I.J. Modern Education and Computer Science, 2018, 2, 54-62
Published Online February 2018 in MECS (https://www.doczj.com/doc/7413864390.html,/)
DOI: 10.5815/ijmecs.2018.02.07
An Effective Technique to Decline Energy Expenditure in Cloud Platforms
Anureet A. Kaur
Chandigarh Engineering College, Landran / Deptt. Of Information Technology, Morinda, 140101, India
Email: anu.shergill43@https://www.doczj.com/doc/7413864390.html,
Dr. Bikrampal B. Kaur
Chandigarh Engineering College, Landran / Deptt. Of Computer Science, Mohali, India
Email: https://www.doczj.com/doc/7413864390.html,tech.bpk@https://www.doczj.com/doc/7413864390.html,
Received: 08 July 2017; Accepted: 08 August 2017; Published: 08 February 2018
Abstract—The cloud computing is the rapidly growing technology in the IT world. A vital aim of the cloud is to provide the services or resources where they are needed. From the user’s prospective convenient computing resources are limitless thatswhy the client does not worry that how many numbers of servers positioned at one site so it is the liability of the cloud service holder to have large number of resources. In cloud data-centers, huge bulk of power exhausted by different computing devices.Energy conservancy is a major concern in the cloud computing systems. From the last several years, the different number of techniques was implemented to minimize that problem but the expected results are not achieved. Now, in the proposed research work, a technique called Enhanced - ACO that is developed to achieve better offloading decisions among virtual machines when the reliability and proper utilization of resources will also be considered and will use ACO algorithm to balance load and energy consumption in cloud environment. The proposed technique also minimizes energy consumption and cost of computing resources that are used by different processes for execution in cloud. The earliest finish time and fault tolerance is evaluated to achieve the objectives of proposed work. The experimental outcomes show the better achievement of prospective model with comparison of existing one. Meanwhile, energy-awake scheduling approach with Ant colony optimization method is an assuring method to accomplish that objective.
Index Terms—Task Scheduling, Energy, Cost, ACO, Cloud Simulator.
I.I NTRODUCTION
Cloud computing is a very rapidly growing technology today. Cloud computing provides its users to access the various cloud services and enables them to access the data storage and the computational resources with low data overhead. Cloud computing is rapidly growing so it has attracted the users to cloud. It offers its users to outsource the data resources from the remote locations when they need them. The cloud computing is fabricated of certain elements: The cloud clients, main datacenter and dispersed servers.
A. Clients
The cloud clients are, in a cloud computing framework, are commonly, the computers or machines that just lie on the counter. Although they can also be client laptops, consumer tablets, the mobiles,—all enormous operators for cloud as a result of their flexibility. Cloud clients are the equipments that the users communicate with them to maintain their data securely on the cloud platform.
B. The Data-center
The data-center is the assemblage of server machines which contain the application to which you want to sign up is stored. It may be a big area in the subbasement of your home where you can acess the web. A spreading tendency in the information technology world is virtualizing server machines, so that the software’s can be equipped by permiting different existences of virtual server machines to be adopted. By using that approach, you can use half of a dozen virtual server machines working on same real servers [4].
C. Dispersed servers
These servers provide the service holder much more adjustability in the options and security concerns. For eg. The Amazon has strong cloud quick fix in server machines in all beyond the world. So if something inexact is happened at one site, inducing a breakdown or error, the service can be accessed over other side [4].
D. Cloud Computing Service Models:
Cloud has a broad collection of services available and organizations and the cloud users can choose these services whenever they want based on how they want to use these cloud services. Cloud computing platform has three types of services and these are: Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). These are also known as cloud services models [5]. The cloud can be classified
in three specific models. The three service models in cloud infrastructure are given below in detail.
Fig.1. Cloud Computing Service Models
From the last several years, the increased growth of the cloud also maximizes the number of cloud data center at unrivalled speeds. In the intervening period of time, the energy expenditure by the data centers has kept increasing
at higher rates. In cloud data-centers, huge bulk of power exhausted by different computing devices.Energy conservancy is an major concern in the cloud computing systems because it provides several important benefits such as diminishing operating costs, improves system accuracy, improve resource utilization, and environmental preservation. Therefore, the focus of cloud resource fulfillment and task scheduling has switched from performance to power efficiency.
Fig.2. Energy Consumption in Cloud
The other issue in cloud is called load offset that is a critical problem that becomes a big hurdle between the speedy developments of the cloud computing. The various number of users from the distinct regions demands services at high frequency in every individual second from cloud service provider [5]. Meanwhile, energy-awake scheduling approach with Ant colony optimization method is an assuring method to accomplish that objective [6]. The researcher M.Dorigo and his companions popularized the ACO is based on the
exploring strategy of various ants that accelerated them through search the best shortend pathway from their home to foodstuff in 1990’s. The Macro Dorgio was the first to study about the ant behavior for computing algorithm design and further developed the ant province optimization method for the excellent solutions. When all pests are alter from their home to foodstuff, all pests has the capability to discover an best path from home to foodstuff.Although when pests moving on the way they drops a chemical stuff called metaphores on all the pathway.Pathways are incidentaly selected by the ant at the beginning.While an confined pest encounter formerly locate on routes, then the pest can reveal the pathway and conclude with higher chances to persue it.Higher rich metaphores guides a pest to select the route and maximum of pests are more fascinate because of the higher metaphore.Hence, the trail is strengthen with their owned metaphore.The chances of a pest can deseperate the finest excellent solution in distinction to various distinctive combination of solutions is percentage to the concentration of a pathway of metaphore[11]. The ant colony algorithm is the swarm intelligent algorithm influenced by the behavior of the real ant relations in the ant colonies. A collaboration of the ants in the finding the food and doing other tasks has been prioritized to achieve the ant colony behavior in the computing environments. The ants store the chemical pheromone for the path devising and following while taking a movement from nest to the source of food. With the raise in the number of the ants on a singular path, the strength of the pheromone increases on that particular path. The ants of that colony select the shortest path on the basis of this pheromone amount or value. The ant province optimization method has been applied for resolving the problem of travelling salesman or other problems where the shortest path is needed.
In the proposed work the Enhanced- ACO is used to balance load and minimize energy consumption in cloud in least possible cost. In the proposed work the VM load and failure rate has been assigned as the main parameters to take the scheduling decisions using ACO. Both of the parameters has been used for the purpose of task scheduling over the given cloud resources. The virtual machine load is the parameter which defines the overall utilization of the resources of the given virtual machine. II. R ELATED W ORK
L.D. Dhinesh, P. Venkata et al. (2013) the author proposed the algorithm named Honey bee behavior Algorithm to balance load in cloud environment. The proposed model worked on the basis of priorities of tasks on the VM’s to Minimize the waiting time of the tasks .In the proposed technique The tasks are removed from the overloaded VM’s .the VM’s will acts as Honey bees And submits the tasks to the under loaded VM’s. Whenever the cloud service provider receives any high priority task it has to submit to that VM, that have minimum number tasks for execution. The all VM’s are scheduled in ascending order and the tasks removed from the VM’s are
Software as a Service Platform as a Service Infrastructure as a
Service
Data center energy use
Network equipment energy use
Consumer client device energy use
Bussiness client device energy use
Manufacturing energy use
ud
submitted to the under loaded VM’s. The current load of all VM’s can be calculated based on the information received from the cloud datacenter [15]. S.K. Dhurandher et al. (2014) introduced a dispersed batch-based method that accomplishes effective load offsets in the cloud. The prospective method represents feature like supports assortment, scalability, low network bottleneck and vacancy of each blockage link because of it are disperse essence, and designed for multiple masters and multiple servers. In the prospective architecture, links are arranged as a batch of sub batches. The internetwork is partitioned into batches using Batch alike method however each node in the internetwork correspond to absolutely one batch and a retainer is the computation component of the inter network. The prospective method is addressed on a huge extent to cloud data-centre, to evaluate the proposed heuristic CloudSim toolkit used as simulation framework. The analysis is completed at the different criterion: load circulation on data-centres in the cloud, no of processes executed and moderate alteration time for completion of jobs [24].V.S Kushwah, S. K Goyal, P. Narwariya et al. (2014) the author proposed that flaw in the cloud filigree is a typical concern although offsets load. Although to subdue the failure, various different methods for fault tolerant are utilized. The Cloud gain unlock to a brand-new vista for usage of assets. Hence, conveniences of the cloud upgrades of the utmost relevant constituents that are envisage and facilitate the existence of the assests and offsets load. Failure resistance is a concern to treat with although facilitates QoS in a cloud, so strengthening the achievement [24].Nidhi Bansal et al. (2015) the authors have worked upon the cost performance optimization based upon the QoS driven tasks scheduling in the cloud computing environments. The authors have focused upon the cost of the computing resources (virtual machines) to schedule the given pool of the tasks over the cloud computing model. The cost optimization has been performed over the QoS-task driven task scheduling mechanism, which did not encounter the cost optimization problem earlier. The authors have shown that the earlier QoS-driven task scheduling based studies has been considered the make span, latency and load balancing. The QoS-based cost evaluation model evaluates the resource computing cost for the scheduling along with the other parameters as in their secondary precedence. The authors have been proved the efficiency of the proposed model in the terms of the cost, and resource utilization [11].S. K Goyal, M.Singh et al. (2012) proposed a new technology that is grid computing involve conjoin and correlated usage of topographically dispensed assets for motivation like huge- scale estimation and dispensed information investigation. Although to achieve the client assumptions in provision of execution and efficiency, the grid system needs appropriate and effective workload offset method to parallel circulate the workload on every computing knob in the network. However, to resolve the load offset issue in computational framework, the author represents the Ant Province Optimization method. Although in the prospective technique, the metaphore is corresponding to assests required for execution, preferably than way. The minimum and maximum of metaphore shows the workload and relies upon process status at assests. So the focused aim of the prospective technique is to assign processes to assests in such a way that offset out the workload results in increased usage of assets [22].N. Kumar, R. Rastogi et al. (2012)the author worked upon the ACO for load balancing of nodes. It is best case for effective balancing load, because in preceding work ant can move in only one direction. But in this algorithm an ant can move forward direction and backward direction. Such as an ant searches the food is called forward direction and homecoming to the nest is called backward direction. This is favorable for balanced the node speedily. This algorithm work fast because an ant can pass simultaneously in both directions for balanced the node and this algorithm also gives the superior usage of assests. The algorithm is performed on the limited parameters suchlike acceleration, internetwork overhead and failure resistance strength [25].
