Abstract: Based on the philosophy of cloud computing,a new intelligently networked manufacturing model called "cloud manufacturing" has been proposed which is service-oriented,highly efficient,consumes less energy,and is knowledge based.By integrating state-of-the-art technologies such as informatized manufacturing,cloud computing,the Internet of things,semantic Web and high performance computing,cloud manufacturing provides secure,reliable,and high quality on-demand services with low prices for users involved in the whole manufacturing lifecycle.Cloud simulation technology based on the cloud simulation platform COSIM-CSP has primarily been applied to the collaborative design of a certain multidisciplinary virtual prototype for flight vehicle.This lays the foundation for further study into cloud manufacturing.

Abstract: Traditional assurance solutions for software-based systems rely on static verification techniques and assume continuous availability of trusted third parties. With the advent of cloud computing, these solutions become ineffective since services/applications are flexible, dynamic, and change at runtime, at high rates. Although several assurance approaches have been defined, cloud requires a step-change moving current assurance techniques to fully embrace the cloud peculiarities. In this paper, we provide a rigorous and adaptive assurance technique based on certification, towards the definition of a transparent and trusted cloud ecosystem. It aims to increase the confidence of cloud customers that every piece of the cloud (from its infrastructure to hosted applications) behaves as expected and according to their requirements. We first present a test-based certification scheme proving non-functional properties of cloud-based services. The scheme is driven by non-functional requirements defined by the certification authority and by a model of the service under certification. We then define an automatic approach to verification of consistency between requirements and models, which is at the basis of the chain of trust supported by the certification scheme. We also present a continuous certificate life cycle management process including both certificate issuing and its adaptation to address contextual changes. Finally, we describe our certification framework and an experimental evaluation of its performance, quality, applicability, and practical usability in a real industrial scenario, which considers Engineering Ingegneria Informatica S.p.A. ENGpay online payment system.

Abstract: Cloud computing has promoted the success of big data applications such as medical data analyses. With the abundant resources provisioned by cloud platforms, the quality of service (QoS) of services that process big data could be boosted significantly. However, due to unstable network or fake advertisement, the QoS published by service providers is not always trusted. Therefore, it becomes a necessity to evaluate the service quality in a trustable way, based on the services’ historical QoS records. However, the evaluation efficiency would be low and cannot meet users’ quick response requirement, if all the records of a service are recruited for quality evaluation. Moreover, it may lead to ‘ Lagging Effect ’ or low evaluation accuracy, if all the records are treated equally, as the invocation contexts of different records are not exactly the same. In view of these challenges, a novel approach named Partial H istorical R ecords-based service evaluation approach ( Partial-HR ) is put forward in this paper. In Partial-HR , each historical QoS record is weighted based on its service invocation context. Afterwards, only partial important records are employed for quality evaluation. Finally, a group of experiments are deployed to validate the feasibility of our proposal, in terms of evaluation accuracy and efficiency.

Abstract: Composing several API-defined services into one composite service per user requirements has become an important service creation approach in the cloud-enabled API economy. Various service selection approaches in support of service composition on demand have been proposed. They usually assume that networking resources are over-provisioned and their usage needs not be considered when making quality-aware service composition decisions. In practice, these approaches often lead to wasteful network resource consumption and impractical end-to-end QoS optimality for cloud-based services. This paper proposes a network-aware cloud service composition approach, named NetMIP, with comparative experimental evaluations for the clouds that adopt the widely deployed fat-tree network topology. By formalizing the service composition goal as a multi-objective constraint optimization problem, we have validated the proposed approach can be used to effectively reduce network resource consumption and deliver QoS optimality while satisfying the end-to-end QoS constraints for the candidate composite services in the cloud. The comparative experimental evaluations are done via a credible cloud infrastructure simulation system, named WebCloudSim. Extensive evaluation results show that NetMIP outperforms several representative cloud service composition approaches in terms of network resource consumption, QoS optimality, and computation time under various service selection workloads and fat-tree network topology settings.

