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ONSP Parallel Overlay Network Simulation Platform 1

ONSP: Parallel Overlay Network Simulation Platform1

Yinghui Wu, Ming Li, Weimin Zheng

Department of Computer Science and Technology, Tsinghua University, Beijing, China {wuyinghui97, lim01}@https://www.wendangku.net/doc/6611227243.html,, zwm-dcs@https://www.wendangku.net/doc/6611227243.html, Abstract. In research of overlay networks, simulator takes a very important

role. However, popular simulators, such as ns and PlanetLab can’t meet the

scale and performance requirement of overlay research. Although it is

necessary for overlay researchers to observe activities of million nodes, current

simulators can not give such simulation result. To improve the overlay network

research efficiency, we design and implement ONSP, a novel parallel overlay

network simulation platform, which provides parallel discrete event simulation

of overlay networks on high performance cluster. With this tool, we are able to

build overlay network simulator in large scale easily and test it in short time.

The test result proves that ONSP can well address the requirement of

performance and scalability.

Keywords

overlay network, discrete event simulation, parallel

1.Introduction

Recently, the lack of suitable simulator greatly encumbers the research of overlay networks. Overlay network is the application-layer overlay on top of the existing internet routing substrate. This layer conceals the details of internet routing, and helps the application on top of it identify and communicate with each other in a large scale. Verifying the protocols prior to their deployment is a fundamental step in research of overlay network protocols. Because the overlay network tends to be large, heterogeneous system with complex interaction between a large numbers of internet nodes, testing it in a real environment is nearly impossible. Most of the time, we employ a network simulator to evaluate the overlay network protocol or application in a controlled environment. There are some simulation tools or environments, such as ns and PlanetLab [4], and they work well on a small scale. But with the advance of overlay network research, protocols today sometimes deal with nearly one million internet nodes, and it is impossible to use the old simulators on such a large scale. 1This research is supported by The National High Technology Research and Development Program of China (G2001AA111010) and Chinese National Basic Research Priority Program (G1999032702)

2 Yinghui Wu, Ming Li, Weimin Zheng

PlanetLab is itself a small scale live network environment. The simulator ns addresses more details about the network layer, so it faces scalability limitation beyond a few hundred nodes. So the research of new large scale simulator for overlay network has attracted a lot of interests recently.

Figure 1 depicts a generic overlay network simulator in our opinion. The simulate-engine is the core of simulation, which drives the execution of the simulator. Design and implementation of this layer determines the performance of whole simulation system. The upper layers are user interface, designed for the convenience of protocol implementation. It could be simply the API interface exported from simulate-engine, such as simulator included in FreePastry [8]; or it could be a complete framework, such as MACEDON [1]. How these layers are designed determines the usability of this simulator. Currently, a lot of work has been done on the upper layers, but little attention has been paid to the simulate-engine itself, so even though new simulators accelerate the implementation of special protocol, they haven’t eventually eliminate the limitation of scale and performance.

Fig. 1. Layers of a generic overlay network simulator

To address this problem, we plan to build our overlay network research environment from the bottom in two steps: (1) build a high performance simulator with coarse interface as the simulate-engine; (2) build complete development framework based on the simulator. ONSP is production of the first step. It is a parallel event driven simulator, which utilizes the parallel nature of high performance cluster to address the limitation of performance and scalability. A parallel protocol is carefully designed to improve the parallelism and make use of all the available resource in the cluster. As a simulator built for overlay network evaluation, it has some extra advantages:

?ONSP omits the low level details of internet routing substrate, because overlay networks conceal the details and pay little attention on them. This simplifies the implementation and greatly improves the performance.

?The parallel protocol and synchronization strategy is carefully designed. With the increment of resources in cluster, this platform can serve larger scale and achieve higher performance.

?ONSP can use most of the topologies, and it is specially optimized for widely used transit-stub topology [2] [3].

With the test result of ONSP, we believe we have well addressed the limitation of performance and scalability in overlay network simulation. For the convenience of protocol implementation, we also include some high level components in ONSP now,

ONSP: Parallel Overlay Network Simulation Platform 3 but they are far from enough; we will build a complete overlay network research environment on ONSP at the second step.

The rest of this paper is organized as follows. We first introduce parallel discrete event simulation in section 2. Then, we detail our design in section 3. In section 4 we give the experimental results, and then we conclude in section 5.

2.Overview of and Parallel Discrete Event Simulation

Parallel discrete event simulation refers to the execution of single discrete event simulation program in parallel computers [9]. A discrete event simulation model assumes the system simulated only changes state at discrete points in simulated time. The simulation model jumps from state to state upon the occurrence of events. In a serial simulation system, there is a global clock and all the events are executed in serial order of timestamp.

