Effects of User Request Patterns on a Multimedia Delivery System, accepted for Multimedia T

Effects of User Request Patterns on a Multimedia Delivery System

Christopher B.Mayer K.Selc?uk Candan

V enkatesh Sangam

Computer Science and Engineering Department

Arizona State University

e-mail:chris.mayer,candan,venkatesh.sangam@https://www.sodocs.net/doc/0d4792385.html,

Abstract

We recently introduced a novel method for creating replication systems where the replicated objects’sizes and/or per-object service times are large[10].Such replication systems are well-suited to delivering multimedia

objects on the Internet.Assuming that user request patterns to the system are known,the method creates repli-

cation systems that distribute read load fairly to servers so that the likelihood of servers overloading is reduced.

Thus,the systems produced are highly available and responsive to user requests.In this paper,we report on results

that reveal(i)how server loads are affected and(ii)the impact of two system design parameters(indicators of a

system’s load distribution qualities)have on server load when user request patterns differ from that for which a

system was designed.

1Introduction

Replication is an accepted method for improving availability and response times of Internet content.The main idea behind replication is that storing copies of an object(?le,database,web page,etc.)on servers throughout a network provides single points of failure.Since the object is available at many servers,high demand loads can be met and the failure of an individual server does not make the object inaccessible.

Multimedia content can be delivered over the Internet and can bene?t from replication.In this paper,we focus speci?cally on systems for delivering(making available for download)multimedia content.When designing a multimedia delivery system the following considerations and constraints apply.

Read Load:In a multimedia delivery system,an object is written by its author and remains accessible for some period of time before it is removed.Since an object is supposed to be a?nished product,it is rarely updated once in publication.Therefore,the load on a system’s servers is due mainly to handling users’read requests and the load for writing can be ignored.Since we are concerned with only the read request load(read load),we will use the term “load”and“read load”interchangeably.

Content Size:Multimedia objects such as video?les tend to be large(tens or hundreds of megabytes)and therefore require special consideration when being replicated.

Large objects can not be rapidly replicated in response to?uctuating demand.Therefore,it is sensible to pre-position multimedia objects.

While pre-positioning is good,over-provisioning can be bad.Creating too many copies of an object having relatively low demand is wasteful.Replication costs for an object should be relative to demand for the object. Service Times:Even if users connect to a server over broadband connections,delivering a multimedia object to a user requires the server’s attention for a long period of time.These long service times occur because the content is either large or streamed,or both.

Server Behavior:A common behavior of servers is that they can support multiple simultaneous requests while maintaining acceptable quality of service.Once a server’s load capacity is exceeded,the server’s service quality rapidly declines,resulting in stalled requests and disappointed users.In combination with the service time property above,this behavior suggests that the best way to maintain the availability of a multimedia object and keep users happy is to ensure that servers operate below their load capacities.

This research funded by NSF grant998404-0010819000.

Techniques for easing the load on,or improve the performance of,multimedia servers(mainly video and streamed media servers)include:caching[1,6,11,14,15],protocols for stream and download sharing[5,8,11], and customized server designs[4,5,7].While all these approaches are bene?cial,they all depend on access to the content’s source.Thus,they are not a panacea for availability and responsiveness;abundant access to the source content,as replication provides,is required.

The profusion of special techniques for video and streamed media delivery are indirect signs that multimedia should be stored and delivered separately from other types of Internet content.Towards this end,several video-only delivery schemes have been proposed([3,12,13]for instance).Common weaknesses of these schemes are that content is assumed to come from a single source(i.e.,only one entity is using the system)and the delivery network is a tree(which networks are commonly not).

In a previous work[10],we introduced a replication architecture and an accompanying design method well-suited for delivery of multimedia content,or any other Internet content where service times or object sizes are large. In our approach,servers are organized into write-sets and read-sets and requests are ful?lled using a speci?c read protocol.Our system structure is more general than that of the tree-based video distribution systems and freely handles multiple content providers.In our system,a server’s share of system load is proportional to the server’s contribution to total system read load capacity.In other words,load is distributed fairly to the servers.Fairly distributing load minimizes the odds that a server will exceed its load capacity.Since servers operate under their load limit,the system is responsive and content is highly available.

In[10],we rigorously examined the implications of our server organization and read protocol and showed that load fairness is a complex non-linear condition that can not be solved easily.Therefore,in[10]we derived two key parameters:(describing good request distribution strategies)and(describing good server organization)that re?ect a system’s load https://www.sodocs.net/doc/0d4792385.html,ing and,we developed and tested a design method for quickly designing replication systems with nearly optimal load fairness.

In this paper,we further the work begun in[10]with a study of(i)server loads and(ii)the importance of and to server loads when the pattern of read requests entering a system differs from that for which the system was designed.In Section2we review our replication approach.We then we explain why divergent request patterns require further investigation in Section3.Next,we describe how we conducted experiments to evaluate the impact of and(Section4).Section5contains the results of our study and Section6summarizes.

2A Replication System Suitable for Multimedia Content Delivery

In this section we describe the features and mechanics of our method for creating a replication system.A detailed accounting of the material summarized here can be found in[10].

2.1System Structure

A replication system has a set of servers,,which it uses to replicate objects.In our approach, we organize these servers into intersecting subsets called write-sets and read-sets.To write an object,a write-set is chosen and the object is replicated on each server in the write-set.To read an object,a read-set is chosen and the object is delivered to the requesting user from a server in the selected read-set.It is allowable for an object to be written to multiple write-sets.We ensure that every written object can be accessed from any read-set by requiring that each read-set and each write-set have at least one server in common.

Although there are many ways to construct write-sets and read-sets while maintaining this requirement,we limit ourselves,for simplicity reasons,to the special case where the write-sets and read-sets are determined by arranging servers in a grid.In a grid fully populated with one server per grid cell,rows correspond to write-sets and columns to read-sets.As Fig.1a indicates,each read-set intersects every write-set with at least one server.Some readers may notice that this grid-based structure resembles grid-based quorum systems([2]and[9]for example).This is intentional since quorum systems feature decentralized operation and have the potential load-balanced server operation.

1Due to the unpredictable nature of Internet routing and to generalize for any request routing scheme,we do not specify how requests are

Read Protocol

1.A user generates a request for an object(an initial-request)which is directed

to one of the system’s servers(a proxy).The distribution of a particular user’s

requests to the proxies is called the user request pattern.1

2.The proxy selects a read-set using a preset,probabilistic proxy strategy.

3.The proxy identi?es the server(s)with the most up-to-date copy(in case the

object has been updated)of the object in this read-set.If more than one server

has the most up-to-date object,one of the servers is picked equiprobably to

serve the data.

4.The proxy redirects the user to the server it has selected.

5.The user receives the object from the appropriate server,thus inducing a read

load on the server.

(a)(b)

Figure1:A grid-based replication system where rows are write-sets and columns are read-sets is shown in(a).The read protocol for the replication system is shown in(b).

2.2Read Protocol

An important part of our replication system is the read protocol shown in1b.Note that locating the appropriate server within a read-set adds to the delay in responding to requests.However,this delay is extremely small compared to the time required to serve a multimedia object and is unlikely to detract much from the user’s experience.Further, this delay can be minimized by using a directory service,especially since object locations rarely change.

Based on the structure of write-sets and read-sets and the read protocol,we now the issue of load fairness.

2.3Fairness

To minimize the likelihood of a server overloading,a server should experience a load proportional to its contribution to the system’s total capacity.In other words,system load should be distributed fairly to servers.This will ensure that no server is pushed beyond its capacity unless the whole system is.This can be accomplished by identifying a good assignment of servers to write-/read-sets(thus de?ning the system’s structure)and

selecting effective proxy strategies for directing initial-requests to read-sets.