R. Mishra et al. (2012) introduced a solution for load offsets in the cloud platforms. The workload can be centre processing capacity (CPU), internal-memory size or internetwork workload. Load Offset is the methodology of spreading the workload beyond miscellaneous links of a system to expand both assests usage and task feedback time although also escapes a condition in which any of the knobs are densely under loaded and other knobs are unavailable. Load balancing make sure that each knob in the internetwork does relatively the same volume of effort in any interval of time. Various techniques to reconcile this issue has been appear into presence alike PSO, Mix hash technique; GA and different organization based method are there in use. In the prospective work author proposed a technique relies upon Ant province optimization to reconcile the issue of workload offset in the cloud environment.But this method does not deal with the fault resistance concerns [26].
III.E XPERIMENTAL D ESIGN
As per the literature survey, it has been found that the load balancing and energy expenditure is an crucial issue in cloud environment, and researchers are giving more consideration towards it so that the proper usage of resources can be done as well as improve the cloud performance with low energy consumption. Processing of computing nodes is done remotely in most of the real time cloud applications. Hence, there is an enlarged necessity for failure province to attain reliability on cloud infrastructure. Researchers have proposed various techniques based on Genetic algorithm, ACO, etc. for workload offset strategy in the cloud environment. Now, In the proposed research work, a technique called Enhanced - ACO that is developed to achieve better offloading decisions among virtual machines when the reliability and proper utilization of resources will also be considered will use ACO algorithm to balance load
and energy consumption in cloud environment. The algorithm fully depends upon the history of the pheromone value to take the further judgments for optimal solutions for any of the computational requirement. The use of artificial ants for the state of development rule for the selection of the optimal resources beyond the grid computation or the cloud environments has been proposed in the prospective work. The artificial ants have been used for the purpose of cloud computing scheduling and shortest path identification. The ant province system adopts the arbitrary -proportional rule, which is the state of transition rule used for the ant system and works on the basis of the probability or a chance to choose the optimal resource out of the k-resources for the task assignment in the cloud. The pheromone value of the resource depends upon the number of available resources, processing cost and estimated time.
The proposed model is aimed at solving the problem of the scheduling in the cloud platforms using the Cloud simulator. The process sequencing is a scheme of put the runtime processes in the memory in the perfect placement or sequence in order to minimize the total tasking and communication overhead in terms of time and load respectively. The proposed technique also minimizes energy consumption and cost of computing resources that are used by different processes for execution in cloud. The earliest finish time and fault tolerance is evaluated to achieve the objectives of proposed work. The experimental outcomes show the better achievement of prospective model with comparison of existing one.
This system is fully based upon the J.L. Deneubourg’s system based on argentine ants as the ant colony optimization, where the author have laid the pheromone trail on the straight and return path of the ants from the foodstuff source to the home. Although straight path has been examined by the use of VM availability and VM load, whereas the return path continuously updates the pheromone amount with each task assignment. This mechanism keeps the current data in the memory, which leads towards taking the correct decision for the optimal path planning for the task scheduling over the cloud environments. Figure 4.2.1 shows the two joins and the mathematical formula is given in equations (1) and (2).
Fig.3. Mathematical Formula for ACO algorithm
i i n
i A B K A K A K ob )()()(Pr ++++=
)1Pr (Pr =+B A ob ob (1)
,1
δ+=+i i A A )1(1δ-+=+i i B B )(i B A i i =+ (2)
Where δ is the stochastic binary variable which contains the value of either 1 or 0 assigned on the basis of the multiple probability levels (ProbA and ProbB). A is the first path moving towards the food source, which is incremented with the entry of every new ant on the path with the pheromone A and value B is incremented with 1 if the ant chooses the path B. The probabilities are rising with the span of time, which depicts the pheromone amounts.
Algorithm: Enchanced Aco Algorithm
1. The input parameters for the ant colony
optimization method are initialized.
2. The available VMs are thoroughly checked over
the cloud computing environment.
3. The available VM list is loaded into the runtime
memory with the RAM and CPU details.
4. The available resource list is updated with the VM
list prepared on the step 3.
}...V ..........V , V ,V ,{V VM n 43 2 1 1= (3)
where VM is the virtual machine list and V 1 to V N to represent the virtual machine IDs.
5. The resource capacity is calculated for each
resource and has been given as the following list:
}......,,,{4321n R VM VM VM VM VM VM = (4)
6. Loop is started for every resource Read and load
the available resources on each VM and update the resource list with current values.
?==N
i i E VM VM 1
(5)
where VM E gives the resource availability after calculating the resource load.
T
VCPU u VCPU L =
(6)
Where L represents the overall resource load on the particular VM, whereas the VCPU u and VCPU T gives the currently used resources and total resources available respectively.
7. End the loop on step 5
8. Load the task list into the runtime memory.
}..........,,,{4321n t t t t t T = (7)
Where T vector represents the task vector and t 1 and t n represents the individual tasks
.
9. Calculate the length of the tasks in the form of
earliest finish time to calculate the length of the each individual task.
)()(Est Eft t t i c -= (8)
Where t c and t i gives the overall time length of each of the task by subtracting the estimated start time from estimated finish time.
10. Subdivide the each task j and create the sub-task
list where each sub-task is assigned with i
11. Scheduling iterations are initialized for the number
of tasks or sub-tasks with i.
12. Calculate the probability value of each available
VM.
13. Compare the task length with probability value
and resource usage on all available VMs.
14. Calculate the VM Load in the form of resource
usage percentage using the following formula, where Aj depicts the availability of the VMs.
i j j TR P A *= (9)
15. Calculate the failure rate of all VMs to determine
the trustworthy VMs to assign the current task. 16. Shortlist the list of available VMs which can
process the given task i.
17. Assign the task i to the resource with minimum
response time and highest probability.
18. Update the resource list with the current values of
VM load
19. Update the pheromone value for each available
resource.
20. End the iteration and process the tasks.
IV. R ESULTS AND D ISCUSSIONS
The proposed model simulation has been prepared by using the Cloud Simulator for the task scheduling procedure testing over the virtual cloud environment. A scenario of multiple user online source has been assumed in this simulation, where the request are being received from the multiple users on same time as per it happens in the social networks like Facebook, Twitter or other online giants such as Google, Amazon, etc. The VM regions has been defined according to the failure rate and virtual machine load status which is transformed into the threshold value using the mathematical equations. Entire simulation is based on the single Time zone scenario, where all users in the given user base are projected as residents of one country or time zone. The simulation can be easily considered for the testing in the peak hours for the point of task scheduling of the samples tasks over the given bunch of resources. The task density has been assumed to be overwhelming during the peak hours, which delays the request response by adding the scheduling delay or processing delay. Hence, the task scheduling method should be enough faster to reduce the
task scheduling delay and assigned resources should be enough vigorous to process the task as fast as possible to minimize the processing delay.