Abstract: The popularity of cloud computing has fueled the growth in multi-tenant service-based systems (SBSs) that are composed of selected cloud services. In the cloud environment, a multi-tenant SBS simultaneously serves multiple tenants that usually have differentiated QoS requirements. This unique characteristic further complicates the problems of QoS-aware service selection at build-time and system adaptation at runtime, and renders conventional approaches obsolete and inefficient. In the dynamic and volatile cloud environment, the efficiency of building and adapting a multi-tenant SBS is of paramount importance. In this paper, we present two service recommendation approaches for multi-tenant SBSs, one for build-time and one for runtime, based on K-Means clustering and Locality-Sensitive Hashing (LSH) techniques respectively, aiming at finding appropriate services efficiently. Extensive experimental results demonstrate that our approaches can facilitate fast multi-tenant SBS construction and rapid system adaptation.

Abstract: Cloud computing is gaining popularity among small and medium-sized enterprises. The cost of cloud resources plays a significant role for these companies and this is why cloud resource optimization has become a very important issue. Numerous methods have been proposed to optimize cloud computing resources according to actual demand and to reduce the cost of cloud services. Such approaches mostly focus on a single factor (i.e. compute power) optimization, but this can yield unsatisfactory results in real-world cloud workloads which are multi-factor, dynamic and irregular. This paper presents a novel approach which uses anomaly detection, machine learning and particle swarm optimization to achieve a cost-optimal cloud resource configuration. It is a complete solution which works in a closed loop without the need for external supervision or initialization, builds knowledge about the usage patterns of the system being optimized and filters out anomalous situations on the fly. Our solution can adapt to changes in both system load and the cloud provider's pricing plan. It was tested in Microsoft's cloud environment Azure using data collected from a real-life system. Experiments demonstrate that over a period of 10 months, a cost reduction of 85% was achieved.

Abstract: In order to provide data storage services,cloud storage employs software to interconnect and facilitate collaboration between different types of storage devices.Compared to traditional storage methods,cloud storage poses new challenges in data security,reliability,and management.This paper introduces four layers of cloud storage architecture:data storage layer connecting multiple storage components,data management layer providing common supporting technology for multiple services,data service layer sustaining multiple storage applications,and user access layer.It then examines a typical cloud storage application—backup cloud (B-Cloud)—and discusses its software architecture,characteristics,and main research questions.

Abstract: Benefiting from the pay-as-you-go business model, cloud-based software applications are becoming more and more popular. A composite cloud system can be constructed by integrating existing component cloud services available over the internet as its system components. In order to fulfill the service-level agreements (SLAs), as well as users' quality of experience (QoE), a stable execution of the constructed system is desirable in the long term. To achieve this goal, system components at high risk of failing must be identified and fault-tolerated. This is extremely challenging in the dynamic cloud environment that host the component cloud services. However, existing approaches are constrained by their lack of modeling and analysis of system components' fluctuating reliability time series. This paper proposes PARS, a perturbation-aware approach, for measuring the reliability sensitivity of component cloud services. It first analyzes the negative perturbations in component cloud services' historical reliability time series. Then, it calculates the reliability sensitivity of the component cloud services by analyzing how their reliability perturbations impact the reliability of the entire cloud system. Based on PARS, we propose a proactive adaptation approach for constructing and operating composite cloud systems. The results of experiments demonstrate the effectiveness and efficiency of the proposed approaches.

Abstract: Cloud computing is a new technology for network computing under the IP architecture,and its potential lies in new ICT business applications.For the majority of operators and enterprises,the main task of cloud computing is data centre transformation.This will ensure cloud computing becomes more widespread among enterprises,institutions,organizations,and operators.Cloud computing will not only provide traditional IT resource usage and application services,but will also support full resource usage and application services—such as IT,communications,video,mobile,as well as Internet of Things under a converged network infrastructure.Some key cloud computing technologies include unified fabric,unified virtualization,and a unified computing system.The formation of an open industry alliance and promotion of open technology standards will also be strategically critical for future development of cloud computing.

