From cloud computing to cloud manufacturing

Cloud computing is changing the way industries and enterprises do their businesses in that dynamically scalable and virtualized resources are provided as a service over the Internet. This model creates a brand new opportunity for enterprises. In this paper, some of the essential features of cloud computing are briefly discussed with regard to the end-users, enterprises that use the cloud as a platform, and cloud providers themselves. Cloud computing is emerging as one of the major enablers for the manufacturing industry; it can transform the traditional manufacturing business model, help it to align product innovation with business strategy, and create intelligent factory networks that encourage effective collaboration. Two types of cloud computing adoptions in the manufacturing sector have been suggested, manufacturing with direct adoption of cloud computing technologies and cloud manufacturing—the manufacturing version of cloud computing. Cloud computing has been in some of key areas of manufacturing such as IT, pay-as-you-go business models, production scaling up and down per demand, and flexibility in deploying and customizing solutions. In cloud manufacturing, distributed resources are encapsulated into cloud services and managed in a centralized way. Clients can use cloud services according to their requirements. Cloud users can request services ranging from product design, manufacturing, testing, management, and all other stages of a product life cycle.     

Model-based services convergence and multi-clouds integration

With the development of cloud computing, IT users (individuals, enterprises and even public services providers) are transferring their jobs or businesses to public online services provided by professional information service companies. These information service companies provide applications as public resources to support the business operation of their customers. However, no cloud computing service vendor (CCSV) can satisfy the full functional information system requirements of its customers. As a result, its customers often have to simultaneously use services distributed in different clouds and do some connectivity jobs manually. Services convergence and multi-clouds integration will lead to new business models and trigger new integration technologies that provide solutions to satisfy IT users’ complicated requirements. This paper firstly reviews the development of cloud computing from business and technical viewpoints and then discusses requirements and challenges of services convergence and multi-clouds integrations. Thirdly, a model based architecture of multi-clouds integration is provided. Business logic modelling for cross-organizational collaboration, service modelling and operation modelling methods with relative model mapping technology are discussed in detail. Some key enabling technologies are also developed. At last, case studies are presented to illustrate the implementation of the technologies developed in the paper.     

Cost model based service placement in federated hybrid clouds

As cloud federation allows companies in need of computational resources to use computational resources hosted by different cloud providers, it reduces the cost of IT infrastructure by lowering capital and operational expenses. This is the result of economies of scale and the possibility for organizations to purchase just as much computing and storage resources as needed whenever needed. However, a clear specification of cost savings requires a detailed specification of the costs incurred. Although there are some efforts to define cost models for clouds, the need for a comprehensive cost model, which covers all cost factors and types of clouds, is undeniable. In this paper, we cover this gap by suggesting a cost model for the most general form of a cloud, namely federated hybrid clouds. This type of cloud is composed of a private cloud and a number of interoperable public clouds. The proposed cost model is applied within a cost minimization algorithm for making service placement decisions in clouds. We demonstrate the workings of our cost model and service placement algorithm within a specific cloud scenario. Our results show that the service placement algorithm with the cost model minimizes the spending for computational services.     

混合云中的一个高效协调器

云计算提供了4种部署模型:公有云、私有云、社区云和混合云.通常,一个私有云中可用的资源是有限的,因此云用户不得不从公有云租用资源.这意味着云用户将会产生额外的费用.越来越多的企业选择混合云来部署它们的应用.在混合云中,为了实现用户的利益最大化,必须满足使用资源的费用最小化和用户的QoS,为此为混合云用户提供了一个既能最小化资源费用又能保证满足QoS的资源分配方法.实验结果表明,该算法在保持低操作成本的同时还满足了用户的QoS.     

浅谈云计算的应用与发展

云计算是基于互联网的商业计算模型,它代表IT产业由硬件转向软件、软件转向服务、分散服务转向集中服务的发展趋势。笔者介绍了云计算的相关概念,云计算的应用形式,最后对云计算的未来发展趋势做了展望。     

Workload-based multi-task scheduling in cloud manufacturing

Cloud manufacturing is an emerging service-oriented business model that integrates distributed manufacturing resources, transforms them into manufacturing services, and manages the services centrally. Cloud manufacturing allows multiple users to request services at the same time by submitting their requirement tasks to a cloud manufacturing platform. The centralized management and operation of manufacturing services enable cloud manufacturing to deal with multiple manufacturing tasks in parallel. An important issue with cloud manufacturing is therefore how to optimally schedule multiple manufacturing tasks to achieve better performance of a cloud manufacturing system. Task workload provides an important basis for task scheduling in cloud manufacturing. Based on this idea, we present a cloud manufacturing multi-task scheduling model that incorporates task workload modelling and a number of other essential ingredients regarding services such as service efficiency coefficient and service quantity. Then we investigate the effects of different workload-based task scheduling methods on system performance such as total completion time and service utilization. Scenarios with or without time constraints are separately investigated in detail. Results from simulation experiments indicate that scheduling larger workload tasks with a higher priority can shorten the makespan and increase service utilization without decreasing task fulfilment quality when there is no time constraint. When time constraint is involved, the above strategy enables more tasks to be successfully fulfilled within the time constraint, and task fulfilment quality also does not deteriorate.     

