基于时间资源共享的分配方法在丰富的协作OFDM中的运用
P r
i m a r
y L i n k Figure 1. Model of the cognitive OFDM network
A Time-Sharing Resource Allocation Method in Heterogeneous Cognitive OFDM Network
Lei Li, Baoyu Zheng, Member, IEEE
Institute of Signal Processing and Transmission
Nanjing University of Posts and Telecommunications, Nanjing, P.R. China
Email: {y080601, zby}@https://www.wendangku.net/doc/e015407163.html,
Abstract —For a multiuser cognitive OFDM network, most existing adaptive resource allocation methods focus solely on fixed data-rate requirement (QoS guaranteed) or variable data-rate (no QoS guaranteed) conditions without any fairness consideration. In this paper, we investigate the resource allocation problem in a heterogeneous cognitive network with different QoS requirements. By decomposing the problem as a convex optimization, an optimal time-sharing resource allocation method is proposed to maximize the throughput of cognitive network under both subcarrier interference temperature limits and heterogeneous data-rate requirements. Finally, the performance of our proposed resource allocation algorithm is investigated by numerical results.
Keywords-Cognitive Wireless Network; Orthogonal Frequency Division Multiplexing (OFDM); Resource Allocation; Quality of Service (QoS)
I. I NTRODUCTION
The cognitive wireless network was proposed as the "NeXt generation" dynamic access technology in [1], which is considered as a reliable, seamless, high-efficient network that support a variety of different QoS guaranteed services. QoS guaranteed services such as voice transmission and video phone are very sensitive to delay and require a fixed data-rate (FDR). Whereas, no QoS guaranteed services like file transmission and web browsing could tolerate large delay and variable data-rate (VDR). Therefore, the cognitive network should dynamically allocate its resource in real-time so as to provide available QoS for different cognitive user while maintaining the performance of primary network. This restriction of primary users is equivalent to the interference temperature limit on all cognitive users' transmission power [2]. Since the design of OFDM ensures that each sub-carrier has a bandwidth less than the coherence bandwidth of the channel resulting sub-carriers experience relatively flat fading [3; 4], the system performance can be significantly enhanced by adaptive resource allocation, especially in multiuser system. Most multiuser OFDM resource allocation methods have focused solely on homogeneous QoS requirements. For a pure FDR QoS, Wong converted the problem to minimizing total transmitting power while maintaining constant rate for each user [5], which is also referred to Margin Adaption (MA). However, cognitive OFDM network usually experiences strict interference constraint by primary network, which can not provide such constant rate for all users. On the other hand, for a pure adaptive rate system, Jang formulated the problem to maximizing network throughput subjected to total transmitting
power limit [6], which is also referred to Rate Adaption (RA). Unfortunately, this technique never takes any fairness among all users into consideration and leads some user to not be assigned any subcarrier if all its subcarrier gains are relatively worse. With the consideration of fairness, Shen proposed a sub-optimal algorithm based on proportional rates constraints among all users in [7], and Tao proposed a time-sharing algorithm based on partly delay-constrained requirement in [8]. In this paper, under the interference temperature limit on each subcarrier, we discuss a time-sharing resource allocation method for heterogeneous cognitive OFDM network, where both FDR and VDR users are supported simultaneously. The rest of the paper is organized as follows: We introduce our system model and decompose the allocation problem by time-sharing in Section II. The multi-level water-filling algorithm for given time-sharing factors is given in Section III. In Section IV, we discuss the calculation of time-sharing factors based on heterogeneous requirements. The performance of our proposed resource allocation method is investigated by numerical results in Section V. Finally, conclusions are given in Section VI. II. S YSTEM M ODEL
We consider a heterogeneous cognitive OFDM network depicted in Fig.1. The system consists of N cognitive users
(CU) sharing K
subcarriers licensed to primary network. According to the "receiver-centricity" concept [2], interference caused by cognitive transmitter must keep below a certain upper-bound determined by the primary user (PU) receiver using the same subcarrier. These interference temperature limits on particular subcarriers is gathered by primary base station and delivered to cognitive control center (CCC). Cognitive channel gains ,n k g and interference channel gains
,n k h are assumed perfectly estimated at user terminal and collected by CCC via a feedback channel. Typically, CCC
This work is partly supported by National Natural Science Foundation of China (Grant No. 60972039), The National High Technology Research and Development Program of China (Grant No. 2009AA01Z241).
978-1-4244-7554-4/10/$26.00 ?2010 IEEE
executes all resource allocation calculations and maintains the reliable communication in the cognitive OFDM network. Let ,n k R denotes the throughput of cognitive user n on subcarrier k in bits/OFDM-symbol, which depends on channel gain ,n k g and allocated power ,n k p , and can be expressed as
2
,,,22,log 1n k n k n k n k n g p R σ????=+??Γ??
, (1) where 2
,n k σ denotes the noise estimated by cognitive receiver. And n Γ
is a constant related to a given BER requirement, which is suitable for estimating practical data-rate. If practical
uncoded MQAM modulation constellation is used, according to [9], we have
()ln 51.5n n BER ?Γ=?. (2)
For a set f Φof FDR cognitive users, the QoS guaranteed constant rates ,f n R n ∈Φ
should be satisfied. And for a set v Φ of VDR cognitive users, best-effort rates ,v m R m ∈Φ should be maximized. Undoubtedly, {}1,2,,f v
N ΦΦ=∪ .
These QoS requirements were also considered in [8] without
fairness considerations among VDR users, which may make some VDR user not be able to communicate if all its subcarrier
gains are relatively worse.
In [8], the author creatively proposed a time-sharing resource allocation by relaxing the constraint that each subcarrier is used by one user only. This method introduced a sharing factor [],0,1n k ρ∈ indicating the portion of time that subcarrier k is assigned to user n during each time fragment,
which was first noticed in [5] and widely used in multiuser
OFDM systems to convert a mixed integer optimization
problem into a convex optimization [6;7;8]. In our work, we
move forward this time-sharing method by introducing
mutually exclusion in time fragment, which can be written as
{}
,,,max
n n k n n k n
k
p R ττ∑∑
(3)
subject to
,0,,n k p n k ≥?,
(4) ,,n k n k
p P n ≤?∑
,
(5) 2
,,,,n k n k k h p T n k ≤?,
(6)
,f
f
n n
R R n =?∈Φ,
(7)
::,,v
i j i j R R i j γγ=?∈Φ, (8)
{}1,2,,f v N ΦΦ=∪ ,
(9)
[]0,1,
1n n n ττ∈≤∑.
