A hierarchical approach for resource allocation in hybrid cloud environments

Cloud computing is a key technology for online service providers. However, current online service systems experience performance degradation due to the heterogeneous and time-variant incoming of user requests. To address this kind of diversity, we propose a hierarchical approach for resource management in hybrid clouds, where local private clouds handle routine requests and a powerful third-party public cloud is responsible for the burst of sudden incoming requests. Our goal is to answer (1) from the online service provider’s perspective, how to decide the local private cloud resource allocation, and how to distribute the incoming requests to private and/or public clouds; and (2) from the public cloud provider’s perspective, how to decide the optimal prices for these public cloud resources so as to maximize its profit. We use a Stackelberg game model to capture the complex interactions between users, online service providers and public cloud providers, based on which we analyze the resource allocation in private clouds and pricing strategy in public cloud. Furthermore, we design efficient online algorithms to determine the public cloud provider’s and the online service provider’s optimal decisions. Simulation results validate the effectiveness and efficiency of our proposed approach.     

浦东新区社区云的设计与实现

云计算被认为是继个人计算机和互联网之后电子信息技术领域的下一次重大革命.本文基于云计算理论,整合分布计算、网格计算与虚拟化理论,从系统架构层介绍浦东新区社区卫生服务中心信息系统.浦东新区社区卫生服务中心信息系统是以浦东新区社区卫生服务中心信息状态为基础,依托社区卫生信息专网,整合社区资源.突破了原有架构模式,解决社区信息化的各种压力,包括:资源分配不均的压力,数据共享的压力,系统扩展性的压力,系统应对变化的压力,维护的压力等.改变以往以应用为核心的资源配置模式,转向以资源为核心的资源分配模式,保证业务对资源需求的完整性.结合当前的热点技术,突破传统模式,为社区卫生服务中心信息化改造提供新的思路.     

Resource-efficient load-balancing framework for cloud data center networks

Cloud computing has drastically reduced the price of computing resources through the use of virtualized resources that are shared among users. However, the established large cloud data centers have a large carbon footprint owing to their excessive power consumption. Inefficiency in resource utilization and power consumption results in the low fiscal gain of service providers. Therefore, data centers should adopt an effective resource-management approach. In this paper, we present a novel load-balancing framework with the objective of minimizing the operational cost of data centers through improved resource utilization. The framework utilizes a modified genetic algorithm for realizing the optimal allocation of virtual machines (VMs) over physical machines. The experimental results demonstrate that the proposed framework improves the resource utilization by up to 45.21%, 84.49%, 119.93%, and 113.96% over a recent and three other standard heuristics-based VM placement approaches.     

Design of energy‐efficient cloud systems via network and resource virtualization

Data centers play a crucial role in the delivery of cloud services by enabling on‐demand access to the shared resources such as software, platform and infrastructure. Virtual machine (VM) allocation is one of the challenging tasks in data center management since user requirements, typically expressed as service‐level agreements, have to be met with the minimum operational expenditure. Despite their huge processing and storage facilities, data centers are among the major contributors to greenhouse gas emissions of IT services. In this paper, we propose a holistic approach for a large‐scale cloud system where the cloud services are provisioned by several data centers interconnected over the backbone network. Leveraging the possibility to virtualize the backbone topology in order to bypass IP routers, which are major power consumers in the core network, we propose a mixed integer linear programming (MILP) formulation for VM placement that aims at minimizing both power consumption at the virtualized backbone network and resource usage inside data centers. Since the general holistic MILP formulation requires heavy and long‐running computations, we partition the problem into two sub‐problems, namely, intra and inter‐data center VM placement. In addition, for the inter‐data center VM placement, we also propose a heuristic to solve the virtualized backbone topology reconfiguration computation in reasonable time. We thoroughly assessed the performance of our proposed solution, comparing it with another notable MILP proposal in the literature; collected experimental results show the benefit of the proposed management scheme in terms of power consumption, resource utilization and fairness for medium size data centers. Copyright © 2013 John Wiley & Sons, Ltd.     

