driven方式开火,质量测试相比

欣赏经久不衰的大旨曲,旋律奔放,给那么些爱情传说扩充了不断罗曼蒂克色彩。 oh,
my love, my darling i’ve hungered for your touch a long, lonely time.
and time goes by so slowly and time can do so much, are you still mine?
………

Python time时间模块学习

time包

time包的底子项目是struct_time。

time.sleep() 让程序 延时,以秒为单位。

time.time() 重临时间戳,浮点型。

time.strptime(string, format)

用法 ,将字符串转换来 struct_driven方式开火,质量测试相比。time,再次来到时间协会struct_time

time_struct = time.strptime(‘2014-10-10 22:22:12’,”%Y-%m-%d
%H:%M:%S”)

time.strftime(format,struct_time)

用法,struct_time将转换到 字符串, 再次回到字符串。

time.strftime(“%Y-%m-%d”,struct_time)

time.mktime(time_struct)

用法,时间组织到时间戳,重返浮点方式的 时间戳。

time.gmtime(time_stamp)

用法,将时刻戳,转化成时间组织,重临时间组织。

time.gmtime(1812950932)

time.localtime()
用法,将时间戳,转化成时间结构,重临时间协会。

time.localtime()

gmtime()和loaltime() 都以将 时间戳 转换到 时间结构 ,区别之处是 gmtime
转化成 格林威治时间, localtime
转换出来的是本土时间,比如我们正是东京时间。 所以1般time.localtime()
括号里都不加参数,暗中认可正是收获当前的小时戳,转换来当前岁月。

总结

字符串 到 time_struct 用 time.strptime()

time_struct 到 字符串 用 time.strftime()

时间戳 到 time_struct 用 time.gmtime(time_stamp) 或者
time.localtime(time_stamp) time_stamp能够省略

time_struct 到 时间戳 用 time.mktime(time_struct)

能够看来 字符串 和 时间戳之间 没有 间接转换的关系 ,能够透过 时间协会来作为 过渡。

简图:

新葡萄京娱乐场网址 1

即使 time.ctime() 也是 将 时间戳 转化为 字符串,但是不可能用 格式化字符
来定制,用处简单。如

time.ctime(121295093贰) 的 重返结果 是 Mon Jun 九 0二:4八:5二 二〇一〇 。

time.asctime(time_struct) 也是将 时间结构体 ,转化成
字符串,通ctime1样,生成的字符串格式,无法定制。如

time.asctime(time_struct) 的回来结果是 Mon Jun 1四 05:2捌:5二 20贰七。

time时间模块学习 time包
time包的底子项目是struct_time。 time.sleep() 让程序 延时,以秒为单位。
time.time() 再次回到时间戳,浮点型。 time.str…

考试场景1:要对同二个list实时添新币素,且放入缓存中。代码如下:

今日学习了peersim的cycle形式,明日开首三番五次悟事件情势。

    @Test  
    public void testMemCacheAndRedis() {  
      this.testInsert(1000);  
    }  

    public void testInsert(int size) {  
      MemCached memCached = (MemCached) ctx.getBean("configMemCache");  
      memCached.flushAll();  
      long before = System.currentTimeMillis();  
      List<Integer> list = new ArrayList<Integer>();  
      for (int i = 0; i < size; i++) {  
        list.add(i);  
        memCached.put("test", list);  
      }  
      long end = System.currentTimeMillis();  

      RedisTemplate<String, Object> redisTemplate = (RedisTemplate<String, Object>) ctx.getBean("bubbleRedisTemplate");  
      redisTemplate.delete("test123");  
      long before2 = System.currentTimeMillis();  
      for (int i = 0; i < size; i++) {  
        redisTemplate.boundListOps("test123").leftPush(i);  
      }  
      long end2 = System.currentTimeMillis();  
      System.out.println("memcached use time is " + (end - before));  
      System.out.println("redis use time is " + (end2 - before2));  
    }  