As a?rst step towards fairness,we further re?ne the system’s structure by splitting each server into one or more virtual servers.Each server in the system,,is represented by its read load capacity,.Denoting a base capacity as,we split each server into virtual servers,

.Populating the cells of a grid with virtual servers(Fig.1),instead of regular servers, results in nearly equal amounts of capacity at each grid cell.If the system read load can be distributed equally to each grid cell,then we have achieved the goal of fairness.Unfortunately,the load on the virtual server in each grid cell,and in turn on the servers,depends on(i)the arrangement of virtual servers in the grid(the grid mapping), (ii)the policy for deciding to which write-sets an item should be written(write-policy),(iii)initial-request loads at the proxies determined by the user strategies,and(iv)the proxy strategies.Hence,load fairness requires more than placing virtual servers in the grid.

Given a grid of write-sets(rows)and read-sets(columns)populated with virtual servers,a total system read load,,the read load of server,,is:

(1)

In the above equation,denotes the probability that write-set contains a server with the requested content;

denotes the probability that read-set is chosen by a proxy given that write-set contains the content; directed to servers.Instead,we rely on the observation that request routing is somewhat predictable and can be expressed probabilistically.

and denotes the probability that server is selected for serving the request given that write-set contains the requested content and read-set is chosen.

To obtain load fairness,we need to ensure that the grid mapping and proxy strategies distribute the total system read load onto individual servers in proportion to each server’s contribution to total system capacity:

(2)

Note that this fairness condition is a complex non-linear equation,and solving it directly is expensive.Also,it is not straightforward to?nd a mapping and determine proxy strategies using(1)and(2)directly.Therefore,we use

(1)and(2)to identify parameters,and,that can be used to construct highly fair replication systems.

2.4Deriving and

To begin the derivation of and,we assume that the write-policy distributes objects to write-sets such that the request load for each write-set is equal.That is,of the system load is directed to the virtual servers in each write-set.With this assumption(1)becomes

(3) Using this equation,we can rewrite the fairness condition,(2),as

(4)

Note that,if a request can be served from write-set and a proxy chooses read-set,then one of the servers having a virtual server in the intersection of and will be selected to serve the content.If there is more than one server in the intersection,then each server has an equal chance of being chosen.Consequently,if we let be the set of servers that have virtual servers in write-set,;i.e.,;

be the set of servers that have virtual servers in read-set,;i.e.,;and

be the set of virtual servers in read-set that have a corresponding server in write-set;i.e.,

,

then,.Hence,(4)becomes

(5)

Notice that(5)has two terms that can be manipulated:and.The?rst term is a function of the proxy strategies and the latter depends on the grid mapping.By isolating these two terms,we gain a measure of insight into how to construct an optimally fair system.

2.4.1Isolating Proxy Strategies:Parameter

In order to extract the term related to proxy strategies,we isolate the term in(5)related to read-set selection by proxies to get

(6) which says that the fraction of requests directed to read-set should be inversely proportional to the number of read-sets,.In other words,initial requests should be directed in equal amounts to each column.This implies that

Figure2:Replication using and

the combined effect of all the proxy strategies should ensure an equal distribution of initial request to each column. We denote the fraction of initial read requests directed to read-set as,or read-set-value.The ideal-value for a read-set is.If is greater(less)than the ideal-value,then,and the virtual servers in,will receive more(less)than their fair share of system load.Likewise,since a server’s load is the sum of the load on its virtual servers,the higher the s of the read-sets in which a server has a virtual server(average server),the more load the server will receive.

2.4.2Isolating the Grid Mapping:Parameter

Assuming that all read-sets have the ideal-value of,we can reduce(5)to

(7) This equation can be satis?ed by ensuring that

(8) holds.The left-hand side of(7)captures the degree of content overlap of server with other servers that share both read-sets and write-sets with.Isolating this overlap we get

(9)

,or server-value,indicates’s vulnerability to being selected for serving a read request.The ideal value for is,the number of virtual servers has.If is too high()or too low(),then will be selected too often or not often enough and will not receive its fair share of load.

As an example of how to calculate an-value,consider server and the second write-set and second read-set highlighted in Fig.1.Server has three virtual servers,thus and.Three virtual servers in the second read-set have a server in the second write-set,so(i.e.,if read-set2is chosen by a proxy and the requested content is on a server in write-set2,then three servers can be selected for download).Since and only have in common..Thus,(i.e.,given that a request is for content contained in write-set2and read-set2was chosen,server has a1-in-3chance of serving the request).Repeating these calculations for server for all rows and columns and summing the results shows that has a perfect-value:.

2.5Replication Based on and

To produce the fairest system possible,-and-values must be as close to their ideal values as possible.While and indicate how fair a system is,they are derivatives of the complex,non-linear fairness condition and,therefore, can not be used directly to construct a replication system either.However,they do provide insight as to how a fair replication system should look.In[10],we exploited and to develop a two-step heuristic approach for creating a replication system(Fig.2).In the?rst step,we set the system’s structure by mapping virtual servers to grid cells so that each server has good-values.This structure is then used to formulate proxy strategies that result in the best possible-values for each grid column.

Given a set of servers and a grid,

1.Put the servers into groups such that servers

in each group have the same number of vir-

tual servers.

2.Try to?ll the grid with the given clusters

3.If such a?lling is not possible,break some

of them into smaller clusters to?t them into

the grid.

The result is a server-to-grid mapping.Given a server set,a grid,and server-to-grid mapping,and user strategies,

1.Identify the linear constraints for

(a)-optimality,

(b)write-policy dependence

(c)fairness,and

(d)column selection restrictions on the proxies.

2.Solve while minimizing error terms in the constraints.

3.Extract proxy strategies from the solution.

The proxy strategies result in optimal-values.

(a)(b)

Figure3:Pseudocode for(a)the cluster-based mapping algorithm and(b)producing-optimizing proxy strategies.

Pseudocode for mapping a grid is shown in Fig.3a.The algorithm clusters virtual servers and then maps the clusters to the grid.Clustering limits the interaction between servers that gives imperfect-values.If all virtual servers can be mapped,while maintaining cluster integrity,then all servers will have perfect-values.Note that clusters must sometimes be split into smaller pieces in order to facilitate placement.This can create imperfections in-values.However,as we showed in[10],the negative effects of splitting are minimal.

Once the system’s structure is set,we use the structure to formulate a linear program(LP)and solve the LP to?nd proxy strategies for the system.In addition to the system’s structure,the LP considers other fairness-related factors such as the system’s write-policy and user request patterns.Figure3b shows pseudocode for the LP’s construction and extraction of proxy strategies.The speci?cs of the LP can be found in[10].The output of the LP are the proxy strategies that produce the best possible(closest to ideal)-values for each read-set.For each proxy,its particular proxy strategy gives the frequency at which it should select a read-set when handling initial-requests from users.

In[10]we studied the impact of and on server load fairness.We showed that grid-based replication systems constructed using our two-step approach are highly fair when operating conditions are exactly that for which the system was designed.In this paper,we investigate what happens when user request patterns no longer match the patterns for which the system was designed.Speci?cally,we examine(i)the importance of and to load fairness and(ii)how server loads are affected user request patterns deviate from expectations.

3Divergent Initial-Request Loads

Since proxies redirect client initial-requests to servers,the performance of the replication system depends on the expected distribution of the users’initial-requests to proxies.As explained earlier as part of the read protocol,the probability that a given user’s initial-requests arrive at a certain proxy,is given as a distribution function called the user request https://www.sodocs.net/doc/0d4792385.html,er request patterns(or at least their cumulative effect on the proxies)are an input to the linear program(LP)that is solved to get the proxy strategies for selecting read-sets.Thus,a replication system is tailored for the user request patterns input into the LP.Since user request patterns are will change over time,using a?xed user request pattern to construct a replication system is a potential weakness of our approach.If request patterns change too much,server load fairness could be lost and the system would perform poorly.

In the remainder of this paper,we present two kinds of results,obtained experimentally,about our proposed replication system.

We show the importance of and to load distribution when user request patterns deviate from those for which the system was designed.

We show that systems built using our two-step construction approach(see previous section)are resilient to changes in user request loads.