The design of the prospective technique can be defined in the form of main modules among the task scheduling procedure. The prospective model can be easily defined in the major four procedure model. The proposed procedure has been designed to attain the task information on the very first step, and then the task cost is calculated by investigating the task length and volume of data associated with the task. Afterwards, the number of available virtual machines (VM) is evaluated for their usability. The usability of the available virtual machines is predicted on the basis of their resource usage. The task cost and the VM availability are evaluated in the terms of resource usage and elapsed time. The VM offering the least time of the task is selected for the task execution. The VM load and failure rate has been assigned as the main parameters to take the scheduling decisions. Both of the parameters has been used for the purpose of task scheduling over the given cloud resources. The virtual machine load is the parameter which defines the overall utilization of the resources of the given virtual machine. The VM load can be used to signify the runtime availability in order to process the given task t on the given time t. The tasks running over the given VM utilize the certain amount of resources. The total percentage of the resources being used during the time t is considered as the VM load.
When the virtual machines are ordered in the workload allocation pool for the process sequencing in the given cloud environment, the load monitoring on each virtual machine becomes very important step to correctly perform the task scheduling tasks. The virtual machine load or overhead is calculated on the base of different parameters like as CPU size, memory size, etc. Each VM load must be calculated on the basis of its local parameters. Any use of general parameter values can result the biased load over the given VMs. The CPU and Memory overhead or usage on the given VM considerably influences the performance of the VM’s in the process sequencing practices.
Fig.4. Response time per task or transaction
Fig. 4. The response time describes the total time taken for the cloud platform to generate the reply to the user’s request. The proposed model has been evaluated for the results obtained from the simulation for the response time, which has been represented graphically in the following figure. The overall response time of process 100 is 0.21 seconds. The response time for process 1 is 13.11 seconds and for process 99 the response time that is 0.83 seconds. The process 8 takes 6.91 seconds to complete the task. Fig. 5. The energy consumption has been also monitored for each of the task in order to assess the overall energy consumption during the simulation. The proposed model has been designed to schedule to task over the virtual machine with the minimum energy and cost indices. The following figure shows the overall energy consumption for all of the tasks in the simulation. The process 100 consumes 0.1239joules of energy to complete the execution. The process 99 consumes 0.4897 joules of energy to complete the execution. The following figure shows the energy consumed by every individual process to complete the execution in the proposed system.
Fig.5. Energy consumption per task or transaction
Fig. 6.The proposed model has been compared on the basis of response time, which is one of the primary performance indicators for the cloud computing models. The task scheduling models are expected to return
the minimum values of response time in order to handle the maximum number of users every second. Fig.6. Graphical Representation of the Response time in seconds
In this comparative model the proposed model has worked expectedly, and consistently shows the robust performance by reducing the response time in comparison with the existing model.
Fig. 7. The imbalance degree of
the proposed model has been found lower than the existing models, which shows the robustness of the proposed model in handling the multiple tasks (in workflows). The proposed model has found more successful in even distribution among the given resources (VMs), than the existing model. Fig.7. Graphical Representation of the imbalance degree
The proposed model has clearly outperformed the existing model on the
basis of the imbalance degree in comparison with the honey bee based existing model. The proposed model has performed better in nearly all of the events assigned with different number of tasks during the experimental study. Fig.8. Graphical Representation for the Make span
Fig. 8. The proposed model based upon the ACO based task scheduling has been compared against the Honey bee algorithm in the existing scenario. The figure 5.3 shows the robustness of the proposed model is comparison with the existing model. The make span has
been recorded significantly lower in the case of proposed scenario in comparison with the honey bee algorithm based existing scenario.
The proposed model has been found efficient with the lower reading than the existing model.
V. C ONCLUSIONS
The cloud computing environments with larger number of user bases are always remain the difficult task to assign the task to the most suitable resources. The most suitable resources consideration can be stated for the resource with the powerful configuration to process the given tasks with quickness and accuracy. The virtual machines are considered as the resources for assigning the tasks in the form of user request and internal system data call and queries. The task assignment becomes the critical job when millions of queries or tasks are coming to the cloud platform from its users. The task assignment or resource scheduling can be categorized according to more number of tasks in less time, critical tasks first, low cost computing resources or any of the combination of these three. In this paper, we have worked upon solving the problem of processing the maximum number of tasks every second, which can definitely enhance the user satisfaction. Also the proposed model is aimed at assigning the resources (VMs) with least resource load and failure rate for the task pool. The virtual machine wills lowest possible load and lowest failure rate will be carrying high probability to reduce the overall time of task and to process it successfully that will also minimizes the amount of tasks in the waiting list. The prospective method results has been compared to the existing models with ACO based load balancing mechanism, GA, SHC and FCFS. The proposed model results have proved to be efficient enough while compared to the existing models for the cloud task processing. The results have obtained and analyzed in the terms of start time, finish time and response time. The experimental results have verified the effectiveness of the prospective system in scheduling and processing the tasks successfully over the given cloud resources.
F UTURE S COPE
In the future, the resource scheduling method can be improved by using the probabilistic mechanism by using the failure rate, current load, average load and other resources properties. The probabilistic mechanism calculates the probability of the successful transaction for the given task, which ensures the correct resources allocation to maximize the success rate and to minimize the failure rate. An adaptive threshold can be computed to discard the virtual machines with higher failure rate than the threshold can be refused from the available resource list to process the critical tasks. The gray wolf optimizer (GWO) may possibly upgrade the performance of the task scheduling process in contrast with the ant colony optimization (ACO) in our system. The proposed algorithm offset the load in cloud and improves the overall performance of cloud environment. This algorithm considers Response time, energy as the major QoS parameter. In the future, the algorithm will be improved by examine other QoS parameters and also contrast the result of the proposed algorithm with new algorithms.
A CKNOWLEDGMENT
I acknowledge with deep feel of obligation and most trustworthy recognition, the helpful guidance and bottomless assistance rendered to me by “Dr. Bikrampal Kaur” Prof essor for their experienced and concerned guidance, useful assistance and enormous help. I have been deep sense of praise for them convict goodness and infinite passion. The proposed work will be finished under the guidance of the official guide and other experts at the campus of the Chandigarh group of Colleges, Landran, and Mohali.
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Author’s Profiles
Anureet A. Kaur Morinda, April 10, 2017. She holds the degree of B.tech in Information Technology from Rayat and Bahra Engineering College Mohali, Punjab, India, 2013, M.tech from Chandigarh Engineering College, landran, Mohali, Punjab, India. She is an Active Researcher who has contributed 3 research papers in various
national & international conferences. She also contributed 2 research papers in Journals .Her areas of interest are Cloud computing.
The lists of publications are: Cost Aware Load balanced Task scheduling with active VM Load Evaluation (Bikaner, Rajasthan, and ACM Conference ID: 39190, 2016). Load balancing in Tasks using Honeybee Behavior Algorithm in Cloud Computing (Rajpura, Punjab, IEEE Conference ID: 38683X, 2016). Energy Aware Task Scheduling with Adaptive Clustering method in cloud computing: (Gurgaon, Haryana, Taylor and Francis, pp. 989-992, 2016).
Dr. Bikrampal B. Kaur is a Professor in the Deptt. Of Computer Science & Information Technology and is also heading the Dept. Of Computer Science in Chandigarh Engineering College, Landran, Mohali. She holds the degrees of B.tech. M.Tech and M.Phil. She has more than 20 years of teaching experience and served many academic
institutions. She is an Active Researcher who has supervised many B.Tech. Projects and MCA, M.tech. Dissertations and also contributed more than 20 research papers in various national & international conferences. Her areas of interest are Information System, ERP.