Serial discrete event simulation suffers from the limitation of performance, so for a long time, parallel discrete event simulation has attracted a considerable amount of interests. To find out the dependency of events, the states of system are first divided into some distinct subsets, which we called Logic Process (LP). Any event can only affect the states of one LP, and LPs only affect each other by sending timestamped messages. So we need not process the events in whole system one by one, instead, we only process the events in one LP in serial order. This is the fundamental rule of parallel discrete event simulation, which we called Local Casuality Constraint. Richard M. Fujimoto [9] gives the definition of Local Casuality Constraint: a discrete event simulation, consisting of logical processes (LPs) that interact exclusively by exchanging timestamped messages, obeys the local casuality constraint if and only if each LP processes events in non-decreasing timestamp order.

There are a lot of parallel simulation protocols which obey the Local Casuality Constraint, and most of them fall into two categories: conservative and optimistic. In conservative protocols, any LP processes one event only when it determines that there is no other event with a smaller timestamp could be sent to itself. Though most of the events are processed ahead of global time, the Local Casuality Constraint is preserved at any point of simulated time. On the contrary, in optimistic protocols LP can execute unsafe events ahead of time and it rollbacks when error occurs. Optimistic protocol can utilize more parallelism than conservative protocol, but it also introduces more cost of rollback. Which one of these two protocols is better is mainly determined by the given application.

When considering overlay network simulation, we prefer conservative protocol for two reasons: first, it is impossible for ONSP to rollback without the help of applications running above it when error occurs; second, we believe that with the optimization based on network topology and carefully designed synchronization strategy, conservative protocol can also offer a high performance.

4 Yinghui Wu, Ming Li, Weimin Zheng

3.Simulation Strategy

3.1.Discrete event simulation of overlay network

Nodes in overlay network maintain their states regarding their local activities and messages from their neighbors. The timer to schedule local activities and the messages are all fired or transmitted at discrete points of time, so any nodes only change their states at discrete points. As we have described before, such a system can be succinctly described in discrete event simulation model. In this model, messages and timers are mapped to events, and states of overlay node are mapped to LPs. Events such as message reception, completion of scheduled timers, and occurrence of user commands and sent to LPs and trigger the overlay protocol to perform protocol actions on the corresponding overlay nodes. Thus, the state of whole system is changed from one state to another. By analyzing the discrete system states and their changings, it is sufficient to capture the intricacy of overlay network.

3.2.Architecture

Figure 2 shows the architecture of ONSP. All the overlay nodes in system, which are mapped to LPs as described before, are firstly partitioned into several parts. To maintain load-balancing, the numbers of nodes in all the parts are nearly equal. Then each part is simulated on one simulator. Simulator is the manager of events. It manages local events in an event-queue, and dispatches events, such as timer events or message events to their destined LP (overlay node) and triggers the execution of overlay protocol at the specified time; during the execution of overlay application, new events are generated, some are rescheduled timer and some are messages sent to other nodes. Simulator conceals the distribution of overlay nodes, and the overlay application can sends messages to any other overlay nodes by invoking the interface of simulator. Simulator collects all the generated events, insert events for local nodes into local event-queue and send events for other nodes to their destined simulator. Simulator is a standard parallel application written in C++. They communicate with each other by message-passing using MPI. Simulators are deployed to workstations. Multi simulators can be deployed to one SMP workstation to utilize multi-processors. To reduce the network latency, workstations are connected with high bandwidth, low latency networks, and here we use Gigabyte Myrinet [16].

We regard simulator as the minimal unit of parallelism; it manages all the local events and processes them in serial order. The execution order between simulators is defined by the conservative parallel protocol, which strictly obeys the Local Casuality Constraint. The synchronization strategy in this protocol is carefully designed in order to reduce the synchronous ratio and improve the speedup. In the simulation, there are two assumptions to simplify the design:

?Overlay node is the minimal unit of logic. Details within node are omitted. There are no other smaller simulation entities, that is to say, it is impossible to schedule events for a hard disk, or something else.

ONSP: Parallel Overlay Network Simulation Platform 5 ?In overlay networks, message transferring latency holds a majority of processing time, and this is also the most important factor affecting the behavior of protocols.

We pay no attention on the message processing time in each node and regard this

Fig. 2. Architecture of ONSP

3.3.Parallel Protocol

ONSP designs and implements a simple conservative protocol. Figure 3 depicts the working flow of simulator in this protocol. Simulators are independent when finding and processing safe events; they only communicate in the synchronization period,

Fig. 3. Working flow of simulator in parallel protocol

6 Yinghui Wu, Ming Li, Weimin Zheng

The key problem in conservative protocols is to determine the safe events. Before explaining this in detail, we first present a prerequisite restrict on topology: the network topology should obey the “triangle inequation”:

()()()c a latency c b latency b a latency V c b a →>→+→∈?,,, (1) With this restrict, we can predict at which time one node could affect other nodes from its next execution point and network latency. “Triangle inequation” is tenable most of the time in internet routing, so this restrict does little harm to the evaluation of overlay networks.