Abstract: Background: The knowledge and application of tools to automate testing is essential to ensure software reliability and therefore its quality. Due to the increasing demand for quality in software projects executed in short time-scales, Testing as a Service (TaaS) appeared in the literature as contributions for cost reduction and productivity of automated tests. Aims: Once quality attributes from these contributions are not deeply discussed by the literature of the area, our goal is to investigate and identify these attributes from the TaaS platforms and providers commonly reported in the literature. Method: A protocol was formulated and executed according to the guidelines for performing systematic literature map in Software Engineering. Results: The TaaS providers and platform proposals found were classified according to the number of mentions in the literature, highlighting the most commonly mentioned and widespread. As well as the propagation and explanation of the main advantages and disadvantages reported in the literature on Testing as a Service. Conclusions: TaaS provides means for cost reduction and increase in productivity in comparison to traditional test approaches. This is a reality observed in 76 options for Test as a Service cloud platforms distributed over 52 papers. In addition, as their quality attributes, we also found eight groups of disadvantages and 21 of advantages. Thus, this systematic mapping is a valuable contribution for decision making on performance testing strategies.

Abstract: Cloud computing is one of the highly flexible, confidential and easily accessible medium of platforms and provides powerful service for sharing information over the Internet. Cloud security has become an emerging issue as network manager eventually encounter its data protection, vulnerability during information exchange on the cloud system. We can protect our data from unwanted access on a hybrid cloud through controlling the respective firewall of the network. But, the firewall has already proved its weakness as it is unable to ensure multi-layered, secured accessibility of the cloud network. Efficient packet utilization sometimes causes high response time in accessing hybrid cloud. In this paper, a Cloud Model with Hybrid functionality and a secure Fuzzy Integrated Firewall for that Hybrid Cloud is proposed and thereby evaluated for the performance in traffic response. Experimental result illustrated that having a fuzzified firewall gives high point-to-point packet utilization decreasing the response time than a conventional firewall. Results from this research work will highly be implemented in transplanting artificial intelligence in future Internet of Things (IoT).

Abstract: Likely system invariants model properties that hold in operating conditions of a computing system. Invariants may be mined offline from training datasets, or inferred during execution. Scientific work has shown that invariants’ mining techniques support several activities, including capacity planning and detection of failures, anomalies and violations of Service Level Agreements. However their practical application by operation engineers is still a challenge. We aim to fill this gap through an empirical analysis of three major techniques for mining invariants in cloud-based utility computing systems: clustering, association rules, and decision list. The experiments use independent datasets from real-world systems: a Google cluster, whose traces are publicly available, and a Software-as-a-Service platform used by various companies worldwide. We assess the techniques in two invariants’ applications, namely executions characterization and anomaly detection, using the metrics of coverage, recall and precision. A sensitivity analysis is performed. Experimental results allow inferring practical usage implications, showing that relatively few invariants characterize the majority of operating conditions, that precision and recall may drop significantly when trying to achieve a large coverage, and that techniques exhibit similar precision, though the supervised one a higher recall. Finally, we propose a general heuristic for selecting likely invariants from a dataset.

Abstract: While great emphasis is given in the current literature about the potential of leveraging the cloud for testing purposes, the authors have scarce factual evidence from real-world industrial contexts about the motivations, drawbacks and benefits related to the adoption of automated cloud testing technology. In this study, the authors present an empirical study undertaken within the ongoing European Project ElasTest, which has developed an open source platform for end-to-end testing of large distributed systems. This study aims at validating the ElasTest solution, and consists of the assessment of four demonstrators belonging to different application domains, namely e-commerce, 5G networking, WebRTC and Internet of Things. For each demonstrator, they collected differing requirements, and achieved varying results, both positive and negative, showing that cloud testing needs careful assessment before adoption.