Cost-benefit analysis of an SLA mapping approach for defining standardized Cloud computing goods

Due to the large variety in computing resources and, consequently, the large number of different types of service level agreements (SLAs), computing resource markets face the problem of a low market liquidity. Restricting the number of different resource types to a small set of standardized computing resources seems to be the appropriate solution to counteract this problem. Standardized computing resources are defined through an SLA template. An SLA template defines the structure of an SLA, the service attributes, the names of the service attributes, and the service attribute values. However, since existing research results have only introduced static SLA templates so far, the SLA templates cannot reflect changes in user needs and market structures. To address this shortcoming, we present a novel approach of adaptive SLA matching. This approach adapts SLA templates based on SLA mappings of users. It allows Cloud users to define mappings between a public SLA template, which is available in the Cloud market, and their private SLA templates, which are used for various in-house business processes of the Cloud user. Besides showing how public SLA templates are adapted to the demand of Cloud users, we also analyze the costs and benefits of this approach. Costs are incurred every time a user has to define a new SLA mapping to a public SLA template due to its adaptation. In particular, we investigate how the costs differ with respect to the public SLA template adaptation method. The simulation results show that the use of heuristics within adaptation methods allows balancing the costs and benefits of the SLA mapping approach.     

Enhancing Federated Cloud Management with an Integrated Service Monitoring Approach

Cloud Computing enables the construction and the provisioning of virtualized service-based applications in a simple and cost effective outsourcing to dynamic service environments. Cloud Federations envisage a distributed, heterogeneous environment consisting of various cloud infrastructures by aggregating different IaaS provider capabilities coming from both the commercial and the academic area. In this paper, we introduce a federated cloud management solution that operates the federation through utilizing cloud-brokers for various IaaS providers. In order to enable an enhanced provider selection and inter-cloud service executions, an integrated monitoring approach is proposed which is capable of measuring the availability and reliability of the provisioned services in different providers. To this end, a minimal metric monitoring service has been designed and used together with a service monitoring solution to measure cloud performance. The transparent and cost effective operation on commercial clouds and the capability to simultaneously monitor both private and public clouds were the major design goals of this integrated cloud monitoring approach. Finally, the evaluation of our proposed solution is presented on different private IaaS systems participating in federations.     

Impact of a firm’s physical and knowledge capital intensities on its selection of a cloud computing deployment model

Cloud computing has become an increasingly common computing infrastructure for contemporary firms. An important decision for firms to make in adopting a cloud computing model is whether to build it in-house (a private cloud) or outsource it (a public cloud). Prior literature has focused on the impact of firms’ characteristics but generated inconsistent results regarding the selection of cloud computing models. To add to this line of inquiry, we consider the relative resource structure, which reflects the importance of physical and knowledge resources for individual firms, and examine their respective effects on the selection of cloud computing deployment models (CCDMs). Using data from 520 companies deploying cloud computing in mainland China, we find that firms with higher physical capital intensity (PCI) tend to outsource cloud computing, whereas those with higher knowledge capital intensity (KCI) tend to use private clouds. Firms with higher codified knowledge capital intensity (CKCI) are found to be more susceptible to the negative relationship between KCI and public cloud selection than those with higher tacit knowledge capital intensity (TKCI). The direct positive influence of regional legal protection on a firm’s preferences for a public cloud is also confirmed, as well as its indirect moderating effect on alleviating the negative relationships between CKCI and deploying a public cloud.     

Ubiquitous manufacturing system based on Cloud: A robotics application

Modern manufacturing industry calls for a new generation of production system with better interoperability and new business models. As a novel information technology, Cloud provides new service models and business opportunities for manufacturing industry. In this research, recent Cloud manufacturing and Cloud robotics approaches are reviewed. Function block-based integration mechanisms are developed to integrate various types of manufacturing facilities. A Cloud-based manufacturing system is developed to support ubiquitous manufacturing, which provides a service pool maintaining physical facilities in terms of manufacturing services. The proposed framework and mechanisms are evaluated by both machining and robotics applications. In practice, it is possible to establish an integrated manufacturing environment across multiple levels with the support of manufacturing Cloud and function blocks. It provides a flexible architecture as well as ubiquitous and integrated methodologies for the Cloud manufacturing system.     

