A Precise and Efficient Evaluation of the Proximity between Web Clientsand their Local DNS
A Precise and Ef?cient Evaluation of the Proximity between Web Clients
and their Local DNS Servers
Zhuoqing Morley Mao,Charles D.Cranor,Fred Douglis,Michael Rabinovich,
Oliver Spatscheck,and Jia Wang
AT&T Labs–Research
Abstract
Content Distribution Networks(CDNs)attempt to im-
prove Web performance by delivering Web content to
end-users from servers located at the edge of the net-
work.An important factor contributing to the perfor-
mance improvement is the ability of a CDN to select
servers in the proximity of the requesting clients.Most
CDNs today use the Domain Name System(DNS)to
make such server selection decisions.However,DNS
provides only the IP address of the client’s local DNS
server to the CDN,rather than the client’s IP address.
Therefore,CDNs using DNS-based server selection as-
sume that clients are“close”to their local DNS servers.
To quantify the proximity between clients and their local
DNS servers,we propose a novel,precise,and ef?cient
technique for?nding the associations of client to local
DNS servers.We collected more than4.2million such
unique associations in three months.From this data,we
study the impact of proximity on DNS-based server se-
lection using four different proximity metrics.We con-
clude that DNS is good for very coarse-grained server
selection,since64%of the associations belong to the
same Autonomous System.DNS is less useful for?ner-
grained server selection,since only16%of the client and
local DNS associations are in the same network-aware
cluster[13](based on BGP routing information from a
wide set of routers).As an application of this method-
ology,we evaluate DNS-based server selection in three
of the largest commercially deployed CDNs to study its
accuracy.
1Introduction
Creating and managing a high-performance,Internet-
scale Web service is a formidable challenge involving
use modi?ed authoritative DNS servers for CDN server selection.The results of a DNS query to one of these DNS servers may vary dynamically depending on fac-tors such as the source of the request and the condition of the network.Typically,the CDN’s authoritative DNS server maps the client’s local DNS server address to a geographic region within a particular network and com-bines that with network and server load information to perform CDN server selection.To enable fast reaction to dynamic resource changes,the answer returned by the CDN’s DNS server has a small TTL.This approach is largely transparent to the client,and works for any Web content(including both HTML and streaming media). Although DNS-based server selection is transparent and general,it has two inherent limitations[15,4].First,it is based on the implicit assumption that clients are close to their local DNS servers.The CDN DNS server per-forming dynamic request routing only has access to the client’s local DNS server’s IP address—it does not know the client’s own IP address.However,the assumption that clients are close to their local DNS server may not be valid.For example,the client might be using a lo-cal DNS server hierarchy in which the outermost local DNS server that communicates with authoritative DNS servers may be far removed from clients;the client may have been con?gured with a local DNS server which is far away;or the client may be using a secondary local DNS server that is more distant from it than its primary local DNS server.Therefore,using only the local DNS server information to select CDN servers has the inher-ent risk of selecting a server farther away from the client than other available CDN servers.
The second inherent limitation of DNS-based server se-lection is that a single request from a local DNS server can represent differing numbers of Web clients—this is called the hidden load factor[8].The hidden load has implications on a CDN’s load balancing algorithm. For example,a DNS request from a local DNS server of a large ISP may result in many more Web requests than a DNS request from a local DNS server of a small site.CDNs need to be able to properly weigh individual DNS requests to distribute Web requests among its CDN servers.If the hidden load factors are known,load bal-ancing algorithms described by Colajanni,et al.[7,8] can be easily deployed to achieve better load distribu-tion.On the other hand,if the hidden load factors are not known,?ne-grained request distribution may be dif-?cult.
We study the extent of the?rst limitation and its impact on CDN server selection.To this end,we developed a simple,non-intrusive,and ef?cient mapping technique to determine the associations between clients and local DNS servers.We deployed this technique on several sites to collect an extensive data set which we use to study the impact of proximity on DNS-based server se-lection using four different proximity metrics.We con-clude that DNS is good for very coarse-grained server selection,since64%of the associations belong to the same Autonomous System(AS).DNS is less useful for ?ner-grained server selection,since only16%of clients use DNS servers in the same network-aware cluster[13] (based on BGP routing information).We also measure the CDN server distribution of several real-world CDNs to evaluate whether the proximity of a client to its local DNS server leads to potentially suboptimal CDN server selection decisions in practice.Our technique could also be used to determine hidden load factors by associating the HTTP request pattern in the Web server logs with the DNS request information.
Our work makes the following contributions.We devel-oped a novel measurement methodology and architec-ture for accurately collecting local DNS server IP ad-dresses of Web clients.We demonstrated its successful deployment on several sites including a large commer-cial site and through the collection of a huge database of associations.Based on this data,we did an extensive analysis of the proximity between clients and their local DNS servers and discovered that signi?cant improve-ment in proximity is possible by con?guring clients to use a closer local DNS server.Finally,we evaluated the impact of the proximity between clients and their local DNS servers on server selection in three of the largest commercially deployed CDNs.We conclude that DNS is good for very coarse-grained server selection,but less suitable for?ne-grained request distribution.
The rest of the paper is organized as follows.Section2 describes our methodology and measurement setup for gathering DNS client associations.In Section3,the as-sociation results are analyzed in detail to evaluate the proximity between the client and its local DNS server. Then,in Section4we study the impact of proximity evaluation on DNS-based server selection in three of the largest commercially deployed CDNs.Related work is covered in Section5.In section6,we discuss future work.Section7concludes.
2Experimental methodology
In this section we describe our novel technique for de-termining a Web client’s local DNS server.This is a necessary?rst step in measuring the closeness of clients to their local DNS servers.We also evaluate the impact
of our technique on end user https://www.wendangku.net/doc/c012407652.html,ter,in Sec-tion5,we will explain how our technique is a signi?cant improvement over related previous work in terms of ef-?ciency,nonintrusiveness,and accuracy.
2.1Measurement setup
There are three main components necessary to use our technique:a specialized authoritative DNS server,an HTTP redirector,and a one-pixel embedded transparent GIF image.To obtain a client population we solicited volunteer Web sites.All the volunteers had to do to par-ticipate in our study was to add a link to our one-pixel transparent GIF to the end of one or more of their com-monly accessed Web pages.Assuming the experiment is hosted by us at https://www.wendangku.net/doc/c012407652.html,,this involves adding the following HTML code towards the end of a web page: 
height=1width=1>
To allow us to easily account for hits from different sites, each participant replaces xxx in the URL with a site identi?er1.This allows us to easily add additional vol-unteer sites without having to make any changes to our Web or DNS server con?guration.
When a Web client loads the one-pixel embedded im-age,our technique allows us to match the address of the local DNS server resolving host names on behalf of the client with the address of the client itself.This process is shown in Figure1.First,the client attempts to get the image from https://www.wendangku.net/doc/c012407652.html,—our HTTP redirector.Rather than serving the image, the redirector determines the client’s IP address and is-sues an HTTP redirect to https://www.wendangku.net/doc/c012407652.html,, where CLI is replaced with a string encoding the IP address of the client(step2).Next,the client contacts its local DNS server to resolve this domain name(step 3).The client’s local DNS server attempts to resolves https://www.wendangku.net/doc/c012407652.html, by sending a DNS request to our authoritative DNS server(step4).At this point our authoritative DNS server logs the IP address of the local DNS server and the client IP address embedded within the query.It then sends the address of the con-tent server hosting the image back to the client’s local DNS server(step5).This resolution is passed on to the client(step6),which retrieves the image from the con-tent server(steps7and8).
Table1:Keynote image overhead measurements
Location Avg download latency(sec)
with image
1.1712%
1.0410%
2.2Measurement impact
Because we propose to use our measurement infrastruc-ture on a production Web site,it is important to evaluate its impact on the server performance and other aspects of its operation.The additional overhead our measurement technique imposes on Web client performance is the re-trieval of the transparent image,including the HTTP redirect and extra DNS requests.Because the image is transparent,it does not visually affect the page.Fur-thermore,the image is small in size—43bytes—which keeps the added delay to a minimum.We also encourage participants to include the image at the end of the HTML page containing it;therefore,browsers will normally re-quest it last.Thus,the extra latency associated with the image is usually hidden from the user’s Web browsing experience.Another advantage of the small size of the image is that when the image is not available for down-load,it does not affect the visual appearance of the Web page at all.
Our custom HTTP redirector is a single-threaded,non-blocking,300-line C program.The redirector responds to all Web requests with a“302Moved Temporarily”HTTP redirect to a URL with the client’s IP address em-bedded in it.Due to the small size and overhead of the redirector,we found it to be highly reliable and more responsive than a standard Web server.