(10) The object function (3) is to maximize cognitive network throughout, where n τ denotes the time-sharing factor that the portion of time subcarriers occupied by user n . Meanwhile, ,n k R is calculated by (1) and the actual rate of user n is
,n n n k k R R τ=∑. Constraint (5) is the total transmitting power
limit of user n . Constraint (6) is the interference temperature
limit given by primary network on each subcarrier. Note that although most existing allocation methods in cognitive radio treat interference limit as a summed-up value over all subcarriers, we hold our opinion that it should be separately
considered on each subcarrier, since primary network allocates
different subcarriers for different users, and then results
different interference channel gains ,n k h .Constraint (7) denotes
the QoS requirements of FDR users. Constraint (8) is the
proportional rates requirement for VDR users, which is firstly introduced in [7] to maintain the fairness among all users.
The sharing factor n τ in (3) under constraint (10) make the resource allocation more tractable by relaxing its exclusion
on k subcarriers under our model assumption, i.e., all
subcarriers are allocated to a single user over a portion of time. This make our optimization problem significantly reduced into
two sub-problems:
? For a given sharing factor set {}n τ, objective function
(3) turn to be {}
,,max n k n k k p R ∑for each user under the power constraints (4)-(6);
? For a given maximized rate set {},,max n k n k k p n R ??????∑, find
the sharing factor n τ for each user under the QoS constraints (7)-(9). The first issue is a power allocation and control problem, as
well as the second belongs to a rate allocation and scheduling
problem. Both these issues will be solved separately and
integrated jointly as the solution of the resource allocation
problem in the following two sections.
III. M ULTI -L EVEL W ATER -F ILLING P OWER A LLOCATION As discussed above, by a given time-sharing factor set {}n τ,
maximizing network's total throughput is equivalent to maximizing each user's throughput, that is
{}
,,max ,n k n k k p R n ?∑ (11)
subject to
,,n k n k
p P ≤∑
(12) 2
,,,n k n k k h p T k ≤?,
(13)
,0,n k p k ≥?.
(14)
The object (11) is concave because positive linear
combination of concave function (1) is concave. Furthermore,
Subcarrier Index
W a t e r F i l l i n g P o w e r
Figure 2. Illustration of multi-level water-filling
since the inequality constraints (12)-(14) are all convex, the feasible set of this problem is convex. For that reason, the power allocation problem defined in (11)-(14) is a convex optimization problem and there exists a unique global optimal point, which can be acquired by iteration. Now we formulate the optimal power allocation. The Lagrangian of the problem is,
{}()
,2
,,2,2,log 1n
n k
n
n k n k
n n k n k
k k n J p g p p P λλ????
??=+
??????Γ????
∑∑, (15) where n λ is the Lagrange multiplier of user n . The boundary conditions in (12) and (14) will be absorbed in Karush-Kuhn-Tucker (KKT) conditions [10] for short Lagrangian ()n J i . If
we let ,n k p ?
as the optimal solution there exist for ,n k ?, applying the KKT conditions, we formulate the sufficient and
necessary conditions for ,n k p ?
by differentiating the Lagrange,
{}()
2
,,,2,,,,0,,0,0,0,0n k k n k
n
n k
n
n k k n k n k
n k p T h J p p T h k p p λ??
?
??
?>=?
??=<
. (16) Moreover, this differentiating of Lagrangian with respect to ,n k p ?
and substituting into the KKT condition (16) follows,
2,2,22,,,,,,2
,222,,,10,0
ln 211,0ln 2ln 21,ln 2n k n
n n k n k n n k n
k n k
n n n k
n k n k n k n k k n n k n k n k g T p g g h T T h g h σλσσσλ?
?Γ??
?ΓΓ?=????
,(17)
which can also be simplified as,
2,,2
2,,1min max 0,,,ln 2n k n
k n k
n n k n k T
p k g h σλ???
??Γ????=?
????????
?
. (18) Meanwhile, the Lagrange multiplier n λ is obtained when we substitute (17) into the total power constraint (12), 2,22,,1min max 0,,ln 2n k n
k n k n n k n k
T
P g h σλ??
????Γ???????
=?????????????
?
∑. (19)
This multi-level water-filling optimal power allocation method given by (17) and (19) is similar to traditional OFDM water-filling principle [11], however, being some difference summarized as follows and illustrated in Fig. 2:
?
The line sketched out the bottom is no longer channel
noise 2,n k σ, but 2
2
,,,n k n k n
n k
g σΩ=Γas Equivalent
Noise adjusted by cognitive channel gains; ?
The power filled on each subcarrier ,n k p does not only depend on the water-line 1ln 2n n L λ=, but also the interference limit 2
,,n k k
n k
I T h =, which is illustrated
in Fig. 2 as Interference Limit Line ,,n k n k I Ω+. Thus, the filled power can exceed neither the water-line nor the interference-line; ?
For different user n , the water-line n L may vary.
However, the water-line 1ln 2n L λ= given by (19) cannot
be calculated in closed-form. Thus, a numerical iterative algorithm is given as follows that incessantly pours total power n P into all subcarriers until total power is used up or interference limits are reached on all subcarriers with least algorithm complexity ()2log K K Ο: 1) Initialization,
a) Set (),min n n k k
L ←Ω as initial water-line;
b) Set 0n F ← as initial total filled-power. 2) Loop until n F approchs n P numerically,
a) Update current water-line ()n n n n L L P F ←+?;
b) Update (),,min max 0,,n n n k n k k F L I ??←?Ω??∑as
current total filled-power;
c) If ,,max()n n k n k L I >Ω+, set ,,max()n n k n k L I ←Ω+
and break the loop. 3) Obtain allocation results,
a) Return current n L as final water-line;
b) Return (),,,min max 0,,n k n n k n k p L I ??←?Ω?? as final
power on subcarrier k .