How to cite this paper: Anureet A. Kaur, Bikrampal B. Kaur, " An Effective Technique to Decline Energy Expenditure in Cloud Platforms", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.2, pp. 54-62, 2018.DOI: 10.5815/ijmecs.2018.02.07
云安全管理平台解决方案 北信源云安全管理平台解决方案北京北信源软件股份有限公司 2010 云安全管理平台解决方案/webmoney 2.1问题和需求分析 2.2传统SOC 面临的问题................................................................... ...................................... 4.1资产分布式管理 104.1.1 资产流程化管理 104.1.2 资产域分布 114.2 事件行为关联分析 124.2.1 事件采集与处理 124.2.2 事件过滤与归并 134.2.3 事件行为关联分析 134.3 资产脆弱性分析 144.4 风险综合监控 154.4.1 风险管理 164.4.2 风险监控 174.5 预警管理与发布 174.5.1 预警管理 174.5.2 预警发布 194.6 实时响应与反控204.7 知识库管理 214.7.1 知识共享和转化 214.7.2 响应速度和质量 214.7.3 信息挖掘与分析 224.8 综合报表管理 245.1 终端安全管理与传统SOC 的有机结合 245.2 基于云计算技术的分层化处理 255.3 海量数据的标准化采集和处理 265.4 深入事件关联分析 275.5 面向用户服务的透明化 31云 安全管理平台解决方案 /webmoney 前言为了不断应对新的安全挑战，越来越多的行业单位和企业先后部署了防火墙、UTM、入侵检测和防护系统、漏洞扫描系统、防病毒系统、终端管理系统等等，构建起了一道道安全防线。然而，这些安全防线都仅仅抵御来自某个方面的安全威胁，形成了一个个“安全防御孤岛”，无法产生协同效应。更为严重地，这些复杂的资源及其安全防御设施在运行过程中不断产生大量的安全日志和事件，形成了大量“信息孤岛”，有限的安全管理人员面对这些数量巨大、彼此割裂的安全信息，操作着各种产品自身的控制台界面和告警窗口，显得束手无策，工作效率极低，难以发现真正的安全隐患。另一方面，企业和组织日益迫切的信息系统审计和内控要求、等级保护要求，以及不断增强的业务持续性需求，也对客户提出了严峻的挑战。对于一个完善的网络安全体系而言，需要有一个统一的网络安全管理平台来支撑，将整个网络中的各种设备、用户、资源进行合理有效的整合，纳入一个统一的监管体系，来进行统一的监控、调度、协调，以达到资源合理利用、网络安全可靠、业务稳定运行的目的。云安全管理平台解决方案 /webmoney 安全现状2.1 问题和需求分析在历经了网络基础建设、数据大集中、网络安全基础设施建设等阶段后，浙江高法逐步建立起了大量不同的安全子系统，如防病毒系统、防火墙系统、入侵检测系统等，国家主管部门和各行业也出台了一系列的安全标准和相关管理制度。但随着安全系统越来越庞大，安全防范技术越来越复杂，相关标准和制度越来越细化，相应的问题也随之出现： 1、安全产品部署越来越多，相对独立的部署方式使各个设备独立配置、管理，各产品的运行状态如何？安全策略是否得到了准确落实？安全管理员难以准确掌握，无法形成全局的安全策略统一部署和监控。 2、分散在各个安全子系统中的安全相关数据量越来越大，一方面海量数据的集中储存和分析处理成为问题；另一方面，大量的重复信息、错误信息充斥其中，海量的无效数据淹没了真正有价值的安全信息；同时，从大量的、孤立的单条事件中无法准确地发现全局性、整体性的安全威胁行为。 3、传统安全产品仅仅面向安全人员提供信息，但管理者、安全管理员、系统管理
中石化 云计算平台工程技术方案 二O一六年四月
目录第1章.基本情况6 1.1.项目名称6 1.2.业主单位6 1.3.项目背景6 1.3.1.XX技术发展方向6 1.3. 2.有关XX公开的相关要求7 1.4.建设规模7 1.5.投资概算10 1.6.设计依据10 1.7.设计范围10 1.8.设计分工11 第2章.现状及需求分析11 2.1.项目意义及建设必要性11 2.2.现状分析13 2.3.需求分析13 2.3.1.长期需求13 2.3.2.本期需求14 第3章.总体设计16 3.1.建设目标16 3.1.1.预期总目标16 3.1.2.阶段性目标17
3.2.建设内容18 3.3.系统的总体结构18 3.3.1.设计原则18 3.3.2.XX本土化战略错误!未定义书签。 3.3.3.建设思路20 3.3. 4.总体拓扑结构22 3.4.信息的分类编码体系25 3.5.质量保证体系26 第4章.建设方案27 4.1.网络资源池28 4.1.1.组网物理拓扑图28 4.1.2.网络负载均衡设计30 4.1.3.网络虚拟化设计32 4.1.4.IP地址及DNS规划36 4.1. 5.网络端口资源估算41 4.2.计算资源池41 4.2.1.计算资源池架构41 4.2.2.应用系统分析42 4.2.3.计算资源池建议配置与选型建议44 4.2.4.计算资源池部署47 4.2. 5.虚拟化软件选型分析48 4.3.云计算管理平台51
4.3.1.云资源管理平台建设方案52 4.3.2.云运营管理平台建设方案61 4.4.云计算安全防护方案71 4.4.1.云计算平台安全威胁71 4.4.2.云计算平台安全防护目标73 4.4.3.云计算平台安全架构74 4.4.4.IaaS层安全74 4.4. 5.PaaS层安全89 4.4.6.SaaS层安全90 4.4.7.公共安全92 4.4.8.安全管理制度98 4.4.9.云安全服务100 4.5.机房方案100 4.5.1.机房设备集中管理100 4.5.2.布线系统101 4.5.3.机房系统102 4.5.4.UPS配置方案104 4.6.标准化工作109 4.6.1.标准规范建设的原则109 4.6.2.标准规范的总体框架110 第5章.设备配置要求112 第6章.项目实施与运行维护117
云计算平台系统建设项目 设计方案
1.1设计方案 1.1.1平台架构设计 **高新区云计算平台将服务器等关键设备按照需要实现的功能划分为两个层面，分别对应业务层和计算平台层。 业务层中，功能区域的划分一般都是根据安全和管理需求进行划分，各个部门可能有所不同，云数据中心中一般有公共信息服务区(DMZ区)、运行管理区、等保二级业务区、等保三级业务区、开发测试区等功能区域，实际划分可以根据业务情况进行调整，总的原则是在满足安全的前提下尽量统一管理。 计算平台层中分为计算服务区和存储服务区，其中计算服务区为三层架构。计算服务区部署主要考虑三层架构，即表现层、应用层和数据层，同时考虑物理和虚拟部署。存储服务区主要分为IPSAN、FCSAN、NAS 和虚拟化存储。 云计算平台中计算和存储支持的功能分区如下图所示：
图云计算平台整体架构 图平台分层架构
基础架构即服务：包括硬件基础实施层、虚拟化&资源池化层、资源调度与管理自动化层。 硬件基础实施层：包括主机、存储、网络及其他硬件在内的硬件设备，他们是实现云服务的最基础资源。 虚拟化&资源池化层：通过虚拟化技术进行整合，形成一个对外提供资源的池化管理（包括内存池、服务器池、存储池等），同时通过云管理平台，对外提供运行环境等基础服务。 资源调度层：在对资源（物理资源和虚拟资源）进行有效监控管理的基础上，通过对服务模型的抽取，提供弹性计算、负载均衡、动态迁移、按需供给和自动化部署等功能，是提供云服务的关键所在。 平台即服务：主要在IaaS基础上提供统一的平台化系统软件支撑服务，包括统一身份认证服务、访问控制服务、工作量引擎服务、通用报表、决策支持等。这一层不同于传统方式的平台服务，这些平台服务也要满足云架构的部署方式，通过虚拟化、集群和负载均衡等技术提供云状态服务，可以根据需要随时定制功能及相应的扩展。 软件即服务：对外提供终端服务，可以分为基础服务和专业服务。基础服务提供统一门户、公共认证、统一通讯等，专业服务主要指各种业务应用。通过应用部署模式底层的稍微变化，都可以在云计算架构下实现灵活的扩展和管理。 按需服务是SaaS应用的核心理念，可以满足不同用户的个性化需求，如通过负载均衡满足大并发量用户服务访问等。 信息安全管理体系，针对云计算平台建设以高性能高可靠的网络安