To validate the claim of a small increase in latency,we measured a simple Web page with Keynote[2]to com-pare the download time with and without the embed-ded calibrating image.Keynote probes are located in 25cities within the US and10cities outside the US.The Web page we measured had a total size of39Kbytes in-cluding13images and was accelerated by a CDN.The increased overhead percentage is therefore higher than we would expect for a regular unaccelerated Web page with more embedded images.Table1shows that the in-creased overhead averages less than140ms,which is 10–12%of the total download time.
We also tested our system to see what would happen in the event of a failure of the redirector,image con-tent server,or DNS server.We found that the impact
Table2:Participating sites in the study Site#of1-pixel
120,816,927
2,3
(commercial domain)3months
Research lab3months
University sites3months
Personal pages
26,563
Count Client-LDNS associations
HTTP requests
Unique client IPs
Unique LDNS IPs
Client-LDNS associations where
56,086 of failure on the user is minimal.We tested the failure of these three components using Microsoft Internet Ex-plorer(MSIE)6and Netscape Navigator6and found that those browsers will?rst load the rest of the Web page and then time out while trying to fetch the im-age.2There is no visible change to the Web page or any pop-up error message;however,the Netscape logo or MSIE browser logo will provide visual feedback until the browser times out.
3Analysis results
We conducted our measurement study for about three months,and nineteen Web sites participated,as de-scribed in Table2.We classify these sites into two cate-gories:commercial(sites1-3)and educational(sites4-19).As we show in Section3.1,the client and local DNS associations visiting these two sites have very different characteristics.For ease of discussion,we use LDNS to represent a local DNS server.A total of4,253,157 unique client and LDNS associations were collected.Ta-ble3presents the statistics of the DNS server and the redirector log for all sites.
To study the proximity between the client and its local
DNS server,we use the following four metrics.
AS clustering.Autonomous System(AS)cluster-ing refers to observing whether a client is in the same AS as its local DNS server.An AS is a re-gion under a single administrative control.A sin-gle AS might contain an entire backbone or a large corporation which might span multiple continents.
Therefore,AS-based clustering is the most coarse-grained metric we use.
Network clustering.This metric observes whether
a client is in the same network-aware cluster(NAC)
as its local DNS server,where network clusters are identi?ed by the network-aware clustering tech-nique[13]using pre?x entries from BGP routing table snapshots from a wide set of routing tables.
Longest pre?x matching is used to map clients to network clusters identi?ed by a network pre?x.All the clients within a network cluster are topologi-cally close together and with a high probability be-long to the same administrative domain.Validation tests(in[13])using nslookup and traceroute show that the accuracy of network clustering is above 90%across all the Web logs from the study by Krishnamurthy and https://www.wendangku.net/doc/c012407652.html,work clustering is much more?ne-grained than AS clustering[12].
For both AS and network clustering,BGP pre?xes and the association of IP CIDR blocks to ASes were extracted from an extensive set of BGP tables col-lected on May27,2001from the sources listed by Krishnamurthy and Wang[13]and Telstra In-ternet[5].There are a total of more than440,000 unique routing entries.
Traceroute divergence.This metric,used previ-ously in[15],is based on the length of divergent paths to the client and its local DNS server from a probe point using traceroute.It is de?ned to be the maximum number of disjoint network hops from a probe location to the client and its LDNS.
Round-trip time correlation.This metric,used previously in both[15]and[4],refers to examin-ing the correlation between the message round-trip times from a probe point to the client and its local DNS server.
AS clustering,network clustering,and traceroute diver-gence are topology-oriented metrics,while round-trip time correlation is a performance-oriented metric.AS and network clustering are passive,requiring no active probing.The other metrics are highly dependent on the Table4:Aggregate statistics of AS/network clustering Metrics#of LDNS
clusters clusters AS clustering8,590
Network clustering53,321
Table5:Percentage of client-LDNS associations sharing the same cluster classi?ed according to the types of domains visited by the clients
Metrics Client IPs HTTP requests
educational combined commercial
70%64%68%
Network cluster16%44%24%
of clients visiting a CDN-accelerated site.In the follow-ing discussion,we consider clients visiting all participat-ing sites.
Using AS clustering,64%of distinct client-LDNS asso-ciations share the same AS.Thus,more than half of the clients use a local DNS server in the same AS.This is expected,since it is common for an administrative do-main to run its own DNS server.If users con?gure their DNS settings correctly,they typically use the LDNS in their administrative domain by default.About69%of the HTTP requests come from clients using an LDNS server in the same AS cluster.This means clients with LDNS in the same AS are slightly more active than those that use an LDNS in another AS.
The above results indicate that in about64%of the cases, CDNs could select appropriate servers using DNS redi-rection with the granularity of ASes.Thus,even if a CDN deployed a cache in every AS in the world,it could select the closest cache according to the AS metric only in64%of the cases.However,AS clustering does not reveal how well redirection works for?ner-grained load-balancing.An AS can span large geographical regions, causing network delays between two hosts within the same AS to be relatively high.For?ner-grained load-balancing it is therefore important to consider network clustering,which groups together IP addresses that are close together topologically and likely to be under the same administrative domain.
The observations using network clustering are signi?-cantly different from the AS clustering results.Only 16%of the client-LDNS associations are in the same network cluster.This shows that most clients are not in the same routing entity as their local DNS servers.If the HTTP request count is taken into account,about24% of the HTTP requests in our logs originated from clients that used an LDNS in the same network cluster.Again, the difference between these two numbers demonstrate that clients with LDNS in the same network clusters are more active than those with LDNS in a different network cluster.Overall,these results indicate that DNS-based redirec-tion can con?dently select appropriate CDN servers with the granularity of an AS.However,for CDNs with mul-tiple servers in the same AS,the selection may not be as accurate.If there is a CDN server in each network cluster,then DNS-based redirection will only select the CDN server in the same network cluster as the client about24%of the time.
3.2Traceroute divergence
Another metric to evaluate the proximity between the client and its local DNS server is the maximum num-ber of disjoint network hops from a probe location to the client and its local DNS server.In[15],this met-ric is referred to as the traceroute cluster size.The smaller the cluster size or traceroute divergence,the closer the client is to the local DNS server.In many of our traceroute results,we found that the network routes from the probe site to the client and its LDNS diverge and converge multiple times due to router load balanc-ing.We use the last point of divergence as the reference for calculating disjoint network hops.For example,Ta-ble6shows the network routes obtained by performing traceroute to the client112.74.197.1633and its LDNS 112.25.195.1.We use hop11instead of2as the point of divergence.Thus,the traceroute divergence in this example is.
We selected four probe sites representing candidate CDN servers and performed traceroute to a sample of clients and local DNS servers from the log.The sample consists of48,908client-LDNS pairs or66,975IP ad-dresses.It is obtained by randomly selecting one client-LDNS pair from the top half of the client network clus-ters generating the most HTTP requests.The number of client-LDNS pairs reached by an individual probe site ranges from9,878to11,935.In about20%of these, both the client and the LDNS belong to the same net-work cluster.And in about75%of these,both the client and the LDNS belong to the same AS cluster.
Table6:Traceroute divergence
1112.0.1.16ms
2112.124.182.1715ms 3112.123.1.107ms
4112.122.5.2467ms
5112.122.2.17325ms
6112.122.2.20631ms
7112.122.2.4134ms
8112.122.2.2668ms
9112.122.2.12175ms
10112.123.145.2572ms 11112.124.23.672ms
12***
13112.25.192.18173ms
Figure2:Proximity evaluation using traceroute diver-gence
Figure2shows the cumulative distribution of traceroute divergence for the sampled client-LDNS pairs.About 14%of them have traceroute divergence of1.The mean divergence varies from5.8to6.2depending on the probe site,and the median traceroute divergence is4from all four probe sites.This means that a large fraction of clients are topologically quite close to their local DNS servers using the hop count metric.At most30%of the client-LDNS pairs have traceroute divergence of size8. This result is slightly inconsistent with the results de-scribed by Shaikh,et al.[15]considering1,090client-LDNS pairs of dial-up ISPs.We believe that the dif-ference can be explained by the fact that our results are based on the analysis of a much larger set of populations visiting both commercial and educational sites.
