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.     

Development of a Hybrid Manufacturing Cloud

Cloud manufacturing is emerging as a novel business paradigm for the manufacturing industry, in which dynamically scalable and virtualised resources are provided as consumable services over the Internet. A handful of cloud manufacturing systems are proposed for different business scenarios, most of which fall into one of three deployment modes, i.e. private cloud, community cloud, and public cloud. One of the challenges in the existing solutions is that few of them are capable of adapting to changes in the business environment. In fact, different companies may have different cloud requirements in different business situations; even a company at different business stages may need different cloud modes. Nevertheless, there is limited support on migrating to different cloud modes in existing solutions. This paper proposes a Hybrid Manufacturing Cloud that allows companies to deploy different cloud modes for their periodic business goals. Three typical cloud modes, i.e. private cloud, community cloud and public cloud are supported in the system. Furthermore, it enables companies to set self-defined access rules for each resource so that unauthorised companies will not have access to the resource. This self-managed mechanism gives companies full control of their businesses and boosts their trust with enhanced privacy protection. A unified ontology is developed to enhance semantic interoperability throughout the whole process of service provision in the clouds. A Cloud Management Engine is developed to manage all the user-defined clouds, in which Semantic Web technologies are used as the main toolkit. The feasibility of this approach is verified through a group of companies, each of which has complex access requirements for their resources. In addition, a use case is carried out between customers and service providers. This way, optimal service is delivered through the proposed system.     

基于证据推理的云计算服务适应性评估

针对云计算服务模式尽管对中小型企业吸引力巨大,但由于受到安全、技术、管理等方面的限制并不是都能适用,企业需要根据自身实际需要作出判断的问题,提出了一个云计算服务适应性评估指标体系。该体系从安全与风险、数据、服务、资源、经济五个维度来分析云计算服务的适应性问题,并利用证据推理方法对实际问题进行建模分析,验证了评估指标体系的有效性与合理性。     

Chargeback for cloud services

With pay-per-use pricing models, elastic scaling of resources, and the use of shared virtualized infrastructures, cloud computing offers more efficient use of capital and agility. To leverage the advantages of cloud computing, organizations have to introduce cloud-specific chargeback practices. Organizations have to allocate IT service costs to business users in a way that reflects service consumption. To help organizations become effective users of cloud services, this article provides an overview of the factors that influence chargeback in the cloud services. This is an initial work that determines the factors influencing the chargeback in the cloud services. The findings of this research facilitate organizations to realize the implications of the cloud for their chargeback.     

Manufacturing in the cloud: A human factors perspective

Cloud manufacturing adopts a cloud computing paradigm as the basis for delivering shared, on-demand manufacturing services. The result is customer-centric supply chains that can be configured for cost, quality, speed and customisation. While the technical capabilities required for cloud manufacturing are a current focus, there are many emerging questions relating to the impact, both positive and negative, on the people consuming or supporting cloud manufacturing services. Human factors can have a pivotal role in enabling the success and adoption of cloud manufacturing, while ensuring the safety, well-being and optimum user experience of those involved in a cloud manufacturing environment. This paper presents these issues, structured around groups of users (service providers, application providers and consumers). We also consider the issues of collaboration that are likely to arise from the manufacturing cloud. From this analysis we discuss the central role of human factors as an enabler of cloud manufacturing, and the opportunities that emerge.     

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.     

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.     

Robot Cloud: Bridging the power of robotics and cloud computing

Cloud computing is shaping the cyber world and evolves as a key computing and service platform for sharing resources including platforms, software applications and everything in the form of services. This is known “X as a Service”. Although it brings our age unparalleled computing ability and economic benefits, the application of cloud computing is still limited currently in the cyberspace due to the cloud services can only reside in cloud instead of our daily life environment. In fact, there are still a plethora of physical position based on-site service demands that cloud computing could help little due to the “cyber limitation”. In this paper, we aim to integrate the cyber world and the physical world by bringing up the idea of “Robot Cloud” to bridge the power of robotics and cloud computing. To make it possible, we design a novel Robot Cloud stack to support our idea and adopt the service-oriented architecture (SOA) to make the functional modules in the Robot Cloud more flexible, extensible and reusable. Then we develop a prototype of Robot Cloud using the popular Google App Engine to demonstrate our design method. Finally, we conduct the simulation experiments with a “robot show” application scenario to evaluate our scheduling policy and identify the effect of different request distributions and robot center solutions.     