A novel approach to manage cloud security SLA incidents

Cloud computing is increasingly playing an important role in the service provisioning domain given the economic and technological benefits it offers. The popularity of cloud services is increasing but so are their customers’ concerns about security assurance and transparency of the Cloud Service Providers (CSPs). This is especially relevant in the case of critical services that are progressively moving to the cloud. Examples include the integrated European air traffic control system or public administrations through the governmental clouds. Recent efforts aim to specify security in cloud by using security service level agreements (secSLAs). However, the paucity of approaches to actually control the fulfillment of secSLAs and to react in case of security breaches, often results in distrust in cloud services. In this paper, we present a solution to monitor and enforce the fulfillment of secSLAs. Our framework is able to (a) detect occurrences that lead to unfulfillment of commitments, and (b) also provide mitigation to the harmful events that may or do compromise the validity of secSLAs.     

Transparent VPN failure recovery with virtualization

Cloud computing is widely used to provide today’s Internet services. Since its service scope is being extended to a wide range of business applications, the security of network communications between clients and clouds are becoming important. Several cloud vendors support virtual private networks (VPNs) for connecting their clouds. Unfortunately, cloud services become unavailable when a VPN failure occurred in a VPN gateway or networks. We propose a transparent VPN failure recovery scheme that can hide VPN failures from users and operating systems (OSs). This scheme transparently recovers from VPN failures by establishing VPN connections in a virtualization layer. When a VPN failure occurs, a client virtual machine monitor (VMM) automatically reconnects to an available VPN gateway which is geographically distributed and connected via leased lines in clouds. IP address changes are hidden from client OSs and servers via a packet relay system implemented by a relay client in the client VMM and a relay server. We implemented a prototype system based on BitVisor, a small client VMM supporting IPsec VPN, and evaluated the prototype system in a wide-area distributed Internet environment in Japan. Experimental results show that our scheme can maintain TCP connections on VPN failures, and performance overhead with the virtualization layer is around 0.6 ms to latency and 8%-30% to throughput.     

Cloud service access control system based on ontologies

Cloud service is a new and distinctive business model for service providers. Access control is an emerging and challenging issue in supporting cloud service business. This work proposes a new access control mechanism called cloud service access control (CSAC). The CSAC mechanism considers payment status and service level as the two essential characteristics of cloud service. Ontology is a theoretical foundation for the CSAC mechanism. Inconsistent access control policies are detected by a set of proposed policy conflict analysis rules. Inappropriate user accesses are inhibited by access control policies according the proposed access denying rules. System architecture is designed to support the CSAC mechanism. A case study is provided to demonstrate how CSAC works. Finally, an evaluation is conducted to measure the concept explosion issue in CSAC.     

Mobile Cloud Middleware

Mobile Cloud Computing (MCC) is arising as a prominent research area that is seeking to bring the massive advantages of the cloud to the constrained smartphones. Mobile devices are looking towards cloud-aware techniques, driven by their growing interest to provide ubiquitous PC-like functionality to mobile users. These functionalities mainly target at increasing storage and computational capabilities. Smartphones may integrate those functionalities from different cloud levels, in a service oriented manner within the mobile applications, so that a mobile task can be delegated by direct invocation of a service. However, developing these kind of mobile cloud applications requires to integrate and consider multiple aspects of the clouds, such as resource-intensive processing, programmatically provisioning of resources (Web APIs) and cloud intercommunication. To overcome these issues, we have developed a Mobile Cloud Middleware (MCM) framework, which addresses the issues of interoperability across multiple clouds, asynchronous delegation of mobile tasks and dynamic allocation of cloud infrastructure. MCM also fosters the integration and orchestration of mobile tasks delegated with minimal data transfer. A prototype of MCM is developed and several applications are demonstrated in different domains. To verify the scalability of MCM, load tests are also performed on the hybrid cloud resources. The detailed performance analysis of the middleware framework shows that MCM improves the quality of service for mobiles and helps in maintaining soft-real time responses for mobile cloud applications.     