The absolute values of traceroute divergence may not be completely indicative of the proximity of a client to its
Figure3:Ratio of common to disjoint path length local DNS server.In Figure3,we plot the ratio of the common path length to the disjoint path length from a probe https://www.wendangku.net/doc/c012407652.html,ing the terminology of Shaikh,et al.[15], the common path length is the minimum number of net-work hops of the shared path from the probe site to the local DNS server and the client before their paths di-verge.For example,the common path length of client 112.74.197.163and its LDNS112.25.195.1(shown in Table6)is.The disjoint path length is the maximum number of network hops of the diverg-ing paths.In this example,the divergent path length is max(14-11,13-11)=3.Again,we use the last point of the divergence as the reference point.For all probe sites, less than34%of the client-LDNS pairs have disjoint paths at least as long as the common path.This means that at least66%of client-LDNS pairs have a common path as long as or longer than their disjoint path.This metric implies that most clients are topologically close to their LDNS as viewed from a randomly chosen probe site.
3.3Round-trip time correlation
Some CDNs select servers based on the round-trip la-tency between the CDN server and the client’s local DNS server[15].It is therefore important to understand the correlation between the round-trip delay to a client and to its LDNS from a third location.
To compare with the results presented in[15],we study how the round-trip delays to the client and its LDNS de-termine the accuracy of the CDN server selection based on round-trip delays to the LDNS.Since our data set consists of more than4.2million pairs of client and
LDNS,much larger than that presented in[15](1,090
pairs),we expect some differences.Let and be the round-trip delays between the probe site and the client,
and between the probe site and the client’s LDNS,re-
spectively.We ask the question whether implies .Depending on the locations of two probe sites and,the percentage of violations ranges from17%to 38%.For instance,among the9,360client-LDNS pairs
responding to traceroute from both probe site1and2,
about38%violate this assumption.This implies that if one selects between two CDN servers located at probe sites1and2based on the round-trip delays to the LDNS, the decisions would be suboptimal38%of the time for the set of clients considered based on the round-trip de-lay metric.On the other hand,among the7,895pairs re-sponding to traceroute from both probe site2and4,only 17%violate this assumption.This means that this metric is highly dependent on probe locations.However,it is a reasonable metric for use to avoid really distant servers.
Another interesting question to answer is whether,if two CDN servers are roughly an equal distance from the LDNS based on the round-trip delay,the same holds from the client’s perspective.Thus,we ask whether
implies,where is a small number(e.g.,a10ms threshold was used by Shaikh et al.[15]).In the sample of our study,it holds in44–75% of the cases depending on the probe sites.This num-ber is bigger than the previously obtained result of12% in[15].
3.4Improved local DNS con?guration
For the client and local DNS associations that are not in the same network cluster,we ask whether there exist any local DNS servers in those clusters.From our log, we collected a set of local DNS servers.Thus,assum-ing the clients have access to those local DNS servers in their network clusters,it is interesting to examine the degree of improvement if all LDNS servers were used optimally.This assumption is not unreasonable,since most IP addresses in the same network cluster are under the same administrative control.From Table4,we can calculate the number of client ASes and network clusters where there are no local DNS servers as observed in our log.There are such AS clusters, and such network clusters. Table7compares the improved percentages of client-LDNS associations and HTTP requests in the same clus-ter with the original results.If the clients in our data cur-rently con?gured to use a LDNS in a different cluster are allowed to use an LDNS in the same cluster,then at least 92%of the HTTP requests come from clients using the Table7:Improvement of the percentage of the client-LDNS associations sharing the same cluster using opti-mal LDNS assignment
Metrics Client IPs HTTP requests
Original Original
64%69%
Network cluster66%70% LDNS in the same AS cluster.That number is70%for network clusters.
3.5Clients using multiple local DNS servers Some client IP addresses in our data are associated with multiple LDNS IP addresses.This may happen due to the following reasons:(1)The?rst LDNS server the client contacts times out and the second LDNS server is contacted.(2)The client’s LDNS server is con?g-ured by a DHCP server that assigns the LDNS server IP addresses from a set of addresses in a round-robin fashion.(3)A client may be con?gured to round-robin among multiple LDNS servers.(4)The client IP address is reused at different times by different users and these users may have different con?gurations for their LDNS servers,resulting in different associations.(5)The client IP address is that of a NAT box or a application-level proxy,so there are multiple actual clients behind this IP address using different LDNS servers.(6)The client is miscon?gured.
Here we examine the distribution of the LDNS servers with which a client IP address is associated.If they all occupy the same cluster as the client,DNS-based server selection can use the local DNS server’s IP address to estimate where the client is even if the client uses multi-ple local DNS servers.However,if they occupy multiple clusters or a single cluster different from the client,it is more dif?cult to use DNS-based server selection.In Ta-ble8,we show how many clients use ten or fewer local DNS servers.In addition,we observe that some IP ad-dresses are associated with up to330local DNS servers occupying up to273different network clusters.Further investigation shows that some of these addresses belong to cache proxies.In general,we observe that the more LDNS servers with which a client IP address is associ-ated,the lower the percentage of associations with the client and LDNS in the same cluster.Fortunately,the majority of client IP addresses are associated with a sin-gle LDNS server.They are responsible for about52% of the requests.However,only about20%in this group
Table8:Clients using ten or fewer multiple local DNS servers
#of clients%of total
(avg#of with client and
requests
the same NAC 2,524,939(78.064)51.8
522,228(16.146)22.4
123,524(3.819)10.4
41,422(1.281) 4.9
13,469(0.416) 2.5
4,555(0.141) 1.8
1,590(0.049) 1.3
713(0.022)0.7
461(0.014)0.7
273(0.008)0.5
4Only7,894of all associations can be reached from both probe sites2and3.
Table9:Comparison of four proximity metrics
Proximity metric
78%in the same cluster
23%in the same cluster
16%:TD=1,32%:TD=2 (TD)
65%:
RTT correlation
62%:
,
5The main tradeoff here is fewer peering links traversed in multi-ISP CDNs versus less traf?c between access and backbone routers as well as lower costs in single-ISP CDNs.
found only16%of these clients have their LDNS in the same network cluster.For clients with their LDNS in different network clusters,the CDN would most likely resolve the DNS query from a client’s LDNS to the CDN server in the LDNS’s cluster and not the cluster where the client resides.In reality,and as we show below,even the biggest CDN today does not have a CDN server in every network cluster.Thus,it is important to examine the impact of DNS-based redirection in a commercial content distribution setting.
We assume that on average a CDN server within the client’s AS/network cluster or smaller traceroute diver-gence(TD)is closer than one in a different cluster or larger TD.For clients with CDN servers in their clusters, if a CDN selects a server not in a client’s cluster,this may be a suboptimal decision in terms of proximity.We also assume that CDNs attempt to optimize for proxim-ity in most https://www.wendangku.net/doc/c012407652.html,work bandwidth is less important, since the content delivered by these CDNs is relatively small in size.Although CDNs may also incorporate the avoidance of overloaded servers in their server selection algorithms,we believe that our assumption is reasonable because CDNs today are highly overprovisioned from the perspective of server capacity.Furthermore,we re-peated our experiments on separate dates to avoid any possibility of a skew due to a?ash event,and the results were always similar.One limitation in our results below is that we do not quantify suboptimal server selection in terms of end user performance,nor how close it is to the optimal server selection.
We?rst describe our measurement methdology then use AS/network clustering and traceroute divergence to study how the proximity between client and LDNS af-fect DNS-based server selection in three commercial CDNs.
4.1Experiment methodology
We use the following three data sets for our study.
1.Client-LDNS associations.These associations be-
tween clients and their LDNS servers are obtained from our measurement study.
2.LDNS-CDN server associations.For a given
CDN,these associations map LDNS servers from the?rst data set to the CDN servers selected by the CDN when resolving a query from these LDNS servers.
3.Available CDN servers.This data set represents a
list of CDN servers available in a given CDN.In the?rst data set,we sampled42,991LDNS servers from our measurement study.We obtained the second data set by sending DNS queries to these42,991LDNS servers using the dig command for a domain name of a Web site that we know is a customer of a given CDN. 27,918of these LDNS servers do not use access con-trol and hence answered the queries from our machines, as if these machines were their clients.To answer our queries,these LDNSs recursively resolved our queries with the CDN in question.The server selected by the CDN for this DNS query is exactly the same server that would be used by any real client associated with this LDNS,as if that client and not our machine initiated the DNS query.6
The third data set was obtained in a similar way,except we added a large number of additional LDNS servers to the27,918LDNS servers above,for a total of41,754 different local DNS servers.This is to increase the like-lihood of?nding all CDN servers of a particular CDN for a given domain.The extensive list of geographically distributed LDNS servers was obtained from DNS server logs for a large Web site.The set of servers to which a given CDN resolved queries from these LDNSs repre-sents the servers available in this CDN at the time of the experiment.We obtained our second and third data sets at around the same time each day to?nd the set of servers available to a CDN at the time it performed its server selection in the second experiment.