云计算的管理、架构、安全、网络与服务 云计算的魅力在于用户只要有身份证和信用卡就可以开始使用，但这也是问题所在。这么简单的服务势必会给毫无准备的IT部门带来许多挑战。之前我们已经多次碰到过这个现象：某项技术易于采用的优点到头来却变成了意料之外的管理难题，比如虚拟化技术导致虚拟机散乱，智能电话带来新的安全风险，即时通讯引发公司治理方面的问题。 作者旨在向IT经理们介绍如何最大限度地发挥云计算的优点，包括使用简单、灵活和较低成本；同时最大限度地减小风险。这篇实用指南包括了许可、管理工具、带宽、安全和架构等方面的内容。 本文表明我们仍处于云计算的早期阶段，这意味着，相关工具和技术还在不断完善中。比方说，经过长达两年的测试后，亚马逊网络服务公司的弹性计算云（Elastic Compute Cloud）服务在去年底才推向市场；监测、管理和负载平衡等企业级功能仍在其规划当中。同样，谷歌应用引擎（App Engine）属于预览版本。微软的Azure云服务也属于预览版本，目前只有Windows开发人员可以使用有限的功能，其他早期采用者无法使用。 不过现在可以开始规划了，你既可以实际感受这种新的IT交付模式（包括了解各种故障和缺陷），又可以比其他在考虑独自利用云服务的公司同事超前一步。 一、管理篇 牢牢控制云计算 管理云计算服务的工具形形色色，既可以使用简单的仪表板，让你在几分钟内就能创建虚拟软件栈；也有能够处理各种配置和管理任务的企业级平台。云计算使用越广泛，就越需要那些高端工具。
亚马逊、谷歌及其他云服务提供商提供了帮助客户入手的基本工具。比方说，谷歌应用引擎的管理控制台可以显示流量大小、带宽、CPU利用率以及谷歌托管应用程序的出错率，这些数据可以帮助你深入研究日志文件，并获得其他详细数据，还可以用它来控制管理权限、管理应用程序的升级。 然而，应用引擎仍属于“预览”版本；这意味着，随着需求越来越高，这些工具将无力满足要求。谷歌的产品经理Pete Koomen承认：“我们还缺少一部分功能。” 我们看到，云服务提供商、新兴公司和系统管理厂商都在竞相为客户提供功能更齐全的工具，以管理云环境中的资源。亚马逊表示，它会“很快”为弹性计算云服务推出新的管理控制台和云监测功能。亚马逊已经在提供一些基本功能，比如使用命令行界面创建亚马逊机器映像（Amazon Machine Images）的功能。管理控制台让用户可以配置及管理EC2资源，而监测功能将包含EC2实例和“可用区域”（availability zones）方面的实时度量――可用区域是客户为了确保冗余和最高可用性而选择的亚马逊基础架构中的一部分。亚马逊还计划在2009年提供负载均衡和自动扩展功能。 专门从事云管理的公司是另一个选择。RightScale公司的托管服务平台包括管理仪表板、数据库和网站管理、批处理、多服务器部署功能以及自动扩展功能。提供基本功能的开发版本可免费使用，但大多数IT部门会需要RightScale的另外三个版本（网站版、网格版和高级版），这些版本的起价为每月500美元，外加2500美元的一次性费用。 RightScale创办于2007年，以管理亚马逊网络服务起家；如今扩大了业务范围，可以管理其他公共云服务，包括FlexiScale和GoGrid的云服务。RightScale 还为加州大学圣巴巴拉分校的Eucalyptus公共云提供了一个平台，把面向云计算的开源Eucalyptus软件部署在集群服务器上。它实际上是一个研究测试项目，但目的是通过RightScale的仪表板，能够管理公共云和基于Eucalyptus的专有云。
云计算平台建设总体技术方案 第1章.基本情况 1.1. 项目名称 XX单位XX云计算平台工程。 1.2. 业主单位 XX单位。 1.3. 项目背景 1.3.1. XX技术发展方向 XX，即运用计算机、网络和通信等现代信息技术手段，实现政府组织结构和工作流程的优化重组，超越时间、空间和部门分隔的限制，建成一个精简、高效、廉洁、公平的政府运作模式，以便全方位地向社会提供优质、规、透明、符合国际水准的管理与服务。 随着网络技术、web2.0、下一代互联网等技术的发展，我国XX建设也发生着变化。2010年10月，国务院发布了《国务院关于加快培育和发展战略性新兴产业的决定》，就把新一代信息技术产业作为十二五时期的重点方向，要推动新一代移动通信、下一代互联网核心设备和智能终端的研发及产业化，加快推进三网融合，促进物联网、云计算的研发和示应用。
1.3. 2. 有关XX公开的相关要求 全国XX领导小组发布了《关于开展依托XX平台加强县级政府XX和政务服务试点工作的意见》，就开展依托XX平台加强县级政府XX和政务服务试点工作提出了相关意见。要求在试点县（市、区），用一年左右时间，建立和完善统一的XX平台，充分利用平台全面、准确发布政府信息公开事项，实时、规办理主要行政职权和便民服务事项，并实现电子监察全覆盖，为在全国全面推行奠定基础、积累经验。 1.4. 建设规模 本期建设规模为（后续根据实际服务器及机房环境进行调整）：
1.5. 投资概算 本项目本期工程概算总投资为XXXX万元（人民币）。 1.6. 设计依据 《中华人民国国民经济和社会发展第十二个五年规划纲要》； 《计算机场地技术条件》（GB2887-89） 《计算站场地安全要求》（GB9361-88） 《电子计算机机房设计规》（GB50174-93） 《供配电系统设计规》（GB50052-92） 《低压配电装置及线路设计规》（GBJ—83） 《建筑物防雷设计规》（GB50057-94） 《电子设备雷击保护守则》（GB7450-87） 《工业企业通信接地设计规》（GBJ79-95） 《中华人民国标准》（BMB3-1999） 《涉密信息设备使用现场的电磁泄漏发射防护要求》（BMZ1-2000）《涉及国家的计算机信息系统技术要求》（BMZ1-2000） 《涉及国家的计算机信息系统安全方案设计指南》（BMZ2-2001）《涉及国家计算机信息系统安全测试指南》（BMZ3-2001） 1.7. 设计围 本方案涉及围包括以下几个部分： （1）基本情况；
云计算数据管理平台项目实施方案
目录 1.项目实施方案 (5) 1.1.项目实施 (5) 1.1.1.实施总体要求响应和承诺 (5) 1.1.2.项目实施内容 (5) 1.2.项目组织架构 (6) 1.2.1.项目实施内部组织架构 (6) 1.2.2.甲乙方联合项目组织架构 (12) 1.3.项目人员配置和管理承诺 (18) 1.4.项目人员保障 (19) 1.4.1.实施工作配置相应资质和数量承诺 (19) 1.4.2.总体资源配置和工作量估算 (19) 1.4.3.具体人力资源配置 (20) 1.5.实施进度计划 (20) 1.6.项目实施过程 (22) 1.6.1.系统运行维护 (22) 1.6.2.系统优化完善 (26) 1.6.3.数据治理 (30) 1.7.项目交付物及质量要求响应 (31) 1.8.项目管理方案 (35) 1.8.1.项目管理方法论 (35)
1.8.3.项目进度管理 (40) 1.8.4.项目需求管理 (40) 1.8.5.项目配置管理 (41) 1.8.6.项目变更管理 (43) 1.8.7.项目质量管理 (45) 1.8.8.项目风险管理 (65) 1.8.9.项目沟通管理 (70) 1.9.测试方案 (73) 1.9.1.总体测试策略 (73) 1.9.2.总体测试方案 (74) 1.9.3.单元测试方案 (112) 1.9.4.集成测试方案 (124) 1.9.5.系统测试方案 (126) 1.9.6.测试组织 (143) 1.9.7.测试工具 (148) 1.9.8.自动化测试 (153) 1.9.9.软件测试知识库 (160) 1.9.10.实施测试 (163) 1.10.应急计划 (164) 1.10.1.本项目的关键成功因素 (164) 1.10.2.重大风险及规避措施 (166)
国际机场节能管理能源管理平台解决方案
目录 1.工程概况 (2) 2.建设背景 (3) 1.1挑战 (4) 1.2需求分析 (5) 3.解决方案概述 (6) 4.系统架构 (9) 4.1能源管理系统主站 (9) 4.2通讯网络 (9) 4.3测控层硬件设备 (9) 5.技术特点 (11) 5.1能源管理可视化 (11) 5.2用能分析图形化 (12) 5.3智能数据统计分析 (13) 5.4管理规范化 (16) 5.5支持多种数据源 (16) 5.6能源系统云服务 (16) 6.应用场景 (17) 6.1能源购进 (17) 6.2能源消耗 (17) 6.3能源转供 (17) 6.4能源运行 (17) 7.计量点设置 (18) 7.1电计量点 (18) 7.235KV变电站计量点设置 (18) 7.3试点变电站（1#变电站）计量点设置 (20) 7.4水计量点设置 (21) 7.5热计量点设置 (23) 8.系统配置及预算 (24) 9.结语 (30)