Abstract: The Cloud testing market share is expected to be over 10 billion USD by 2022. Cloud migration for applications has become an attractive phenomenon, and end users have, as a result, achieved various benefits such as autonomy, scalability and agility and improved return on investment by migrating to Cloud. Cloud environment is inherently elastic with respect to applications, infrastructure and platform resources which consequentially translate to the benefits mentioned. The rapid consumer adoption of Cloud paradigm mandates software testing in order to ensure that services over the Cloud are working as expected. In addition to the need for testing Cloud services and applications, the emergence of Cloud computing has opened new avenues for providing testing services over the Cloud in the form of testing as a service (TaaS). Many quality assurance (QA) processes which have a direct impact on testing cycles, like test environment management and test data management, can be provisioned via the Cloud, resulting in immense additional benefits. This chapter sets the context of Cloud computing and its growing significance for the software industry before focusing on Cloud TaaS. Additionally, different types of Cloud deployments in testing ecosystem are discussed in this chapter including: testing on the Cloud, testing models, test processes relating to the Cloud, tools and frameworks.

Abstract: The invention discloses a cloud test configuration method and device, computer equipment and a storage medium. The method comprises the steps of receiving a user identifier and a to-be-tested softwarepackage uploaded by a client; obtaining a package name and a suffix name of the to-be-tested software package, and determining a test platform; analyzing the to-be-tested software package to obtain version information of the to-be-tested software package; obtaining user portrait information corresponding to the user identifier from a user portrait database; determining a customized configurationscheme of the to-be-tested software package according to the user portrait information; determining a total configuration scheme matched with the version information according to the test platform andthe version information; and performing cloud testing on the to-be-tested software package by using the customized configuration scheme or the total configuration scheme on the test platform. According to the technical scheme, automatic configuration and testing of cloud testing are achieved, the labor cost is reduced, the cloud testing efficiency is improved. Meanwhile, a reasonable configuration scheme is obtained through the user portrait information, and the intelligent level of testing configuration is improved.

Abstract: The resource scheduling ability of hidden fault channel cloud test platform based on cache data replacement method is not good. A hidden fault channel cloud test platform resource access algorithm based on deep learning is proposed. The resource access and data mining structure model of hidden fault channel cloud test platform is constructed, the task code is distributed to the multi-path monitoring node by distributed code execution and KD tree data index, the two-dimensional KD tree is constructed to realize the path data monitoring, the cloud computing core technology is used to test the hidden fault channel cloud test platform, and the hidden fault channel cloud test platform is designed with the resource integration method. Improve the parallel processing ability of hidden fault channel cloud test platform. The simulation results show that the output stability and reliability of the hidden fault channel cloud test platform designed by this method are good, and the ability of hidden fault channel cloud testing is improved.

Abstract: Nowadays, more and more cloud testing platforms provide enterprise developers with solutions for cloud device debugging and automatic testing. It is a great challenge for these cloud platforms to schedule the arriving requests to run on the specific smart device resources in real-time and efficiently. The traditional scheduling algorithm is difficult to adapt to the application interface call request with a vast difference in volume and behaviour ability. To solve this problem, we integrate these smart devices into a device cloud and propose a measurement method of the service capability of a single device in the group. Then we build an adaptive scheduling algorithm model according to the characteristics of the serviceability of a single device to improve the scheduling efficiency of the group. Practice shows that the adaptive scheduling algorithm can effectively control the network traffic. Finally, through the analysis and optimization, we get the method of obtaining the optimal parameter combination in the algorithm.

Abstract: Traditional testing service incurs high cost and low efficiency because of the expenditure on testing tools and the geographical location. Cloud testing service platform (CTSP) uses cloud infrastructure for testing service, which leads to a more cost-effective testing solution. However, how to realize the intelligent matching among the various testing services and the testing demands is one of the common issues and aims for CTSP. This paper investigates a semantic demand-service matching method for CTSP. Considering the diverse, heterogeneous and dynamic characteristics of cloud testing services, an Input, Output, Precondition, Effect (IOPE) matching model based on Web Ontology Language for Service (OWL-S) is proposed, and a three-phase matching process is developed consisting of parameter matching, attribute matching and global matching. To compute the matching degree between a testing service and a testing demand during the matching process, a quantitative matching method is put forward. At last, the effectiveness and feasibility of the proposed method is tested by a case study.