A method for trust management in cloud computing: Data coloring by cloud watermarking

With the development of Internet technology and human computing, the computing environment has changed dramatically over the last three decades. Cloud computing emerges as a paradigm of Internet computing in which dynamical, scalable and often virtualized resources are provided as services. With virtualization technology, cloud computing offers diverse services (such as virtual computing, virtual storage, virtual bandwidth, etc.) for the public by means of multi-tenancy mode. Although users are enjoying the capabilities of super-computing and mass storage supplied by cloud computing, cloud security still remains as a hot spot problem, which is in essence the trust management between data owners and storage service providers. In this paper, we propose a data coloring method based on cloud watermarking to recognize and ensure mutual reputations. The experimental results show that the robustness of reverse cloud generator can guarantee users embedded social reputation identifications. Hence, our work provides a reference solution to the critical problem of cloud security.     

Contract RBAC in cloud computing

Cloud computing is a fast growing field, which is arguably a new computing paradigm. In cloud computing, computing resources are provided as services over the Internet and users can access resources based on their payments. The issue of access control is an important security scheme in the cloud computing. In this paper, a Contract RBAC model with continuous services for user to access various source services provided by different providers is proposed. The Contract RBAC model extending from the well-known RBAC model in cloud computing is shown. The extending definitions in the model could increase the ability to meet new challenges. The Contract RBAC model can provide continuous services with more flexible management in security to meet the application requirements including Intra-cross cloud service and Inter-cross cloud service. Finally, the performance analyses between the traditional manner and the scheme are given. Therefore, the proposed Contract RBAC model can achieve more efficient management for cloud computing environments.     

Heavy traffic optimal resource allocation algorithms for cloud computing clusters

Cloud computing is emerging as an important platform for business, personal and mobile computing applications. In this paper, we study a stochastic model of cloud computing, where jobs arrive according to a stochastic process and request resources like CPU, memory and storage space. We consider a model where the resource allocation problem can be separated into a routing or load balancing problem and a scheduling problem. We study the join-the-shortest-queue routing and power-of-two-choices routing algorithms with the MaxWeight scheduling algorithm. It was known that these algorithms are throughput optimal. In this paper, we show that these algorithms are queue length optimal in the heavy traffic limit.     

On the security of auditing mechanisms for secure cloud storage

Cloud computing is a novel computing model that enables convenient and on-demand access to a shared pool of configurable computing resources. Auditing services are highly essential to make sure that the data is correctly hosted in the cloud. In this paper, we investigate the active adversary attacks in three auditing mechanisms for shared data in the cloud, including two identity privacy-preserving auditing mechanisms called Oruta and Knox, and a distributed storage integrity auditing mechanism. We show that these schemes become insecure when active adversaries are involved in the cloud storage. Specifically, an active adversary can arbitrarily alter the cloud data without being detected by the auditor in the verification phase. We also propose a solution to remedy the weakness without sacrificing any desirable features of these mechanisms.     

基于云计算的数据安全风险及防范策略探讨

云计算依托计算机网络系统,目前已经成为人们生活的重要部分,随着网络化、虚拟化生活的加速发展,诸如Google、Microsoft、Apple、Amazon、IBM等互联网IT和手机、网络运营商巨头开始重新定位企业发展的战略核心.云计算作为IT商业计算模型,它将计算任务分布在各种类型的广域网络和局域网络组成计算机网络系统,使用户能够借助网络按需获取计算力、存储空间和信息服务.云计算的用户通过PC、手机以及其他终端连接到网络使用云资源;随着云计算的广泛应用,云计算的环境安全环境、数据安全成为突出问题,如何保障云计算的安全成为当前急需解决的问题.本文介绍了云计算相关概念,以及对云计算数据安全风险进行分析,并提出了防范策略.     

The challenge of networked enterprises for cloud computing interoperability

Manufacturing enterprises have to organize themselves into effective system architectures forming different types of Networked Enterprises (NE) to match fast changing market demands. Cloud Computing (CC) is an important up to date computing concept for NE, as it offers significant financial and technical advantages beside high-level collaboration possibilities. As cloud computing is a new concept the solutions for handling interoperability, portability, security, privacy and standardization challenges have not been solved fully yet. The paper introduces the main characteristics of future Internet-based enterprises and the different CC models. An overview is given on interoperability and actual standardization issues in CC environments. A taxonomy on possible connecting forms of networked enterprises and cloud-based IT systems with reference on interoperability is introduced, parallel presenting four use cases as well. Finally, an example of connecting cloud and NE is presented as an effective application of cloud computing in manufacturing industry.     

DRM cloud framework to support heterogeneous digital rights management systems

The DRM(digital rights management) techniques have been rapidly developed to protect the digital media contents. The growth of smart device and cloud computing makes the environment in which various services can be provided anywhere and anytime, so the DRM technologies have to react to such changes. From this aspect, we previously proposed the architecture of DRM-as-a-Service that provides various functionalities of DRM as some services on the cloud environment, and we referred to it as the DRM Cloud. In this paper, we define a reference model of DRM Cloud to represent some DRM functions that are provided by the DRM Cloud, and several service scenarios are proposed on the DRM Cloud. Also we simulate the DRM Cloud on the testbed and then discuss some security issues and how to handle the interoperability in the DRM Cloud. We conclude that the DRM Cloud allows the content consumers to use many contents with various smart devices, also let the DRM developers and the content service providers reduce the costs of development and business.     