Then we explain the way how simulator determines the safe events. In ONSP, each simulator maintains a moving “safe window” of time. All the events with timestamp falling into this window are believed to be safe, that is to say, the simulator will never receive other events which could fall into this window. The lower bound of the window indicates when the simulator should start event processing from. It is defined as the local simulation time of the simulator. Below is the definition of local simulation time of simulator S i , in which T(e) stands for the timestamp of event e:

()(){}i j j i S e e T S LST ∈=,min (2)

The upper bound of the window indicates when the simulator could be affected by other new events and must stop to synchronize. To give the definition of upper bound, we first define the latency between two simulators as follow:

()(){}j y i x y x j i S LP S LP LP LP latency S S latency ∈∈?=,,min , (3) As we have indicated, each LP in ONSP corresponds to one simulated network node, so the latency between LPs is the same as latency between network nodes. With equation 3, it is easy to calculate the inter-simulator latency given the network topology and all the nodes in each simulator.

With the definition of latency between simulators, we can give the definition of upper bound of safe window as follow, in which τ(S i ) stands for the upper window bound:

()()(){}i k i k k i S S S S latency S LST S ≠?+=,min τ (4)

All the events before τ(S i ) are believed to be safe, the reason is: if any LP in any other simulator S k sends events to S i , the events could not affect the state of S i before τ(S i ) because these events must occur after LST(S k ) and they have at least transfer latency of latency(S k , S i ). So simulator S i is free to process any events with timestamp lower than τ(S i ) without violating the Local Casuality Constraint.

Conservative protocols are often criticized to be over pessimistic. In equation 4, though S k could affect S i at τ(S i ), it seldom happens actually. So conservative protocols can only exploit a little part of all parallelism in the simulation system. But we think this is not a problem in large scale network simulation. The reason is that with the increment of network scale, more events will be generated in a small period, and the processor is kept busy even with small safe window.

ONSP: Parallel Overlay Network Simulation Platform 7

To calculate the upper window bound, any simulator must know the local simulation time of all other simulators. The simulators gain this information by synchronization. The detail of synchronization is introduced in the next part.

3.4. Synchronization strategy

One key contribution in ONSP is the one-step-synchronization strategy. The ONSP system is running in synchronous manner during synchronization, so the performance of synchronization greatly affects the speedup. During synchronization, the simulator must finish two tasks:

1. Send outgoing events to their destined simulators;

2. Recalculate the LST after all new events have been received, then broadcast it to all other simulators;

Task 2 can not be started before finishing of task 1, because simulator can not calculate LST before all new events received. So in normal strategy, any simulator must communicate with each other twice during synchronization.

The key of one-step-synchronization is that instead of broadcasting the LST, we send some premature data along with the events to other simulators, and the LSTs of all simulators are calculated out by each simulator. We mark the local simulation time after last synchronization of simulator S i as LST ia , the minimal timestamp of all events in simulator S i before this synchronization as LST ib , and the local simulation time after this synchronization as LST ic . Then, according to the definition of local simulation time, LST ic can be calculated with equation below:

(){}{}{}i k e T LST LST ki ib ic ≠?=min min ,min (5)

In this equation, min{T(e ki )} represents the minimal timestamp of all the events sent from simulator S k to simulator S i . Though LST ic can not be determined before synchronization, LST ib can be calculated by S i and min{T(e ki )} can be calculated by S k . All the simulators can send these values together with the events in one request to other simulators, and along with the completion of this data exchange, each simulator get enough information for calculating other simulators’ LST. So the synchronization is finished in one request. This improvement greatly reduces the cost of synchronization, especially on a high latency network.

3.5. Partition policy

One key problem in parallel simulation is how to partition all the nodes among simulators. In ONSP, we partition the nodes according to the latency between nodes and try to aggregate near nodes to same simulator.

The first consideration about such a partition policy is to optimize ONSP for the locality-prone overlay network protocol. Though overlay networks conceal the detail of internet routing substrate, they often utilize the locality to accelerate message transferring. For a protocol with locality, nearer neighbor is more preferable as the next hop of message, so if ONSP partitions near nodes to the same simulator, more of the events should be sent within the simulator and less of them need to pass the

8 Yinghui Wu, Ming Li, Weimin Zheng

boundary of simulator. This will reduce the throughput on network and save the time of synchronization.

The second consideration concerns the performance of conservative parallel protocol. In PDES research, performance of conservative protocol is determined by the lookahead ability; in ONSP, it is represented by the upper window bound. Refer to equation 4, the upper window bound is mainly determined by the latency between simulators. Considering the whole simulation system, the parallelism exploited is related to the minimal of latencies between simulators. As we partition near nodes to same simulator, we also increase the latency between simulators. With a higher inter-simulator latency, we can reduce the synchronization frequency and improve the performance.

Though this partition policy is easy to express, it is some difficult to achieve. For a generic topology, there are some universe partition algorithms to gain high inter-simulator latency. For example, we can increase a threshold of latency continuously and congregate the nodes with smaller latency until we find a suitable partition. Unfortunately, the complexities of these generic algorithms are mostly too high for large scale network. For special topology, such as widely used transit-stub topology, there could be a simpler policy.