Note that our set of available servers is conservative, since we might not have discovered all available CDN servers.However,if a CDN performs a suboptimal server selection among a subset of all available servers, its server selection will remain suboptimal for a larger set:suboptimal means that we already found a closer server to the client than the one selected by the CDN.A superset of the list of servers would suffer from the same suboptimal assignment.
Many CDNs claim a much larger number of caches. However,CDNs do not utilize all servers for all Web sites and many of their locations may contain multiple caches.The statistics we gathered are for a particular domain served by a CDN.For example,when examining multiple different domain names served by the largest CDN in our study,we found multiple CDN IP address sets of approximately equal size which only partly over-lapped.Each unique server IP address we discover may also account for multiple servers.
Table10:CDN cache servers for a particular domain name
#of network
clusters servers IPs
with servers
6221,567
120195
60154 Table11:The evaluation of server selection according to AS clustering
CDN CDN Y
1,679,515618,897 server in cluster
Veri?able clients961,382 Misdirected clients752,822
(60%)(82%) (%clusters occupied)(94%)
MC w/LDNS
443,394262,713 (%misdirected
(55%)(60%) Table10shows the statistics of the CDN server IP ad-dresses of the three CDNs studied for a single domain name obtained on August7,2001.These numbers were fairly stable during the course of our study.All three CDNs examined appear to redirect client requests by us-ing DNS,although they may differ in the details of the algorithms.This table lists the total number of CDN servers discovered and the number of AS and network clusters these CDN servers represent.The data in Table 10con?rm our conjecture that CDNs today cover only a small number of all available network clusters for a sin-gle domain they serve.While the overall list of LDNSs used for generating the third data set represents5,788 AS and21,786network clusters,the discovered CDN servers represent only a small fraction of these,even in the case of the largest CDN in our study.
With the three data sets above,we evaluate the quality of server selection by these CDNs by examining what percentage of clients are actually redirected to servers in their own cluster,among those clients that have at least one server in their cluster.Table12:The evaluation of server selection according to network clustering
CDN CDN Y
264,743103,448 server in cluster
Veri?able clients132,567 Misdirected clients125,449
(68%)(96%) (%clusters occupied)(82%)
MC w/LDNS
145,27684,737 (%misdirected clients)(93%)
anomaly of clients belonging to a small number of clus-ters,we also show in the third row the percentage of clusters occupied by these clients relative to the total
number of clusters of veri?ed clients.The cluster per-centage values are at least as big as the client percentage values.This means that the misdirected clients are fairly spread out in the number of clusters they occupy.
We conjecture that the reason that these clients are mis-directed is that their LDNS servers are topologically dis-tant from these clients.CDNs select a server close to the LDNS servers.The servers selected may therefore be suboptimal from the client’s perspective.The last row of the tables shows misdirected clients with their LDNS outside their clusters.This row indicates the number of clients that inherently cannot be directed to the most proximal server using a DNS-based mecha-nism.According to Table11,for AS clustering,they represent only half of misdirected clients.To understand why CDNs choose a CDN server in a different AS than the one containing the client and its LDNS server,we sampled a dozen of these clients using traceroute fol-lowed by DNS name resolution of the last-hop router IP address to estimate the geographic locations7of the client,CDN servers in the client’s AS,and selected CDN servers in a different AS.We found that in most cases, the selected CDN servers by CDNs are geographically closer to the client than CDN servers in the same AS. Assuming peering links between the client’s AS and the selected CDN server’s AS are not congested,redirect-ing to a nearby CDN server in a different AS may be a better decision than redirecting to a distant CDN server in the same AS.This observation also con?rms our?nd-ing that AS clustering is a very coarse-grained metric for evaluating proximity.
For network clustering,the last row of Table12indicates that an overwhelmingly majority of misdirected clients have their LDNS servers in a different network cluster. This con?rms our hypothesis that such misdirection is due to the fact that clients and their LDNS servers are of-ten not proximal.It also shows the usefulness of network clustering because it is a?ne-grained metric for eval-uating proximity.We emphasize that we do not know the exact server selection policy used by a commercial CDN,so we cannot fully evaluate the effectiveness of its server selection decisions.However,given that there is such a strong correlation between misdirection and an LDNS being in a different cluster,we can infer that when the LDNS and client do not belong to the same network cluster,this limits the accuracy of server selection.
CDN X
2,105
Clients with CDN servers at smaller1,724 TD than ones redirected to(79%)
11
Median TD of closest CDN9 servers to clients
6
8We were unable to include CDN Y in the traceroute experiment, since most of its CDN servers are unreachable using traceroute.
on AS numbers and domain names to decide whether a client and a nameserver did in fact belong together. This heuristic removed miscon?gured client-nameserver pairs and did not assure the correctness of associations. They also obtained a set of1090client-LDNS associa-tions from accounts with9commercial ISPs to study the proximity correlations.
In comparison,our method provides accurate associa-tions eliminating any need for validation.Furthermore, our study has more than4.2million associations,con-sisting of clients from a diverse set of ISPs,far exceed-ing their data set of1090associations.
More recently,Bestavros,et al.[4]have also developed a method for?nding client-LDNS associations by assign-ing multiple IP addresses to a Web server and correlat-ing DNS lookups with client IPs based on the server IP used.This method is slow in discovering client-LDNS pairs due to the limited number of IP addresses a Web server can have.In addition,their method is complicated to implement,requiring reassignment of server IPs and modi?cation of the Web server.
Compared to both works,the distinguishing features of our measurement methodology are ef?ciency,nonintru-siveness,and accuracy.This allowed us to collect more extensive data,which we used to evaluate the effective-ness of DNS-based server selections using four different proximity metrics in several real-world CDN settings. To our knowledge,we are the?rst to conduct such an ex-haustive proximity evaluation between clients and their local DNS servers using such a representative data set. We are also not aware of other work in examining the impact that the proximity between the local DNS server and the client has on DNS based server selection in com-mercial CDNs.
There has been a recent effort within the IETF to cat-egorize different mechanisms for request routing in CDNs[3].DNS-based redirection is one of those mech-anisms,and our methodology may prove useful in eval-uating the effectiveness of this technique in that context.
6Future work
There are three areas of future work we plan to pursue. First,we plan to study the hidden load factors due to dif-fering amounts of HTTP load corresponding to a DNS name resolution request from an LDNS server.With the help of a busy Web site,we will be able to gather statis-tics on the number of HTTP requests and clients behind each LDNS server.Identifying LDNS servers resulting in large numbers of HTTP requests is essential for proac-tive load balancing and?ash crowd protection. Second,we plan to improve existing DNS-based server selection algorithms by considering the properties of known client-LDNS associations for an LDNS that re-quests a server name resolution.The following charac-teristics of the associations can be explored based on data collected using our methodology:known client proximity to the LDNS,known client distribution,and hidden load factor.
Given a name resolution request from an LDNS,if the known client proximity to the LDNS is good,then a CDN server close to the LDNS would also be close to its clients.If the proximity correlation is low,known client distribution and client cluster request patterns would be considered.If the majority of HTTP requests belong to a single network cluster,?nding a CDN server close to or within that network cluster would also be close to clients issuing a majority of requests.Along with these factors, the hidden load factor of the LDNS is also considered to select lightly loaded CDN servers for an LDNS with a large hidden load factor.If the proximity correlation is low between LDNS and its clients,then server selection is optimized using other metrics such as server load. Finally,we would like to apply the results of this work to improving content distribution internetworking(CDI), which refers to the interoperation among multiple CDNs for additional?exibility.A prototype of CDI,called CDN Brokering[6],uses a DNS-based brokering mech-anism to forward requests among DNS servers of the interoperating CDNs.As a third area of future work, we plan to improve CDN brokering algorithms by us-ing hidden load factors and client-LDNS proximity in-formation.The client-LDNS proximity?ndings in our work justify DNS-based brokering,because the major-ity of the clients and their LDNS belong to the same AS. 7Conclusion
In this paper,we propose a novel technique for?nding client and local DNS server associations and potentially hidden load factors in a fast,non-intrusive,and accu-rate manner.Based on the results,we evaluate the prox-imity between clients and their LDNS using four met-rics:AS clustering,network clustering,traceroute diver-gence,and round-trip time correlation.