Exploring vendor capabilities in the cloud environment: A case study of Alibaba Cloud Computing

Cloud computing is being viewed as an effective enabler for enhanced operational efficiency and flexibility in competitive business environments. Despite the increased knowledge on cloud computing, very few studies have been conducted to explore vendors' capabilities for service development and delivery. This gap prevents us from developing a full understanding of the service provision process and the actions through which vendors develop cloud services and create value for clients. In this paper, we present an in-depth qualitative case study of Alibaba Cloud Computing, China’s biggest Infrastructure-as-a-Service (IaaS) vendor, and identify five important capabilities, namely, cloud platform development, cloud platform deployment, IaaS imitation, IaaS commercialization, and IaaS improvement. We suggest that these capabilities and the associated actions are central to vendors’ adoption of cloud computing, acquisition of knowledge, and delivery and improvement of their cloud service provision. This paper contributes incrementally to the evolving scholarship on cloud computing and also offers useful guidelines for current and aspiring vendors.     

Highly reliable architecture using the 80/20 rule in cloud computing datacenters

Cloud datacenters host hundreds of thousands of physical servers that offer computing resources for executing customer jobs. While the failures of these physical machines are considered normal rather than exceptional, in large-scale distributed systems and cloud datacenters evaluation of availability in a datacenter is essential for both cloud providers and customers. Although providing a highly available and reliable computing infrastructure is essential to maintaining customer confidence, cloud providers desire to have highly utilized datacenters to increase the profit level of delivered services. Cloud computing architectural solutions should thus take into consideration both high availability for customers and highly utilized resources to make delivering services more profitable for cloud providers. This paper presents a highly reliable cloud architecture by leveraging the 80/20 rule. This architecture uses the 80/20 rule (80% of cluster failures come from 20% of physical machines) to identify failure-prone physical machines by dividing each cluster into reliable and risky sub-clusters. Furthermore, customer jobs are divided into latency-sensitive and latency-insensitive types. The results showed that only about 1% of all requested jobs are extreme latency-sensitive and require availability of 99.999%. By offering services to revenue-generating jobs, which are less than 50% of all requested jobs, within the reliable subcluster of physical machines, cloud providers can make their businesses more profitable by preventing service level agreement violation penalties and improving their reputations.     

Peer to peer trade in HTM5 meta model for agent oriented cloud robotic systems

Cloud computing is a methodology and not a technology. Adaptation of cloud computing services for robotic applications is relatively straightforward while adaptation of underlying ideas will require a new design attitude. Cloud computing is a cost-effective and dynamic business model. Currently cloud robotics is understood as a client server methodology which enables robots utilize resources and services placed at centralized servers. These cloud servers treat robots as any other client computer offering them platform, infrastructure, process or algorithm as a service. HTM5 is an OMG MDA based multi-view meta-model for agent oriented development of cloud robotic systems. HTM5 encourages design of peer-to-peer service ecosystems based on an open registry and matchmaking mechanism. In peer-to-peer cloud robotics, a robot can trade its hardware, software and functional resources as a service to other robots in the ecosystem. The peer-to-peer trade in such systems may be driven by contracts and relationships between its member agents. This article discusses trade-view model of HTM5 methodology and its use in developing a cloud robotic ecosystem that implements peer-to-peer, contract based economy. The article also presents a case study with experiments that implement distributed artificial intelligence and peer-to-peer service oriented trade on simulated and real robot colonies.     

Fast GA-based project scheduling for computing resources allocation in a cloud manufacturing system

Cloud manufacturing is becoming an increasingly popular enterprise model in which computing resources are made available on-demand to the user as needed. Cloud manufacturing aims at providing low-cost, resource-sharing and effective coordination. In this study, we present a genetic algorithm (GA) based resource constraint project scheduling, incorporating a number of new ideas (enhancements and local search) for solving computing resources allocation problems in a cloud manufacturing system. A newly generated offspring may not be feasible due to task precedence and resource availability constraints. Conflict resolutions and enhancements are performed on newly generated offsprings after crossover or mutation. The local search can exploit the neighborhood of solutions to find better schedules. Due to its complex characteristics, computing resources allocation in a cloud manufacturing system is NP-hard. Computational results show that the proposed GA can rapidly provide a good quality schedule that can optimally allocate computing resources and satisfy users’ demands.