Integration of firefly optimization and Pearson service correlation for efficient cloud resource utilization

Cloud Computing (CC) environment presents a simplified, centralized platform or resources to usage while necessitated at minimum cost. In CC, the main processes in is the allocation of resources of web applications. However, with the increasing demands of Cloud User (CU), an efficient resource allocation technique for web applications is required. According to the request made by the user and response obtained, the cost of resources has also to be optimized. To overcome such limitations, Pearson service correlation‐based firefly resource cost optimization (PSC‐FRCO) technique is designed. Pearson service correlation‐based firefly resource cost optimization technique not only improves the performance of cost aware resource allocation but also achieves higher efficiency while rendering services in cloud computing environment for web applications. Pearson service correlation‐based firefly resource cost optimization technique initially uses Pearson service correlation in which the user‐required service is identified by correlating the available services provided by cloud owner. This helps in improving the Response Time (RT) of cloud service provisioning. Next, firefly resource cost optimization algorithm is applied to identify and allocate the cost‐optimized cloud resources to users to afford required service from the cloud server. Thus, PSC‐FRCO technique improves the Resource Utilization Efficiency (RUE) of web applications with minimal computational cost. This technique conducts experimental works on parameters such as RT, Bandwidth Utilization Rate (BUR) computational cost, Energy Consumption (EC), and RUE. Experimental analysis reveals that PSC‐FRCO technique enhances enhances RUE and lessens RT as compared to state‐of‐the‐art works.     

车辆网络多平台卸载智能资源分配算法

为了降低计算任务的时延和系统的成本,移动边缘计算(MEC)被用于车辆网络,以进一步改善车辆服务。该文在考虑计算资源的情况下对车辆网络时延问题进行研究,提出一种多平台卸载智能资源分配算法,对计算资源进行分配,以提高下一代车辆网络的性能。该算法首先使用K临近(KNN)算法对计算任务的卸载平台(云计算、移动边缘计算、本地计算)进行选择,然后在考虑非本地计算资源分配和系统复杂性的情况下,使用强化学习方法,以有效解决使用移动边缘计算的车辆网络中的资源分配问题。仿真结果表明,与任务全部卸载到本地或MEC服务器等基准算法相比,提出的多平台卸载智能资源分配算法实现了时延成本的显著降低,平均可节省系统总成本达80%。     

An Approach Towards Amelioration of an Efficient VM Allocation Policy in Cloud Computing Domain

Cloud computing is on the horizon of the domain of information technology over the recent few years, giving different remotely accessible services to the cloud users. The quality-of-service (QoS) maintaining of a cloud service provider is the most dominating research issue today. The QoS embraces with different issues like virtual machine (VM) allocation, optimization of response time and throughput, utilizing processing capability, load balancing etc. VM allocation policy deals with the allocation of VMs to the hosts in different datacenters. This paper highlights a new VM allocation policy that distributes the load of VMs among hosts which improves the utilization of hosts’ processing capability as well as makespan and throughput of cloud system. The experimental results are obtained by utilizing trace based simulation in CloudSim 3.0.3 and compared with existing VM allocation policies.     

云环境中基于异构资源的资源分配算法

随着"云计算"的出现和快速发展,"云"作为一种新型的资源形式被越来越多的用户所使用。云环境中的资源分配问题成为了云计算中不可忽略的问题。在云资源管理平台中,如何既满足用户的任务需求,又节省云资源成本,是云运营商尽快希望解决的问题之一。实际上云用户对云资源的请求是有差异的,而且用户任务的完成通常由多个异构的云资源来实现。文中作者考虑了异构云资源间的差异,提出了一种基于异构资源的资源分配算法。该算法先从任务的全局角度考虑,将用户提交的云任务划成不同的组合,再根据云资源间的差异,为相应的组合分配相应的资源。实验仿真表明,在异构云环境中,该算法能在满足用户需求的前提下,在节省云资源使用上有较好的表现。     

Resource allocation in cloud virtual machines based on empirical service traces

One of the key technologies in cloud computing is virtualization. Using virtualization, a system can optimize usage of resources, simplify management of infrastructure and software, and reduce hardware requirements. This research focuses on infrastructure as a service, resource allocation by providers for consumers, and explores the optimization of system utilization based on actual service traces of a real world cloud computing site. Before activating additional virtual machines (VM) for applications, the system examines CPU usage in the resource pools. The behavior of each VM can be estimated by monitoring the CPU usage for different types of services, and consequently, additional resources added or idle resources released. Based on historical observations of the required resources for each kind of service, the system can efficiently dispatch VMs. The proposed scheme can efficiently and effectively distribute resources to VMs for maximizing utilization of the cloud computing center. Copyright © 2013 John Wiley & Sons, Ltd.     