Figure 3. Procedure of time-sharing resource allocation
2C o g n i t i v e N e t w o r k T h r o u g h p u t
2Figure 4. Throughput versus cognitive and interference channel gains
IV. T IME -S HARING A LLOCATION WITH Q O S R EQUIREMTS Although the transmitting power is given by (17) and (19), the actual transmitting rate of user n also depends on its time-sharing factor n τ, i.e., ,n n n k k R R τ=∑. As discussed above, time-sharing factors denote the portion of time subcarriers occupied by user n , which play a leading role in multiuser scheduling under heterogeneous QoS requirements. Thus, the sharing factors allocation problem can be expressed as, {}
,max n n n k
n k R ττ∑∑ (20) subject to
,f f n n R R n =?∈Φ, (21) ::,,v
i j i j R R i j γγ=?∈Φ, (22)
{}1,2,,f v N ΦΦ=∪ , (23) []0,1,1n n n ττ∈≤∑, (24)
Fortunately, this convex optimization problem is a linear program (LP) without any inequality constraint, which leads to the existence of close-form solution [10]. The FDR constraint (21) should be satisfied foremost not only for its reliable requirement, but also for its equality property, which yields,
,,f f
n n n k
k R n R τ=?∈Φ∑. (25) Note that these factors may dissatisfy constraint (24) when the FDR requirements are beyond the capacity of cognitive network, which is also called service outage. In this situation, there is no feasible point for (20)-(24).
On the other hand, all VDR users share the remaining
throughput of cognitive network under the proportional rate fairness constraint (22). Their total sharing factors must satisfy,
1,,v f
m n m n m n ττ=??∈Φ?∈Φ∑∑. (26) Additionally, proportional rates constraint (22) implies ,,:(/):(/)i j i i k j j k k k R R ττγγ=∑∑, ,v i j ?∈Φas the
proportional sharing factors constraint, which yields, ()
,,,m m k
v
k m m
m m m k
m k R m R γττγ=?∈Φ∑∑∑∑. (27) Hence, the time-sharing factors allocation including both FDR and VDR users and given by (25) and (27) respectively.
As a summarization, the diagram of our time-sharing resource allocation is given in Fig. 3, where the right dashed box represents the heterogeneous QoS requirements of cognitive users. Furthermore, each resource allocation cycle must be finished in a stable cognitive period, during which the
channel parameters and interference limit do not change.
V. S IMULATION AND A NALYSIS
In our simulations, we consider a heterogeneous cognitive
OFDM network with 8 users and 64 subcarriers. Uncoded
MQAM constellation is used and the BER for both FDR and VDR users are set to 510?. Noise gap 2
,n k σ for every cognitive
receiver on each subcarrier in normalized to unit, i.e. 0dB. In
addition, all cognitive and interference channel gains are treat
as i.i.d. Rayleigh variables with mean square 2,E[||]n k g and
2,E[||]n k h respectively. Thus, Monte Carlo simulations are applied over 310 times to reduce the influence of various channel realizations.
Fig. 4 shows the relation between cognitive network
throughput ,n n k n k R τ∑∑ in unit bits/OFDM-symbol and two average channel gains. We vary one channel gain from -20 dB to 0 dB and lock another at -10 dB. Meanwhile, all users
are set as VDR equal proportional rates (1:1::1 ) on power
constraint n P =+∞
for a independent comparison between different k T . We see that throughput rises when 2,E[||]n k
g
increases or 2,E[||]n k h decreases for fixed k T . This is because
cognitive network enjoys advantageous access capacity in good
cognitive or bad interference channel conditions. Moreover, as expected, throughput rises with k T since more interference
primary network can endure. Last but not least, the gaps grow
up as shown in the figure for the joint beneficial impact of
interference limit and channel gains. Owing to the direct constraint of cognitive transmitting
power by (5) and (6), cognitive network throughput definitely
depends on both user power constraint n P and subcarrier interference limit k T . This relation is given in Fig. 5 as a mesh plot with all channel gains at a same mean value -10dB. Above
10
20
30P n
(dB)
T k
(dB)
C o g n i t i v e N e t w o r k T h r o u g h p u t
Figure 5. Throughput versus sub-carrier interference temperature limit and
user transmitting power limit
5
101520
2500.20.40.60.81Average Throughput of FDR Users
S e r v i c e O u t a g e P r o b a l i t y
Figure 6. Service outage probability versus average throughput requirement
of each fixed data-rate user
all, at small value of n P or k T , no matter how large the other is, the throughput hardly increases. This trend is expected because the low value constraint plays a dominant role in transmitting power limitation. In addition, for a fixed large k T (corresponding large 2,E[||]k k n k I T h =), the throughput's variation can be generalized into three phases: Power Limited Phase — small n P restricts transmitting power and then makes the cognitive network in a low throughput, i.e., n k P I <; Boost Phase — median n P brings effective multi-level water-filling among all subcarriers and then makes rapid growth in throughput, i.e., k n k k I P I <<∑; Interference Limited Phase — large n P
exceeds the interference temperature limit and then makes little change in throughput, i.e.,n k k P I >∑.Note that a similar phenomenon occurs if we extend the value range
of k T for a fixed large n P .
For a cognitive heterogeneous network, access availability of FDR users can only be guaranteed in a probabilistic manner for the variable cognitive conditions and finite transmitting power. The service is said to be in an outage if FDR users' requirements exceed system capacity and then cannot be satisfied. This relation is illustrated in Fig. 6 with parameters n P =∞ and 0dB k T =. We define the range of average FDR users' requirements with outage probability lower than 1% as
Non-Outage Range, and the range with outage probability between 1% and 99% as Partial-Outage Range. Undoubtedly, FDR users' requirements by (7) can be supported in probability 1out P ? in both these ranges, but hardly guaranteed after the partial-outage range. In our simulation, both these two ranges shrink with the growth in FDR users' amount rapidly. This characteristic tells us that FDR users' requirements should be set at most in the non-outage range or low values of partial outage range to reduce service outage probability in a real system.
VI. C ONCLUSION
A time-sharing resource allocation method for cognitive heterogeneous OFDM network under the constraint of each subcarrier interference limit is considered in this paper. FDR and VDR users are simultaneously supported by constant rate and proportional rate access respectively. We investigated the problem of maximizing network throughput while satisfying both interference and total power constraints. This problem was transformed into two sub-problems of convex optimization by introducing the time-sharing factor, and solved separately. Simulation results showed that network throughput increases with the increasing of cognitive channel gains, the decline of interference channel gains, the increment of each subcarrier interference temperature limit, and the growth of user power constraint. Moreover, the upper-bound of FDR requirements is determined in a service outage probabilistic manner.