Transit-stub topology [2] is one type of hierarchy topology. Figure 4 shows the structure of a transit-stub graph. Nodes in transit-stub topology are categorized into two types: stub nodes and transit nodes. Stub nodes are the edge nodes and transit nodes are the router nodes. Stub nodes aggregate into stub domains; transit nodes aggregate into transit domains. Stub nodes in different domains can not directly communicate with each other and messages should pass through transit node as gateway, so the inter-domain latency should be larger than in-domain latency most of the time. It should be reasonable to partition all the stub nodes in a same domain into the same simulator, so partition on this topology can be simply done on the level of domains. Because each stub domain is at least connected to one transit node, so we can simply partition the transit nodes first and distribute the stub domains according to the transit nodes they are connected to. This policy is easy to implement and work with a quite low complexity, but it is too simple to achieve the highest latency.

Fig. 4. Example of transit-stub topology

ONSP: Parallel Overlay Network Simulation Platform 9 Increment of inter-simulator latency will accelerate the locality-prone application, but for ONSP, higher latency does not always lead to higher performance. The first reason is that in large scale network simulation, processor is not the only factor affecting the performance; memory and network bandwidth are also very important factors. With a higher inter-simulator latency, simulator can get a larger safe window and process more events before next synchronization; but at the same time, more pending events are generated and they will engage more memory and buffer on network adaptor, this could bring a heavy burden to the system and greatly increase the cost. Experiments later will prove that with higher latency, the overall performance does not improve as expected, and sometimes it drops. The second reason is that in large scale network simulation, even with a small safe window, there are still a great number of events fallen into this window, so we can still keep the processor busy and gain enough speedup upon parallel simulation. Anyway, we suggest set the latency to a proper level according to the simulation scale and available resources.

3.6.Management of Events Queue

The simulator uses a calendar queue [13] as the data structure of events queue. The calendar queue consists of a list of buckets and each bucket stands for a period of time. Events are placed into buckets according to their timestamps. There are two operations on calendar queue: insert events and pop out the earliest event. The cost of each operation is O (1). Calendar queue is a high performance priority queue, and in ONSP, the period of each bucket is simply set to 1. This could be a waste of memory resource when there are no enough events in the queue, but it seldom happens in large scale network simulation.

4.Evaluation

For the limitation of time, we haven’t ported any available overlay network to ONSP; instead, we use the sample application “relay”, which we built along with the ONSP, to evaluate the performance. Message-passing is the fundamental operation in overlay networks, and it is simulated by this application in a simple way. The pseudo code below represents the event logic of “relay” :

OnMessage(Message msg) {

If (msg->hops < PROTO_HOPS) {

neighbor = SelectNeighbor();

msg.hops = msg.hosp+1;

Network->Transfer(msg, local, neighbor);

}}

OnTimer(Timer t) {

Message msg = new Message();

neighbor = SelectNeighbor();

msg->hops = 0;

Network->Transfer(msg, local, neighbor);

}

10 Yinghui Wu, Ming Li, Weimin Zheng

We also defined “ratio of locality” in this application. Locality affects the way one node selects its neighbors. The ratio of locality is defined as how much percent of their neighbors are within the same simulator.

ONSP and the sample application are implemented with C++ language. All the experiments are performed on a cluster connected by 2 Gigabytes Myrinet. Each workstation in the cluster consists of 4 700MHz Xeon CPUs and 1G memory. In this experiment, the largest topology scale we used is 100,000 nodes; this is due to the limitation of gt-itm [14], and we believe current evaluation result is sufficient to evaluate the performance of ONSP.

Fig. 5. Speedup of different scale overlay network simulated with different parallel simulator

count.

Fig. 6. Speedup of different scale overlay network simulated with different setting of latency

between simulators

ONSP: Parallel Overlay Network Simulation Platform 11 Figure 5 shows the speedup of different scale overlay network simulated with different simulator count. In these experiments latency between simulators are set to the max value calculated on the node partitions, and the locality ration is set to 50%. The result shows an acceptable overall performance of ONSP. The performance of 2k scale network simulated with 7 parallel simulators is one exception in Figure 5; it drops sharply. The reason of this phenomenon is the greatly drop of latency between simulators, and this is caused by the partition way of nodes. In figure 6, we plot the speedup of different scale under different setting of latency, and the curve of 2K gives the reason of sharp drop in figure 5. As we have mentioned, though increment in latency between simulators can reduce the frequency of synchronization, but it can not always improve the performance. Curves of 20K, 50K and 100K all prove this. The reason is that when one simulator continues to work for a long period, it will generates a lot of pending outgoing events, and this could lead to a great burden on memory and buffers on network adaptor. We suggest set the latency according to the scale and the available resources. In figure 7, we evaluate the performance with different locality settings. The experimental result proves that the overall performance of simulation gains obvious improvement when the application level protocols exploit more localities in topology.