Abstract: There exists a close relationship between cloud computing and distributed systems. In fact, Cloud computing technology brings new organizational and technological capabilities to build an underlying infrastructure for distributed systems. Therefore, even if cloud computing offers opportunities to improve productivity and reduce costs, it also introduces a number of technical challenges, especially in testing area. In this paper, we introduce concepts such as cloud computing, cloud testing and Infrastructure as a Service (IaaS). Then, we present our distributed testing architecture for applications running in an elastic cloud computing environment using these concepts.

Abstract: Cloud computing facilitates service providers to rent their computing capabilities for deploying applications depending on user requirements. Applications of cloud have diverse composition, configuration and deployment requirements. Quantifying the performance of applications in Cloud computing environments is a challenging task. In this paper, we try to identify various parameters associated with performance of cloud applications and analyse the impact of resource management and scalability among them.

Abstract: Recent expansions of Internet-of-Things (IoT) applying cloud computing have been growing at a phenomenal rate. As one of the developments, heterogeneous cloud computing has enabled a variety of cloud-based infrastructure solutions, such as multimedia big data. Numerous prior researches have explored the optimizations of on-premise heterogeneous memories. However, the heterogeneous cloud memories are facing constraints due to the performance limitations and cost concerns caused by the hardware distributions and manipulative mechanisms. Assigning data tasks to distributed memories with various capacities is a combinatorial NP-hard problem. This paper focuses on this issue and proposes a novel approach, Cost-Aware Heterogeneous Cloud Memory Model (CAHCM), aiming to provision a high performance cloud-based heterogeneous memory service offerings. The main algorithm supporting CAHCM is Dynamic Data Allocation Advance (2DA) Algorithm that uses genetic programming to determine the data allocations on the cloud-based memories. In our proposed approach, we consider a set of crucial factors impacting the performance of the cloud memories, such as communication costs, data move operating costs, energy performance, and time constraints. Finally, we implement experimental evaluations to examine our proposed model. The experimental results have shown that our approach is adoptable and feasible for being a cost-aware cloud-based solution.

Abstract: Cloud computing services now different widely in how they are packaged and labelled. Cloud computing is just like that bus, carrying data and information for several users and allows to use its service with minimal cost. Cloud computing is a model for enabling suitable, on-demand network admission to a shared pool of configurable computing resources that can be speedily provisioned and released with least management attempt. Cloud management software and the underlying cloud computing communications must support the ability to physically or logically segregate the traffic and data storage associated with different customers. This paper aims to provide a means of understanding and investigating IaaS This paper talks on the IaaS model of the cloud computing. Authors of this paper, gathered, analysed and drafted all the up to date information on the IaaS.

Abstract: ract Cloud computing is commonly used for the delivery of software, infrastructure and storage services over the internet. The delivery of services can be done in the private cloud or public cloud. Private cloud resources will be within our data center and it is a secure environment where only specified client can operate. Public cloud resources are provided in a virtualized environment, which provides a pool of shared resources. Hybrid cloud is integration of private, public and in some cases community cloud to perform unique functions within the same organization. Small and medium scale organizations cannot effort to setup IT infrastructure so hybrid cloud is one prominent solution for them. This paper deals with the hybrid cloud computing and architecture of the hybrid cloud computing. The paper also depicts advantages, disadvantages, challenges and differences in hybrid cloud computing implementations.

Abstract: Low-code is a growing development approach supported by many platforms. It fills the gap between business and IT by supporting the active involvement of non-technical domain experts, named Citizen Developer, in the application development lifecycle.Low-code introduces new concepts and characteristics. However, it is not investigated yet in academic research to point out the existing challenges and opportunities when testing low-code software. This shortage of resources motivates this research to provide an explicit definition to this area that we call it Low-Code Testing.In this paper, we initially conduct an analysis of the testing components of five commercial Low-Code Development Platforms (LCDP) to present low-code testing advancements from a business point of view. Based on the low-code principles as well as the result of our analysis, we propose a feature list for low-code testing along with possible values for them. This feature list can be used as a baseline for comparing low-code testing components and as a guideline for building new ones. Accordingly, we specify the status of the testing components of investigated LCDPs based on the proposed features. Finally, the challenges of low-code testing are introduced considering three concerns: the role of citizen developer in testing, the need for high-level test automation, and cloud testing. We provide references to the state-of-the-art to specify the difficulties and opportunities from an academic perspective. The results of this research can be used as a starting point for future research in low-code testing area.