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1991.
基于非完全信息博弈的云资源分配模型
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大数据下的资源整合和知识共享上(2020)
大数据下的资源整合和知识共享（上）（2020） 卷1 1.本讲提到，“工业4.0”是指利用物联信息系统，将生产中的供应、制造、销售信息（），最后达到快速、有效、个人化的产品供应。（10.0分） A.立体化 B.数据化 C.表面化 D.方便化 2.2015年5月19日，经李克强总理签批，国务院印发《中国制造2025》，部署全面推进实施（）战略。（10.0分） A.全面发展 B.工业强国 C.制造强国 D.创新强国 3.本讲提到，大数据在给社会带来巨大的社会价值，也对（）构成严重威胁。（10.0分） A.个人隐私 B.个人安全 C.个人信用 D.社会公平
4.本讲提到“互联网+”行动将重点促进以移动互联网、云计算、物联网、大数据等与（）相结合。（10.0分） A.金融业 B.旅游业 C.现代制造业 D.林业 1.本讲提到，云计算的核心技术有（）。（10.0分）） A.虚拟化技术 B.分布式数据存储技术 C.分布式并行编程模式 D.大规模数据管理 E.分布式资源管理 2.本讲提到，《中国制造2025》的核心目标就是推动产业结构迈向中高端，坚持（）,加快从制造大国转向制造强国。（10.0分）） A.创新驱劢 B.提高产量 C.智能转型 D.强化基础 E.绿色发展 1.”互联网+“对传统行业的影响巨大而深远，它将来会替代传统行业。（10.0分）
2.以纸牌屋为例，Netflix可以通过大数据分析电影题材、挑选演员、播放形式，再根据这些内容拍摄用户感兴趣的电影。（10.0分） 3.工业 4.0称之为第四次工业革命，它是基于信息、物理融合系统，基于大数据和物联网传感器融合的系统，在生产中大规模使用。（10.0分） 4.“互联网+”是互联网和传统行业融合的新形式和新业态，“互联网+”就等于“互联网+传统行业”。（10.0分） 卷2 1.本讲讲到，云计算是一种按（）付费的模式。（10.0分） A.会员 B.下载量 C.使用量 D.使用空间 2.本讲提到，除了3“V”,大数据还有一个隐含的特征，称之为（）。（10.0分） A.价值洼地 B.价值增值 C.数据总量 D.数据更新
论文校园教学资源共享与交流平台设计与实现
本科毕业论文（设计） 校园教学资源共享与交流平台的设计与实现The Design and Implementation of Sharing Campus Teaching Resources and Exchanging Platform 学生姓名： 学院： 专业： 班级： 学号： 指导教师： 审阅教师： 完成日期：
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摘要 在教育领域,IT技术已经打破了空间和时间的限制,使任何人、在任何地方和任何时间,都可以全球性地获得信息。它正在改变着教与学的环境、主旨、内容和实施方法。新的一代正在“网上数字式成长”,他们的学习方法、需要和兴趣,与以往的时代是不同的。依托校园网络的优势设计和开发网络教学平台，为教师、学生提供全面与快捷的教学信息，实现教学的资源共享，增强师生之间的交流，更好的促进学生的学习和对知识的掌握，并对高校的教育模式的改革具有重要的意义。 本文首先进行了项目概述，简单介绍了项目开发的背景、项目开发的目的和项目开发的意义；在系统规划阶段，对系统进行了功能需求分析、可行性分析及总体设计原则；在系统分析阶段，采用面向对象的分析方法进行系统分析；系统设计阶段主要完成了类图的设计，并介绍了系统的开发工具和采用的技术。 采用MVC模式、应用JSP+MySql 设计实现校园教学资源共享与交流平台的设计与实现，通过本网站可以进行下载教学资源、阅览教师校园博客、论坛交流等功能，减轻了教师在传统教学中的工作负担，同时也为学生的学习提供了更广阔的学习空间，方便学生学习，教师管理，提高教学质量以及加强了师生的交流。 关键词：师生交流；资源共享；教师博客；论坛 -I-
云计算资源池的构建讲课稿
云计算资源池构建必须考虑的五个问题 近日，IDC发布最新中国云计算市场的研究报告。报告显示，2011年中国用户为建设云计算基础架构的投资已经达到2.86亿美元，同比增长42.0%。IDC 预计，中国云计算基础架构市场还将保持高速发展，到2016年其规模将超过10亿美元。可见，建设云计算基础架构已经成为许多企业的计划。 构建一个合理的资源池，是实现从传统的“烟囱式IT”迈向云计算基础架构的第一步。在传统的“烟囱式IT”基础架构中，应用和专门的资源捆绑在一起，为了应对少量的峰值负载，往往会过度配置计算资源，导致资源利用率低下，据统计，在传统的数据中心里，IT资源的平均利用率不到20%。 构建资源池也就是通过虚拟化的方式将服务器、存储、网络等资源全面形成一个巨大的资源池。云计算就是基于这样的资源池，通过分布式的算法进行资源的分配，从而消除物理边界，提升资源利用率，统一资源池分配。 图传统的“烟囱式”IT结构中，应用与固定的资源绑定 作为云计算的第一步，资源池的构建在实现云计算基础架构的过程中显得尤为重要，只有构建了合理的资源池，才能实现云计算的最终目的——按需动态分配资源。那么，在借助虚拟化手段构建资源池时，需要考虑哪些问题?通过与一
些已经或正在实施云计算的企业用户交流时发现，在搭建云计算资源池时，如下五个问题是必须要考虑的。当然，除了这些问题之外，还有其他需要考虑的问题，需要视情况而定。 底层软硬件平台的可靠性 要搭建虚拟资源池，首先需要具备物理的资源，然后通过虚拟化的方式形成资源池。一个物理服务器可以虚拟出几个甚至是几十个虚拟的服务器，每一个虚拟机都可以运行不同的应用和任务。 听到这里，可能很多用户都会感觉到某种危险性，要是这一个物理服务器崩溃了，那这个物理机上的所有虚拟机以及虚拟机上的应用都会受到影响甚至是崩溃(当然，可以去做实时的动态迁移，这是我们后面要谈到的话题)。这就好比是把许多鸡蛋放在一个篮子里，篮子破了，所有的鸡蛋都会摔碎。这对于许多连续性要求较高的用户来说，比如金融、电信等行业的用户，是无法接受的。 为了降低“鸡蛋”全部摔碎的风险，企业用户必须要保证“篮子”的质量。也就是硬件资源(服务器、存储、网络等)的安全性、稳定性。 民族证券信息部主任颜阳表示，“证券行业的核心业务对于业务连续性要求很高，一秒钟的中断都会带来巨大的损失，因此，在搭建资源池的时候，必须要考虑到硬件平台的可靠性”。 资源粒度最小化 “医疗信息化是配合业务流程的，比较复杂，并且跟人的生命健康息息相关，因此云计算平台的安全性十分重要，我们希望把每个元素都放到最小的粒度，打造出与业务流程十分契合的医疗云平台”，首都医科大学附属北京儿童医院信息中心主任孙宏国表示。
高校课程资源共享问题研究
高校课程资源共享问题研究 摘要：课程是实现教育目的的主要载体,在人才培养过程中起到 至关重要的作用。课程资源的利用、管理直接影响着高校课程的数量和质量。随着我国高校扩招,大学生在校人数激增,高校课程资源日益紧张,许多高校都不同程度地存在课程资源短缺、生师比偏高、无法满足规模庞大的在校生需求等问题。为提高资源利用率,学习国外先进经验，实现课程资源优化共享,已成为各高校在探索新的人才培养模式过程中的必然选择。本文结合当前中国高校课程资源的共享现状，查阅相关资料，从资源共享现状，值得肯定的做法，存在的问题及解决方案等方面做了研究。 一．资源共享的意义和主要形式 高校课程资源的共享实现教学资源共享，提高办学质量和教学水平可谓意义重大：优质资源的有效共享，可以减少教学资源的浪费，提高利用率，减轻教师教学负担，同时也可以使课程种类大幅增加，方便高校培养复合型、技能型人才，从而有效地提高高等教育整体质量和教学效率。 这里值得一提的就是哈弗大学的公开课，哈弗大学向全世界公开教学视频，像哲学，物理等课程，既可以让世界上的在校大学生享受到哈弗的教学成果，学到知识，感受世界一流讲师的魅力，另一方面，哈弗也能通过这种手段提高自身的影响力和知名度，于己于人都是有益无害。