总的看,笔者个人认为事件情势是周期情势的升官版,或许说,周期方式只是事件情势的二个子效益。

 

事件格局是基于时间和事件的(没打错),每一次试验的发端会设定甘休时间和多少要处理的风云,当岁月甘休,也许事件全体做完,实验就甘休,而在终止以前,也是足以周期性的履行一些事件,所以说周期方式只是事件方式的3个子效益。

新葡萄京娱乐场网址,当n=一千时的4意三次打字与印刷结果:

memcached use time is 6282
redis use time is 1156

memcached use time is 6547
redis use time is 1172

memcached use time is 6234
redis use time is 1375

学学格局依然直接看代码,看不懂的地方直接去找对应的源文件,一难得一见从子类往上看。

当n=三千时的妄动三回打字与印刷结果:

memcached use time is 22313
redis use time is 2328

memcached use time is 22969
redis use time is 2907

memcached use time is 22938
redis use time is 2234

贴上样例代码,功用和周期方式1样,首先是对互连网中的每种节点赋起初值(那里运用线性分布),接着各样节点和隔壁节点求平均值

当n=三千时的即兴二次打印结果:

memcached use time is 48859
redis use time is 3297

memcached use time is 48156
redis use time is 4547

memcached use time is 47765
redis use time is 3375

**试验壹结论(只对本次试验负责):当n在20条以内时,两者反差相当小,但当n大于20时,redis在质量上的优势就会日益显示出来。

调查场景二:对程序做些稍许的改动,即将memCached.put(“xwq”,
list);这一行移到for循环外面执行,代码如下:**

 

 1      @Test  
 2     public void testMemCacheAndRedis() {  
 3       this.testBatchInsert(3000);  
 4     }  
 5       
 6     public void testBatchInsert(int size) {  
 7       MemCached memCached = (MemCached) ctx.getBean("configMemCache");  
 8       memCached.flushAll();  
 9       long before = System.currentTimeMillis();  
10       List<Integer> list = new ArrayList<Integer>();  
11       for (int i = 0; i < size; i++) {  
12         list.add(i);  
13       }  
14       memCached.put("test", list);  
15       long end = System.currentTimeMillis();  
16       
17       RedisTemplate<String, Object> redisTemplate = (RedisTemplate<String, Object>) ctx.getBean("bubbleRedisTemplate");  
18       redisTemplate.delete("test123");  
19       long before2 = System.currentTimeMillis();  
20       for (int i = 0; i < size; i++) {  
21         redisTemplate.boundListOps("test123").leftPush(i);  
22       }  
23       long end2 = System.currentTimeMillis();  
24       System.out.println("memcached use time is " + (end - before));  
25       System.out.println("redis use time is " + (end2 - before2));  
26     }  

 

大家再做下调查:

样例代码:

当n=一千时的自由3回打字与印刷结果:

memcached use time is 15
redis use time is 1188

memcached use time is 32
redis use time is 1188

memcached use time is 31
redis use time is 1234

  1 /*
  2  * Copyright (c) 2003 The BISON Project
  3  *
  4  * This program is free software; you can redistribute it and/or modify
  5  * it under the terms of the GNU Lesser General Public License version 2 as
  6  * published by the Free Software Foundation.
  7  *
  8  * This program is distributed in the hope that it will be useful,
  9  * but WITHOUT ANY WARRANTY; without even the implied warranty of
 10  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 11  * GNU Lesser General Public License for more details.
 12  *
 13  * You should have received a copy of the GNU Lesser General Public License
 14  * along with this program; if not, write to the Free Software
 15  * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 16  *
 17  */
 18 
 19 package example.edaggregation;
 20 
 21 import peersim.vector.SingleValueHolder;
 22 import peersim.config.*;
 23 import peersim.core.*;
 24 import peersim.transport.Transport;
 25 import peersim.cdsim.CDProtocol;
 26 import peersim.edsim.EDProtocol;
 27 
 28 /**
 29 * Event driven version of epidemic averaging.
 30 */
 31 public class AverageED extends SingleValueHolder
 32 implements CDProtocol, EDProtocol {                     //这里除了ED协议,还实现了CD协议,正是因为在实验中会周期性的执行一些事件,所以要实现CD协议             
 33 
 34 //--------------------------------------------------------------------------
 35 // Initialization
 36 //--------------------------------------------------------------------------
 37 
 38 /**
 39  * @param prefix string prefix for config properties
 40  */
 41 public AverageED(String prefix) { super(prefix); }
 42 
 43 
 44 //--------------------------------------------------------------------------
 45 // methods
 46 //--------------------------------------------------------------------------
 47 
 48 /**
 49  * This is the standard method the define periodic activity.
 50  * The frequency of execution of this method is defined by a
 51  * {@link peersim.edsim.CDScheduler} component in the configuration.
 52  */
 53 public void nextCycle( Node node, int pid )        //这里就是周期模式的核心代码部分,定义了周期性的行为
 54 {
 55     Linkable linkable = 
 56         (Linkable) node.getProtocol( FastConfig.getLinkable(pid) );    
 57     if (linkable.degree() > 0)
 58     {
 59         Node peern = linkable.getNeighbor(
 60                 CommonState.r.nextInt(linkable.degree()));          //这部分的功能是得到本地节点的一个邻居节点,而不是所有相邻节点
 61         
 62         // XXX quick and dirty handling of failures
 63         // (message would be lost anyway, we save time)
 64         if(!peern.isUp()) return;
 65         
 66         ((Transport)node.getProtocol(FastConfig.getTransport(pid))).     //这里涉及到了传输层,功能是像邻居节点发送信息,把本地的值发过去
 67             send(
 68                 node,
 69                 peern,
 70                 new AverageMessage(value,node),
 71                 pid);
 72     }
 73 }
 74 
 75 //--------------------------------------------------------------------------
 76 
 77 /**
 78 * This is the standard method to define to process incoming messages.
 79 */
 80 public void processEvent( Node node, int pid, Object event ) {        //这里是ED模式的核心代码部分,用来处理事件
 81         
 82     AverageMessage aem = (AverageMessage)event;                       //从邻居节点那里接受到的信息
 83     
 84     if( aem.sender!=null )                                           //如果邻居节点的发送者不为空,说明本节点并不是目的节点,所以继续帮他转发
 85         ((Transport)node.getProtocol(FastConfig.getTransport(pid))).
 86             send(
 87                 node,
 88                 aem.sender,
 89                 new AverageMessage(value,null),
 90                 pid);
 91                 
 92     value = (value + aem.value) / 2;                             //如果为空,则说明本节点就是目的节点,所以把对方的值和本地的平均值替换本地值
 93 }
 94 
 95 }
 96 
 97 //--------------------------------------------------------------------------
 98 //--------------------------------------------------------------------------
 99 
100 /**
101 * The type of a message. It contains a value of type double and the
102 * sender node of type {@link peersim.core.Node}.
103 */
104 class AverageMessage {                                   //信息类,非常简单,只有一个value和sender
105 
106     final double value;
107     /** If not null,
108     this has to be answered, otherwise this is the answer. */
109     final Node sender;
110     public AverageMessage( double value, Node sender )
111     {
112         this.value = value;
113         this.sender = sender;
114     }
115 }

当n=三千时的私行三遍打字与印刷结果:

memcached use time is 47
redis use time is 2437

memcached use time is 31
redis use time is 2344

memcached use time is 47
redis use time is 2265

以上便是ed方式下的一个简便的样例,作用也相当粗略,比较前一周期格局,作者以为她是如此工作的:在各类定义的周期内向邻居发送节点,当节点接受到发送来的音讯后,则会处理(求平均值),在那之中内部一些是经过贰个堆来保管这么些事件(send()方法本身就是把事件添加到堆个中)