4Experimental Setup

In order to test the performance of replication systems that use our write-/read-set structure and read protocol,we have constructed a testbed system that uses real web servers.The use of real servers adds a degree of realism that

ordinary simulation does not provide.Because of space constraints we can not go into the details of the testbed system in this paper.However,we do describe the conditions for conducting experiments.

To prepare for an experiment,servers are arranged into their write-sets and read-sets using the grid structure and given their proxy strategies(calculated in advance based on expected loads).A different object is written to each write-set(a row of the grid).All data items have the same size and hence the same download times.Having each object be the same size and having each write-set contain a single object captures the effects of a perfectly tuned write-policy.The download time of an object is simulated by having servers execute a sleep operation of5seconds. The running time for an experiment is40times the sleep time,or200seconds.This is the minimum time needed for an experiment to show long-term loading behavior.Once the setup stage is complete,the experiment can begin.

Performing an experiment consists of generating user requests for objects and the handling of those requests by the https://www.sodocs.net/doc/0d4792385.html,er requests are regularly-spaced over a second to meet a speci?ed request rate.For example,if the request rate is10requests/sec,then a new request is generated every tenth of a second.Uniform request generation, while simple,is adequate since object sizes,and hence download times,are relatively large compared to request inter-arrival times.For each request,an object is selected uniformly at random.The proxy server that will receive a newly generated initial-request is selected at random using a probability distribution that models the effects of the user request patterns.

Experiments were performed using20sets of servers.A server set is the servers available for use by a replication system.Server sets were generated so that the number of virtual servers in each set equalled64and would?ll an 8x8grid.The number of virtual servers per server was randomly generated according to the following distribution: of the servers have1,have2,have3,have4,and have5virtual servers.

In order to observe the effect of as request patterns change,we map a server set to a grid using two different mapping strategies:

Random.This strategy randomly maps virtual servers to a grid.This results in server-values that differ greatly,both up and down,from their ideals.

Cluster.Grids are mapped using an algorithm based on the clustering pseudocode of Fig.3a.Clustering results in ideal or nearly ideal-values for all servers in a grid.

To observe the effect of,we used two methods for formulating proxy-strategies that result in favorable and unfavorable-values.

Not-optimized.Each proxy redirects initial-requests equiprobably to the read-sets(grid columns)in which it has virtual servers.As such,read-set s can vary greatly,being highly in?uenced by the system’s structure.

-optimized.Here a linear program is formulated and solved to obtain proxy-strategies that produce optimal s.As with the above non--optimized strategy,proxies can only redirect initial-requests to read-sets in which the proxy has virtual servers.Even with this restriction,resulting s are close to ideal regardless of mapping strategy.

Mixing mapping and proxy strategies results in four replication systems(mapping/proxy systems or MP-systems) for each server set.The mix of good and bad-and-values in the four systems allows us to observe the in?uence of and on server load as initial-request loads at the proxies vary in response to changing user request patterns. We refer to an MP-system by the mapping method used and the presence of-optimization as shown in Fig.4and listed below.

R ANDOM:not-or-optimized R ANDOM-:not-optimized,but-optimized

C LUSTER:-optimized,but not-optimized C LUSTER-:-and-optimized.

For the tests,a baseline load of6.4requests per second is the arrival rate of initial-requests to each proxy(256 requests per proxy divided by the40second experiment length).Thus,the-optimized systems were constructed for user request patterns whose cumulative effect is that each proxy is equally loaded with initial-requests.

In order to systematically explore the effects of varying user request patterns,we randomly selected subsets of proxies in each server set and subjected them to increased initial-request loads.2We refer to the combination 2Note that we only increase initial-request loads at proxies.Since Internet demand only grows over time and unevenly,this is a reasonable thing to do.

Figure4:Naming concept for the four types of replication systems created from a server set.

of proxies selected to receive extra load and the extra load they are given as an initial-request-combination(IR-combo).For a given server set,there are seven IR-combos which form an initial-request-set(IR-set).An IR-set is built as follows.The?rst IR-combo in the set is each proxy receiving the baseline load In this combination, 0%of the proxies receive0%extra load.We call this the0%-0%IR-combo or the baseline system.Next,three proxy subsets of sizes,,and of the number of proxies in the server set are formed,with the larger subsets reusing servers from the smaller ones.The proxies in these subsets will receive25%and then50% extra initial-request load above the baseline https://www.sodocs.net/doc/0d4792385.html,bining the proxy subset sizes and extra request percentages produces the remaining six IR-combos in an IR-set:10%-25%,10%-50%,20%-25%,20%-50%,40%-25%,and 40%-50%.

Example4.1We now illustrate how to create an initial-request-set.Consider a server set with thirty servers num-bered1through30and a baseline initial-request load of10requests per second(req/sec).

1.The0%-0%combination is all proxies receiving10req/sec.

2.For the10%proxy subset we pick three proxies,say5,9,and21.For the10%-25%combination,we increase

the number of requests to these three proxies by25%;they will each receive12.5req/sec.Proxies not in the subset still get only10req/sec.To create the10%-50%combination,initial-requests are increased by50%to 15req/sec at the three proxies.

3.To build the20%subset,2,5,9,11,21,29,the10%subset is augmented by three more servers:2,11,and

https://www.sodocs.net/doc/0d4792385.html,binations20%-25%and20%-50%are created by increasing the initial loads at these proxies by25%

and50%,respectively.

4.The40%subset is the20%subset plus servers1,15,17,23,24,and28.Increasing the initial-requests by

25%and50%at the selected proxies gives combinations40%-25%and40%-50%.

By running a server set through each of its four MP-systems and each of its seven IP-combos(each server set is run28times),we can detect trends in server read loads and compare the in?uence of and on server load as user request patterns vary.

5Results and Observations

In this section,we answer six questions about the effects of and when initial-request loads to proxies diverge from their expected values.To do this,we observe the extra load experienced by a server when operating as part of the four MP-systems created from the server set of which the server is a member.Extra load is the difference in a server’s read load in an experiment where user request patterns have changed and in an experiment where user request patterns are exactly what the system was designed for(the0%-0%IR-combo or baseline system).Server read load is the average number of reads(downloads)experienced by a server in each second of an experiment.For example,if server had a load of5read requests per second in a baseline system experiment and then had a load of 7requests per second in a20%-50%combination then’s extra load is2,an increase of40%.

The evidence supporting answers to Questions One through Four involve twenty different server sets.Questions Five and Six are answered using results from four rounds of repeated experiments on the second of the twenty server sets.In the four rounds,the initial-request-sets were not changed.Since space is limited,we present evidence representing behavioral trends seen throughout all the experiments.

R ANDOM strategies.

(a)(b)

Figure6:Differences in the percentage of extra server load,,as average server changes between(a)the

R ANDOM and C LUSTER systems and(b)the C LUSTER and C LUSTER-systems.

5.1Questions and Answers

Question One:If a server’s-value increases or decreases between different mapping strategies,does the extra

read load experienced by the server also increase or decrease?

Figure5displays the difference in the percentage of extra read load,,experienced by servers as their-

values improve under different mapping strategies.The x-axis shows the difference in a server’s-value,, when cluster-mapped(C LUSTER systems)versus randomly-mapped(R ANDOM systems).Each data point represents a server.We see that a positive(negative)move in a server’s-value causes a likewise change in extra load at the server.In all experiments,the correlation in movement in and extra load is between83%and86%.This behavior indicates that,a function of the grid structure,has a strong in?uence on where extra load is distributed. Question Two:If the s of a server’s read sets increase or decrease under different proxy strategies,does the extra read load experienced by the server also increase or decrease?

