Environmental-aware virtual data center network

Cloud computing services have recently become a ubiquitous service delivery model, covering a wide range of applications from personal file sharing to being an enterprise data warehouse. Building green data center networks providing cloud computing services is an emerging trend in the Information and Communication Technology (ICT) industry, because of Global Warming and the potential GHG emissions resulting from cloud services. As one of the first worldwide initiatives provisioning ICT services entirely based on renewable energy such as solar, wind and hydroelectricity across Canada and around the world, the GreenStar Network (GSN) was developed to dynamically transport user services to be processed in data centers built in proximity to green energy sources, reducing Greenhouse Gas (GHG) emissions of ICT equipments. Regarding the current approach, which focuses mainly in reducing energy consumption at the micro-level through energy efficiency improvements, the overall energy consumption will eventually increase due to the growing demand from new services and users, resulting in an increase in GHG emissions. Based on the cooperation between Mantychore FP7 and the GSN, our approach is, therefore, much broader and more appropriate because it focuses on GHG emission reductions at the macro-level. This article presents some outcomes of our implementation of such a network model, which spans multiple green nodes in Canada, Europe and the USA. The network provides cloud computing services based on dynamic provision of network slices through relocation of virtual data centers.     

A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters

Information and communication technology (ICT) has a profound impact on environment because of its large amount of CO₂ emissions. In the past years, the research field of “green” and low power consumption networking infrastructures is of great importance for both service/network providers and equipment manufacturers. An emerging technology called Cloud computing can increase the utilization and efficiency of hardware equipment. The job scheduler is needed by a cloud datacenter to arrange resources for executing jobs. In this paper, we propose a scheduling algorithm for the cloud datacenter with a dynamic voltage frequency scaling technique. Our scheduling algorithm can efficiently increase resource utilization; hence, it can decrease the energy consumption for executing jobs. Experimental results show that our scheme can reduce more energy consumption than other schemes do. The performance of executing jobs is not sacrificed in our scheme. We provide a green energy-efficient scheduling algorithm using the DVFS technique for Cloud computing datacenters.     

Energy-efficient data centers

Energy consumption of the Information and Communication Technology (ICT) sector has grown exponentially in recent years. A major component of the today’s ICT is constituted by the data centers which have experienced an unprecedented growth in their size and population, recently. The Internet giants like Google, IBM and Microsoft house large data centers for cloud computing and application hosting. Many studies, on energy consumption of data centers, point out to the need to evolve strategies for energy efficiency. Due to large-scale carbon dioxide ( $\mathrm{CO}_2$ ) emissions, in the process of electricity production, the ICT facilities are indirectly responsible for considerable amounts of green house gas emissions. Heat generated by these densely populated data centers needs large cooling units to keep temperatures within the operational range. These cooling units, obviously, escalate the total energy consumption and have their own carbon footprint. In this survey, we discuss various aspects of the energy efficiency in data centers with the added emphasis on its motivation for data centers. In addition, we discuss various research ideas, industry adopted techniques and the issues that need our immediate attention in the context of energy efficiency in data centers.     

Novel resource allocation algorithms to performance and energy efficiency in cloud computing

The rapid growth in demand for computational power has led to a shift to the cloud computing model established by large-scale virtualized data centers. Such data centers consume enormous amounts of electrical energy. Cloud providers must ensure that their service delivery is flexible to meet various consumer requirements. However, to support green computing, cloud providers also need to minimize the cloud infrastructure energy consumption while conducting the service delivery. In this paper, for cloud environments, a novel QoS-aware VMs consolidation approach is proposed that adopts a method based on resource utilization history of virtual machines. Proposed algorithms have been implemented and evaluated using CloudSim simulator. Simulation results show improvement in QoS metrics and energy consumption as well as demonstrate that there is a trade-off between energy consumption and quality of service in the cloud environment.     

Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers

The use of High Performance Computing (HPC) in commercial and consumer IT applications is becoming popular. HPC users need the ability to gain rapid and scalable access to high-end computing capabilities. Cloud computing promises to deliver such a computing infrastructure using data centers so that HPC users can access applications and data from a Cloud anywhere in the world on demand and pay based on what they use. However, the growing demand drastically increases the energy consumption of data centers, which has become a critical issue. High energy consumption not only translates to high energy cost which will reduce the profit margin of Cloud providers, but also high carbon emissions which are not environmentally sustainable. Hence, there is an urgent need for energy-efficient solutions that can address the high increase in the energy consumption from the perspective of not only the Cloud provider, but also from the environment. To address this issue, we propose near-optimal scheduling policies that exploit heterogeneity across multiple data centers for a Cloud provider. We consider a number of energy efficiency factors (such as energy cost, carbon emission rate, workload, and CPU power efficiency) which change across different data centers depending on their location, architectural design, and management system. Our carbon/energy based scheduling policies are able to achieve on average up to 25% of energy savings in comparison to profit based scheduling policies leading to higher profit and less carbon emissions.     

面向HPC和DC的可重构光互连网络体系结构综述

互连网络是高性能计算系统和数据中心的核心组件之一,也是决定其系统整体性能的全局性基础设施。随着高性能计算、云计算和大数据技术的迅速发展,传统的电互连网络在性能、能耗和成本等方面无法满足高性能计算应用和数据中心业务的大规模可扩展通信需求,面临着严峻的挑战。为此,近年来相关研究者提出了多种面向高性能计算和数据中心的可重构的光互连网络结构。首先阐明了光互连网络相对于电互连网络的优势;然后介绍了几种典型的可重构光互连网络体系结构,并对其特点进行了分析比较;最后探讨了可重构光互连网络的发展趋势。     

Deadline-aware multi-objective IoT services placement optimization in fog environment using parallel FFD-genetic algorithm

Nowadays, Internet of things has become as an inevitable aspect of humans’ IT-based life. A huge number of geo-distributed IoT enabled devices such as smart phones, smart cameras, health care systems, vehicles, etc. are connected to the Internet and manage users’ applications. The IoT applications are generally time sensitive, so that giving them up to Cloud and receiving the response may violate their required deadline, due to distance between user device and centralized Cloud data center and consequently increasing network latency. Fog environment, as an intermediate layer between Cloud and IoT devices, brings a smaller scales of Cloud capabilities closer to user location. Processing real time applications in Fog layer helps more deadlines to be met. Although Fog computing enhances quality of service parameters, limited resources and power of Fog nodes is a challenge in processing applications. Furthermore, the network latency is still an issue for communications between applications’ services and between user device and Fog node, which seriously threatens deadline condition. Regarding to mentioned points, this paper proposes a 3-partite deadline-aware applications’ services placement optimization model in Fog environment which optimizes total power consumption, total resources wastage, and total network latency, simultaneously. The proposed model prioritizes applications in 3 levels based on their associated deadline, and then the model is solved using a parallel model of first fit decreasing and genetic algorithm combination. Simulations results indicates the superiority of proposed approach against counterpart algorithms in terms of reducing power consumption, resource wastage, network latency, and service rejection rate.     

绿色网络节能方案综述

随着互联网的迅速发展、移动通信的广泛普及，信息通讯技术（Information and Communication Technology，ICT）发挥着越来越重要的作用。与此同时，网络能源的消耗也急剧攀升。为了应对业务量的增长，无法预测的实时流量，以及保证连接和服务质量（Quality of Service，QoS），现有网络通常是按照网络业务量的峰值来设定，而且网络设备的耗能有时也是按照峰值来设定。但是网络的业务量在大部分时间里不会达到峰值，甚至远远小于峰值。这就意味着能量的利用率很低，大部分的能量会被浪费掉。因此，降低能耗成本，提高能耗利用率已经成为目前急需解决的问题。这一类问题被统称为绿色网络节能问题。针对目前已有的绿色网络节能方案，将这些方案根据在不同网络中的节能问题分成了三类，并对不同分类中的具体方案进行详细描述，对比了它们之间的优缺点，总结分析了这些方案的贡献及不足，并进一步提出了未来的研究方向。     

Artificial bee colony based energy‐aware resource utilization technique for cloud computing