1.工程概况 **国际机场位于*市东南方向，距*市？km，始建于？年，曾于？年进行过扩建。经过扩建后航站楼面积为？万平方米，跑道及滑行道延长至？米，并加宽跑道及滑行道道肩，飞行区等级由？升格为？级，可满足当前最大机型A380等飞机的备降要求，为国内干线机场及首都国际机场的备降场。 经中国民用航空总局批准，“**机场”更名为“**国际机场”。机场已开通航线*多条，通达国内外60多个城市，保障机型近20种。
2.建设背景 节能减排已经被全社会普遍关注。就民航业而言，民航总局明确要求，到2020年我国民航单位产出能耗和排放要比2005年下降22%，达到航空发达国家水平。 目前，机场能耗占民航业能耗的3%。其中，供暖、制冷、照明又占了机场能耗的70%。 在这一背景下，****国际机场的能源管理也提上日程。如何降低运营成本，在保持优质服务水平的基础上减少能源消耗，将耗能大户变为节能大户，树立良好的社会形象，为社会节能减排做贡献，也成为****国际机场运营管理的关注焦点之一。 ****国际机场设有飞行区、航站区、办公生活区、塔台和通讯导航站、气象观测站、供油站、机务维修区、消防应急等区域设施，其面积大，分布广，负荷密集，供电容量大，不仅对于系统的安全性和可靠性要求极高，而且航空级的设施水平和服务水平也决定了机场对管理水平的高度要求。 **国际机场对于能源管理的需求主要包括： 1)持续安全可靠运行。由于机场交通枢纽有大量的人群聚集，为确保人员和设备的安全，对设施的照明、通风、航班的通讯导航等系统的持续可靠运行提出了极高的要求。而且机场功能决定了其站房和相关设施必须长时间持续稳定运行，以便确保设施的高利用率，从而也要求能源管理系统持续可靠地运行。 2)实现能源成本管控。由于机场航空级的设施水平和一系列人性化的体验要求，空调、照明通风的能耗必然很大，因此需要对能耗进行分类监测和统计，找出无效能耗，针对实际客流变化进行合理调控，以降低整体运营能耗。 3)降低运营管理强度。对于规模大、设施分布广、客流密度高的**** 国际机场，其日常运营的管理强度极大，仅仅靠传统的管理模式无法满足正常功能和可靠性保障的要求，必须借助现代自动化技术手段以降低传统的人工管理强度。
安全与管理 云盾 云盾是阿里巴巴集团多年来安全技术研究积累的成果，结合阿里云计算平台强大的数据分析能力，为客户提供DDoS防护，主机入侵防护，以及漏洞检测、木马检测等一整套安全服务。 购买云服务器ECS时已自动开通云盾 我们的优势 免部署，免维护，即时开启 无需采购昂贵的设备，免部署 无需复杂配置、免维护 开通云服务器即开启云盾安全防护 多层防御体系 网络层提供流量清洗中心 主机层提供客户端防护功能 应用，数据层提供防火墙功能 海量数据分析
收集攻击行为数据 深入挖掘海量数据 分析判断安全趋势决定防护决策 产品功能 云盾帮您轻松应对各种攻击、安全漏洞问题，确保云服务稳定正常。 十年攻防，一朝成盾。 DDoS防护 提供四到七层的DDoS攻击防护，防护类型包括CC、SYN flood、UDP flood等所有DDoS攻击方式。 主机入侵防护 提供包括密码暴力破解、网站后门检测和处理、异地登录在内的反入侵服务。 安全体检 提供Web漏洞检测、网页木马检测、端口安全检测等安全检测服务。 WEB防火墙 提供WEB攻击防护防火墙，能有效拦截SQL注入，XSS跨站等类型的WEB攻击。 案例 北京乐汇天下科技有限公司
北京乐汇天下科技有限公司，是一家专注于手机网游产品研发和运营的公司，拥有业内顶尖的游戏设计人才，拥有充满激情的研发团队，更拥有健康成熟的游戏理念。我们致力于通过数字娱乐方式提升人们的生活乐趣，为用户创造一流的娱乐产品和交流环境。 例如游戏作品 口袋海贼王 游戏类型：角色扮演 在线人数：100000人 使用产品：云服务器、负载均衡、内容分发网络、云盾、云监控 开发语言：Java、PHP 开发引擎：Cocos2d-x 如下不熟构架图： 架构解读 我们的web端更新是走cdn更新，应用服务器端的更新是批量脚本更新我们采用了多种数据库相结合的形式，既能保证数据的安全性和很高的可分析性，又保证了高效的运行效率，起初重要的玩家数据用了从库被动备份，随着玩家数据的变大，我们不得不取消从库备份，以节约不必要的内存成本，后来改成了每日备份到数据备份机器。游戏并采用了单服单库架构，以把以外损失和玩家损失降到最低。 客户反馈 我们的web端更新是走cdn更新，应用服务器端的更新是批量脚本更新，我们采用了多种数据库相结合的形式，阿里云既能保证数据的安全性，又保证了高效的运行效率。
下一代云计算平台 建设方案
目录 第一章背景介绍 (4) 1.1 云计算介绍 (4) 1.2云计算与我国教育领域 (5) 第二章预期建设目标 (8) 2.1建设目标 (8) 2.2建设内容 (8) 第三章平台整体架构及特色 (10) 3.1 云平台背景简介 (10) 3.2 云平台架构及特色 (11) 3.3 云平台特色功能 (14) 第四章平台的管理与维护 (19) 4.1功能概述 (19) 4.2访问接口 (20) 4.3集群管理软件客户端 (20) 4.4集群管理软件WEB客户端 (20) 4.5远程桌面及命令行界面 (20) 4.6主要功能介绍 (20) 第五章应用的支撑 (31) 5.2分布式缓存 (42) 5.3迁移的支持 (45) 第六章集群管理软件虚拟化实现 (47) 6.1计算虚拟化 (47) 6.2存储虚拟化 (53) 6.3网络虚拟化 (64) 第九章平台发展与案例 (73) 9.1平台发展历程 (73)
9.2政府支持 (74)
第一章背景介绍 云计算是计算机科学和互联网技术进一步融合发展的产物，也是引领未来信息产业创新的关键战略性技术和手段。云计算在教育领域应用前景广阔，未来将在促进教育公平、降低教育成本、变革教学活动方式、提高管理效率和助推终身教育等五个方面对教育产生深远影响。 1.1 云计算介绍 云计算本质是将计算任务分布在大量计算机构成的资源池上，使各种应用系统能够根据需要获取计算力、存储空间和各种软件服务。微软把云计算定义在云+端、软件+服务上；谷歌(Googe)认为，云计算就是以公开的标准和服务为基础，以互联网为中心，提供安全、快速、便捷的数据存储和网络计算服务；IBM则认为云计算是一个虚拟化的计算机资源池，一种新的IT资源提供模式。虽然他们对云计算的定义不同，但认识较一致的地方是：云计算即“按需服务”，将数据存储和计算能力作为可以通过互联网来获取的“服务”向客户提供。因此，云计算具有以下两点优势： 1.1.1 降低信息化成本 在信息化不断向广度、深度发展的今天，日常工作处理的数据急剧增长，这些数据中还有相当一部分保存在本地。大多数情况下，网络只是让人们能更方便地获得信息，数据处理主要还是依靠本地硬件设备及运行在本地的应用程序来进行。面对海量数据的存储与计算，人们对计算机系统升级的要求不断提高。对计算机系统的要求越高，给个人或单位带来的经济压力就越大。云计算的出现，为投入较低成本，换取高计算能力提供了技术支持。云计算只要求用户端设备能运行简单的操作系统和浏览器软件即可，也就是说，云计算对用户端设备要求很低。应用云计算技术，可以避免本地建设和维护价格不菲的计算机系统，只需支付低廉的服务费用，即可完成原来需要高配置的本地计算才能完成的计算任务。 1.1.2 使用方便快捷 个人计算机是日常工作中的重要信息处理工具，人们需经常不断地进行系统软件的
能耗管理系统技术优势 能耗监测系统为政府能耗监管部门、能耗企业提供能耗监测解决方案及配套产品，为客户提供能耗数据（如水电燃气热量等）采集、上传、统计、分析、公示、动态监控等服务。系统通过通讯方式与智能模拟屏进行数据交换，形象显示整个系统运行状况。保证计算机监测系统的正常供电，在整个系统发生供电问题时，保证站控管理层设备的正常运行。 能耗数据都是依赖可靠地能耗数据采集网关来完成。采集端口同各种仪表相联，获取实时数据，通过的通讯方式将这些数据上报给数据中心具备较强的运算能力与开放性。并且内置了信息自动采集组件，能根据信息变化主动向网络上报信息，取代传统的数据库轮循，保证数据的实时传递的同时，大幅度减少网络数据通讯量和中心服务器的负担，保证中心服务器的稳定可靠。同时，内置存储可选模块，可以根据用户需要保存一定时间的数据，保证在通讯链路故障时的数据不会遗失。 能耗管理系统技术优势：