Fig. 7. Speedup of different scale network topology simulated with different locality ratio

5.Conclusion and Future work

In this paper we present ONSP as a simulation platform for large scale overlay network. As the first step in solving the problem of overlay network simulation, we pay more attention on the performance and scale ability. We make use of parallel

12 Yinghui Wu, Ming Li, Weimin Zheng

discrete event simulation and implement our system based on high performance cluster. The result proves that this architecture can provide an acceptable performance for large scale network simulation. Though we have provided some useful tools to accelerate the implementation of special application level protocols, it is still far from enough. In the future, we will change our focus on setting up a complete framework for overlay network study and implementation based on ONSP.

References

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[8]Rice University, FreePastry, 2004, https://www.wendangku.net/doc/6611227243.html,/CS/Systems/Pastry/FreePastry

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[15]The Message Passing Interface (MPI) standard. https://www.wendangku.net/doc/6611227243.html,/mpi/.

[16]Myricom, Inc. Myrinet Overview. https://www.wendangku.net/doc/6611227243.html,/myrinet/overview/index.html

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1.1.2、增加操作员单击【新建】按钮，画面如下：输入用户名称、初始密码、选择用户权限，可对用户进行适当描述，按【保存】后就点【退出】，就完成了新操作员的添加，效果如下图。

1.1.3 删除操作员选择要删除的操作员，单击【删除】按钮。 1.1.4 修改操作员选择要修改的操作员，单击【修改】按钮，可对操作员作相应修改，修改后需保存。 1.1.5 用户操作权限选择要修改的操作员，单击【修改】按钮，出现以下画面，点击【用户权限】栏下的编辑框，出现对号后点【保存】，该操作员就有了此权限。 1.2数据初始化 1.2.1进入界面单击【系统设置】，选择其中的【数据初始化】，画面如下：

1.2.2数据清除选择要清除的数据，即数据前出现对号，按【确定】后点【退出】，就可清除相应数据。 1.3 修改我的登录密码 1.3.1进入界面单击【系统设置】，选择其中的【修改我的登录密码】，画面如下： 1.3.2密码修改输入原密码、现密码，然后对新密码进行验证，按【确定】后关闭此窗口，就可完成密码修改。 1.4 切换用户 1.4.1进入界面单击【系统设置】，选择其中的【切换用户】，画面如下：

对等网络(P2P)总结整理解析

对等网络(P2P 一、概述 (一定义对等网络(P2P网络是分布式系统和计算机网络相结合的产物,在应用领域和学术界获得了广泛的重视和成功,被称为“改变Internet的新一代网络技术”。对等网络(P2P:Peer to Peer。peer指网络结点在: 1行为上是自由的—任意加入、退出,不受其它结点限制,匿名; 2功能上是平等的—不管实际能力的差异; 3连接上是互联的—直接/间接,任两结点可建立逻辑链接,对应物理网上的一条IP路径。 (二P2P网络的优势 1、充分利用网络带宽 P2P不通过服务器进行信息交换,无服务器瓶颈,无单点失效,充分利用网络带宽,如BT下载多个文件,可接近实际最大带宽,HTTP及FTP很少有这样的效果 2、提高网络工作效率结构化P2P有严格拓扑结构,基于DHT,将网络结点、数据对象高效均匀地映射到覆盖网中,路由效率高 3、开发了每个网络结点的潜力结点资源是指计算能力及存储容量,个人计算机并非永久联网,是临时性的动态结点,称为“网络边缘结点”。P2P使内容“位于中心”转变为“位于边缘”,计算模式由“服务器集中计算”转变为“分布式协同计算”。

4、具有高可扩展性(scalability 当网络结点总数增加时,可进行可扩展性衡量。P2P网络中,结点间分摊通信开销,无需增加设备,路由跳数增量小。 5、良好的容错性主要体现在:冗余方法、周期性检测、结点自适应状态维护。二、第一代混合式P2P网络 (一主要代表混合式P2P网络,它是C/S和P2P两种模式的混合;有两个主要代表: 1、Napster——P2P网络的先驱 2、BitTorrent——分片优化的新一代混合式P2P网络 (二第一代P2P网络的特点 1、拓扑结构 1混合式(C/S+P2P 2星型拓扑结构,以服务器为核心 2、查询与路由 1用户向服务器发出查询请求,服务器返回文件索引 2用户根据索引与其它用户进行数据传输 3路由跳数为O(1,即常数跳 3、容错性:取决于服务器的故障概率(实际网络中,由于成本原因,可用性较低。

多文件夹的自动同步和各向同步工具

多文件夹的自动同步和各向同步工具出处：小建の软件园作者：佚名日期：2008-06-25 关键字：同步对于经常需要备份文件，同步文件的网友，Allway Sync 可谓不可多得，虽然不能激活其专业版，对文件数量多和经常性的同步操作可能会超过免费版的限制，不过对于一般文件数量不多同步操作可以完全满足，Allway Sync 使用相当简单，多种同步方式能满足你不同需求。对重要文件进行备份是文件恢复最好的方法，而 Allway Sync 可以简化你许多备份的过程，能实现自动备份，如果你“胃口”不大，免费版应当已经可以满足。下载地址：https://www.wendangku.net/doc/6611227243.html,/soft/23495.html Allway Sync 可以进行自动同步，可以对的文件/文件夹进行筛选，只备份需要的东东。