SNA based QoS and reliability in fog and cloud framework

Fog and Cloud computing are ubiquitous computing paradigms based on the concepts of utility and grid computing. Cloud service providers permit flexible and dynamic access to virtualized computing resources on pay-per-use basis to the end users. The users having mobile device will like to process maximum number of applications locally by defining fog layer to provide infrastructure for storage and processing of applications. In case demands for resources are not being satisfied by fog layer of mobile device then job is transferred to cloud for processing. Due to large number of jobs and limited resources, fog is prone to deadlock at very large scale. Therefore, Quality of Service (QoS) and reliability are important aspects for heterogeneous fog and cloud framework. In this paper, Social Network Analysis (SNA) technique is used to detect deadlock for resources in fog layer of mobile device. A new concept of free space fog is proposed which helps to remove deadlock by collecting available free resource from all allocated jobs. A set of rules are proposed for a deadlock manager to increase the utilization of resources in fog layer and decrease the response time of request in case deadlock is detected by the system. Two different clouds (public cloud and virtual private cloud) apart from fog layer and free space fog are used to manage deadlock effectively. Selection among them is being done by assigning priorities to the requests and providing resources accordingly from fog and cloud. Therefore, QoS as well as reliability to users can be provided using proposed framework. Cloudsim is used to evaluate resource utilization using Resource Pool Manager (RPM). The results show the effectiveness of proposed technique.     

Data storage auditing service in cloud computing: challenges,methods and opportunities

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.     

Towards an autonomic performance management approach for a cloud broker environment using a decomposition–coordination based methodology

Efficient resource allocation of computational resources to services is one of the predominant challenges in a cloud computing environment. Furthermore, the advent of cloud brokerage and federated cloud computing systems increases the complexity of cloud resource management. Cloud brokers are considered third party organizations that work as intermediaries between the service providers and the cloud providers. Cloud brokers rent different types of cloud resources from a number of cloud providers and sublet these resources to the requesting service providers. In this paper, an autonomic performance management approach is introduced that provides dynamic resource allocation capabilities for deploying a set of services over a federated cloud computing infrastructure by considering the availability as well as the demand of the cloud computing resources. A distributed control based approach is used for providing autonomic computing features to the proposed framework via a feedback-based control loop. This distributed control based approach is developed using one of the decomposition–coordination methodologies, named interaction balance, for interactive bidding of cloud computing resources. The primary goals of the proposed approach are to maintain the service level agreements, maximize the profit, and minimize the operating cost for the service providers and the cloud broker. The application of interaction balance methodology and prioritization of profit maximization for the cloud broker and the service providers during resource allocation are novel contributions of the proposed approach.     

A utility-based matching mechanism for stable and optimal resource allocation in cloud manufacturing platforms using deferred acceptance algorithm

Cloud Manufacturing (CM) as a successful manufacturing business model and a major driver of Industry 4.0 has attracted a lot of attention in recent years. CM idea aims to streamline the on-demand provisioning of manufacturing resources and capabilities as services, providing end-users with flexible and scalable services accessible through global networks. This idea created many opportunities and challenges. One of the critical challenges is resource allocation, which determines who interacts with whom and how in the CM platform. The type of the platform is a determining factor for the selection of the appropriate resource allocation. To analyze the impact of the allocation on the utilities, the paper models the behavior of the manufacturing providers and consumers based on their preference attributes. Then, the paper discusses the influence of the platform, matching algorithm, and resource availability on the utility of the manufacturing providers and consumers. As a result, the paper presents a framework to obtain managerial insights to decide about the appropriate matching algorithm under different situations. The framework suggests Consumer as Proposer Deferred Acceptance algorithm for public platforms when the resources are greater than or equal to the demand, and Provider as Proposer Deferred Acceptance algorithm when the resources are less than the demand in the same platform. In private platform, Consumer-oriented Kuhn-Munkres is suggested when the resources are greater than or equal to the demand, and Provider-oriented Kuhn-Munkres when the resources are less than the demand.     

Performance issues and performance analysis tools for HPC cloud applications: a survey

Cloud Computing is an eminent emerging technology that surpasses Grids from their IT resource administrations and arduous Grid middleware solutions. At present, users could access an abundant number of pre-defined cloud services or run their programs on demand as a pay-as-you-go processing model without much distribution problems. In addition, the IT business market has pumped enough revenue for establishing salient common-use cloud solutions. Despite adequate researchers have been involved in the cloud development, scientific application developers are still reluctant to execute their applications in the cloud due to the performance concerns, such as, scalability, availability, and service level agreement violations of the cloud providers. In this paper, a survey of various High Performance Computing (HPC) applications and possible performance concerns while executing applications in cloud is presented. Pointing out the need for Performance Analysis (PA) tools, this paper focuses on the study of cloud-based PA tools in detail. This paper could leverage HPC application developers to cope with the performance issues and to best utilize the available performance analysis tools of clouds.