Yes,however,the trend is not as pronounced as it was for.Figures6a and6b show,as a function of

the difference,,in the average-values of a server’s read-sets(average server),under differently formulated proxy strategies.Figure6a compares R ANDOM and C LUSTER systems whereas Fig.6b compares C LUSTER and C LUSTER-systems.Note that the tilt of the regression trend-lines in both of these?gures is less than the tilt caused by changes in(Fig.5),suggesting that the trend is not as strong as it was for.Indeed,the correlation between changes in average server and extra load ranged from only61%to65%in all experiments.Furthermore, in Figs.6a and6b the data points are spread to all four quadrants of the graph,whereas in Fig.5data points are

mostly in the all-positive or all-negative quadrants.This suggests that has a more pronounced effect than.Note

(a)(b)(c)

Figure 7:Histograms showing differences in percentage of extra server load,

,between (a)the C LUSTER and R ANDOM systems (b)the R ANDOM -and R ANDOM systems,and (c)the C LUSTER -and C LUSTER systems.that the data points are more widely spread in Fig.6a than in 6b indicating that ’s effect on extra load distribution is more visible only after the grid is already optimized for .

Question Three:How is extra read load distributed in systems with good -values versus systems with bad -values?

The answer to this question can already been seen in Figs.5and 6a which compare extra loads in randomly-mapped grids (large errors)with those of clustered grids (small errors).In Fig.6a,it appears that the data points

are mostly in the

region.A histogram showing the distribution of values con ?rms this observation (Fig.7a).When cluster mapping is used,a large portion (an average of 63%across all experiments)of the servers get higher amounts of extra load than when randomly mapped.This indicates that systems with good -values more evenly distribute extra load to servers.

Question Four:How is extra read load distributed in systems that are -optimized versus non--optimized?

Load distribution is minimally affected by optimizing for .When comparing randomly-mapped grids before

and after optimization,extra load changes for a server range from

to (Fig.7b).Similarly for clustered grids,the extra load changes range from

to (Fig.7c).Comparatively,load shifts in the range of to were observed for randomly mapped grids versus clustered grids without optimizing for (Fig.7a).Thus,the shift in load is due mainly to changes in server https://www.sodocs.net/doc/0d4792385.html,bined with the behavior seen in the answers to Questions One through Three,we conclude that grid structure,as captured by -values,and not proxy strategies,which determine s,is the main factor in where extra read load is distributed.

Also,note that unlike in Fig.7a,the histograms in Figs.7b and 7c are centered on zero load difference.This means that the overall effect of optimization on extra load distribution is minimal.

Question Five:What does a server’s -value indicate about its extra read load?

We have already partially answered this in Questions One and Three,but provide additional analysis here.

Figure 8shows the percentage of extra read load,

,experienced by servers for the 20%-25%and 20%-50%initial-request-combinations (IR-combos).Figure 8a shows results for the R ANDOM strategy and Fig.8b shows the C LUSTER -strategy.Trend lines help distinguish between the two IR-combos.In both graphs we see that smaller -values have highly variable extra read loads.This implies that systems could be made robust to variances in initial-request loads by making -values large.However,increasing -values would negatively affect load fairness.

Note also that extra read load hardly changes from that of a baseline system for the 20%-25%proxy/load combi-

nation in Fig.8(i.e.,the linear regression line lies right on the zero

line).However,read loads jump noticeably for the 20%-50%combination.Since this behavior appears regardless of mapping strategy,this suggests that the grid structure and read protocol has a good deal of resilience to variations in initial-request loads and that beyond a certain threshold performance signi ?cantly degrades.

Question Six:What do the s of a server’s read-sets indicate about the server’s extra read load?

(a)

(b)Figure 8:Percent differences in server loads plotted by server

-values for the second server set’s (a)R ANDOM

system and (b)C LUSTER -system.

(a)

(b)Figure 9:Percent differences in server loads plotted by average server

for the second server set’s (a)R ANDOM

system and (b)the C LUSTER -system.Results,arranged by the average -values of a server’s read-sets (average server )for the second server set’s R ANDOM system are in Fig.9a and results for the second server set’s C LUSTER -system are in Fig.9b.In both

?gures,we see that the largest swings in extra read load,

,occur when average server equals the ideal for the grid (which is 0.125)and is the sole factor in server loading.The R ANDOM system also has large differences

in extra load for other average s (most notably at 0.136).This is due to the fact that

errors are large in the randomly-mapped systems,making nearly irrelevant to server loading.

Notice that in the R ANDOM system extra load decreases as average server increases,which is counter to our expectations.The explanation for this behavior is that,for this particular server set.the proxies receiving extra initial-requests all have average server s that are less than the ideal of 0.125.Therefore,the extra read requests are directed mostly to read-sets with less than ideal causing the servers in those read-sets to receive the bulk of the extra load.

5.2Summary of Results

Overall,the experiments reveal the following notable observations about the proposed replication system when user request patterns deviate from that for which a system was originally designed.

,which is a factor of the grid structure,has the greatest in ?uence on where extra server load is distributed.can also have an effect.However,’s impact is often masked by .

Systems with good -values more evenly distribute extra load to servers than do systems with bad -values.

Servers in systems built according to our construction method and operating protocols can be insulated from read load increases when initial-request loads increase at a subset of the proxies.

6Conclusion

In[10]we proposed and validated an approach for developing replication systems which specialize in hosting large objects,or where user requests have long service times,or both.The replication strategy attempts to prevent servers from overloading by fairly distributing read loads to servers based on their relative capacities.Our approach to developing a fair replication strategy depends on optimizing two key parameters,and.In[10]we showed that systems designed according to our approach are highly load-fair when user request patterns(the arrival rate of users’initial-requests)to servers in the system match that for which the system was designed.

In this paper we further studied the performance of systems constructed with our approach.We showed the in?uence and have on server load when user request patterns vary from that for which a system was designed. Experimental results indicate that system structure,captured by,has the most in?uence on load distribution. also affects load distribution,but not as much as.We also saw that server loading is relatively unaffected by mild to moderate shifts in user request patterns.

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企业环保标语大全

企业环保标语大全 企业环保标语大全 1．保护中发展，发展中保护 2．落实环保责任，完善环保制度 3．环境与人类共存，开发与保护同步 4．垃圾混置是垃圾，垃圾分类是资源 5．建设绿色企业，坚持绿色经营 6．降低生产成本，节约地球资源 7．生产绿色产品，节约地球资源 8．珍惜资源、永续利用 9．依靠科技进步、促进环境保护 10．合理利用资源、保护生态平衡、促进经济持续发展11．保护环境山河美、持续发展事业兴 12．发展经济不能以牺牲环境为代价 13．使用环保材料，减少环境污染 14．推行清洁生产，促进经济社会可持续发展15．推行清洁生产，实现碧水蓝天 16．推行循环经济，提高资源利用水平 17．依靠科技进步，推行清洁生产 18．环境与人类共存，发展与环保同步

19．贯彻环境教育，宣导环保资讯 20．全员参与改善，持续环保社区 21．提高环保意识，争做环保公民 22．让地球远离污染，让绿色走进家园23．有限的资源，无限的循环 24．降低损耗齐用心，开源节流增效益25．节约的是利润，损耗的是财富 26．树立节约意识，倡导节约行为 27．浪费不因量小而为之，节约不因细微而不为28．保护环境降污染，千方百计增效益29．资源节约齐参加，珍惜使用靠大家30．改变观念，废物利用，资源回收，持续发展31．保护环境、就是保护生产力 32．既要金山银山、更要青山绿水 33．提倡绿色生活、实施清洁生产 34．树立节水意识、反对浪费水源 35．提高环境道德水平、建设文明小康城区36．保护环境、造福后代 37．全面建设小康社会、同心共创美好家园38．当环保卫士、做时代公民 39．让大气清新、让天空蔚蓝、让河山碧绿40．企业求发展、环保须先行