We evaluate the potential effectiveness of DNS-based server selection in CDNs based on these metrics.We conclude that DNS is good for very coarse-grained
server selection,since64%of the associations belong to the same AS.DNS is less useful for?ner-grained server selection,since only16%of clients use a DNS server in the same network-aware cluster.These values can be im-proved to88%and66%respectively,if clients are con-?gured to use a closer local DNS server.Since current CDNs are not present in many network-aware clusters, we conclude that although DNS-based server selection has inherent limitations due to potentially poor proxim-ity correlation between a client and its LDNS,the impact is small due to the sparse distribution of CDN servers in today’s CDNs.
At least one CDN has stated a goal of ultimately placing CDN servers in every edge network.The high fraction of clients using LDNS servers in different network-aware clusters suggests that CDNs may be unable to use DNS request routing for such?ne-grained server selection un-less DNS itself scales to provide each edge network with a local DNS server that communicates directly with the Internet.Thus,from an economic perspective,due to the inherent limited precision of DNS-based server selec-tion,it is less bene?cial to have so many CDN servers that the performance to two nearby servers is indistin-guishable.
In addition to the proximity evaluation and the novel measurement methodology,our work also provides two additional contributions in improving DNS-based CDNs in general.From our observation,client-LDNS asso-ciations are fairly static.Thus,CDNs can build up a database of such associations to infer the geographic lo-cation of clients associated with an LDNS IP address to improve server selection.Furthermore,based on the URL-rewriting technique in our measurement method-ology,CDNs can completely eliminate the originator problem by embedding the client IP addresses in the URLs of the Web pages,when a client initially requests the base page.
8Acknowledgement
We thank all participants in our measurement study.We especially thank Ted Kowalski of AT&T,Danielle Gallo of AT&T Research,Alex Brown and Milan Andric of UC Berkeley,Frans Kaashoek of MIT,and Mike Dahlin of UT Austin for generously offering their main Web pages for our study.We are grateful to Robert Szewczyk and Alec Woo for instrumenting the TinyOS Web page. We also thank Hyunseok Chang,Yuan Gao,Jason Hong, David Oppenheimer,Amit Sehgal,Wilson So,and Hao Zhang,who took part in our measurement using their personal Web pages.References
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医科达Precise型直线加速器真空故障维修研究
医科达Precise型直线加速器真空故障维修研究 发表时间：2019-07-18T12:42:30.247Z 来源：《科技尚品》2018年第10期作者：丁春江冯惠华戴晓敏梁兵梁卫聪 [导读] 随着我国科学技术的发展，医学设备也在不断的增加，而医科达Precise型直线加速器作为医院重要的医学设备，提高对其的故障检修是其中的重点和关键，本文从医科达加速器的概述、真空系统的组成以及医科达Precise型直线加速器真空故障维修等方面进行简要的分析和研究，进而为研究故障出现的原因和因素，针对其因素采取相应的解决方式，进而保障其加速器的正常运行。 开平市中心医院放疗中心 前言 在医院的临床手术中，医科达Precise型直线加速器是其中的重要设备，它的作用是避免加速管内放电击穿，防止电子枪阴极中毒，即钨丝材料的热子或灯丝氧化，减少电子与残余气体杂质的碰撞损失，因此，对医科达Precise型直线加速器真空系统的故障进行排除是其中的重要工作，在故障的排除中，要加强对原因的分析，并对故障进行科学的检修，进而保障加速器的正常使用。 1.医科达加速器的概述 1.1特点 在进行医科达加速器的真空系统的维修中，要先对医科达加速器进行熟悉和了解，通过对其结构和特征的熟悉，能够更加针对的预测其故障发生，其主要有以下几个特点： 首先，其加速器在使用的过程中可以避免加速管内放电击穿，进而提高加速器的使用效率，故此，该加速器具有较大的优势。 其次，在医院的使用中，医科达加速器可以避免出现钨丝材料灯丝或者热子氧化的问题，同时防止电子枪的阴极出现中毒的问题。 最后，医科达加速器能够有效降低电子和残余气体发生碰撞的概率，避免造成势能损失。 由此可见该直线加速器在医院的使用中能够提高其使用效率，并为医院的手术等提供高效率的使用，保障手术的正常进行。 1.2结构 在对医科达Precise型直线加速器进行分析中，要先对其结构进行熟悉，医科达Precise直线加速器是行波加速，用的是可拆卸密封的行渡加速管，其结构示意图如下： 从上图中可以看出，医科达Precise直线加速器的结构较为复杂，在进行故障的维修过程中，呀针对其结构进行故障分析，进而得出故障所在。故此，要加强对医科达直线加速器的结构分析，进而提高维修效率。 2.真空系统 在医科达Precise型直线加速器真空系统中，要对真空系统进行分析，其加速器的真空系统构成较为复杂，在进行故障的分析中，可以借助科学技术和信息技术对医科达直线加速器的真空系统进行检测，进而对其故障因素进行分析，进而保障真空系统的安稳运行，同时，加速器对真空的检测主要是通过对离子泵的工作电流的检测换算过来的，真空监测主要是加速器可以对真空度、真空联锁以及真空电路进行监测，在监测的过程中可以对加速器的情况进行观察。 3.医科达Precise型直线加速器真空故障维修 3.1故障表现 在对医科达Precise型直线加速器真空维修前要先对故障表现进行了解，进而能够快速的找到其故障所在。医科达加速器在使用的过程中，如果长时间的没有进行检修，其真空系统就会出现故障，进而影响加速器的使用，因此，在使用加速器要定期对其进行检修，医科达Precise直线加速器使用时起，如果在6MV电子线治疗模式时剂量不稳，同时低剂量联锁，治疗暂停，经常报Magnetron timer，其它电子线及X线治疗模式均正常，这时就是出现故障，需要对其加速器进行检修，这时，需要对加速器进行检修，故此，在医科达Precise型直线加速器使用过程中，要加强对其加速器故障的检测，如果一旦出现故障要及时进行处理，避免由于故障而影响其使用。 3.2故障分析和排除 在对医科达Precise型直线加速器的故障分析中，首先要先对其真空系统出现故障的因素进行分析，一般医科达直线加速器的真空故障有两个原因，其一是可能加速器的真空系统有泄漏点，该泄漏点导致真空系统出现故障，进而影响其正常使用，其二，可能加速器的 离子泵泵体故障损伤无法工作，这些是加速器真空系统可能出现故障的因素，通过对因素的分析进而对故障进行分析，在医科达工程师到达后，初步诊断为离子泵性能减退，通过测试，关闭枪端钛泵，靶端真空值上升，但是真空值还是未达到工作条件。再次判断为真空系统有泄漏点，在对故障分析完成之后，要对其可能出现的故障进行排除，找出真正出现故障的因素，在进行故障排除的过程中，针对一些可能出现的特殊故障要采取针对性的措施，进而进而提高故障排除的效率。 3.3故障处理 故障处理是加速器真空系统运行中的重要环节，在对医科达Precise型直线加速器真空系统的故障分析和排除之后，要对其故障进行处