高校课程资源的共享形式主要包括物力资源的共享和人力资源的共享，而物力资源资源的共享又包括教学设施的开放，如教室，实验室，机房等；图书馆资源的开放，现在好多高校之间可以互办借书证，免费到其他学校借书，如北大；网络资源的共享，中国目前正在办精品课程，好多学校有自己特色专业，办的这些精品课程通过网络可以惠及许多人。 人力课程资源的共享主要包括教师的交流与互聘，名家资源共享，校际选课和学分共认教师的交流与互聘教师互聘主要采用聘用客座 教授，邀请名师和著名学者举办讲座或报告会种形式，但还有临时聘任、短期聘或双聘任其他高校的教师的形式，除此之外还会通过人才借用和人才租赁等一些方式和途径来引进吸收外校优秀师资力量；名家资源共享专家资源的共享存在两种情况，一是校内专家资源跨专业的共享，一是高校校际间专家资源的共享，我们学校有星期四人文讲座，请了各校的名师来我校讲学，为学生提供了良好的接触人文气息的机会；校际选课和学分共认，这种形式在中国最为普遍，如南京的仙林大学城，开办了120多门的选修课，还有北京的学院路，高校之间可以共选选修课。跨校选修课程可以说是实现资源共享、发挥各自办学优势与办学特色的良好形式，特别是跨校选修课程能够使学生学到外校的优秀课程，聆听到外校优秀教师的教诲，这样各高校的名师资源就能够在本地区范围内发挥更多的作用。 二．高校课程资源共享存在的问题 1.教育资源共享内动力不足
存储管理动态异长存储资源分配算法
. 存储管理—动态异长存储资源分配算法 一、设计目的 理解动态异长存储分区资源管理，掌握所需数据结构和管理程序，了解各种 存储分配算法的优点和缺点。 二、设计内容 （1）分析UNIX最先适应（First Fit,FF）存储分配算法，即map数据结构、存储分配函数malloc()和存储释放函数mfree()，找出与算法有关的成分。 (2) 修改上述与算法有关的成分，使其分别体现BF（Best Fit,最佳适应） 分配原则和WF(Worst Fit，最环适应)分配原则。 三、设计准备（理论、技术） 1.最先适应（First Fit，FF）算法 指对于存储申请命令，选取满足申请长度要求且起始地址最小的空闲区域。在实现时，可以将系统中所有的空闲区域按照起始地址由小到大的次序依次记录于空闲区域表中。当进程申请存储空间时，系统由表的头部开始查找，取满足要求的第一个表目。如果表目所对应的区域长度恰好与申请的区域长度相同，则将该区域全部分配给申请者，否则将该区域分割为两部分，一部分的长度与申请长度相同，将其分配给申请者；另一部分的长度为原长度与分配长度之差，将其记录在空闲区域表中 2.最佳适应（Best Fit，BF）算法 是为了克服最先适应算法缺点提出的。它在分配时取满足申请要求且长度最小的空间区域。在实现时，可以将系统中所有的空闲区域按照长度由小到大的次序依次记录于空闲区域表中。当进程申请存储空间时，系统由表的头部开始查找，取满足要求的第一个表目。 3.最坏适应（Worst Fit，WF）算法 是为了克服最佳适应算法的缺点而提出的。它在分配时取满足申请要求且长度最大的空闲区域。在实现时，可以将系统中所有的空闲区域按照长度由小到大的次序依次记录于空闲区域表中。当进程申请存储空间时，取第一个表目。 4.程序设计技术分析 按题目题目首先对存储分配表进行初始化；然后对用户输入的请求和释放，按照动态更新存储分配表，并将每次更新之后的存储分配表在屏幕上显示出来 动态分区分配需要解决三个问题：A.对于请求表中的要求内存长度，从可用表或自由链寻找出合适的空闲区域分配程序。B.分配空闲区后更新自由链或可用表。 C.进程或作业释放内存资源时，合并相邻空闲区并刷新可用表。 四、设计过程（设计思想、代码实现）
网站资源共享模块的设计思路及代码
今天实训内容： 1、资源共享模块开发演示 2、检查剩余没有完成教学公告部分的同学 期末上课没回过问题、中期验收没通过、缺席、期末作品一塌糊涂，重修。
网络教学支撑平台：（1）平台的简介，静态页面（参考课本多媒体技术基础网络课程）（2）教学公告模块，包括公告标题显示、上下翻页显示、教学公告具体内容浏览、教学公告的录入。（做出任何一个二级学院网站，甚至一个中学网站）
（3）资源共享模块，包括资源上传功能、资源标题浏览、点击可以下载。这部分功能可以参考“广东省中职教育教学平台” 如何进行资源上传：使用fileupload控件 演示： （1）添加upload.aspx
页面，在页面添加一个fileupload控件以及button控件 （2）在网站目录下添加一个upload目录保存上传的所有课件和视频 （3）双击button编写上传文件代码： Try catch语句主要用在可能出现异常的地
方，例如访问数据库或者文件读写 （4）核心的一行代码： FileUpload1.PostedFile.SaveAs(Server.MapPath("upload/" + FileUpload1.FileName)); //fileupload1.postedfiel表示用户选择的文件 //fileuplaod1.postedfile.saveas(保存的路径)，其中saveAs是一个方法 //Server.MapPath(),表示服务器的虚拟路径 //fileupload1.filename是用户选择的文件的名称，例如"讲课备注11.doc" 第2个问题： 如何将上传后的资源以列表方式显示出来？ 答：使用数据库保存上传的资源路径。（1）表Res
大数据下的资源整合和知识共享(上)2020考试答案
大数据下的资源整合和知识共享（上） 1.本讲提到“互联网+”行动将重点促进以移动互联网、云计算、物联网、大数据等与（）相结合。（10.0分） A.金融业 B.旅游业 C.现代制造业 D.林业 我的答案：C√答对 2.2015年5月19日，经李克强总理签批，国务院印发《中国制造2025》，部署全面推进实施（）战略。（10.0分） A.全面发展 B.工业强国 C.制造强国 D.创新强国 我的答案：C√答对 3.本讲提到，“工业 4.0”是指利用物联信息系统，将生产中的供应、制造、销售信息（），最后达到快速、有效、个人化的产品供应。（10.0分） A.立体化 B.数据化
D.方便化 我的答案：B√答对 4.本讲提到，除了3“V”,大数据还有一个隐含的特征，称之为（）。（10.0分） A.价值洼地 B.价值增值 C.数据总量 D.数据更新 我的答案：A√答对 1.本讲中，大数据的3“V”特征是指（）。（10.0分）） A.vast B.volume C.velocity D.variety E.vapor 我的答案：BCD√答对 2.本讲提到，通过利用不同的云计算平台管理技术，云计算的云可分为（）。（10.0分）） A.数据云
C.私有云 D.混合云 E.电子云 我的答案：BCD√答对 1.大数据不是万能的，所以我们要将大数据方法结合传统的推理预测方法，才得到一个更加精确的结果。（10.0分） 我的答案：正确√答对 2.工业4.0称之为第四次工业革命，它是基于信息、物理融合系统，基于大数据和物联网传感器融合的系统，在生产中大规模使用。（10.0分） 我的答案：正确√答对 3.“互联网+”是互联网和传统行业融合的新形式和新业态，“互联网+”就等于“互联网+传统行业”。（10.0分） 我的答案：错误√答对 4.以纸牌屋为例，Netflix可以通过大数据分析电影题材、挑选演员、播放形式，再根据这些内容拍摄用户感兴趣的电影。（10.0分） 我的答案：正确√答对