初中生物第三单元(生物圈中的绿色植物)知识点

第三单元生物圈中的绿色植物 第一章生物圈中有哪些绿色植物 ?1、绿色植物分为四大类群：藻类、苔藓、蕨类、种子植物 一、藻类、苔藓、蕨类植物 ?2、藻类、苔藓、蕨类植物的区别： ?3、藻类，苔藓，蕨类植物的共同点：藻类，苔藓，蕨类植物都不结种子，它们的叶片背面 有孢子囊群，可产生孢子，称为孢子植物，孢子是一种生殖细胞，在温暖潮湿的地方才能萌发和生长，所以说孢子植物的生活离不开水。 4.藻类植物：“春水绿于染” 5.藻类植物既有单细胞，也有多细胞。既有生活在淡水中，也有海水中。 6.多细胞藻类植物整个身体浸没在水中，几乎全身都可以吸收水和无机盐。可以进行光合作用，但没有专门吸收和运输养料以及进行光合作用的器官。 7.藻类植物没有根茎叶的分化。 8.苔藓植物生存在阴湿环境中。有类似根和茎的分化，但是茎中没有导管，叶中没有叶脉，根很简单，称为假根，具有固定作用。 9.苔藓植物可作为监测空气污染程度的指示植物。 10.蕨类植物：有根茎叶的分化，具有输导组织。有些蕨类植物比较高大。 11.蕨类植物用处：①卷柏、贯众可药用②满江红是一种优良绿肥和饲料③蕨类植物遗体层层堆积，经过漫长年代、复杂变化，逐渐变成了煤。 12.藻类植物，苔藓植物，蕨类植物，生殖依靠孢子，所以也被统称为孢子植物。 第二节 二、种子植物 ? 1.、种子的结构 菜豆种子：种皮、胚(胚芽、胚轴、胚根、2片子叶)【双子叶植物，网状叶脉】

玉米种子：果皮和种皮、胚(胚芽、胚轴、胚根、1片子叶)、胚乳【单子叶植物，平行叶脉】种皮：保护里面幼嫩的胚。 胚：新植物体的幼体，由胚芽（发育成茎和叶）、胚轴（发育成连接根和茎的部位）、胚根（发育成根）和子叶组成。胚乳不是胚的结构。 ?2、子叶和胚乳里有营养物质，供给胚发育成幼苗。 ?3、菜豆种子中与子叶相连的结构是胚轴，玉米种子中滴加碘液变蓝的结构是胚乳，说明胚 乳中含有淀粉。 菜豆种子（双子叶植物）： 胚轴：发育成根和茎的连接部 胚芽：发育成茎叶芽 胚根：发育成根 子叶（两片）：贮存营养物质 种皮：保护 玉米种子（单子叶植物）： 果皮和种皮：保护胚 胚乳：贮存营养物质（含有淀粉，遇碘变蓝） 子叶（一片）：转运营养物质 胚芽：发育成茎叶芽 胚轴：发育成茎和根的连接部 胚根：发育成根 ?4、菜豆种子和玉米种子的异同 （严格意义来说，一粒玉米就是一个果实） ?5、种子植物比苔藓、蕨类更适应陆地的生活，其中一个重要的原因是能产生种子。 ?6、种子植物包括两大类群：裸子植物和被子植物。 裸子植物：种子裸露着，不能形成果实，常见的裸子植物有松、杉、柏、苏铁、银杏； 被子植物（绿色开花植物）：种子外面有果皮包被着，是陆地上分布最广泛的植物类群。（苹果，梨，葡萄，水稻） 第二章被子植物的一生

环保企业文化标语

环保企业文化标语 导读：本文是关于环保企业文化标语，如果觉得很不错，欢迎点评和分享！ 1、维护社会公德，保护生态环境。 2、环保时代，健康生活。自由追逐，财富就在咱们手中。 3、引领环保新时尚，创造自由新生活。 4、你环保健康，一起财富自由。 5、改善环境，保护家园；净化环境，美化家园。 6、百业要兴，环保先行。 7、给你“更环保、更健康、更成功”的选择。 8、低碳生活，自由财富。 9、环保新主义，财富由你创，健康自然来！ 10、拯救地球，一起动手。 11、人类若不能与其他物种共存，便不能与这个星球共存。 12、做世界一流环保产品，让地球人都身体康健，给财富自由的翅膀。 13、节能环保缔造健康生活，健康生活成就财富自由。 14、环保进万家，财富赢天下。 15、改善环境，创建美好未来是我们共同的愿望。 16、尽环保力量，谋健康财富。 17、蓝天白云，绿水青山；咱们的发奋，自然的美丽。

18、绿色环保，引领时尚。 19、咱们所拥有的不仅仅是财富，还有干净的水滋养身体。 20、一草一木要爱惜，环境生活创佳绩。 21、落实环境职责，完善环保制度。 22、既要金山银山，更要青山绿水。 23、树立企业形象，协调人与自然。 24、我们期待世界更蓝的天空，期待你灿烂的微笑，期待社会自由的财富。 25、悠然一心，财富自由。 26、争做环保使者，共创绿色礼貌。 27、改善生态环境，营造绿色家园。 28、节能为环保，财富又自由。 29、环保创造健康，财富携手自由。 30、我们所拥有的不仅是财富，还有干净的水滋养身体。 31、财富我创造，自由交给你，享受健康，享受绿色，享受美好生活。 32、绿色与生命共存，健康与财富共进。 33、环保又健康，财富享自由。 34、你我环保，一齐健康，共创财富，共享自由。 35、树环保意识，创健康财富。 36、创意节能环保，缔造健康生活，共创幸福财富，分享快乐自由。

初中生物生物圈中的绿色植物知识点习题及答案

第三专题 生物圈中的绿色植物 一、绿色植物与生物圈的水循环 [知识网络结构] 水就是植物体的重要组成成分 原因 水保持植物直立的姿态,有利于进行光合作用 1、绿色植物的生活需要水 无机盐只有溶解在水中,才能被吸收与运输 水影响植物的分布:降水量大的地方,植被茂密 根吸水的部位:主要就是根尖成熟区, 吸水 成熟区的特点:生有大量根毛,增大根吸水的表面积 2、水分进入植物体内的途径 途径:木质部的导管 水分的运输 方向:自下而上 树皮:韧皮部中有筛管,输导有机物 茎的结构 形成层:细胞能分裂(木本植物有此结构) 木质部:有导管,输导水分与无机盐 实验:观察叶片的结构(练习徒手切片) 表皮(上、下表皮) 叶片的结构 叶肉 叶脉 重要结构:气孔,就是植物蒸腾失水的门户,也就是气体交换的窗口,由成对的保卫细 胞围成,气孔的开闭由保卫细胞控制。当保卫细胞吸水膨胀时,气孔张开, 当保卫细胞失水收缩时,气孔关闭。 概念:水分以气态从植物体内散发到体外的过程。 蒸腾作用 主要部位:叶片 降低了叶表面的温度 意义 促进对水与无机盐的运输 促进对水的吸收 绿色植物促进了生物圈中的水循环 绿色植物参与生物圈的水循环 提高大气湿度,增加降水量 保持水土 [课标考点解读] 绿色植物促进了生物圈中的水循环,因此保护森林与植被有非常重要的意义。本章重点阐明水就是绿色 植物生存的必要条件,水对植被的影响,植物吸水的主要部位及特点,水运输的途径,蒸腾作用的意义。在能力培养方面,通过解读数据,培养学生的探究能力。 [典型例题剖析] 例1、把发蔫的菠菜泡在清水中,菠菜又重新挺起来,这说明( ) A 、水就是光合作用的原料 B 、水就是溶解与运输物质的溶剂 C 、水能保持植物体固有的姿态 D 、水就是植物体重要的组成成分 [解析]:植物体内只有水分充足时,才能保持挺立的姿态,叶片才能舒展,有利于光合作用。本题没有数据说明水就是植物体的重要组成成分,也没有说明水就是如何作为溶剂来溶解无机盐的。本题考察学生对植物需水原因的分析与理解。答案:C 例2、下图所示,植物吸收水分的主要部位就是( ) [解析]:根尖成熟区就是吸水的主要部位,表皮细胞突起,形成大量根毛,大大增加了吸水的面积。本题主要考察学生识图的能力。答案:A 例3、3月12日就是我国的全民植树节,之所以选择这个时候,就是因为( ) A 、正值农闲时节 B 、土壤松散 C 、雨水较多 D 、叶没长出,蒸腾作用很弱 3、绿色植物参与生物圈的水循环

