling, Load balancing, Makespan, Energy-aware, Turnaround time, Response time, Cost of task, QoS, Multi-objective. DOI: 10.7176/IKM/12-5-03 Publication date: September 30 th 2022

Cloud computing (CC) has many benefits, so its use has spread rapidly, particularly in the business sector. An important consideration in the acceptance of CC is whether the CC system is dependable, and it can differ among industry and service type. However, little research has considered the effect of dependability (composed of availability, reliability, security, maintainability) on CC acceptance. Especially, group comparisons between high IT-intensive (Hi-ITi) and low IT-intensive (Lo-ITi) industries have not been reported, nor have comparisons between software-as-a-service (SaaS) and platform-as-a-service (PaaS)/infrastructure-as-a-service (IaaS). This study aims to explore how the dependability of CC affects users’ intent to accept it, with focus on how this intent is affected by intensity of IT use (by industry) and by the type of CC service used. To validate the proposed model, this study applied structural equation modeling and conducted multi-group analysis. A total of 230 business managers in South Korea represent the sample for our study. For the full dataset, the three dependability attributes (availability, reliability, security) do not affect the usefulness of CC, but do affect the ease of use of CC. The usefulness of CC is a determinant for positive intention to accept CC, whereas the ease of use of CC is not. Maintainability is the strongest determinant of CC adoption for the full dataset, and for all individual groups, except those that use SaaS. For Hi-ITi and Lo-ITi industries, results show that managers show no differences in their perceptions of the effect of dependability attributes (availability, reliability, security) on the usefulness and the ease of CC. The absence of such a difference in managers’ perception also applies to the relationship between two core variables of TAM (i.e., perceived usefulness, perceived ease of use) and behavioral intention to accept CC. For SaaS and PaaS/IaaS, managers have different perceptions of security on the usefulness of CC, and the effect of the usefulness of CC on the intention to accept CC. The findings can provide academic researchers and industry practitioners with a differentiated and in-depth perspective on the understanding and the spread of CC.

PurposeThis bibliometric study is steered to compute the impact of literature published on cloud computing within the fields of information science and library science. The research has been conducted on concentrating the term “Cloud Computing” to search the literature published in both fields, i.e. information science and library science from the time span 2007 to August 2023. This study aims to investigate the top productive country, organizations and highly cited publications.Design/methodology/approachThe period of the exploration was from 2007 to August 2023 for bibliometric analysis and data was collected from the ISI Web of Science. Total 401 documents were retrieved and analyzed to highlight the year-wise distribution of documents type, year-wise most cited articles, prominent journals of the subjects, productivity of organizations, impact of countries and cooccurrences of keywords. The results are grounded on the basis of documents types (articles, early access articles, proceeding papers, book review, editorial material, news items and reviews).FindingsThe findings reveal that the most productive year of publication on cloud computing services was 2013. The top productive source is “International Journal of Information Management.” The articles entitled “Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors” found as the most cited article and the significant increase in citations is also noteworthy. The most productive organizations on the topic include “Islamic Azad University of Iran,” “University Cologne of Germany” and “University Nova Lisboa of Portugal.” The results confirmed that the USA dominates in the production of research on “Cloud Computing Services” and the most repeated keyword in the literature is cloud computing. The research articles are the most cited sources of research.Originality/valueThis bibliometric research is an original piece of work that has been conducted to measure the research production in the field of information science and library science during 2007−2023. This piece of work is valuable for those who want to study the literature on cloud computing in the area of information science and library science.