基于LTE的D2D资源分配最优算法
Resource Sharing Optimization for Device-to-Device Communication Underlaying Cellular Networks Chia-Hao Yu,Klaus Doppler,C′a ssio B.Ribeiro,and Olav Tirkkonen Abstract—We consider Device-to-Device(D2D)communication underlaying cellular networks to improve local services.The system aims to optimize the throughput over the shared resources while ful?lling prioritized cellular service constraints.Optimum resource allocation and power control between the cellular and D2D connections that share the same resources are analyzed for different resource sharing modes.Optimality is discussed under practical constraints such as minimum and maximum spectral ef?ciency restrictions,and maximum transmit power or energy limitation.It is found that in most of the considered cases,optimum power control and resource allocation for the considered resource sharing modes can either be solved in closed form or searched from a?nite set.The performance of the D2D underlay system is evaluated in both a single-cell scenario,and a Manhattan grid environment with multiple WINNER II A1of?ce buildings.The results show that by proper resource management, D2D communication can effectively improve the total throughput without generating harmful interference to cellular networks. Index Terms—Cellular networks,device-to-device,D2D,peer-to-peer,resource sharing,underlay. I.I NTRODUCTION T HE increasing demand for higher data rates for local area services and gradually increased spectrum conges-tion have triggered research activities for improved spectral ef?ciency and interference management.Cognitive radio sys-tems[1]have gained much attention because of their poten-tial for reusing the assigned spectrum among other reasons. Conceptually,cognitive radio systems locally utilize“white spaces”in the spectrum for,e.g.,ad hoc networks[2][3] for local services.Major efforts have been spent as well on the development of next-generation wireless communication systems such as3GPP Long Term Evolution(LTE)1and WiMAX2.Currently,the further evolution of such systems is speci?ed under the scope of IMT-Advanced.One of the main concerns of these developments is to largely improve the services in the local area scenarios.Device-to-Device (D2D)communication as an underlaying network to cel-lular networks[4][5]can share the cellular resources for Manuscript received November26,2010;revised February11,2011and March23,2011;accepted April20,2011.The associate editor coordinating the review of this paper and approving it for publication was N.Kato. C.-H.Yu and O.Tirkkonen are with the Department of Communi-cations and Networking,Aalto University,Finland(e-mail:{chiahao.yu, olav.tirkkonen}@aalto.?). K.Doppler and C.B.Ribeiro are with Nokia Research Center,Nokia Group (e-mail:{klaus.doppler,cassio.ribeiro}@https://www.wendangku.net/doc/e015407163.html,). Digital Object Identi?er10.1109/TWC.2011.060811.102120 1see https://www.wendangku.net/doc/e015407163.html,/ 2see https://www.wendangku.net/doc/e015407163.html,/better spectral utilization.In addition to cellular operations where the network services are provided to User