生态环境绿色标杆企业创建方案

市生态环境绿色标杆企业创建 实施方案（试行） 为深入贯彻落实生态环境部《关于加强重污染天气应对夯实应急减排措施的指导意见》和《市打赢蓝天保卫战作战方案暨2018-2020年大气污染防治攻坚行动实施方案》，激励企业积极创建环保绿色标杆，主动减排，制定本方案。 一、总体要求 全面贯彻落实党的十九大精神和绿色发展理念，坚持“污染排放空间换时间”原则，鼓励企业切实承担污染治理主体责任，强化企业自我监管，积极落实各项减排措施。对达到绿色标杆标准的企业，在错峰生产和重污染天气应急（红色应急除外）期间，实行差别化管理，准予不停产、不限产，实现生产经营和污染减排的双赢。 二、实施范围 钢铁、焦化、炭素、陶瓷、玻璃、石灰窑、铸造、炼油与石油化工、制药、农药、涂料、油墨、工业锅炉、页岩砖、木业加工、表面涂装、家具、印刷（限书报刊、本册平板印刷、无VOCs排放的数字印刷）。（其他行业绿色标杆创建标准根据污染治理进程补充制定。） 三、创建要求 （一）基本要求 企业建设和运行有合法手续，上年度亩产效益评级为良好以上、 - 1 -

无重大环境投诉及群体性上访、未发生重大环境事故、没有被国家和省各类督察巡查发现问题并通报、无环境违法排污行为，按要求实现废气污染物排放在线监控，开展自行监测并实现信息公开，有完善的环保管理制度并有专职环保管理人员。 （二）行业创建标准 各行业创建标准见附件1。 四、豁免政策 对于列入错峰生产、重污染应急减排清单企业，达到绿色标杆企业创建标准要求的，在错峰生产、重污染应急（红色除外）期间免予停产、限产（国家和省明确规定必须执行停限产要求的除外）。 五、实施程序 采用企业自愿申报、县区审核、社会公示、市级核发的工作方式，开展生态环境绿色标杆企业认定工作。 （一）企业申报。按照企业自愿参与的原则，由企业向所在县区大气办提交绿色标杆企业申请表（见附件2，纸质版，A4纸打印并装订成册，一式三份）和电子版。 （二）县区审核。各县区大气办依据绿色标杆企业创建标准和豁免政策对申请表进行复核，筛选出符合条件、综合评价较高的绿色标杆企业。 （三）社会公示。对遴选出的绿色标杆企业，各县区大气办在官方网站和主流媒体进行公示，公示时间不少于5个工作日。对公示无异议的企业，报市大气办备案。 - 2 -

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1.初步具有收集、鉴别和利用课内外的图文资料及其他信息的能力。 2.关注绿色植物的生存状况，形成环保意识。 3.描述细胞分裂的基本过程。 4.描述各类植物的主要特征和生活环境。 5.说出植物在自然界的作用和人类的关系。 主题单元问题设 生物圈中有哪些绿色植物, 计 专题一:藻类植物 (2课时) 专题二:苔藓和蕨类植物 专题划分 (2课时) 专题三:种子植物 (3课时) 专题一专题一藻类植物 所需课时本专题使用2课时 专题一概述 本专题内容在整个单元中起到引导的作用。通过本专题的学习，学生能够知道藻类植物的基本特征和生活环境，明白藻类植物在自然界中的作用及人类对藻类植物的利用。 专题学习目标 知识目标: 概述藻类植物的主要特征和生活环境。 能力目标: 说出藻类植物在自然界中的作用和与人类的关系。 情感态度价值观: 关注藻类植物的生存现状，形成环保意识。

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27、与绿色拥抱，生活更美好。 28、回家吧，回到绿色生产的美好。 29、环保是健康之基，绿色是和谐之源 30、坚持水务发展，促进环境保护。 31、用我们的双手，描绘绿色的家园。 32、绿色成就石化。 33、绿色环保低碳行，节能创效做先锋。 34、低碳环保，绿色生产，我们一直在行动。 35、保护石化环境，共建绿色家园。 36、守护蓝天白云，碧水青山，绿色石化等你回家。 37、万绿丛中一点“石化”。 38、汽油柴油，不如绿水长流；春风十里，不如绿色有你。 39、足下留一寸，换来四时春。 40、让中国石化阳光普照，让绿色企业神圣美妙。 41、水乡油田我的家，绿色生产人人夸。 42、绿色石化福社会，青山绿水笑开颜。 43、建优美环境，筑优良业绩，做优秀社管人。 44、让家园更美，让生活更好。 45、绿色发展，科技创新——中石化，带您畅享低碳生活。 46、垃圾分类回收是物有价值，分类投放是人有素质。 47、共建青山绿水，共享幸福安康。 48、聚焦碧水蓝天净土心动，攻关安全环保节能技术。 49、爱的魔力转圈圈，绿色生产让人心花怒放不知疲倦。 50、手牵手维护绿色家园，心连心构筑和谐石化。 51、爱护环境，是我们每一位石油人应尽的责任。 52、发展诚可贵，效益价更高；若为环保故，两者皆可抛。 53、开发资源立足绿色生态，油气稳产呵护碧水蓝天。 54、石化实说：我喜欢绿色。 55、将绿色进行到底，让绿荫遍布天下。

初中生物生物圈中的绿色植物知识点

初中生物生物圈中的绿 色植物知识点 Company number【1089WT-1898YT-1W8CB-9UUT-92108】

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7.藻类植物没有根茎叶的分化。 8.苔藓植物生存在阴湿环境中。有类似根和茎的分化，但是茎中没有导管，叶中没有叶脉，根很简单，称为假根，具有固定作用。 9.苔藓植物可作为监测空气污染程度的指示植物。 10.蕨类植物：有根茎叶的分化，具有输导组织。有些蕨类植物比较高大。 11.蕨类植物用处：①卷柏、贯众可药用②满江红是一种优良绿肥和饲料③蕨类植物遗体层层堆积，经过漫长年代、复杂变化，逐渐变成了煤。 12.藻类植物，苔藓植物，蕨类植物，生殖依靠孢子，所以也被统称为孢子植物。 第二节 二、种子植物 1.、种子的结构 菜豆种子：种皮、胚(胚芽、胚轴、胚根、2片子叶)【双子叶植物，网状叶脉】 玉米种子：果皮和种皮、胚(胚芽、胚轴、胚根、1片子叶)、胚乳【单子叶植物，平行叶脉】 种皮：保护里面幼嫩的胚。 胚：新植物体的幼体，由胚芽（发育成茎和叶）、胚轴（发育成连接根和茎的部位）、胚根（发育成根）和子叶组成。胚乳不是胚的结构。 2、子叶和胚乳里有营养物质，供给胚发育成幼苗。 3、菜豆种子中与子叶相连的结构是胚轴，玉米种子中滴加碘液变蓝的结构 是胚乳，说明胚乳中含有淀粉。

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19、保护环境，持续发展。 20、这天节约一滴水，留给后人一滴血。 21、守护健康人生，畅享财富自由。 22、产品买环保，处理少烦恼。 23、全面建设小康社会，同心共创完美家园。 24、需求无限，资源有限；善用有限，发展无限。 25、有限的资源，无限的循环。 26、以法保护自然环境，以德建立社会礼貌。 27、环保生活，健康财富。 28、做良心企业，为您的完美生活的保驾护航。 29、改善环境，创建美好未来是我们共同的愿望。 30、欣赏荒野、回归自然。 31、天蓝水清，地绿居佳。 32、心飞翔，身康健，天下一片绿色。 33、与环保同行，与财富共进。 34、用心迎接挑战，实现持续发展。 35、环保畅享绿色人生，健康乐活财富自由。 36、做世界一流环保产品，让地球人都身体康健，给财富自由的翅膀。 37、环保健康手牵手，财富自由心连心。 38、绿色是性命之源，绿色是人类之根。 39、美好的环境来自我们每个人的珍惜和维护。

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A.藻类植物 B.蕨类植物 C.裸子植物 D.被子植物 15. “人间四月芳菲尽，山寺桃花始盛开”。造成这一差异的非生物因素主要是