A novel clustering and optimal resource scheduling in vehicular cloud networks using MKMA and the CM-CSO algorithm

Various opportunities aimed at the employment of delay-sensitive applications in the vehicular environment are presented by the vehicular cloud (VC). Contrary to diverse wireless networks, VC networks possess exceptional features among others, namely, shorter transmission time along with a higher dynamic topology. Although integration with the cloud offers higher storage along with computation capabilities, it as well entails restricted resource availability. The restrictions on the number of resources serve as a challenge in servicing the applications with their necessary quality of service (QoS) guarantees as the number of service requests for applications keeps on augmenting with diverse circumstances. Thus, the need for an effective scheduling methodology arises to decide the sequence of servicing application requests and successful utilization of a broadcast medium, along with data transmission. To do efficient resource scheduling on VC networks, an optimization algorithm, namely, the crossover and mutation (CM)-centered chicken swarm optimization (CSO) is proposed and implemented with the help of a publicly available dataset. Initially, the VC infrastructure is initialized and some vehicle information is extracted as features. Next, the Brownian motion-centered bacteria foraging optimization (BM-BFO) algorithm chooses the essential features. Centered on the chosen features, the vehicles are clustered using the modified K-means algorithm. Next, as for the cloud server's virtual machines (VMs), the resource information is extracted. Lastly, the CM-CSO algorithm carries out the optimal scheduling in the VC by means of the clustered features of vehicles and features of the VM. The proposed techniques' findings are scrutinized and analogized to the other prevailing methodologies to confirm that the proposed work performs effectively and gives optimal resource allocation (RA) to the VC.     

VM migration algorithm for the balance of energy resource across data centers in cloud computing

Cloud computing makes it possible for users to share computing power. The framework of multiple data centers gains a greater popularity in modern cloud computing. Due to the uncertainty of the requests from users, the loads of CPU(Center Processing Unit) of different data centers differ. High CPU utilization rate of a data center affects the service provided for users, while low CPU utilization rate of a data center causes high energy consumption. Therefore, it is important to balance the CPU resource across data centers in modern cloud computing framework. A virtual machine(VM)migration algorithm was proposed to balance the CPU resource across data centers. The simulation results suggest that the proposed algorithm has a good performance in the balance of CPU resource across data centers and reducing energy consumption.     

基于云计算的视频监控和资源整合优化系统

视频监控系统在日常生活中担当着越来越重要的作用,将云计算技术应用于视频监控系统中,以此构建了基于云计算的视频监控和资源整合优化系统;然后,改进了视频监控中的一个重要的算法—动态目标检测算法,以提高视频监控的准确性;最后,研究了如何对于云计算平台中的服务器资源进行整合优化,并对所实现的系统进行了测试,结果表明,该系统可以提高视频监控的准确性和服务器的响应时间.     

FedRec: Trusted rank-based recommender scheme for service provisioning in federated cloud environment

The emergence of on-demand service provisioning by Federated Cloud Providers (FCPs) to Cloud Users (CU) has fuelled significant innovations in cloud provisioning models. Owing to the massive traffic, massive CU resource requests are sent to FCPs, and appropriate service recommendations are sent by FCPs. Currently, the Fourth-Generation (4G)-Long Term Evolution (LTE) network faces bottlenecks that affect end-user throughput and latency. Moreover, the data is exchanged among heterogeneous stakeholders, and thus trust is a prime concern. To address these limitations, the paper proposes a Blockchain (BC)-leveraged rank-based recommender scheme, FedRec, to expedite secure and trusted Cloud Service Provisioning (CSP) to the CU through the FCP at the backdrop of base 5G communication service. The scheme operates in three phases. In the first phase, a BC-integrated request-response broker model is formulated between the CU, Cloud Brokers (BR), and the FCP, where a CU service request is forwarded through the BR to different FCPs. For service requests, Anything-as-a-Service (XaaS) is supported by 5G-enhanced Mobile Broadband (eMBB) service. In the next phase, a weighted matching recommender model is proposed at the FCP sites based on a novel Ranking-Based Recommender (RBR) model based on the CU requests. In the final phase, based on the matching recommendations between the CU and the FCP, Smart Contracts (SC) are executed, and resource provisioning data is stored in the Interplanetary File Systems (IPFS) that expedite the block validations. The proposed scheme FedRec is compared in terms of SC evaluation and formal verification. In simulation, FedRec achieves a reduction of 27.55% in chain storage and a transaction throughput of 43.5074 Mbps at 150 blocks. For the IPFS, we have achieved a bandwidth improvement of 17.91%. In the RBR models, the maximum obtained hit ratio is 0.9314 ?at 200 million CU requests, showing an improvement of 1.2% in average servicing latency over non-RBR models and a maximization trade-off of QoE index of 2.7688 at the flow request 1.088 and at granted service price of USD 1.559 million to FCP for provided services. The obtained results indicate the viability of the proposed scheme against traditional approaches.     