Abstract: Cloud computing is regarded as massively extensible, an on-demand configurable resources computing model. It approaches the cloud infrastructure in a distributed rather than dedicated infrastructure where users can have full access to the extensible, reliable resources. Data generated by IoT attached objects is high, cloud is a key to store the incalculable data generated by these attached devices and it is the forward stepped towards the green computing, it removes the setups and installation steps as the cloud user accessing the hardware resources co-exist on different platform in distributed way. Cloud computing environment furnished a great flexibility and availability of computing resources at a very lower cost. This arriving technology opens a new era of e-services in different disciplines. In this paper, we seen cloud computing with its applications, most common Cloud Service Provider such as Google, Microsoft, Amazon, HP, and Sales force and we present innovative applications for cloud computing in Enterprise Resource Planning.

Abstract: Modern cloud computing platforms based on virtual machine monitors (VMMs) host a variety of complex businesses which present many network security vulnerabilities. In order to protect network security for these businesses in cloud computing, nowadays, a number of middleboxes are deployed at front-end of cloud computing or parts of middleboxes are deployed in cloud computing. However, the former is leading to high cost and management complexity, and also lacking of network security protection between virtual machines while the latter does not effectively prevent network attacks from external traffic. To address the above-mentioned challenges, we introduce a novel customized network security for cloud service (CNS), which not only prevents attacks from external and internal traffic to ensure network security of services in cloud computing, but also affords customized network security service for cloud users. CNS is implemented by modifying the Xen hypervisor and proved by various experiments which showing the proposed solution can be directly applied to the extensive practical promotion in cloud computing.

Abstract: Cloud computing is an evolution of web based internet application and describes an advance consumption, supplement and delivery model for Information Technology and ICT services based on the global network. This enables allocation of resources and costs across a large pool of users while providing on-demand services with dynamic scalability. So we can say that a technology that has the capability and potential to offer solutions for IT Industries of cloud computing. Cloud computing provide service-oriented access to users least compromising on security. In today's era software and their services are biggest cost concern for the implementation of IT environment in an organization. Cloud has the capability to reduce the cost in dramatic way for the all kind of the organization even it is small scale Industry or a big corporate organization. This makes Cloud an excellent platform to host IT based services and application. The basic intention of this article is whatever improvement happening in cloud technology and in web technology mention sub sequentially. If we apply them in existing IT Industries application running under various department then we can minimize the some of the most basic affected components of application software like cost of the software in its execution, optimal time for usage and running of application software, storage capacity for storing of the data and network infrastructure used for the functioning of the application software.

Abstract: Cloud computing is now a well-adopted computing paradigm. With unprecedented scalability and flexibility, the computational cloud is able to carry out large scale computing tasks in parallel. The datacenter cloud is a new cloud computing model that uses multi-datacenter architectures for large scale massive data processing or computing. In datacenter cloud computing, the overall efficiency of the cloud depends largely on the workload scheduler, which allocates clients’ tasks to different Cloud datacenters. Developing high performance workload scheduling techniques in Cloud computing imposes a great challenge which has been extensively studied. Most previous works aim only at minimizing the completion time of all tasks. However, timeliness is not the only concern, reliability and security are also very important. In this work, a comprehensive Quality of Service (QoS) model is proposed to measure the overall performance of datacenter clouds. An advanced Cross-Entropy based stochastic scheduling (CESS) algorithm is developed to optimize the accumulative QoS and sojourn time of all tasks. Experimental results show that our algorithm improves accumulative QoS and sojourn time by up to 56.1 and 25.4 percent respectively compared to the baseline algorithm. The runtime of our algorithm grows only linearly with the number of Cloud datacenters and tasks. Given the same arrival rate and service rate ratio, our algorithm steadily generates scheduling solutions with satisfactory QoS without sacrificing sojourn time.