（1）严格按照国家相关规范与技术导则要求进行研发，易于组网实施省、市、区域性政府能耗监测平台和企业集团能耗监测平台、硬件架构、软件功能、数据传输可与上下级监测平台系统无缝对接。产品设计深入贴近用户需求，提供专业的能耗数据采集、上传、统计、对比、分析，建筑信息管理、能效公示等功能与服务。 （2）采用功能强大的工业级能耗数据采集终端进行能耗数据采集，提供多种可靠的安全性策略包括支持断点续传功能等，避免数据丢失和迟滞，确保系统安全可靠使用。 (3）适应客户分期建设的需求，满足用户基础应用、小型应用、中型应用与大型应用需求的不断扩充，制定灵活的部署方案，有效控制初投资。不仅提供国家规范的能耗检测功能，更可根据各地政府、能耗企业能源管理需求研发定制专业能源管理功能，提升工作效率。 通过对能耗数据统计、分析，结合能耗对比，确定产品能耗状况和设备能耗效率，从而提供能源管理优化措施。能耗数据分析模块是能耗管理软件的精髓所在，目前市场上各家软件的算法不尽相同，其效果还需市场验证。然而，以模糊语言变量及模糊逻辑推理为基础的计算机智能控制技术的发展将极大推动能源管理水平。对目标企业的机电设备进行信息化融合，实现水、电、气、风等能耗的优化可控和合同能源管理！
能源管理云平台系统简介 鲁东大学信息与电气工程学院与烟台振华电气有限公司联合共建“能源管理和节能智能控制研究中心”，致力于“能源管理云平台”系统建设，研究中心汇聚了多位资深行业专家和能源行业优秀的能源管理师组成的专业的服务团队，为用户提供全天候的高质量的能源服务。倡导用能的“安全、可靠、经济、高效、洁净”目标。提倡能源管理的数字化。通过“能源管理云信息平台”，让您得到一个数字式量化的能耗指标数据，准确掌握自己的用能和设备运行指标。通过能源管理网络化平台和互联网----经济、快捷、透明、无界的服务手段，让您在地球的任何地方都能把握自己的能耗动态和设备工况。通过能源管理可视化平台，让您像管理人、财、物一样管理能源，提高能源使用效率,降低生产成本。 一、能源管理云服务平台简介 振华电气有限公司于 2007年在烟台创建，是一家专业致力于能源行业的能源管理技术研究与能源系统整体解决方案的专业公司。公司拥有目前国际、国内领先的能源管理技术与能源分析、优化、评估技术,并且拥有自主的知识产权，开发了具有自主知识产权的云服务能源管理平台系统,能帮助企业“向管理要效益”。系统以新一代数据处理平台为核心，不仅拥有能源监控、计量计费、用能分析、负荷预测、用能计划、能源质量、
设备运维管理、智能报表等常用功能模块，还可针对企业实际需求实现功能定制。 完善的现场测控与计量系统，采用专业测控与计量产品，依托智能通讯网络，实现供配电、供排水、燃气、供热等能源站的自动化测控与计量功能。 系统以智能化节能潜力分析为先导，包含设备节能、工艺节能和管理节能等多维度节能措施，并辅之以节能效果评估及EPC 项目管理跟踪等手段，真正打造立体化能源管控体系。 振华电气实行企业现代管理机制，企业构架扁平化，目前有综合部、研究所、营销中心、系统集成部、技术部、运维管理中心六大职能机构。 振华电气追求以客户为服务价值重心、合作共赢的经营理念，在能源服务行业中不断优化价值链分配，共享智慧能源的成就与喜悦。 二、平台首页
旅游云计算平台要求 本平台是旅游信息化的核心平台，方案要充分考虑旅游的现状和未来发展需要，并在深入了解旅游产业市场需求、管理服务、技术、功能、性能需求的基础上，提出如何依托云计算打造旅游产业智能型的信息服务平台。平台应采用具有动态架构的云计算技术或产品，保证平台的先进性、安全性、开放性、兼容性、共享性、可升级、可扩充，确保系统实施和服务的效率和弹性。 一、业务需求 一是内部基础业务。主要是内部的协同办公（电子邮件、日程管理和文档管理等）、及时通讯和在线会议等内部业务； 二是旅游管理业务。这主要包括在景区、饭店、旅行社和导游等的在线业务办理、电子合同监管、网上旅游投诉、导游考试和网络培训等； 三是旅游企业IT服务。存储服务、运维服务，安全服务、数据管理、旅游同业交流、网站建设、电子商务平台建设和推广等。 四是公众信息服务。旅游目的地营销、假日旅游预报、旅游信息查询、旅游网上预定、旅游行程规划、旅游人才服务。 二、功能需求 1、实现一站式、低成本、高性能IT资源虚拟（包括服务器、存储设备、网络资源等）租赁服务。支持按需计费的运营模式、通过共享资源和信息降低个业务单位的运营成本并提升运营效果。 2、利用瘦客户机和桌面云系统构建旅游服务中心的IT办公环境。 3、设计部署为景区、饭店、旅行社等旅游企业提供基于Saas独立电子商务平台建设的支撑环境（数据库、操作系统和中间件）和平台服务； 4、实现全省数据共享，不同业务单位之间和各种异构应用的互联互通，相互协作和关键数据复用，构建动态、统一和具有全局数据视图的数据中心，满足各系统间的灵活调用和相互联动。 5、可为旅游企业提供完善的数据中心运营支撑，以全省旅游公共数据库为基础，为全省旅游企业提供可上传、下载的公共旅游数据资源。用户可基于Web 的软件来进行企业管理和经营活动。 6、利用云架构平台使各业务单位可快速拆分各自应用和管理平台，并可随意、快速、便捷的调用各种应用模块，通过可视化界面构建自己的应用系统，使新应用的开发更加简单快速。 7、充分考虑旅游局已有应用系统和平台的移植，并支持未来用户的IaaS、PaaS和SaaS服务需要。 二、建设要求 1、产品与方案 根据所采用的云计算平台整体架构和所提供的技术参数指标，说明搭建平台主要采用哪些硬件产品，云计算管理软件、平台软件及平台IT运维安全操作风险管理、系统集成主要解决方案，设备选型依据和说明。 2、内容与功能 平台是旅游服务中心的中枢，因此平台建设直接影响着整个网络的安全稳定运行。根据旅游服务中心旅游云计算平台数据集中、系统集中的要求，对云计算
H3C CAS云计算管理平台 产品概述 自上世纪90年代开始，IT行业在全球范围内得到了迅猛的发展，IT平台的规模和复杂程度出现了大幅度的提升，与此同时，很多企业的IT机构却因为这种提升而面临着新的困境，如高昂的硬件成本和管理运营成本、缓慢的业务部署速度以及缺乏统一管理的基础架构。 H3C公司依托其强大的技术实力、产品与服务优势，以及深入人心的以客户为中心的理念，为企业数据中心IaaS云计算基础架构提供最优化的虚拟化与云业务运营解决方案。通过H3C CAS云计算管理平台实现数据中心IaaS云计算环境的中央管理控制，以简洁的管理界面，统一管理数据中心内所有的物理资源和虚拟资源，不仅能提高管理员的管控能力、简化日常例行工作，更可降低IT环境的复杂度和管理成本。 H3C CAS云计算管理平台改变了传统IT行业的消费模式和商业模式，IT部门通过网络提供软硬件和服务，消费者从以前的“购买软硬件产品”向“购买IT服务”转变，并通过网络浏览器来获取和使用服务。在这一新的服务模式下，IT部门成为了业务部门的云计算服务提供方。利用H3C CAS 云计算管理平台，构建灵活的IaaS服务资源平台，从而为用户提供前所未有的虚拟基础架构访问体验。此外，IT 部门还可以通过提高整合率、任务自动化和简化管理来降低成本。 产品特点 直观的配置与管理 完全基于B/S架构的管理控制台，不仅让您轻松组织和快速部署整个IT环境，而且还能对包括CPU、内存、磁盘I/O、网络I/O等重要资源在内的关键元件进行全面的性能监测，为管理员实施合理的资源规划提供详尽的数据资料。 智能的资源配置优化 H3C CAS云计算管理平台为虚拟机中运行的应用程序提供简单易用、成本效益高的高可用性功能。硬件故障导致的服务器或虚拟机宕机再也不会造成灾难性的后果，H3C CAS提供的资源智能调度能力会为这些服务器或虚拟机自动选择最佳的重新运行位置。 支持IEEE 802.1Qbg 支持IEEE 802.1Qbg（EVB）协议，与H3C S5800系列交换机及iMC网管组件配合，能够实现对虚拟机流量的全面监控。 自助服务管理 通过将计算、存储和网络等物理资源抽象成按需提供的弹性虚拟资源池，以消费单元（即组织或虚拟数据中心）的形式对外提供服务，各个消费单元之间完全隔离，由各自的管理员进行监
工企业安全生产管理云平台 中石科技以实现企业安全生产全要素数字化管理为目标，围绕风险分级管控和隐患排查治理体系、化工过程安全管理、安全生产标准化等内容，建设集重大危险源监控信息、可燃有毒气体检测报警信息、企业安全风险分区信息、生产人员在岗在位信息和企业生产全流程管理信息等于一体的安全生产信息化管理平台，切实落实企业安全生产主体责任，全面提升企业安全生产管理水平。 四大系统模块，全方位“数字化”升级 一、重大危险源检测预警系统 主要用于监测化工企业构成重大危险源的危险化学品储存及生产装置实时数据和预警、可燃有毒气体数据及预警、危险化工工艺安全参数监测预警、监控视频等信息。
二、安全风险分区管理系统 通过生产过程危险和有害因素的辨识，运用定性或定量的统计分析方法确定其风险程度，在信息系统中企业厂区平面图上用红、橙、黄、蓝“四色图”进行标绘，形成“两单三卡”。 三、人员在岗在位管理系统