医科达电子直线加速器技术参数(上海
医科达电子直线加速器技术参数 1、双模式的数字化加速器，提供宽范围的X线和电子线能量，充分满足放射治疗外照射的临床需要。 2、射线束能量：多能量可定制性：多至2档X射线能量(4~18MV)和6档电子线能量(4~20MeV) 3、主机性能及配置： （1）独特设计的滚筒式机架：高度可靠性和稳定性，开放的机架结构，便于维修，最低的等中心高度（124cm），最大的等中心到治疗头的净空间距离45cm。 （2）高效能的行波加速管：行波加速管二十年无条件保用，允许较低的电压梯度，对行波加速管的真空要求低，使电子枪等部件可快速拆卸并易于更换。 （3）大功率FasTraQ磁控管：专门的紧凑型微波功率源，5MW功率输出，具有快速调谐的能力，快速的束流切换特性<0.1秒，提供24个月的保用期。 （4）滑雪式偏转系统：完全的消色散系统，并维持射束的对称性，伺服控制的三极磁偏转系统，精确的靶点聚焦，极佳的半影。（5）可单独拆卸更换灯丝的电子枪：电子枪伺服系统反应快速，确保束流能量的精度。
（6）六通道开放式结构的电离室：最新型超薄壁陶瓷材料电离室，自动校正KTP（温度、湿度、气压），监测射线的剂量、对称性和平坦度，具有长期的高灵敏和高稳定性，适合精确的伺服控制射线束流，重复精度：+/-0.5%，线性精度：+/-1%，2-10MU时的线性精度对保证IMRT的放疗精度尤其重要，旋转（运动束流）投照时的稳定性：±1%，分辨率：0.1MU。 （7）运动系统：用于操纵治疗头、机架及病人床的运动，手控盒可操纵加速器的所有动作，治疗头上有四个控制钮，可操纵治疗头的所有运动，治疗床两边各有一个控制板，可操纵床的所有运动，所有运动都是无线调速。 （8）安全连锁系统：通过硬件限位和软件防碰撞二种方式，确保病人和操作人员的安全。 （9）真空系统：维持加速管和电子枪的真空状态，在加速器中有效使用离子泵，有助于减少能源消耗，保护环境，并维持高的开机率。（10）水冷系统（内循环）：保证加速器的频率稳定，进而保证能量的稳定，用于加速器的热交换。 4、控制系统：全新的第三代全集成、全数字控制系统，确保更为平顺的流程工作方式，有效地提高治疗病人的周转率，基于Windows 平台的图形用户界面，易学习和使用，模块化软件结构，配置安装各
医科达直线加速器参数
Precise全数字直线加速器 双模式的数字化加速器，提供宽围的X线和电子线能量，充分满足放射治疗外照射的临床需要。 具有如下详述的特征和配置： 1.0 射线束能量 Precise数字化加速器具有无可匹敌的多能量可定制性：2档X射线能量（4~15MV）和9档电子线能量（4~22MeV） 2.0 Precise全数字直线加速器主机系统包含如下特性： 独特设计的滚筒式机架直线加速器 -由强劲的刚性结构带来的高度可靠性和稳定性 -开放的机架结构，便于维修，需维护的重要部件均分布在易于接近的位置 -最低的等中心高度（124cm），具有最优的临床可用性 -最大的等中心到治疗头的净空间距离45cm 高效能的行波加速管 -行波加速管二十年无条件保用 -允许较低的电压梯度，对行波加速管的真空要求低，使电子枪等部件可快速拆卸并易于更换 大功率FasTraQ磁控管： -专门的紧凑型微波功率源，5MW功率输出，具有快速调谐的能力 -快速的束流切换特性<0.1秒 -提供24个月的保用期 独有的滑雪式偏转系统： -完全的消色散系统，并维持射束的对称性 -伺服控制的三极磁偏转系统 -精确的靶点聚焦，极佳的半影 可单独拆卸更换灯丝的电子枪 -电子枪伺服系统反应快速，确保束流能量的精度 -易于更换，维护费用低 六通道开放式结构的电离室 -最新型超薄壁瓷材料电离室 -自动校正KTP（温度、湿度、气压），监测射线的剂量、对称性和平坦度 -具有长期的高灵敏和高稳定性，适合精确的伺服控制射线束流 -重复精度：+/-0.5% -线性精度：+/-1% -2-10MU时的线性精度对保证IMRT的放疗精度尤其重要 -旋转（运动束流）投照时的稳定性：±1% -分辨率：0.1MU 运动系统 -用于操纵治疗头、机架及病人床的运动 -手控盒可操纵加速器的所有动作
加速器相关英语缩略语定义(医科达)
加速器缩略语定义Abbreviation Definition ABCActive BreathingCoordinator主动呼吸协调器 ACBarm control board 臂控制板 AFCautomatic frequencycontrol 自动频率控制 AFDaxis filmdistance 轴向膜距 AIangiographicimage血管造影图像 ALARPas low as reasonablepracticable尽可能低 AmSiAmorphousSilicon 非晶硅 APSAutomatic PositioningSystem?自动定位系统? ASUassisted setup辅助设置 BCBendingCoarse弯曲粗 BEVbeam eyeview光束视角 BFBendingFine弯曲细度 BLDbeam limitingdevice限束装置 BLSbeam limitingsystem束流限制系统 BMPBitmap (imageformat)位图(图像格式) BOMbill ofmaterials材料单 CANcontroller area network控制器局域网 CATcustomer acceptancetests客户验收试验 CAXcentralaxis中央轴，中心轴 CBcircuitbreaker]断路器 CCcouchcontrol 床控制 CCPCAN calibrationprotocol CAN校准协议 CCTVclosed circuittelevision 闭路电视 CDcompactdisc CITP clients interface terminal panel (interface between Elekta product and client hardware)客户界面终端面板（Elekta产品与客户端硬件之间的接口） CMMcorrective maintenancemanual校正维护手册 CMUMClinical Mode User’s Manual 临床模式用户手册 CRPcommon referencepoint 公共参考点 CTComputerizedTomography计算机X射线断层
感受现代科技
感受现代科技 【学习目标】 1、知识：感受现代科技给人类生活带来的新变化，认识科技与生活，科技发展与社会发展的关系，懂得“科学技术是第一生产力”的道理。 2、能力与情感：感悟现代科技的神奇与力量，理解科技是社会发展的强大推力，激发学生 对科技重要性的认识，增强学生对科学的兴趣，培养学生热爱科学的精神。 3、过程与方法：依据教学内容和学生的认识规律设置了“课前预习”、“课堂助学”、“课堂巩固”、“课后拓学”、“教学反思”五个模块的教学整合，运用多媒体等教学手段，采用自主体验、 探究活动、案例情境等方法来完成教学目标。 【学习重点、难点】 领略现代科技的神奇与力量，理解“科技是第一生产力”。 【学习过程】 一、预习初探： （一）快快行动，书外的知识真有趣： 1、生活体验：观察生活，请你说说我们身边有哪些科技产品？例举实例说说这些科技产品给我们的生活带来哪些新变化？ 2、想象天地：展现你的想象天份，想象你准备发明一样科技产品，使你的未来生活更美好。 3、图片收集：上网收集有关科技产品的图片，准备创办科技小展览，领略现代科技的神 奇与力量。 （二）阅读课本，书本的知识真寻味： 4、我们现在的生活与科技________________。丰足的衣食，舒适的住行，千百年来一直是人类_________________。 5、科学技术是________________的强大推力，是________生产力。______________已成为当代经济发展的火车头。 6、________________是人类文明的标志。科学技术的进步为人类创造了巨大的 ______________和_________________。
医科达加速器离子泵监护装置的设计与实现