Equipment (UE)through the Base Stations(BSs),UE may communicate directly with each other over D2D links while remaining control under the BSs.Due to its potential of improving local services,D2D communication has received much attention recently[6][7][8][9][10][11][12][13][14][15][16]. The idea of enabling D2D connections in cellular networks for handling local traf?c can be found in,e.g.,[17][18][19], where ad hoc D2D connections are used for relaying pur-poses.However,with these methods the spectral utilization of licensed bands cannot be improved as D2D connections take place in license-exempt bands.Furthermore,ad hoc D2D connections may be unstable as interference coordination is usually not possible.In[20],non-orthogonal resource shar-ing between the coexisting cellular and ad hoc networks is considered.As the operations of both types of networks are independent(with independent traf?c loads),interference coordination between them considers only the density of transmitters.Recent works on D2D communication assume the same air interface as the underlaying cellular networks. In[21],the cellular resources are reused by D2D connections in an orthogonal manner,i.e.,D2D connections use reserved resources.Although orthogonal resource sharing eases the task of interference management,better resource utilization may be achieved by non-orthogonal resource sharing.In[4][5],a non-orthogonal resource sharing scheme is assumed.Cellular users can engage in D2D operation when it is bene?cial for the users or system.Further,D2D power control when reusing Uplink(UL)cellular resources,where cellular signaling for UL power control can be utilized,is addressed to constrain the interference impact to cellular operations. To better improve the gain from intra-cell spatial reuse of the same resources,multi-user diversity gain can be achieved by properly pairing the cellular and D2D users for sharing the resources[8][9][10].In[10],the resource allocation scheme over multiple cellular users and D2D users considers the local interference situations,making it possible for inter-cell interference avoidance.Interference randomization through resource hopping is considered in[11].This provides more homogeneous services among users in challenging interfer-ence environments,e.g.,when one cellular connection shares resources with multiple D2D pairs at the same time.Integra-tion of D2D communication into an LTE-Advanced network is investigated in[13][14],where schemes for D2D session setup 1536-1276/11$25.00c?2011IEEE
(完整版)政务信息资源共享交换平台建设方案设计
政务信息资源共享交换平台建设方案 2012年XX月
目录 一、建设背景 (3) 二、建设原则 (3) 三、建设目标 (4) 四、建设内容 (5) 4.1总体架构 (5) 4.2信息资源目录体系 (6) 4.2.1目录体系建设目标 (6) 4.2.2目录体系标准建设 (6) 4.2.3信息资源目录平台架构 (7) 4.2.5信息资源目录平台功能 (9) 4.2.4目录体系内容建设步骤 (10) 4.3信息资源交换体系 (11) 4.3.1交换体系建设目标 (12) 4.3.2信息资源交换平台架构 (12) 4.3.2信息资源交换平台功能 (13) 4.3.3交换体系内容建设步骤 (14) 4.4政务信息资源库 (15) 4.4.1政务信息资源库架构 (15) 4.4.2政务信息资源库功能 (15) 4.4.3资源库建设范例 (18) 五．扩展主题应用 (24) 六．建设步骤 (24) 6.1准备阶段 (24) 6.2信息资源调研阶段 (24) 6.3系统软件建设阶段 (25) 6.4内容建设阶段 (25) 6.5培训阶段 (25) 6.6验收阶段 (25) 6.7服务阶段 (25)