Allway Sync 备份方式介绍 - 同步方式有源文件夹同步和各向同步两种方式： 1、源文件夹同步方式将以一个文件夹为基准，删除或覆盖其余文件夹与源文件相比较不相同的文件。 2、各向同步方式则自动将更新的文件覆盖几个同步文件夹中的旧文件。软件带有一个小型数据库，监视每次更新后的文件状态。如果在一次同步之后，你删除了同步文件夹中某些文件，它在同步的时候将其它的几个文件夹的副本也删除，而不会将不需要的未删除文件重复拷贝到已更新的文件夹。由于软件自己会对文件进行删除和覆盖，它提供了使用回收站进行文件备份的措施，使用者可以在不慎执行错误的同步动作之后，从回收站将错误删除或覆盖的文件找回来（默认禁用该功能，请到软件选项处激活相应设置）。主程序在 AllwaySync＼Bin＼里面，此为多国语言版，在语音选项那里选择中文即可。不过退出的时候会有错误提示（貌似没影响？）

博客系统需求分析报告1

系统需求分析和概要设计 1 系统需求分析 1.1 开发背景过去很多人都喜欢写文章写日记以及交流自己的文章和作品，以求实现相互间的沟通、展现自己的才华和让别人了解自己的想法观点。现在的网络已经成为人们生活中不可或缺的一个元素，所以自然而然诞生了博客这样一个新兴事物，它不仅仅能取代前面所说的功能，还能加入图片，而且使得作者更能无所拘束地生动地写出自己想写的，旁人也能非常便捷地阅读并且加以评论，并且它还能作为展示个人个性的窗户。个人博客现在已经成为很多人生活中必不可少的一个部分，方便了人与人之间的沟通和交流。 1.2 系统实现目标概述基于个人博客以上的特点，本系统要实现个人博客的主要基本功能有主界面，博客用户登录发表文章（心情、日志），用户登录/退出，游客发表评论，分页浏览文章和评论等。这里其中比较主要的是区分了个人博客用户和游客。博客用户可以在任何时候写下自己的主张，记录下自己的点点滴滴。而游客主要的权限是阅读博客所有注册用户写的文章，阅读后可以发表评论和留言，还可以分页浏览所有注册用户上传的图片。以上是个人博客的系统功能目标，当然由于个人博客的网络流行特点以及个人个性的展示，还适当要求界面比较漂亮轻快，直观便捷，操作方式简单以及人性化。 1.3 系统功能需求根据对系统的特点和应用的分析，可以得到本系统主要有如下功能：（1）登录这部分功能又分为用户登录、用户退出两个部分。登录：主要用于验证博客网站用户信息的真实身份，以便对博客网站进行管

理和维护。通过系统管理员写入的用户名，密码登录到网站。网站检测用户的用户名，密码并给予其相应的权限对博客网站进行操作。用户退出：已经登陆的用户可以退出，释放自己所占有的各种信息资源。（2）文章管理文章管理主要有文章的发表、查询、浏览、评论和删除功能。博客的系统管理员除了可以查询、浏览和评论文章外，还可以对系统中的所有文章以及评论进行修改、删除操作。这些维护和管理拥有最高权限，并且系统自动更新在服务器端数据库中的数据。文章的发表：博客用户可以发表自己的文章，文章包括主题、正文、表情、图片等信息，作者通过各种元素来展示自己的想法和思想。系统接受这些信息并且存储在服务器端的数据库中。文章的删除：博客用户可以删除自己已经发表的文章内容和各项信息，系统自动在服务器端数据库中删除这些记录。文章的浏览：游客和博客用户根据所获得的用户权限获取服务器端数据存储的各篇文章并且浏览阅读文章的所有信息，包括标题、正文、表情、图片以及其它读者的留言评论。文章的评论：文章的读者可以评论和回复所阅读的文章，发表自己的看法。系统自动将这些评论存储在服务器端的数据库中，并且可供博客作者以及其它读者浏览。文章的查询：博客用户可以按文章题目或作者来查询想要查的文章。文章中还可能包含一些图片视频等多媒体,所以文章管理中还包含了网站中媒体的管理。媒体管理有添加，浏览、删除和查询功能。博客用户可以添加自己喜欢的图片或视频等，还可以查询和浏览系统中的所有媒体信息。游客只能浏览博客系统中的媒体信息。系统管理员拥有以上的所有权限，除此之外还可以删除媒体信息。（3）博客管理员管理博客管理员可以添加、删除新用户，用户的角色又分为订阅者、作者、编辑、投稿者、管理员。还可以对博客主页的外观、博客使用的插件、工具进行添加、删除、设置。