研究论著RESEARCH WORK 引言 随着放疗技术的发展，放疗作为肿瘤治疗的三大主要治疗手段之一，越来越受到广大肿瘤患者的认可，约有75%的患者需要采用放射治疗[1]。目前，国内外医院放疗用设备主要是瑞典的医科达和美国的瓦里安医用直线加速器，医科达加速器具有电子枪可拆卸的行波加速管，加速管内的真空度靠枪端和靶端的两个溅射式离子泵维持[2]。由于加速管可拆卸接口较多，密封度很难保证，两个离子泵必须24 h通电工作以维持加速管内真空度在10-6 mbar以下[3-4]。当发生断电几个小时以上，真空度下降[5]，来电后离子泵开始工作时负荷较大（或发生局部漏气时），泵体温度上升[6]。虽然工作电流大到一定值时，离子泵电源会因过流而自我保护，但此时离子泵的温度已达100℃以上，远远超出了其正常工作的温度[7-9]。如果不及时断电冷却，会严重影响其工作性能和使用寿命，甚至直接烧坏[10-12]。因加速器上没有对离子泵工作状态的指示和提示，工作人员对这种情况不能及时发现，直至加速管真空度下降到出现连锁保护。更危险的是夜间或周六、周日无人值班时发生以上情况。两个离子泵的价格50余万元，不但会导致严重的经济损失，而且影响病人的正常放疗。为解决这一问题，笔者设计了一种离子泵监护装置，可有效地避免以上情况的发生。 1 设计方法与工作原理 1.1 离子泵的结构 加速管真空系统是由枪端和靶端两个溅射式离子泵维持，其内部结构见图1。阳极为纵横排列的薄壁不锈钢筒，阴极为放置于阳极两端的两块钛板，阳极、阴极一起封装于不锈钢的外壳中，壳外加一U形永磁体，磁力线方向平行于阳极筒轴，垂直于阴极钛板。阴阳极之间加有7.3 kV 的直流高压，泵体内的残余气体分子在正交电磁场的作用下发生潘宁放电，产生的阳离子轰击阴极钛板，溅射出的钛原子在阳极筒内壁和阴极轰击较少的部位形成新鲜的钛 医科达加速器离子泵 监护装置的设计与实现 魏绪国1a，李修磊1a，王宏英1a，张明臣1b，王绪刚1a，王永明2 1. 聊城市人民医院 a. 放疗科；b. 设备科，山东聊城 252000； 2. 南京君茂医疗器械有限公司，江苏南京 210000 [摘 要] 目的 为避免医科达加速器离子泵高温损坏，及时发现工作异常，设计一种离子泵监护装置。方法选取我院小儿康根据枪靶端离子泵的工作特性和结构特点，设计一种有效的监护方法，并将各监护部件集成于监控箱内，安装在加速器机架的合适位置。结果经过应用检验，该装置实现了离子泵工作温度的实时显示、超温报警及断电保护功能，达到预设目标。结论离子泵监护装置安装简单可靠，能有效地保护离子泵，弥补了加速器设计的缺陷，避免了经济损失。 [关键词] 直线加速器；离子泵；真空度；离子泵监护装置 Design and Implementation of the Monitoring Device for the Elekta Accelerator Ion Pump WEI Xuguo1a, LI Xiulei1a, WANG Hongying1a, ZHANG Mingchen1b, WANG Xugang1a, WANG Yongming2 1. a. Department of Radiotherapy; b. Department of Equipment, Liaocheng City People’s Hospital, Liaocheng Shandong 252000, China; 2. Nanjing Junmao Medical Devices Co., Ltd., Nanjing Jiangsu 210000, China Abstract: Objective In order to avoid the high temperature damage of the Elekta accelerator ion pump and detect the abnormal work in time, a monitoring device of ion pump was designed. Methods According to the working characteristics and structural characteristics of the ion pump at the gun target, an effective monitoring method was designed. The monitoring components were integrated into the monitoring box and installed in the proper position of the accelerator frame. Results After application test, the device could realize real-time display of working temperature of ion pumps, over temperature alarm, and power failure protection function, suggesting the achievement of the preset target. Conclusion The ion pump monitoring device is simple and reliable, which can effectively protect the ion pump, make up for the defects of accelerator design, and avoid economic loss. Key words: linear accelerator; ion pump; vacuum degree; ion pump monitor [中图分类号]TH774 [文献标识码] A doi：10.3969/j.issn.1674-1633.2019.04.020 [文章编号] 1674-1633(2019)04-0076-04 收稿日期：2018-09-29 修回日期：2018-11-05 作者邮箱：sdlcwxg@https://www.wendangku.net/doc/c012407652.html, 中国医疗设备 2019年第34卷 04期 V OL.34 No.04 76
医用直线加速器比较表
医用直线加速器比较表 厂商Elekta VARIAN SIEMENS 说明 机型Precise Clinac EX Primus 基本结构Elekta加速器的高度集成化控制系统、性能绝佳的敞开式设 计保证可加速器的高开机率。其他厂家的产品都是封闭式设 计，常因机器设计不佳而停机，更换加速管时间长。 加速管行波驻波驻波最低的加速器使用消耗费用：驻波加速管对真空度的要求、能 量的转换、能谱的宽度等几个方面都优于驻波加速管。Elekta 保持和发展了行波管加速原理，通过独特的设计使一台加速器 可提供3档电子线。一机多用。其他厂家加速器最多产生两个 光子线。Elekta加速器具有低功耗、高效率、长寿命，自1953 年生产世界上第一台直线加速器以来，从未更换过加速管。 机架类型滚筒式中心轴承式中心轴承式Elekta滚筒机架磨损小，等中心变化小，十年精度1mm，终身 保证机架等中心精度在2mm（V和S都采用中心轴承式，十年 等中心偏差超过2mm），且为敞开式设计，散热性能好，连续 工作时间长，便于维修。 微波功率源仅用磁控管（ 5.5MW） 即可需速调管（5.5MW）加 微波驱动 需速调管（7MW）加 微波驱动 Elekta使用EEV公司的长寿命磁控管，停机时间短，运行费 用低，且无条件保修2年。 低运行费用的微波功率源：Elekta公司采用的微波功率源是 磁控管，集振荡器和放大器为一体，结构简单，不需额外的微 波振荡器（或微波驱动器）等组件，从而简化功率源的结构。 磁控管的体积小，能安装在机架上，直接把微波馈送到加速管， 不需特殊接头，且易更换，停机时间短（而速调管必须配上配 上微波振荡器才能实现磁控管的功能，磁控管的寿命比速调管 短一半，但由于振荡器的寿命与磁控管差不多，导致使用速调 管的费用为使用磁控管的4～5倍。
医科达直线加速器参数(精)
Precise 全数字直线加速器 双模式的数字化加速器,提供宽范围的 X 线和电子线能量,充分满足放射治疗外照射的临床需要。 具有如下详述的特征和配置: 1.0 射线束能量 Precise 数字化加速器具有无可匹敌的多能量可定制性:2档 X 射线能量 (4~15MV 和 9档电子线能量(4~22MeV 2.0 Precise 全数字直线加速器主机系统包含如下特性: 独特设计的滚筒式机架直线加速器 -由强劲的刚性结构带来的高度可靠性和稳定性 -开放的机架结构,便于维修,需维护的重要部件均分布在易于接近的位置 -最低的等中心高度(124cm ,具有最优的临床可用性 -最大的等中心到治疗头的净空间距离 45cm 高效能的行波加速管 -行波加速管二十年无条件保用 -允许较低的电压梯度, 对行波加速管的真空要求低, 使电子枪等部件可快速拆卸并易于更换 大功率 FasTraQ 磁控管: -专门的紧凑型微波功率源, 5MW 功率输出,具有快速调谐的能力 -快速的束流切换特性 <0.1秒
-提供 24个月的保用期 独有的滑雪式偏转系统: -完全的消色散系统,并维持射束的对称性 -伺服控制的三极磁偏转系统 -精确的靶点聚焦,极佳的半影 可单独拆卸更换灯丝的电子枪 -电子枪伺服系统反应快速,确保束流能量的精度 -易于更换,维护费用低 六通道开放式结构的电离室 -最新型超薄壁陶瓷材料电离室 -自动校正 KTP (温度、湿度、气压 ,监测射线的剂量、对称性和平坦度-具有长期的高灵敏和高稳定性,适合精确的伺服控制射线束流 -重复精度:+/-0.5% -线性精度:+/-1% -2-10MU 时的线性精度对保证 IMRT 的放疗精度尤其重要 -旋转(运动束流投照时的稳定性:±1% -分辨率:0.1MU 运动系统 -用于操纵治疗头、机架及病人床的运动
医用直线加速器