但n=2000时的任意三次打印结果:

memcached use time is 62
redis use time is 3406

memcached use time is 47
redis use time is 3391

memcached use time is 47
redis use time is 3688

试验二结论(只对此次考试负责):因此能够看出,memcached叁遍性将list放入到缓存中,比redis每一趟换代质量是要高很多的。

考察场景3:对相同大小的五个list,同时开展同样次数的读操作,代码如下:
 

 1  @Test
 2   public void testMemCacheAndRedis() {
 3     // this.testInsert(3000);
 4     // this.testBatchInsert(3000);
 5     this.testRead(200);
 6   }
 7 
 8   public void testRead(int size) {
 9     MemCached memCached = (MemCached) ctx.getBean("configMemCache");
10     memCached.flushAll();
11     List<Integer> list = new ArrayList<Integer>();
12     for (int i = 0; i < 1000; i++) {
13       list.add(i);
14     }
15     memCached.put("test", list);
16 
17     long before = System.currentTimeMillis();
18     for (int i = 0; i < size; i++) {
19       memCached.get("test");
20     }
21     long end = System.currentTimeMillis();
22 
23     RedisTemplate<String, Object> redisTemplate = (RedisTemplate<String, Object>) ctx.getBean("bubbleRedisTemplate");
24     redisTemplate.delete("test123");
25     for (int i = 0; i < 1000; i++) {
26       redisTemplate.boundListOps("test123").leftPush(i);
27     }
28 
29     long before2 = System.currentTimeMillis();
30     for (int i = 0; i < size; i++) {
31       redisTemplate.boundListOps("xwq123").range(0, -1);
32     }
33     long end2 = System.currentTimeMillis();
34     System.out.println("memcached use time is " + (end - before));
35     System.out.println("redis use time is " + (end2 - before2));
36   } 

 

可是难题并不是这,而是布署文件部分。。。shit :(

当n=100时的专擅一回打字与印刷结果:

memcached use time is 6765
redis use time is 281

memcached use time is 2187
redis use time is 297

memcached use time is 9547
redis use time is 313

贴代码

当n=200时的人身自由三遍打字与印刷结果:

memcached use time is 34250
redis use time is 546

memcached use time is 12171
redis use time is 547

memcached use time is 8297
redis use time is 516

 1 # network size                             #首先是类似宏定义的一样定义了几个值,方便一会使用,果然换了ED模式连这里都高级起来了
 2 SIZE 1000
 3 
 4 # parameters of periodic execution
 5 CYCLES 100
 6 CYCLE SIZE*10000                     
 7 
 8 # parameters of message transfer
 9 # delay values here are relative to cycle length, in percentage,
10 # eg 50 means half the cycle length, 200 twice the cycle length, etc.
11 MINDELAY 0
12 MAXDELAY 0
13 # drop is a probability, 0<=DROP<=1
14 DROP 0
15 
16 random.seed 1234567890
17 network.size SIZE
18 simulation.endtime CYCLE*CYCLES
19 simulation.logtime CYCLE
20 
21 ################### protocols ===========================   #这部分比较麻烦,全是新的玩意
22 
23 protocol.link peersim.core.IdleProtocol          #网络还是选的之前的一样
24 
25 protocol.avg example.edaggregation.AverageED
26 protocol.avg.linkable link
27 protocol.avg.step CYCLE          #这里的step类似于周期的概念
28 protocol.avg.transport tr        #这里的tr在下面有定义
29 
30 protocol.urt UniformRandomTransport       #这里是具体的传输层协议,功能是以随机的延迟(随机范围在下面定义)发送消息,发送的实质是向事件的堆中添加事件
31 protocol.urt.mindelay (CYCLE*MINDELAY)/100
32 protocol.urt.maxdelay (CYCLE*MAXDELAY)/100
33 
34 protocol.tr UnreliableTransport          #一个不稳定的传输层协议,以下面的drop的值随机丢包,实际传输还是用的上面urt
35 protocol.tr.transport urt
36 protocol.tr.drop DROP
37 
38 ################### initialization ======================
39 
40 init.rndlink WireKOut                    #网络布局,节点以k出度相连
41 init.rndlink.k 20
42 init.rndlink.protocol link
43 
44 init.vals LinearDistribution            #给每个节点的赋初值,线性分布,具体分布方法是定义step= (max.doubleValue()-min.doubleValue())/(Network.size()-1);   setter.set(i,Math.round(i*step)+min.longValue()); 详细见源文件