Cloud computing is a form of distributed computing, which promises to deliver reliable services through next‐generation data centers that are built on virtualized compute and storage technologies. It is becoming truly ubiquitous and with cloud infrastructures becoming essential components for providing Internet services, there is an increase in energy‐hungry data centers deployed by cloud providers. As cloud providers often rely on large data centers to offer the resources required by the users, the energy consumed by cloud infrastructures has become a key environmental and economical concern. Much energy is wasted in these data centers because of under‐utilized resources hence contributing to global warming. To conserve energy, these under‐utilized resources need to be efficiently utilized and to achieve this, jobs need to be allocated to the cloud resources in such a way so that the resources are used efficiently and there is a gain in performance and energy efficiency. In this paper, a model for energy‐aware resource utilization technique has been proposed to efficiently manage cloud resources and enhance their utilization. It further helps in reducing the energy consumption of clouds by using server consolidation through virtualization without degrading the performance of users’ applications. An artificial bee colony based energy‐aware resource utilization technique corresponding to the model has been designed to allocate jobs to the resources in a cloud environment. The performance of the proposed algorithm has been evaluated with the existing algorithms through the CloudSim toolkit. The experimental results demonstrate that the proposed technique outperforms the existing techniques by minimizing energy consumption and execution time of applications submitted to the cloud. Copyright © 2014 John Wiley & Sons, Ltd.     

云计算数据中心虚拟机节能管理机制研究

云计算数据中心的耗电量巨大,但绝大多数的云计算数据中心并没有取得较高的资源利用率,通常只有15%-20%,有相当数量的服务器处于闲置工作状态,导致大量的能耗白白浪费。为了能够有效降低云计算数据中心的能耗,提出了一种适用于异构集群系统的云计算数据中心虚拟机节能调度算法(PVMAP算法),仿真实验结果表明：与经典算法PABFD相比,PVMAP算法的能耗明显更低,可扩展性与稳定性都更好。与此同时,随着〈Hosts,VMs〉数目的不断增加,PVMAP 算法虚拟机迁移总数和关闭主机总数的增长幅度都要低于PABFD算法。     

Power‐aware provisioning of virtual machines for real‐time Cloud services

Reducing power consumption has been an essential requirement for Cloud resource providers not only to decrease operating costs, but also to improve the system reliability. As Cloud computing becomes emergent for the Anything as a Service (XaaS) paradigm, modern real‐time services also become available through Cloud computing. In this work, we investigate power‐aware provisioning of virtual machines for real‐time services. Our approach is (i) to model a real‐time service as a real‐time virtual machine request; and (ii) to provision virtual machines in Cloud data centers using dynamic voltage frequency scaling schemes. We propose several schemes to reduce power consumption by hard real‐time services and power‐aware profitable provisioning of soft real‐time services. Copyright © 2011 John Wiley & Sons, Ltd.     

Energy-Aware Scheduling for Tasks with Target-Time in Blockchain based Data Centres

Cloud computing infrastructures have intended to provide computing services to end-users through the internet in a pay-per-use model. The extensive deployment of the Cloud and continuous increment in the capacity and utilization of data centers (DC) leads to massive power consumption. This intensifying scale of DCs has made energy consumption a critical concern. This paper emphasizes the task scheduling algorithm by formulating the system model to minimize the makespan and energy consumption incurred in a data center. Also, an energy-aware task scheduling in the Blockchain-based data center was proposed to offer an optimal solution that minimizes makespan and energy consumption. The established model was analyzed with a target-time responsive precedence scheduling algorithm. The observations were analyzed and compared with the traditional scheduling algorithms. The outcomes exhibited that the developed solution incurs better performance with a response to resource utilization and decreasing energy consumption. The investigation revealed that the applied strategy considerably enhanced the effectiveness of the designed schedule.

     

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.     

Resource-aware hybrid scheduling algorithm in heterogeneous distributed computing

Today, almost everyone is connected to the Internet and uses different Cloud solutions to store, deliver and process data. Cloud computing assembles large networks of virtualized services such as hardware and software resources. The new era in which ICT penetrated almost all domains (healthcare, aged-care, social assistance, surveillance, education, etc.) creates the need of new multimedia content-driven applications. These applications generate huge amount of data, require gathering, processing and then aggregation in a fault-tolerant, reliable and secure heterogeneous distributed system created by a mixture of Cloud systems (public/private), mobile devices networks, desktop-based clusters, etc. In this context dynamic resource provisioning for Big Data application scheduling became a challenge in modern systems. We proposed a resource-aware hybrid scheduling algorithm for different types of application: batch jobs and workflows. The proposed algorithm considers hierarchical clustering of the available resources into groups in the allocation phase. Task execution is performed in two phases: in the first, tasks are assigned to groups of resources and in the second phase, a classical scheduling algorithm is used for each group of resources. The proposed algorithm is suitable for Heterogeneous Distributed Computing, especially for modern High-Performance Computing (HPC) systems in which applications are modeled with various requirements (both IO and computational intensive), with accent on data from multimedia applications. We evaluate their performance in a realistic setting of CloudSim tool with respect to load-balancing, cost savings, dependency assurance for workflows and computational efficiency, and investigate the computing methods of these performance metrics at runtime.     