医用直线加速器 医用加速器是生物医学上的一种用来对肿瘤进行放射治疗的粒子加速器装置。带电粒子加速器是用人工方法借助不同形态的电场，将各种不同种类的带电粒子加速到更高能量的电磁装置，常称“粒子加速器”，简称为“加速器”。要使带电粒子获得能量，就必须有加速电场。依据加速粒子种类的不同，加速电场形态的不同，粒子加速过程所遵循的轨道不同被分为各种类型加速器。目前国际上，在放射治疗中使用最多的是电子直线加速器。 电子直线加速器 电子直线加速器是利用具有一定能量的高能电子与大功率微波的微波电场相互作用，能量电子直接引出，可作电子线治疗。电子打击重金属靶，产生韧致辐射，发射X射线，作X线治疗。 根据电子与微波电场的作用方式不同，电子直线加速器分为行波加速器和驻波加速器。 一个最简单的电子直线加速器至少要包括，一个加速场所（加速管），一个大功率微波源和波导系统，控制系统，射线均整和防护系统。当然市场上作为商品的设备要远比这些复杂，但这些基本部件都是必不可少的。医用加速器的分类 分类情况 医用加速器按照能量区分可以分为低能机、中能机和高能机。 按照X能量的档位加速器分为单光子、双光子和多光子。 低中高能机的区分主要在于给出的电子线的能量。 医用加速器用于放疗的适应症 1、当其用于常规放疗时其适应症为： 医用加速器适应症广泛，可用于头颈、胸腔、腹腔、盆腔、四肢等部位的原发或继发肿瘤，以及手术后残留的术后或手术前的术前治疗等。 西门子直线加速器 它所产生的高能X线具有照射深度强，射线集中等优点为，不仅能有效杀死癌细胞，而且能保护正常组织少受损伤，是治疗深部肿瘤的理想设备。它优于国产加速器，可以产生六档电子线，为肿瘤治疗提供了更好的方法。 高能X线具有皮肤损伤小，照射量高，保证正常组织效果好的特点，主要用于治疗深部肿瘤，高能电子束，能量可变，可根据不同的肿瘤深度进行调节选择，可用于恶性肿瘤和偏心性肿瘤的治疗，术中放疗也多用于高能电子束。 恶性肿瘤患者的治疗提倡的是综合治疗，放射治疗是不可缺少的手段，约有70%的患者需行放射治疗，医用直线加速器是现今国际上先进的放疗设备。 瓦里安直线加速器 该加速器融合了现代医学影像技术、立体定位技术、计算机、核医学、放射物理、自动化智能控制等多种现代高新科技，可实现对全身肿瘤的常规放射治疗、三维立体定向精确放疗、立体定向放射外科治疗(俗称X-刀治疗)、适形调强放射治疗(诺力刀治疗)等。该直线加速器能最大限度杀死肿瘤，保护人体正常组织提供更加准确、高效、安全的技术保障。 1具有双光子、多档能量的电子线，保证各种不同深度的肿瘤治疗剂量组要。肿瘤隐藏的再深，他也能像海底捞针一样抓住它2。具有多叶准直系统，能完成精确放疗计划。可从三维方向上随着肿瘤的不同形状变化，产生与肿瘤形状近似的照射体积，使治疗更精确。3。具有三维调强治疗计划系统，调强放射治疗是当今世界上
感受现代科技
第十四课感受现代科技 项目一现代科技在身边 教师寄语：今日沟通于昔日之最大差异：在于科技的介入，已超越时间、空间，甚至权利与阶级的围墙。 学习目标： ●情感态度价值观：感受现代科技发展带来的新变化，培养学生热爱科学的情感和品质。 ●能力：培养学生观察、收集、整理、归纳信息的能力。 ●知识：了解科技对社会发展具有推动作用，理解科学技术是第一生产力。 重难点：科技是社会发展的强大推动力 学习过程： 一、课前预习（提前预习课本，顺便做以下小题，相信你能完成!） 1.我们现在的生活与科技_________。 2.___________________是第一生产力。 3._______________已成为当代经济发展的火车头。 4.科学技术是_______________的标志。 5.科技是社会发展的______________。 二、合作探究，共同进步（合作有助于提高学习效率，要努力哦！） 知识点一：现代科技给生活带来新变化 1：（见教材P58页-59页）图片1：液晶彩色电视图片2：利用网络学习 图片3：山里人用上了手机图片4：磁悬浮列车 说一说：观察生活，列举实例，说说现代科技给我们的生活带来哪些新的变化？ 2：阅读教材（P59页教材正文），说说现代科技的发展给人类的生活产生了怎样的影响？（学生讨论交流） 。3：（见教材P59页-60页）想象和推测一下，随着科技的发展，5年、10年、20年、50年后人们的生活可能是怎样的？ 。 知识点二：科技——社会发展的强大推力 1：（见教材P61页），材料中的数字变化说明了什么？（学生讨论交流） 上述材料中的数字变化说明：“”。 2：为什么说“科学技术是第一生产力”？ ① ② 实践证明：高新技术及其产业已经成为当代经济发展的火车头。 3：（见教材P62页）说一说：网络学校的发展将会给人们的学习方式带来了哪些变化？ 4、①科学技术的进步使精神文明建设有了新的载体. 说一说：在思想文化传播的载体方面，你知道有哪些新的传播手段呢？ ②科学技术的进步丰富了人们的 . 5、科学技术的进步为人类创造了巨大的财富和财富 三、课堂小结 通过学习我学会：
《交通规划》课程教学大纲
《交通规划》课程教学大纲 课程编号：E13D3330 课程中文名称：交通规划 课程英文名称：Transportation Planning 开课学期：秋季 学分/学时：2学分/32学时 先修课程：管理运筹学，概率与数理统计，交通工程学 建议后续课程：城市规划，交通管理与控制 适用专业/开课对象：交通运输类专业/3年级本科生 团队负责人：唐铁桥责任教授：执笔人：唐铁桥核准院长： 一、课程的性质、目的和任务 本课程授课对象为交通工程专业本科生，是该专业学生的必修专业课。通过本课程的学习，应该掌握交通规划的基础知识、常用方法与模型。课程具体内容包括：交通规划问题分析的一般方法，建模理论，交通规划过程与发展历史，交通调查、出行产生、分布、方式划分与交通分配的理论与技术实践，交通网络平衡与网络设计理论等，从而在交通规划与政策方面掌握宽广的知识和实际的操作技能。 本课程是一间理论和实践意义均很强的课程，课堂讲授要尽量做到理论联系实际，模型及其求解尽量结合实例，深入浅出，使学生掌握将交通规划模型应用于实际的基本方法。此外，考虑到西方在该领域内的研究水平，讲授时要多参考国外相关研究成果，多介绍专业术语的英文表达方法以及相关外文刊物。课程主要培养学生交通规划的基本知识、能力和技能。 二、课程内容、基本要求及学时分配 各章内容、要点、学时分配。适当详细，每章有一段描述。 第一章绪论（2学时） 1. 交通规划的基本概念、分类、内容、过程、发展历史、及研究展望。 2. 交通规划的基本概念、重要性、内容、过程、发展历史以及交通规划中存在的问题等。
第二章交通调查与数据分析（4学时） 1. 交通调查的概要、目的、作用和内容等；流量、密度和速度调查；交通延误和OD调查；交通调查抽样；交通调查新技术。 2. 交通中的基本概念，交通流量、速度和密度的调查方法，调查问卷设计与实施，调查抽样，调查结果的统计处理等。 第三章交通需求预测（4学时） 1. 交通发生与吸引的概念；出行率调查；发生与吸引交通量的预测；生成交通量预测、发生与吸引交通量预测。 2. 掌握交通分布的概念；分布交通量预测；分布交通量的概念，增长系数法及其算法。 3. 交通方式划分的概念；交通方式划分过程；交通方式划分模型。 第四章道路交通网络分析（4学时） 1. 交通网络计算机表示方法、邻接矩阵等 2. 交通阻抗函数、交叉口延误等。 第五章城市综合交通规划（2学时） 1. 综合交通规划的任务、内容；城市发展战略规划的基本内容和步骤 2. 城市中长期交通体系规划的内容、目标以及城市近期治理规划的目标与内容 第六章城市道路网规划（2学时） 城市路网、交叉口、横断面规划及评价方法。 第七章城市公共交通规划（2学时） 城市公共交通规划目标任务、规划方法、原则及技术指标。 第八章停车设施规划（2学时） 停车差设施规划目标、流程、方法和原则。 第九章城市交通管理规划（2学时） 城市交通管理规划目标、管理模式和管理策略。 第十章公路网规划（2学时） 公路网交通调查与需求预测、方案设计与优化。 第十一章交通规划的综合评价方法（2学时） 1. 交通综合评价的地位、作用及评价流程和指标。 2. 几种常见的评价方法。 第十二章案例教学（2学时）
走进都市农业 感受现代科技
走进都市农业感受现代科技 --"大东农业观光园"基地实践活动 设计者：大兴区第九小学窦雪征 指导教师：大兴区教师进修学校柏东河 ( 2008-05-27 13:40:19 ) 一、环境分析 我国是个农业大国。当前，在农业科技领域，中国不断缩小与发达国家的差距，农业科技部门在生物技术、高新技术、基础研究方面均取得较大进展，植物细胞和组织培养、花药培养、单倍体育种及其应用研究处于国际先进地位。但是，作为国家希望的小学生却对我国农业的了解知之甚少，甚至有的学生只认识餐桌上的蔬菜，田地里面的蔬菜就不认识了。而且由于社会环境的影响，学生对于农业生产的认识非常极端，认为农业生产只是辛苦的体力劳动，对其间蕴含的高科技文化知识基本不了解。 大兴区作为北京市的一个远郊区县，拥有着得天独厚的农业教育资源，在我们的身边就有向"大东农业观光园"这样的一个很好的农业教育基地。在大东农业观光园里面充分体现了现代农业。它目前拥有现代化日光温室34个，塑料大棚34个，露地80亩，为观光采摘、实验、示范、生产提供了场所。农业观光园里引进福建有机茶叶树，通过土壤改质能使其在北方正常生长；蔬菜嫁接的试验技术；大棚中的科学技术，如：所有地面具备节能、日光温室跨度大、内设供热、微喷、保湿、补光等多项功能；另外园区内设有科普知识长廊，为学生了解我国农业的历史文化和发展，创造了很好的条件。此基地实践活动适合小学高年级学生，他们可以在基地里面，进行参观、体验、学习了解高科技农业技术。通过基地的实践活动，可以增长学生现代农业知识，提高劳动能力，体验劳作的艰辛，培养学生良好的道德品质。可以很好的促进学生学习方式的变革，使学生直接从事实践性主题活动，实现学生自主学习和直接体验为主的学习方式，从而不