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17、绿色节能，创造财富。 18、低碳生活，让你要拥有一切。 19、健康有保障，财富更自由。 20、呼吸洁净空气，享受财富生活。 21、环保生活，健康一生，财富自由，不悔此生。 22、现在做环保，未来当富豪。 23、环保是健康的保证，健康是财富的本钱，财富是自由的天地。 24、节能为环保，财富为健康。 25、悠然一心，财富自由。 26、做节能环保小先锋，享自由财富新生活。 27、健康，环保，财富，三者良性循环。 28、环保创财富，健康奔自由。 29、快乐环保，享受健康，经济自由。 30、做世界一流环保产品，让地球人都身体康健，给财富自由的翅膀。 31、尽环保力量，谋健康财富。 32、践行环保理念，扞卫健康生活。 33、节能就是创造财富，节能就是环保健康。 34、打造中国最优节能环保品牌。 35、更环保，更健康，更自由。 36、环保靠大家，健康你我他，财富和自由就有啦。

最新初中生物绿色植物试题

绿色植物 姓名_________ 班级___________ 一、选择题 3.春季玉米播种后，接着下了一场大雨，土壤长时间浸水结果玉米出苗很少，其原因主要是 A.土壤中养分流失 B.土壤的派度过低 C.土壤中水分不足 D.土壤中空气不足 9.百色市右江河谷是全国著名的亚热带水果基地、南菜北运基地和“芒果之乡”，盛产芒果、荔枝等名优水果。芒果、甜美多汁的芒果肉和芒果种子分别是由花的哪些结构发育而来的A.子房、子房壁、胚珠 B.子房壁、子房、胚珠 C.子房、胚珠、子房壁 D.胚珠、子房、子房壁 4．一粒小小的种子能长成参天大树，主要是因为种子中具有 A．胚芽B．胚C．胚乳D．子叶 7．图1 为桃花的结构示意图，图中④在受精作用完成后，将发育成 A．种子B．种皮 C．果实D．果皮 9．农业生产中的施肥，主要为农作物的生长提供 A．有机物B．水C．无机盐 D．氧气 10．呼吸作用的实质是 A．分解有机物，释放能量B．合成有机物，释放能量 C．分解有机物，贮存能量D．合成有机物，贮存能量 11．自然界中，被称为“天然的碳氧平衡器”的生物是 A．植物B．动物C．病毒D．细菌和真菌13．植物体吸收和运输水分的主要结构分别是 A．根毛、导管B．导管、根毛C．根毛、根毛D．导管、导管14．“芒果、龙眼、荔枝”是百色市右江河谷盛产的名优水果，按植物的类群来分，它们都属于 A．藻类植物B．蕨类植物C．裸子植物D．被子植物15．“人间四月芳菲尽，山寺桃花始盛开”。造成这一差异的非生物因素主要是A．光B．水C．湿度D．温度 4.百色盛产芒果，被誉为“芒果之乡”。芒果的种子中，幼小的生命体是 A.胚乳 B.子叶 C.胚 D.胚芽 5.播种在水涝地的种子一般很难萌发，这是因为缺少种子萌发所需要的 A.水分 B.空气 C.养料 D.温度 6.没有输导组织的植物类群是 A.苔藓植物 B.蕨类植物 C.裸子植物 D.被子植物 7.农业生产上能有效提高光合作用的效率，从而提高作物产量的措施是 A.带土移栽 B.中耕松土 C.合理密植 D.及时排涝 10.绿色开花植物要形成果实和种子，必须经过的两个必要生理过程是 A.开花和传粉 B.开花和受精 C.传粉和受精 D.自花传粉和异花传粉 5．能进行光合作用，但没有真正的根也没有输导组织的植物类群是 A．蕨类植物B．裸子植物C．苔藓植物D．被子植物8．下列不符合生物进化总趋势的表述是

绿色环保协会有哪些

目前在中国已经成立了许多的绿色环保协会，相对权威性的是中国环境保护协会，其他的绿色环保协会分别是绿色风环保协会、绿色青春环保协会等组织。下面由绿色环保厂家亚太水处理有限公司为大家介绍下我国权威机构——中国环境保护协，帮助大家更多了解我国绿色环保事业的发展状况。 中国环境保护协会的目标是促进我国环保技术的进步与发展，提高我国环保产品的产业结构；搭建政府与企业之间的桥梁；团结、凝聚各社团组织以及各方面的力量，共同参与和关爱环保工作。 加强环境监督，维护公众和社会环境权益，协助和配合政府实现国家环境目标、任务，促进中国环境保护事业的顺利发展；确立中国环保社团应有的国际地位，参加双边、多边与环境相关的国际民间交流与合作，维护我国良好的环境国际形象，推动全人类环境事业的进步与发展。

中国环境保护协会的工作是为政府提供环境决策建议。围绕国家环境与发展的目标和任务，充分发挥政府与社会之间的桥梁和纽带作用，为各级政府及其环境保护行政主管部门提供决策建议； 为公众和社会提供环境法律权益的维护。组织开展维护环境权益和环境法律援助的理论与实践活动，推动维护环境权益的立法，建立环境权益保障体系，设立维护环境权益中心，下设维护环境权益法律咨询委员会、维护环境权益项目管理部、维护环境权益专项基金和环境律师事务所，对环境权益受到侵害的公民、法人尤其是弱势群体进行救助――调查取证、法律援助、调解协商、帮助申诉、支持诉讼等等，维护公众和社会的环境权益。 中国环境保护协会是中国环境保护公益组织；宗旨是服务于中国的环境保护事业；中国环保协会坚持以中共中央总书记胡锦涛同志指出的：“加强环境保护，促进绿色繁荣”为指导思想，坚持以拥护中国共产党的路线方针政策为重任，持之以恒。

企业环境方针标语

企业环境方针标语 1、善待地球就是善待自己。 2、提高环境意识，保护完美家园。 3、环保一份心，享受健康财富生活。 4、倡导环保理念，引领健康生活，实现财富自由。 5、多环保，多健康，更多财富自由。 6、青山绿水蓝天，共创完美人间。 7、争做环保使者，共创绿色文明。 8、环保在首，健康长久。 9、为世界节能，让财富共享。 10、需求无限，资源有限；善用有限，发展无限。 11、让绿色覆盖世界，让动力流向未来。 12、除了足迹，咱们什么也没有留下；除了摄影，咱们什么也没有带走。 13、环保创造健康，财富携手自由。 14、节能环保，健康人生，财富随身。 15、争当节能环保标兵，树立财富自由模范，畅享健康写意人生。

16、生态环保，节能先锋，享受生活。 17、领创环保经济，构筑财富梦想。 18、宝岛做环保，青山绿水常围绕。 19、愿未来由您主宰，助梦想财富自由。 20、环保生活，健康财富。 21、保护生态环境，倡导礼貌新风。 22、大家做环保，子孙才有保。 23、地球为家，环保无处不在。 24、树立企业形象，协调人与自然。 25、地球能满足人类的需要、但满足不了人类的贪婪。 26、拯救地球，一齐动手。 27、合理利用自然资源，防止环境污染和生态破坏。 28、健康就是财富，环保才有未来。 29、健康节能，共享财富。 30、环保心，人健康。 31、更环保，更健康，更自由。 32、人造环境，环境育人。 33、合理利用资源保护生态平衡，促进经济持续发展。

34、消费重环保，地球才有保。 35、环保健康手牵手，财富自由心连心。 36、保护环境，保存希望。 37、我环保，我健康，自由财富任我想。 38、做健康环保社会的引领者。 39、当环保卫士，做时代公民。 40、遵守法律法规，防治环境污染。 41、环保一小步，健康一大步。 42、珍爱性命，保护环境，造福人类。 43、垃圾混置是垃圾，垃圾分类是资源。 44、改善环境，建立完美未来是咱们共同的愿望。 45、人人参与环保，共创绿色世纪。 46、自由就是财富，环保就是健康。 47、健康环保保障，财富自由先行。 48、快乐环保，享受健康，经济自由。 49、绿色生活，源于细节环保。 50、保护环境山河美，持续发展事业兴。 51、重视生态功在千秋，保护环境造福万代。