45 init.vals.protocol avg
46 init.vals.max SIZE
47 init.vals.min 1
48 
49 init.sch CDScheduler                    #这里很关键,定义了一个周期调度管理器,会在每个周期的开始随机打乱节点的部分,每隔一个cycle值就打乱一次
50 init.sch.protocol avg
51 init.sch.randstart
52 
53 ################ control ==============================
54 
55 control.0 SingleValueObserver         #这里的step指定了周期值,使得监测者也会在每个指定的周期调用一次,否则control则会想周期模式一样调度
56 control.0.protocol avg
57 control.0.step CYCLE

当n=500时的任意3遍打字与印刷结果:

memcached use time is 69437
redis use time is 1297

memcached use time is 43781
redis use time is 1516

memcached use time is 37750
redis use time is 1281

考查3结论(只对本次试验负责):同样是从缓存中读取数据,redis的性质是要远远超过memcached。

归纳结论(只对此番次试验负责):通过上述3回考试,redis在
读操作和写操作上是健全超过memcached的。但有一点,memcached能够一遍性将集聚的多寡放入本身的缓存中,而redis却卓殊(有依然木
有?),所以memcached并不是不对的,在重重情景下,2者是急需交叉使用的,要基于实际处境实行抉择。

谈到底贴上试验结果:

EDSimulator: resetting
Network: no node defined, using GeneralNode
EDSimulator: running initializers

