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.     

IoTScal‐H : Hybrid monitoring solution based on cloud computing for autonomic middleware‐level scalability management within IoT systems and different SLA traffic requirements

Wireless sensor network (WSN) technologies have enabled ubiquitous sensing to intersect many areas of modern day living. The creation of these devices offers the ability to get, gather, exchange, and consume environmental measurement from the physical world in a communicating‐actuating network, called the Internet of Things (IoT). As the number of physical world objects from heterogeneous network environments grows, the data produced by these objects raise uncontrollably, bringing a delicate challenge into scalability management in the IoT networks. Cloud computing is a much more mature technology, offering unlimited virtual capabilities in terms of storage capacity and processing power. Ostensibly, it seems that cloud computing and IoT are evolving independently on their own paths, but in reality, the integration of clouds with IoT will lead to deal with the inability to scale automatically depending on the overload caused by the drastic growth of the number of connected devices and/or by the huge amount of exchanged data in the IoT networks. In this paper, our objective is to promote the scalability management, using hybrid mechanism that will combine traffic‐oriented mechanism and resources‐oriented mechanism, with adaption actions. By the use of autonomic middleware within IoT systems, we seek to improve the monitoring components's architectural design, based on cloud computing‐oriented scalability solution. The intention is to maximize the number of satisfied requests, while maintaining at an acceptable QoS level of the system performances (RTT of the system, RAM, and CPU of the middleware). In order to evaluate our solution performance, we have performed different scenarios testbed experiments. Generally, our proposed results are better than those mentioned as reference.     

一种基于异构系统的H.264/AVC运动估计算法

运动估计是H.264/AVC视频编码标准中计算量最大、耗时最多的模块。为加速编码过程,基于NVIDIA公司提出的统一计算设备架构(CUDA)组成的异构系统,设计了一种并行运动估计算法。该算法采用顺序运动搜索方式,并且使用合成的方式得到宏块在不同划分模式下的绝对帧差和(SAD)值。将运动估计分为3个步骤:小宏块SAD值计算,合成其他形状块SAD值,归约获取最佳结果。各个步骤串行执行,而每个步骤内并行执行大量的线程。实验结果表明,在GTS450硬件平台上,与传统的基于CPU算法相比获得了平均超过50倍的加速比。     

可信物联网的研究

针对物联网发展中安全体系尚不完善的问题,从物联网三层技术体系架构出发,分析了可信计算在物联网安全中的应用前景,并且以RFID协议为基础,利用可信计算技术,提出了一种基于平台完整性证明的可信接入方案,并对其进行了仿真,证明了通过本方案能够建立一个可信的数据交互环境。     

基于云模型的负载均衡问题研究

云环境是一个复杂多变的环境,其不同层次提供不同的服务,在处理服务请求时其服务类型与服务器节点供给都是一个动态,随机的变化过程,如何高效的组织以及利用这些节点的处理能力,是该环境中负载技术应该考虑的关键问题.本文对云计算中私有云模型做了概括总结;并对负载均衡调度问题在不同平台中的研究现状做了相关描述,最后、指出了负载均衡调度问题在私有云中未来的研究方向.