Energy‐aware cross‐layer resource allocation in mobile cloud

This paper proposed an energy‐aware cross‐layer mobile cloud resource allocation approach. In this paper, a hybrid cloud architecture is adopted for provisioning mobile service to mobile device users, which include nearby local cloud and remote public cloud. The computation‐intensive tasks can be processed by the remote public cloud, while the delay‐sensitive computation can be processed by the nearby local cloud. On the basis of the system context and mobile user preferences, the energy‐aware cross‐layer mobile cloud resource allocation approach can optimize the consumption of cloud resource and system performance. The cooperation and collaboration among local cloud agent, public cloud supplier, and mobile cloud user are regulated through the economic approach. The energy‐aware cross‐layer mobile cloud resource allocation is performed on the local cloud level and the public cloud level, which comprehensively considers the benefits of all participants. The energy‐aware cross‐layer mobile cloud resource allocation algorithm is proposed, which is evaluated in the experiment environment, and comparison results and analysis are discussed.     

Dynamic resource allocation in cloud download service

Cloud download service, as a new application which downloads the requested content offline and reserves it in cloud storage until users retrieve it, has recently become a trend attracting millions of users in China. In the face of the dilemma between the growth of download requests and the limitation of storage resource, the cloud servers have to design an efficient resource allocation scheme to enhance the utilization of storage as well as to satisfy users' needs like a short download time. When a user's churn behavior is considered as a Markov chain process, it is found that a proper allocation of download speed can optimize the storage resource utilization. Accordingly, two dynamic resource allocation schemes including a speed switching (SS) scheme and a speed increasing (SI) scheme are proposed. Both theoretical analysis and simulation results prove that our schemes can effectively reduce the consumption of storage resource and keep the download time short enough for a good user experience.     

云存储在高校数字资源建设中的应用

随着高校数字资源建设步伐的加快,传统的存储技术已不能满足如今资源建设的需求。云存储技术这一新的服务体系,为海量资源存储提供了一个新的方式。本文在介绍了云计算和云存储的基础上,讨论研究了云存储在高校数字资源存储的应用。     

Multiuser computation offloading for edge-cloud collaboration using submodular optimization

A computation offloading scheme based on edge-cloud computing was proposed to improve the system utility of multiuser computation offloading.This scheme improved the system utility while considering the optimization of edge-cloud resources.In order to tackle the problems of computation offloading mode selection and edge-cloud resource allocation,a greedy algorithm based on submodular theory was developed by fully exploiting the computing and communication resources of cloud and edge.The simulation results demonstrate that the proposed scheme effectively reduces the delay and energy consumption of computing tasks.Additionally,when computing tasks are offloaded to edge and cloud from devices,the proposed scheme still maintains stable system utilities under ultra-limited resources.     

A Modelling of Context-Aware Elderly Healthcare Eco-System-(CA-EHS) Using Signal Analysis and Machine Learning Approach

In cloud computing, more often times cloud assets are underutilized because of poor allocation of task in virtual machine (VM). There exist inconsistent factors affecting the scheduling tasks to VMs. In this paper, an effective scheduling with multi-objective VM selection in cloud data centers is proposed. The proposed multi-objective VM selection and optimized scheduling is described as follows. Initially the input tasks are gathered in a task queue and tasks computational time and trust parameters are measured in the task manager. Then the tasks are prioritized based on the computed measures. Finally, the tasks are scheduled to the VMs in host manager. Here, multi-objectives are considered for VM selection. The objectives such as power usage, load volume, and resource wastage are evaluated for the VMs and the entropy is calculated for the measured objectives and based on the entropy value krill herd optimization algorithm prioritized tasks are scheduled to the VMs. The experimental results prove that the proposed entropy based krill herd optimization scheduling outperforms the existing general krill herd optimization, cuckoo search optimization, cloud list scheduling, minimum completion cloud, cloud task partitioning scheduling and round robin techniques.

     

Load-Balancing Based Cross-Layer Elastic Resource Allocation in Mobile Cloud

The paper proposes a hybrid mobile cloud computing system, in which mobile applications can use different resources or services in local cloud and remote public cloud such as computation, storage and bandwidth. The cross-layer load-balancing based mobile cloud resource allocation optimization is proposed. The proposed approach augments local cloud service pools with public cloud to increase the probability of meeting the service level agreements. Our problem is divided by public cloud service allocation and local cloud service allocation, which is achieved by public cloud supplier, local cloud agent and the mobile user. The system status information is used in the hybrid mobile cloud computing system such as the preferences of mobile applications, energy, server load in cloud datacenter to improve resource utilization and quality of experience of mobile user. Therefore, the system status of hybrid mobile cloud is monitored continuously. The mathematical model of the system and optimization problem is given. The system design of load-balancing based cross-layer mobile cloud resource allocation is also proposed. Through extensive experiments, this paper evaluates our algorithm and other approaches from the literature under different conditions. The results of the experiments show a performance improvement when compared to the approaches from the literature.

     

Two-User Relay Protocol Based on Energy Harvesting and Cognitive Radio Techniques

Wireless Personal Communications - Cloud computing environment supply the computing resources based on the demand of cloud user requirements. It builds the resource allocation model through...