云环境下绿色任务调度策略

针对云计算数据中心的能耗问题,提出了绿色云计算体系理论,设计了绿色云系统架构;基于该架构,将能量作为一种系统资源进行分配,提出了三种绿色任务调度算法分别是STF-OS、LTF-OS和RT-OS算法;对三种绿色任务调度算法可行性做了相关的理论分析,三种算法可以有效地减少能源消耗;通过扩展云计算仿真平台CloudSim实现了模拟实验,结果表明STF-OS算法降低数据中心能耗的能力最优。     

Energy-saving model for SDN data centers

With the development of the Internet, data centers have become vital infrastructures which provide computing, storage and other services for the networks. According to statistics, data centers consume large amount of electricity all around the world. In most cases, the majority of network devices in data centers are relatively idle, resulting in a waste of energy. Software defined network (SDN) was proposed by UC Berkeley and Stanford University around 2008, which allows the administrators to manage the network and set configurations through abstraction of lower level functionality. It also separates the control plane and the data plane, so administrators can control the network traffic through centralized controller instead of access to physical devices. This paper discusses the energy-saving model in data center networks based on SDN. We propose two different energy-saving algorithms, which can be applied to different data centers. Through centralized management and preprocessing traffic by SDN, we get better energy efficiency and reduce the energy cost by 30–40 %. To the best of our knowledge, this is the first work on energy saving in SDN architecture which provides two different algorithms that can be applied in different scenarios.     

An energy-efficient virtual machine placement and route scheduling scheme in data center networks

The increasing requirements of big data analytics and complex scientific computing impose significant burdens on cloud data centers. As a result, not only the computation but also the communication expenses in data centers are greatly increased. Previous work on green computing in data centers mainly focused on the energy consumption of the servers rather than the communication. However, for those emerging applications with big data-flows transmission, more energy consumption could be consumed by communication links, switching and aggregation elements. To this end, based on data-flows’ transmission characteristics, we proposes a novel Job-Aware Virtual Machine Placement and Route Scheduling (JAVPRS) scheme to reduce the energy consumption of data center networks (DCN) while still meeting as many network QoS (Quality of Service) requirements as possible. Our proposed scheme focuses on not just migrating large data flows, but also integrating small data flows to improve the utilization rate of the communication links. With more idle switches turned off, DCN’s energy consumption will thus be reduced. Besides the data flows’ migration and integration, the Traffic Engineering (TE) technique is also applied to decrease the transmission delay and increase the network throughput. To evaluate the performance of our proposed scheme, a number of simulation studies are performed. Compared to the selected benchmarks, the simulation results showed that JAVPRS can achieve 22.28%–35.72% energy saving while reducing communication delay by 5.8%–6.8% and improving network throughput by 13.3%.     

基于云计算的旅游气象服务研究

云计算是在分布式计算、并行计算、网格计算的基础上提出的一种新型计算模型。它提供了可靠安全的数据存储、强大的计算能力和方便快捷的五联网服务。云计算将给IT行业带来重大的变革,同样将对旅游产业信息化产生深远影响。本文介绍了云计算的相关概念并指出了云计算的主要技术特点,最后重点提出了云计算在旅游景区气象预测的应用。     

云数据中心资源调度机制研究

云数据中心包含大量计算机,运作成本很高。有效整合资源、提高资源利用率、节约能源、降低运行成本是云数据中心关注的热点。云数据中心通过虚拟化技术将计算资源、存储资源和网络资源构建成动态的虚拟资源池;使用虚拟资源管理技术实现云计算资源自动部署、动态扩展、按需分配;用户采用按需和即付即用的方式获取资源。因此,数据中心对提高资源利用率的迫切需求,促使人们寻求新的方式以建设下一代数据中心。     

云计算环境下的安全问题浅析

云计算是一种基于Internet的新兴应用计算机技术。其愿景是以互联网为中心,提供可靠安全的数据存储、方便快捷的互联网服务和强大的计算能力。在这个特殊的云计算环境下,如何保证存储在云上数据的安全,将是云计算面临的一个大问题。本文将从云计算的特征及目前已存在的问题出发,浅析云计算环境下的安全问题。