布谷鸟粒子群优化算法的多目标任务调度

高效的调度方法促使云计算更快更好地服务,一般采用优化算法来解决云计算中的调度问题。将布谷鸟搜索(CS)和粒子群优化(PSO)两种算法相结合,提出多目标布谷鸟粒子群优化算法(MO-CPSO),主要目的是提高云计算的服务质量。使用Cloudsim仿真工具对MO-CPSO算法的性能进行了评估。仿真结果表明,与CS、ACO和Min-Min算法相比,MO-CPSO算法使makespan、开销和截止时间违背率均最小。     

基于移动路径预测的车载边缘计算卸载切换策略研究

车载云计算环境中的计算卸载存在回程网络延迟高、远程云端负载大等问题,车载边缘计算利用边缘服务器靠近车载终端,就近提供云计算服务的特点,在一定程度上解决了上述问题。但由于汽车运动造成的通信环境动态变化进而导致任务完成时间增加,为此该文提出一种基于移动路径可预测的计算卸载切换策略MPOHS,即在车辆移动路径可预测情况下,引入基于最小完成时间的计算切换策略,以降低车辆移动性对计算卸载的影响。实验结果表明,相对于现有研究,该文所提算法能够在减少平均任务完成时间的同时,减少切换次数和切换时间开销,有效降低汽车运动对计算卸载的影响。     

电信BS架构系统迁移及云化研究

深入分析了电信BS架构系统迁移的相关问题及解决方案,并对BS架构系统IaaS及PaaS云化方案进行了研究,提出了两种IaaS和一种PaaS解决方案,能有效地解决电信BS架构系统Web及应用层云化相关问题,同时给出了迁移及云化演进路径和分阶段推进建议,对于推进云计算在IT支撑系统中的应用,具有较好的价值贡献。     

基于可信计算的移动终端签名方案

针对当前移动终端支付系统的安全性问题,分析了可信计算技术的基本思想,利用双线性对及离散对数问题的数学知识,提出了一种在可信移动平台上结合SIM卡的数字签名方案,增加了移动终端系统的安全性。为了防止消息在公共信道被篡改,利用用户与签名者的一次一密密码加密系统对消息和签名进行加密处理。最后,给出该方案的正确性证明和安全性分析,证明该方案在理论上可行且具有很高的安全性。     

云计算安全相关标准研究现状初探

云计算带来了广阔的应用前景,但也对传统的信息安全技术在云计算模式下的应用提出了新的挑战,安全和标准化是云计算所面临的众多问题中的关键所在。目前,各国政府机构和研究组织正在积极着手研究相关问题,文中围绕云计算安全标准研究展开,介绍当前主要的云安全标准组织情况,并就各自在云计算安全领域的标准研究情况进行概述,对其主要研究成果进行扼要分析,希望为国内云计算安全标准的研究提供部分借鉴。     

云计算在智能电网中的应用及其安全问题研究

文章认为在智能电网中引入云计算,构建智能电网云可以为智能电网的发展提供有效支持。基于智能电网的特征,依托作为一种崭新的存储和计算模式的云计算技术,文章阐述了云计算技术如何为智能电网的数据存储和分析提供技术支持,设计了一种智能电网云计算的结构,分析了云计算技术给智能电网带来的诸多益处;并就智能电网云可能存在的安全威胁和相应的防范措施进行讨论。     

云计算环境下的网络技术研究

提出了以数据中心为核心的云计算网络技术体系框架,通过对云计算环境下的网络新需求分析,对虚拟机本地互访网络、数据中心二层互访网络、跨数据中心二层互访网络、用户接入网络以及SDN (software defined network)等新技术进行了详细探讨,并提出了云计算网络发展建议,使其为未来云计算网络技术落地做好准备。     

基于TPM的云计算平台双向认证方案

为了解决云计算服务环境中用户和云服务器之间的双向认证问题,提出一种基于可信平台模块的云计算平台双向认证方案。将可信计算技术和传统的智能卡口令认证方法相结合应用于云计算服务平台,实现云计算中双方身份的认证,协商生成会话密钥,同时对云服务器的平台可信状况进行了验证。实验分析表明,该方案可以抵抗常见的各种攻击,安全性较高。计算时间复杂度在云计算服务中能够满足要求。     

Task scheduling mechanisms in cloud computing: A systematic review

Today, cloud computing has developed as one of the important emergent technologies in communication and Internet. It offers on demand, pay per use access to infrastructure, platforms, and applications. Due to the increase in its popularity, the huge number of requests need to be handled in an efficient manner. Task scheduling as one of the challenges in the cloud computing supports the requests for assigning a particular resource so as to perform effectively. In the resource management, task scheduling is performed where there is the dependency between tasks. Many approaches and case studies have been developed for the scheduling of these tasks. Up to now, a systematic literature review (SLR) has not been presented to discover and evaluate the task scheduling approaches in the cloud computing environment. To overcome, this paper presents an SLR‐based analysis on the task scheduling approaches that classify into (a) single cloud environments that evaluate cost‐aware, energy‐aware, multi‐objective, and QoS‐aware approaches in task scheduling; (b) multicloud environment that evaluates cost‐aware, multi‐objective, and QoS‐aware task scheduling; and (c) mobile cloud environment that is energy‐aware and QoS‐aware task scheduling. The analytical discussions are provided to show the advantages and limitations of the existing approaches.     

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

The massive growth of cloud computing has led to huge amounts of energy consumption and carbon emissions by a large number of servers. One of the major aspects of cloud computing is its scheduling of many task requests submitted by users. Minimizing energy consumption while ensuring the user's QoS preferences is very important to achieving profit maximization for the cloud service providers and ensuring the user's service level agreement (SLA). Therefore, in addition to implementing user's tasks, cloud data centers should meet the different criteria in applying the cloud resources by considering the multiple requirements of different users. Mapping of user requests to cloud resources for processing in a distributed environment is a well‐known NP‐hard problem. To resolve this problem, this paper proposes an energy‐efficient task‐scheduling algorithm based on best‐worst (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The main objective of this paper is to determine which cloud scheduling solution is more important to select. First, a decision‐making group identify the evaluation criteria. After that, a BWM process is applied to assign the importance weights for each criterion, because the selected criteria have varied importance. Then, TOPSIS uses these weighted criteria as inputs to evaluate and measure the performance of each alternative. The performance of the proposed and existing algorithms is evaluated using several benchmarks in the CloudSim toolkit and statistical testing through ANOVA, where the evaluation metrics include the makespan, energy consumption, and resource utilization.