用于管理化工企业作业人员定时、定人、定岗履职的物联网信息系统，可通过生物识别、智能门禁、实时定位等技术，能够有效识别、跟踪作业人员及车辆的位置和行为。 四、生产全流程管理系统 主要包括安全生产目标责任管理、安全制度管理、教育培训、现场管理、安全风险管控及隐患排查治理、应急管理、事故管理、考核评审、持续改进等为一体的信息管理系统。
平台优势 一、全面监测厂区安全 能够及时、准确地监测全厂区安全生产信息。 二、多终端展示 通过PC端、大屏显示、移动终端等多种设备展示信息。 三、综合信息展示监测 综合展现企业安全生产状况、应急管理状况和各类企业管理应用，实现服务与应用的便捷访问和可视化展现。 （内容来自中石科技https://www.doczj.com/doc/7413864390.html,）
云计算咨询项目 云计算管理平台规划设计方案
目录 一. 概述 (4) 1.1 建设背景 (4) 1.2 建设目标 (5) 二. 规划思路 (6) 2.1 云计算整体规划思路 (6) 2.2 云管理平台总体目标架构 (6) 三. 基础设施云管理平台建设规划 (8) 3.1 功能架构描述 (8) 3.2 服务管理功能域 (11) 3.2.1 服务目录要求 (11) 3.2.2 服务目录管理 (12) 3.2.3 服务流程支撑 (14) 3.2.4 自助服务门户 (14) 3.2.5 配置数据管理 (15) 3.3 监视功能域 (17) 3.3.1 数据采集管理 (18) 3.3.2 性能管理 (23) 3.3.3 健康管理 (25) 3.3.4 容量监视 (28) 3.3.5 告警管理 (31) 3.3.6 拓扑管理 (32) 3.4 资源调度功能域 (33) 3.4.1 资源纳管封装 (33) 3.4.2 模板管理 (39) 3.4.3 部署调度 (41) 3.5 采集与控制功能域 (46) 3.5.1 性能采集 (47) 3.5.2 告警采集 (49) 3.5.3 配置采集 (50) 3.5.4 操作控制 (52) 3.6 管理流程说明 (53) 四. 虚拟桌面云管理平台建设规划 (55) 4.1 整体功能架构描述 (55) 4.2 桌面虚拟化云管理平台规划 (56) 4.2.1 接入层支持 (58) 4.2.2 身份认证管理 (61) 4.2.3 会话管理 (62) 4.2.4 资源管理 (65) 4.2.5 用户和用户组管理 (67) 4.2.6 系统维护管理 (69)
基于 NB-IoT 的车间能耗管理平台
引言：企业概况 海信集团成立于1969 年，始终坚持“技术立企、稳健经营”的发展战略，围绕着多媒体、家电、信息系统、房地产、新兴服务业等几大产业核心，使海信的技术创新工作始终走在国内同行的前列。目前，海信全球员工72000 余人，在南非、墨西哥、捷克等地拥有生产基地，在全球设有20 余个海外分支机构，产品远销130 多个国家和地区。2016 年实现销售收入1003.31 亿人民币。 海信拥有品类完善的家电产品（电视、冰箱、空调、洗衣机、手机等）及新兴的 B2B 产业。海信以电视起家，海信电视连续13 年中国第一，全球首台ULED 电视、全球首家推出激光影院，推出中国首款自主研发画质引擎处理芯片。海信的B2B 业务囊括交通、医疗、宽带三个版块。2003 年，海信成立了光通信公司，涉足光宽带业务；1998 年，开始涉足智能交通领域；海信医疗则开始于2014 年。海信的城市交通连续七年国内市场第一，宽带接入网光模块连续五年全球市场第一。海信智能商用也是海信集团IT 板块的核心，致力于流通信息化系统解决方案的研究。2017 年，海信成立智慧家居公司，基于海信智能家电的产品和技术优势，为顾客提供便捷、健康、舒适、安全、节能的生活环境。 进入21 世纪，海信以强大的全球研发人才组成的研发团队为后盾，以优秀的国际化经营管理团队为支撑，加快了产业扩张的速度，从B2C 向B2B 的产业延伸，已形成了以数字多媒体技术、智能信息系统技术、现代通信技术、绿色节能制冷技术、城市智能交通技术、光通讯技术、医疗电子技术、激光显示技术为支撑，涵盖多媒体、家电、IT 智能信息系统和现代地产的产业格局。 —关键词 基于NB-IoT 的车间能耗管理平台旨在提供一个可推广的与NB-IoT 技术相结合的车间能耗管理平台，该平台能够利用先进的IoT 技术、通信、云端数据计算等技术对车间内的能耗统一管理。具有如下优点：
能耗管理系统设计方案 近年来，随着企业能耗的急剧增加和电价的日益上涨，期望节约营维成本的管理人员，正越来越关注能源的成本问题。在不可再生能源日益稀缺和成本日益上升的今天，要求我们应采取必要的技术措施和管理手段，来建立具有“增容不增耗”的节能降耗型“绿色企业” 随着各企业自动化和信息化建设的开展，很多企业逐步装配了大量的自动计量仪表，并在此基础上建立了DCS系统、MES系统等，为企业能源消耗管理提供了基础。效率是企业的生命，如何提高工作效率也是摆在企业面前的头等大事情，通过不断的实践，信息化，自动化以其迅速、快捷、稳定、可靠的应用特征可以很好的满足能源管理的需求。 能耗管理系统是一个持续的过程，包括能源管理计划、能源管理计划和改进措施实施、改进结果检查、进一步改善等环节。每一环节是下一环节的前提条件，各个环节循环前进，推动企业能源管理工作不断优化和提升。建立一个能源管理的服务平台，在此平台上运用信息化的强大优势，利用各种先进的技术成果，建立一个能源管理体系，长期的、持久的为企业的能源管理服务。具体来说，在逻辑维上实现
战略管理、科技管理、过程管理和文化管理。在空间维上实现能耗数据采集、能耗指标管理和能耗分析预测。在时间维上实现规划、设计、实施、监控和提升。 其目标可以确定为以下4个： （1）能耗管理系统管理系统实现对能源的自动测量、采集、记录、汇总、分析和监控实际能耗数据，并于目标对比。而且要具备必要的分析和展现工具，包括模拟和优化等现代技术。作为一个完整的能源管理系统， （2）建立能耗指标体系 能耗指标体系的建立包括能耗装置维护、能耗考核单元维护、能耗指标维护、能耗计算函数维护、能耗计划下达等。能耗指标体系的建立是后续各项管理目标实现的基础。 （3）建立能耗数据库 通过各种方式获取能耗数据形成企业能耗数据库，包括从DCS系统获取数据、从MES系统获取数据，对于部分无法自动采集或获取的数据采用手工方式补充录入。能耗数据库的建立是后续统计分析的数据来源。 （4）能源消耗分析 在前两步的基础上，对能耗单元的实际执行状况进行统计分析，了解能耗实际执行情况，比较各能耗考核
云安全管理平台解决方案
目录 1前言 (4) 2安全现状 (5) 2.1问题和需求分析 (5) 2.2传统SOC面临的问题 (6) 3应对方案 (8) 4某云安全管理平台解决方案 (10) 4.1资产分布式管理 (11) 4.1.1资产流程化管理 (11) 4.1.2资产域分布 (12) 4.2事件行为关联分析 (13) 4.2.1事件采集与处理 (13) 4.2.2事件过滤与归并 (14) 4.2.3事件行为关联分析 (14) 4.3资产脆弱性分析 (15) 4.4风险综合监控 (16) 4.4.1风险管理 (17) 4.4.2风险监控 (18) 4.5预警管理与发布 (18) 4.5.1预警管理 (18) 4.5.2预警发布 (20) 4.6实时响应与反控 (21) 4.7知识库管理 (22) 4.7.1知识共享和转化 (22) 4.7.2响应速度和质量 (23) 4.7.3信息挖掘与分析 (23) 4.8综合报表管理 (23) 5某云安全管理平台方案特性 (25)
5.1终端安全管理与传统SOC的有机结合 (25) 5.2基于云计算技术的分层化处理 (26) 5.3海量数据的标准化采集和处理 (27) 5.4深入事件关联分析 (28) 5.5面向用户服务的透明化 (29) 6某云安全管理平台部署 (31) 7方案总结 (32)
1前言 为了不断应对新的安全挑战，越来越多的行业单位和企业先后部署了防火墙、UTM、入侵检测和防护系统、漏洞扫描系统、防病毒系统、终端管理系统等等，构建起了一道道安全防线。然而，这些安全防线都仅仅抵御来自某个方面的安全威胁，形成了一个个“安全防御孤岛”，无法产生协同效应。更为严重地，这些复杂的IT资源及其安全防御设施在运行过程中不断产生大量的安全日志和事件，形成了大量“信息孤岛”，有限的安全管理人员面对这些数量巨大、彼此割裂的安全信息，操作着各种产品自身的控制台界面和告警窗口，显得束手无策，工作效率极低，难以发现真正的安全隐患。另一方面，企业和组织日益迫切的信息系统审计和控要求、等级保护要求，以及不断增强的业务持续性需求，也对客户提出了严峻的挑战。 对于一个完善的网络安全体系而言，需要有一个统一的网络安全管理平台来支撑，将整个网络中的各种设备、用户、资源进行合理有效的整合，纳入一个统一的监管体系，来进行统一的监控、调度、协调，以达到资源合理利用、网络安全可靠、业务稳定运行的目的。