博客作用

1.过滤信息在这个网络信息泛滥的时代，网上的信息太多、太杂、太乱，学习者无法判别哪些信息是有价值的，哪些是重要的。教师可以通过博客将经过过滤过的信息传递给学生，而学生也可以通过博客将信息传递给他的伙伴。通过浏览别人的博客日志，知识获取的效率将得到很大的提高。 2.提供学习的丰富情境通常的教辅网站，只是提供一些参考资料的链接，而博客则提供更多的评价，更广泛的背景资料。有一些学者通过博客日志反映他们对某些问题的认识，开始对于这些问题的看法可能也是粗糙的，但是他们将这些思想表达出来，然后在博客上发表后续的看法。在这一过程中，专家可以将最近看了哪些书，读了哪些人的文章，听取了哪些意见都通过博客方式表达出来。这样，阅读者了解的不仅仅是专家静态的、目前的观点，而重要的是可以把握专家思想的流程。同样，这一方式对于学生来讲也是有效的，学生的博客日志可以反映出他们在学习过程中产生的问题、关于问题的想法与思路、问题的解决过程，使得教师可以更有效地了解学生的学习状况。 3.提高学生的媒体文化水平博客（blog）的个人化使得博客们（blogger）在信息发布的过程中，要采用最适当的方式对信息进行过滤与说明，使得他的博客日志能够为更多的人接受，使得他的思想和资源为更多的人所了解。与传统BBS相比，博客日志具有更强的规范性，博客们具有更强的自律性。由于博客一般是由个人或小组拥有的，通常具有共同的主题，所谓敝帚自珍，所以在博客的世界中，很少出现在BBS中常见的不负责任的"胡说八道"。 4.鼓励参与者发表自己不同的观点博客的模式是平等的，博客更看重的是参与的过程而不是结果。对于教师或书本上的观点，学生可以通过博客的方式发表他对于这些问题的理解，博客并不要求意见的统一，但要求意见的针对性和独立性。另外，在课程设置的过程中可以设置多个不同的议题，允许学生自由地选择他们感兴趣的议题。 5.提供对信息的评价博客的重要特征就是对信息的过滤，使得信息可以转换成有用的知识。但是

对等网络的网络弹性分析

对等网络的网络弹性分析摘要：网络弹性研究的是网络在节点失效或被有意攻击下所表现出来的特征。分析Gnutella网络的网络弹性，包括对于随机攻击的容错性和对于选择性攻击的抗攻击性，并与ER模型和EBA模型进行了对比。Gnutella网络对于随机攻击具有很好的容错性，但是对于选择性攻击却显得脆弱。最后对网络弹性进行了理论分析，给出了网络在出现最大集团临界点之前的平均集团大小的公式解。关键词：对等网络；无标度；网络弹性；脆弱性中图分类号：TP393.02文献标识码：A 文章编号：1001-9081(2007)04-0784-04 0 引言在过去的40多年里，科学家习惯于将所有复杂网络看作是随机网络。随机网络中绝大部分节点的连结数目会大致相同。1998年开展的一个描绘互联网的项目却揭示了令人惊诧的事实：基本上，互联网是由少数高连结性的页面串联起来的，80%以上页面的连结数不到4个，而只占节点总数不到万分之一的极少数节点，例如门户网Yahoo和搜索引擎Google等类似网站，却高达上百万乃至几十亿个链接。研究者把包含这种重要集散节点的网络称为无标度网络[1]。

具有集散节点和集群结构的无标度网络，对意外故障具有极强的承受能力，但面对蓄意的攻击和破坏却不堪一击[2]。在随机网络中，如果大部分节点发生瘫痪，将不可避免地导致网络的分裂。无标度网络的模拟结果则展现了全然不同的情况，随意选择高达80%的节点使之失效，剩余的网络还可能组成一个完整的集群并保持任意两点间的连接，但是只要5%―10%的集散节点同时失效，就可导致互联网溃散成孤立无援的小群路由器。许多复杂网络系统显示出惊人的容错特性，例如复杂通信网络也常常显示出很强的健壮性，一些关键单元的局部失效很少会导致全局信息传送的损失。但并不是所有的网络都具有这样的容错特性，只有那些异构连接的网络，即无标度网络才有这种特性，这样的网络包括WWW、因特网、社会网络等。虽然无标度网络具有很强的容错性，但是对于那些有意攻击，无标度网络却非常脆弱。容错性和抗攻击性是通信网络的基本属性，可以用这两种属性来概括网络弹性。对等网络技术和复杂网络理论的进展促使对现有对等网络的拓扑结构进行深入分析。对网络弹性的认识可以使从网络拓扑的角度了解网络的脆弱点，以及如何设计有效的策略保护、减小攻击带来的危害。本文研究Gnutella网络的网络弹性，并与ER模型和EBA模型进行了比较，对比不同类型的复杂网络在攻击中的网络弹性。当网络受到攻击达到某

备份软件使用方法v1.0

备份软件使用方法一Bestsync2012使用说明 1 软件运行点击BestSync2012运行软件 2 设置任务在编辑菜单下点击追加任务（如果任务列表下没有任务可以在文件菜单下选择新建任务选项）软件会弹出任务窗口，用来设置同步任务