  • Running initializer init.rndlink: class peersim.dynamics.WireKOut
  • Running initializer init.sch: class peersim.edsim.CDScheduler
  • Running initializer init.vals: class
    peersim.vector.LinearDistribution
    EDSimulator: loaded controls [control.0]
    Current time: 0
    control.0: 1.0 1000.0 1000 500.5 83416.66666666667 1 1
    Current time: 10000000
    control.0: 37.5 919.0 1000 500.5 25724.159091250687 1 1
    Current time: 20000000
    control.0: 206.7109375 767.890625 1000 500.5 8096.807036889389 1 1
    Current time: 30000000
    control.0: 352.373046875 695.453125 1000 500.5 2578.022573176135 1 1
    Current time: 40000000
    control.0: 412.430419921875 625.474609375 1000 500.5 801.1082179446831 1
    1
    Current time: 50000000
    control.0: 436.43787479400635 570.459858417511 1000 500.5
    243.53994072762902 1 1
    Current time: 60000000
    control.0: 470.7608990445733 527.0359845032217 1000 500.49999999999994
    74.13788674564383 1 2
    Current time: 70000000
    control.0: 483.6040476858616 518.0301055684686 1000 500.49999999999903
    23.428974301677556 1 1
    Current time: 80000000
    control.0: 490.5196089811798 512.0301471857779 1000 500.4999999999993
    7.285566419597019 1 1
    Current time: 90000000
    control.0: 494.97216907397836 506.0375954180854 1000 500.4999999999999
    2.1798299307442246 1 1
    Current time: 100000000
    control.0: 497.18190345272336 503.5837144460532 1000 500.5000000000001
    0.6073148838336206 1 1
    Current time: 110000000
    control.0: 498.54320551492475 502.3533156558903 1000 500.5
    0.1786794435445898 1 2
    Current time: 120000000
    control.0: 499.4023441821402 501.4962048486104 1000 500.49999999999966
    0.055257607540637785 1 1
    Current time: 130000000
    control.0: 500.0032071191514 501.09832936709677 1000 500.4999999999995
    0.017914865984002482 1 1
    Current time: 140000000
    control.0: 500.2115701087355 500.83018846682955 1000 500.5000000000003
    0.005681541785868155 1 1
    Current time: 150000000
    control.0: 500.34130672550015 500.6817038675913 1000 500.49999999999983
    0.0018027434356275712 1 1
    Current time: 160000000
    control.0: 500.4099993828274 500.593872603867 1000 500.50000000000006
    5.362825330674977E-4 1 1
    Current time: 170000000
    control.0: 500.45257293353984 500.54348273631905 1000 500.50000000000057
    1.540724446790086E-4 1 1
    Current time: 180000000
    control.0: 500.4730336913324 500.5308245606462 1000 500.49999999999983
    4.870521656989873E-5 2 1
    Current time: 190000000
    control.0: 500.48375224793585 500.5157654498322 1000 500.4999999999995
    1.6009524787167412E-5 1 1
    Current time: 200000000
    control.0: 500.48865909872677 500.50651976294216 1000 500.50000000000017
    5.077729859315717E-6 1 1
    Current time: 210000000
    control.0: 500.4951192430326 500.5047676794802 1000 500.5000000000002
    1.6155684864646324E-6 1 1
    Current time: 220000000
    control.0: 500.4974085478983 500.502935896735 1000 500.50000000000045
    5.105843169016166E-7 1 1
    Current time: 230000000
    control.0: 500.4987222960319 500.5016402188968 1000 500.5000000000002
    1.6512563666543207E-7 1 1
    Current time: 240000000
    control.0: 500.49903235648463 500.50092451432386 1000 500.49999999999994
    5.299286046125987E-8 1 1
    Current time: 250000000
    control.0: 500.49958979342233 500.50059026547086 1000 500.5000000000001
    1.5440470612681545E-8 1 1
    Current time: 260000000
    control.0: 500.4996881991682 500.500274943262 1000 500.5000000000001
    4.8652048911656945E-9 1 1
    Current time: 270000000
    control.0: 500.49977403242923 500.5001382754437 1000 500.4999999999995
    2.0101744759906163E-9 1 1
    Current time: 280000000
    control.0: 500.49987495360983 500.50007640960587 1000 500.5
    1.7479778052092316E-10 1 1
    Current time: 290000000
    control.0: 500.49994445650935 500.5000501190701 1000 500.5000000000004
    0.0 1 1
    Current time: 300000000
    control.0: 500.4999661034419 500.50002345424616 1000 500.49999999999994
    3.2046259762169247E-10 1 1
    Current time: 310000000
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    Current time: 450000000
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    Current time: 490000000
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    Current time: 500000000
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    Current time: 670000000
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    Current time: 710000000
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    Current time: 750000000
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    Current time: 760000000
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    Current time: 770000000
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    Current time: 790000000
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    Current time: 800000000
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    Current time: 810000000
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    Current time: 830000000
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    Current time: 840000000
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    Current time: 850000000
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    Current time: 860000000
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    Current time: 870000000
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    Current time: 880000000
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    Current time: 890000000
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    Current time: 900000000
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    Current time: 910000000
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    Current time: 920000000
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    Current time: 930000000
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    Current time: 940000000
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    Current time: 950000000
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    Current time: 960000000
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    Current time: 970000000
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    Current time: 980000000
    control.0: 500.5 500.5 1000 500.5 0.0 1000 1000
    Current time: 990000000
    control.0: 500.5 500.5 1000 500.5 0.0 1000 1000
    EDSimulator: queue is empty, quitting at time 999980413

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