公司创建“绿色环保企业”实施方案

公司创建“绿色环保企业”实施方案 为了全面贯彻《国务院关于落实科学发展观,加强环境保护的决定》,认真落实中共中央宣传部、国家环保总局、国家教育部联合颁布实施的《全国环境宣传教育行动纲要》精神,树立绿色财富理念,倡导生态文明,促进公司绿色生产与绿色消费,坚持科学发展观,走新型工业化道路,从源头控制环境污染和生态破坏,加快生态县的建设步伐,推动公司节约资源、保护环境,根据自治区《关于开展广西“绿色环保企业”创建活动的通知》(桂环字[2006]69号)要求,经公司领导研究决定,从2009年起开展“绿色环保企业”创建活动。 一、创建“绿色环保企业”的宗旨 随着绿色文明时代的来临,以人为本,全面、协调、可持续发展的科学发展观是新世纪、新阶段党和国家各项事业发展的重大战略思想。创建绿色环保企业,旨在创建成为资源利用率高、污染排放少、经济效益显著、具有国际竞争力的企业。转变生产经营观念,将环保、安全理念贯穿生产全过程,保护和改善生态环境,使公司所处的环境同经济协调发展,实现生产经营生态化、产品技术绿色化、增长发展持续化。 “绿色环保企业”指的是按照生态学和生态经济学原理,建立生态型生产经营管理体系,实行清洁生产,发展无毒无害生产工艺和综合利用技术,组织生态化的物质生产过程或服务过程,使整个企业技术工艺过程和经营管理过程生态化,变现代企业物质生产过程或服务过程为自然生态过程或纳入自然生态过程,形成生态生产和生态生活的企业化生态格局。在投入生产过程中各 种自然资源和原料能源得到最有效、最充分地利用,提高资源能源利用率,减少乃至消除废弃物,形成一个投入少、消耗低、质量高、无污染或少污染而又生产符合生态环境标准的产品的生态经济有机整体,实现企业生态经济良性循环与可持续发展。

环保企业愿景宣传标语有哪些

环保企业愿景宣传标语有哪些 1、要环保，要健康，享受你的财富自由。 2、绿色与生命共存，健康与财富共进。 3、节能与环保齐美，健康与财富共享。 4、引领环保新时尚，创造自由新生活。 5、享财富自由，做世界健康环保大使。 6、我们的使命就是让您自由的呼吸纯净的空气。 7、我们的财富需要健康，我们的健康需要环保。 8、引领环保健康新时尚，缔造财富自由新企业。 9、爱护环境，关注健康，创造财富，追求自由。 10、从环保健康之路踏上财富自由之路。 11、以环保赢得健康，以品质换来财富自由。 12、携手共行环保之路，共享绿色节能财富。 13、节能环保，健康生活，畅享财富自由。 14、环保，健康，财富，自由，是我们永远的追求。 15、环保-健康-财富自由，是我们永远的追求。 16、共建地球生态环境，共享健康财富人生。 17、做节能环保小卫士，创自由财富新生活。 18、环保靠大家，健康你我他，财富和自由就有啦。 19、与环保同步，和健康同行，享受财富自由。

20、做节能环保小先锋，享自由财富新生活。 21、实实在在做环保人士，轻轻松松享财富自由。 22、怀着激情的信心而来，带着财富的自由而归。 23、做良心企业，为您的美好生活的保驾护航！ 24、环保你我他，健康永相伴，财富自由共分享。 25、节能就是创造财富，节能就是环保健康。 26、环保带给你健康，健康让你享受财富和自由。 27、发展中国环保事业，提高民族健康水平，实现百姓财富自由。 28、支持环保，别让你的健康与财富自由流失。 29、给你“更环保、更健康、更成功”的选择。 30、环保时代，健康生活。自由追逐，财富就在我们手中。 31、环保畅享绿色人生，健康乐活财富自由。 32、投资节能环保，稳赚健康体魄，享受财富自由。 33、争当节能环保标兵，树立财富自由模范，畅享健康写意人生。 34、争做节能环保企业标杆，缔造健康财富自由生活。 35、环保好方向，健康好身体，财富自由来。 36、绿色环保的健康，呼吸自由的空气，享受财富的积累。 37、节能环保缔造健康生活，健康生活成就财富自由。 38、让环保常驻你我心中，换回财富健康自由成长。 39、缔造环保节能新时代，成就业内最优品牌。 40、倡导环保理念，引领健康生活，实现财富自由。 41、蓝天白云，绿水青山；我们的努力，自然的美丽。

初中生物生物圈中的绿色植物知识点、习题及答案

第三专题生物圈中的绿色植物 一、绿色植物与生物圈的水循环 [知识网络结构] 水是植物体的重要组成成分 原因水保持植物直立的姿态，有利于进行光合作用1、绿色植物的生活需要水无机盐只有溶解在水中，才能被吸收和运输 水影响植物的分布：降水量大的地方，植被茂密 根吸水的部位：主要是根尖成熟区， 吸水 成熟区的特点：生有大量根毛，增大根吸水的表面积 2、水分进入植物体内的途径途径：木质部的导管 水分的运输方向：自下而上 树皮：韧皮部中有筛管，输导有机物 茎的结构形成层：细胞能分裂（木 本植物有此 结构） 木质部：有导管，输导水 分和无机 盐 实验：观察叶片的结构（练习徒手切片）

表皮（上、下表皮） 叶片的结构叶肉 叶脉 重要结构：气孔，是植物蒸腾失水的门户，也是气体交 换的窗口，由成对的保卫细胞围成，气孔3、绿色植物 的开闭由保卫细胞控制。当保卫细胞吸水 膨胀时，气孔张开，当保卫细胞失水收缩 时，气孔关闭。 概念：水分以气态从植物体内散发到体外的 过程。 蒸腾作用主要部位：叶片 降低了叶表面的温度 意义促进对水和无机盐的运输 促进对水的吸收 绿色植物促进了生物圈中的水循环 绿色植物参与生物圈的水循环提高大气湿度，增加降水量 保持水土 [课标考点解读] 绿色植物促进了生物圈中的水循环，因此保护森林和植被有非常重要的意义。本章重点阐明水是绿色植物生存的必要条件，水对植被的影响，植物吸水的主要部位及特点，水运输的途径，蒸腾作用的意义。在能力培养方面，通过解读数据，培养学生的探究能力。

[典型例题剖析] 例1、把发蔫的菠菜泡在清水中，菠菜又重新挺起来，这说明（） A.水是光合作用的原料 B.水是溶解和运输物质的溶剂 C.水能保持植物体固有的姿态 D.水是植物体重要的组成成分 [解析]：植物体内只有水分充足时，才能保持挺立的姿态，叶片才能舒展，有利于光合作用。本题没有数据说明水是植物体的重要组成成分，也没有说明水是如何作为溶剂来溶解无机盐的。本题考察学生对植物需水原因的分析和理解。答案：C 例2、下图所示，植物吸收水分的主要部位是（） [解析]：根尖成熟区是吸水的主要部位，表皮细胞突起，形成大量根毛，大大增加了吸水的面积。本题主要考察学生识图的能力。答案：A 例3、3月12日是我国的全民植树节，之所以选择这个时候，是因为（） A.正值农闲时节 B.土壤松散 C.雨水较多 D.叶没长出，蒸腾作用很弱 [解析]：移栽植物时，会损伤幼根和根毛，降低根吸水的能力，因此，要使移栽植物成活率高，必须降低其蒸腾作用。植树节选在3月12日，是因为这时叶还没有长出，蒸腾作用弱。本题考察学生对叶是蒸腾作用的主要部位的理解。答案： D 例4、不属于蒸腾作用应用的是（） A.在阴天傍晚移栽植物 B.移栽后的植物去掉部分枝叶

企业环保宣传标语

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