以其中一个任务为例

选择好同步的文件夹和同步方向，点击下一步，按照要求设置任务即可。 3 查看任务在以有任务中点击设置任务（任务必须是未在同步状态，否者不能点击设置任务选项）

点击后软件会弹出设置同步任务窗口，在这里可以在里面进行任务修改和设置

目前我们设置的同步任务只需要修改一般和日程两个窗口下的内容，其他暂时不需要修改。 BestSync2012这款同步软件目前还不是很稳定，需要不定期检查一下软件是否运行正常，如果发现软件出错，就关闭软件后在打开BestSync2012软件，因为打开软件后软件不会自动启动同步功能，所有需要手动启动所有任务注意： 1 在修改任务在开启后，必须将修改的任务停止一下在开启，不然同步任务不能正常同步。 2 现有BestSync2012同步软件在16.15和151.247这两台机器上。

二Backup Exec 2010 R2 SP1使用说明 1 软件运行点击Backup Exec 2010运行软件 2 设置任务在作业设置选项中可以看到作业的作业名称、策略名称和备份选这项列表。其中作业名称里放有现有作业，双击其中一个作业就可以看到作业属性。作业属性默认显示设备和介质窗口，在设备和介质窗口下可以选择设备和介质集。目前设备选项中因为只有一台磁带机工作，所有只有一个选项，而介质集一般选择永久保留数据-不允许覆盖选项。

博客需求分析

博客系统需求分

博客简介

漫漫教学路，博客伴我行能在互联网上拥有一个真正属于自己的空间，是我的梦想，而今天这个梦想在“博客”中实现了。我怀着一颗好奇心，在博客上流连，申请了一方属于自己的免费空间，置身于梦幻秋天的背景下，设置自己喜欢的几个栏目，于是我便拥有了博客。当我第一次在博客中添加文章的时候，兴奋得无法入眠。我想：平素与网络无缘的我也终于拥有了一个网上家园。一个可以让我任意挥洒激情、记录人生轨迹的网上家园。感谢博客给我一块自由的空间，让我展翅飞翔！在与博客“亲密接触”的日子里，我深深地感觉到博客对教育的促进，对自身专业化成长的帮助。在开始的时候，我也只是摘录一些自己感兴趣的信息，很少有经过自己思考的原创日志。随着对博客认识的加深，以及浏览其他著名博客所受到的启示，我也试着把自己在教育中的思考及时记录下来。就这样我在博客里“书写着，记录着，思考着，分享着，品味着，学习着”，在不断地积累中感受着学习的乐趣。在博客里写作已经逐渐成为我的一种习惯，在博客中我不断地阅读、书写，在阅读、书写中释放心情，这让我感到在博客中学习竟是如此快乐。我在博客中开设了心灵随笔，教学案例，教学反思，教学相长，教学设计，教学论文，主题中队会等栏目…… 作为一名教师，我深深地认识到：要想鼓励、指导学生写出好的文章，教师必须要有过硬的写作基本功，博客中的心灵随笔这个栏目正好为我提供了这样一个平台，我在这个栏目中及时捕捉教学生活中细微的瞬间，从中悟出深刻的道理，并马上形成文字。例如：《让心灵跟着爱飞翔》、《如何赏识学生》、《感动》、《怎样转变学生的不良习惯》等文章。心灵的感悟，出乎意料的发挥了作用，有了这样的历练，对学生进行写作指导和评改，就驾轻就熟了。学生也会在老师的指导下逐渐明白，写作并不是一件很难的事情，只要真实的记录自己在生活中的所见、所闻，有了自己的感悟，慢慢就会写出具有真情实感的文章。在博客中记录教学过程是一个不断充实自己，提高自己的过程，教学中我也曾遇到过很多困惑，于是把这些困惑书写到我的博客中，期待与博友们交流和切磋。博友们的热心触发了我很多灵感，常常使我茅塞顿开……我现在博客中的教学案例就是平时点滴的积累。《位置与方向》《那只松鼠》《笔算除法》《商中间、末

管理软件使用说明书

目录 1 软件介绍...................................................... １ 2 软件运行环境 ................................................. １ 3 软件安装步骤 ................................................. １ 4 软件卸载步骤 ................................................. ４ 5 软件使用...................................................... ４5.1、创建数据库.............................................................................................................................. ４ 5.2、创建数据数据表................................................................................................................... ６ 5.3、历史数据读取 ........................................................................................................................ ７ 5.4、查看历史数据、通道信息.............................................................................................. ８ 5.5、打印数据、曲线或图片输出 .................................................................................... １３ 5.6、数据实时采集 .................................................................................................................... １５ 6 软件使用中可能出现的问题与解决方法.................. １８6.1、不出现对话框 .................................................................................................................... １８ 6.2、数据库不能建立............................................................................................................... １８ 6.3、U盘不能数据转存........................................................................................................... １８ 6.4、U盘上没有文件 ................................................................................................................ １８ 6.5、U盘数据不能导入计算机；...................................................................................... １８