一、建设背景 在“信息化带动工业化”的国家战略大背景下，随着电子政务建设政策支撑环境快速发展和政府部门纵向系统日趋成熟，我县各部门间对于共享交换和信息资源的发开利用需求越来越迫切。XXX县根据中办发［2002］17号、［2004］34号等有关文件的精神和国家电子政务“十二五”规划，并结合我县实际，以科学发展观为指导，坚持以需求为导向，以应用促发展，加强电子政务建设和政务信息资源共享整合，从而增强政府监管和服务能力，提高行政质量和效率，带动全县行业、领域和社会信息化建设，加快我县振兴，促进国民经济持续快速健康发展和社会全面进步。 二、建设原则 1、统一规划，分步实施 在统一规划的前提下，分阶段分解建设任务，各参建单位按照规划的要求，明确建设目标、重点和步骤，分工负责，分类指导，分步实施，分层推进。2、需求主导，讲求实效 从应用需求出发，紧密结合政府职能转变和管理方式创新，突出重点，强化应用。以应用带动电子政务和信息资源整合的工作的推进 3、统一平台，资源共享 充分利用现有的全县电子政务网络平台，加强资源整合，促进互联互通，实现信息共享，使有限资源发挥更大的效用。 4、统一标准，保障安全 标准先行，用标准规范电子政务建设，促进信息资源整合、流程再造和应用系统整合；正确处理发展与安全的关系，综合平衡安全成本和风险，把强化技术手段与健全管理体制紧密结合起来，建立健全全县电子政务标准规范体系和安全保障体系。
云计算的资源分配现状
云计算的资源分配现状 云计算的资源分配是指在一个共同的云环境中使用者根据一定是使用规则来调度资源的过程。目前云计算资源调度的研究主要集中在三个方面: (1)人工智能算法 人工智能算法是指以学习的方式对解空间进行人工搜索，以减少任务的平均时间，提高资源的利用率 (2)云计算的负载均衡 不同的用户对云计算有不同的需求，云计算必须满足服务器网络带宽、吞吐量、延迟和抖动等负载需求。因此，在进行云计算时，更应该注意云计算的负载均衡。 (3)云计算的能耗管理 数据中心作为云计算的中心，能耗过大，不仅浪费电能，还会降低系统的稳定性，影响环境。因此，加强云计算能耗管理也是云计算资源配置中需要解决的重要问题。 本章小结 本章对于多目标优化、遗传算法、SPEA-II做出了详细的基础知识介绍，通过数学模型以及流程图对于该问题进行了解析分析。通过此小结可大致了解多目标问题的优劣端以及如何利用遗传算法和SPEA-II进行修饰，避免局部最优解，从而获得优秀的目标最优解集。
基于改进 SPEA-II动态资源配置 资源调度方案的实现 通过分组编码和多目标优化模型可知，根据遗传算法在交叉和突变阶段提出的TMR，便可以指出基因的类型及其在染色体上的分布。选择已经分层的Pareto前沿时，使用预筛选操作来维持种群分布的均匀性。当达到一定的进化代数时，上一代种群中平均功耗最低的个体被输出。 MOGAISP可以采用自适应概率突变和交叉概率突变进行遗传操作,以帮助我们防止遗传算法进化的过程陷入局部停滞的状态,保持遗传算法种群的多样性,提高了遗传算法进化和全局最优搜索的速度和能力。MOGAISP选择机制选择EFP种群的最优个体，使EFP种群中的个体尽可能均匀地分布在多维目标空间中，从而减少进化过程中陷入局部最优的可能性。通过改进轮盘赌注的选择策略从EFP种群中随机选择遗传的子代，尽量避免仅选择较优个体进行遗传而陷入局部最优解的缺陷，以提高遗传个体的种群多样性和加速遗传算法的全局搜索能力和速度进化。 TMR规则 物理节点上的虚拟机迁移过程可以被视为一个包装的问题，将项目（虚拟机）的合理的装入每个盒子（物理节点）中，项目的大小代表了资源使用的虚拟机大小,容量的盒子是使用资源物理节点阈值,每个负载都会对应一个资源调度方案。本设计所涉及的物理节点的资源为CPU，因此建立了虚拟机的折线图如图3.1所示。 从图中可以看出，横坐标为CPU，纵坐标为容量，即资源大小。由于不同的应用程序和不同类型的资源对所需要的应用程序的需求不同，当4台虚拟机在物理节点上运行时，不同纬度的节点资源呈现下降趋势，但下降程度不同。 多目标优化模型的建立 物理节点 物理网络节点是一个连接到网络的有源的电子设备，是可以通过通信通道发送、接收或转发信息。而在优化模型中，物理节点的多少是一个重要的参考点。本文用0，1的二维矩阵来模拟单个物理节点在每一时刻每一个虚拟机的位置。0表示每一时刻每一
D2D网络中基于强化学习的路由选择与资源分配算法研究
D2D网络中基于强化学习的路由选择与资源分配算法研究 随着通信网络的发展,终端直连通信技术(Device-to-Devic,D2D)被广泛关注,它的应用将满足用户日益增长的流量需求。然而,D2D技术的引入使得蜂窝网络内部的干扰冲突加剧,用户难以满足服务质量(Quality-of-Service,QoS)的需求。 一些传统算法基于网络“抓拍”信息可以计算得到各采样时刻的网络控制策略,却难以适应复杂多变、高度动态的网络环境。因此,本文着手于动态环境下的D2D网络中的通信问题进行了深入地研究,并结合正在兴起的机器学习技术,提出了更加智能化的解决方案。 在本文中我们将分别研究“多跳D2D网络”与“D2D直连通信”两类D2D应用场景的通信问题,提出了在两种场景下基于强化学习的在线学习方法,从而解决多跳网络中的路由问题与D2D直连网络中的资源分配问题。而随着问题复杂程度的增加,强化学习算法也相应由浅入深。 在路由问题中,因问题复杂程度较低,我们利用传统强化学习算法中的值迭代算法求解,而在资源分配问题中因问题规模变大,本文依次提出了基于深度Q 学习(Deep Q-Learning,DQN)的资源分配算法和深度确定性策略梯度(Deep Deterministic Policy Gradient,DDPG)的资源分配算法分别解决了问题中状态空间连续与动作空间连续的问题,而这两种算法都是深度强化学习(Deep Reinforcement Learning,DRL)中的经典算法。在多跳D2D网络路由问题中,我们考虑了三类随网络动态变化的QoS指标,并利用值迭代算法求解,同时提出了分布式的强化学习算法解决了集中式算法学习周期过长的问题。 仿真发现,在动态环境中,所提算法在性能与时间复杂度方面相较于传统算
云计算中业务配置的纳什均衡
按需服务，资源利用率比较高。冗余可靠性比较好，维护成本比较低，可扩展性能比较强。 1.Target： 多SaaS竞争IaaS资源，提出一个资源分配和管理的即时性高效算法，此算法基于纳什均衡的博弈论思想。 2.Basic knowledge： (1)云计算应用在一个订购模式上收费，终端用户按使用时间付费，而不是一次 性购买终身使用。 (2)互联网应用服务GAE（基于PaaS）和EC2（基于IaaS，用户租用云电脑实现 应用）。 IaaS将硬件设备等基础资源封装成服务供用户使用，用户相当于在使用裸机和磁盘，但用户必须考虑如何才能让多台机器协同工作起来。 PaaS提供应用程序的运行环境，自身负责资源的动态扩展和容错管理，用户应用程序不必过多考虑节点间的配合问题，但是用户的自主权利降低，必须使用特定的编程环境并遵照特定的编程模型。 SaaS针对性更强，它将某些特定应用软件功能封装成服务只提供专门用途的服务供应用调用。 (3)终端用户从基础设施获得收益而不需要实现和维护，也就是用时付费。 (4)SaaS的利润=从SLA下获得的收益—IaaS的花费 SaaS之间竞争IaaS提供的资源，而IaaS也想最大化收益去提供资源。(5)IaaS提供多个业务，每个业务对应一个应用，每个应用需要资源。 SaaS从IaaS中获得服务，与终端用户签订协议(合同)。 一个VM实现一个应用，多个VM可以并行实现同一个应用，分享工作量。 (6)SaaS每个小时都预测WS工作量来分配VMs，估计VM的性能（平均响应时 间Rk），维持一个VM队列实现WS类。 IaaS提供三种VMs：flat VM、on demand VM、on spot VM。 (7)maxθp（SaaS）与maxθk（IaaS） (8)从NASH equilibrium concept，SaaS和IaaS采取一种策略：如果只改变自己的策略不能够提高或者降低他们的收益。 个人思考：文献的思想其实还是基于云的基础思想，在此基础上针对多SaaS问