DNS-B

ASED LOAD BALANCING FOR WEB SERVICES

Alan Nakai, Edmundo Madeira and Luiz Eduardo Buzato

Institute of Computing, Unicamp - University of Campinas, Campinas, SP, Brazil

Keywords:

Load balancing, Web services.

Abstract:

A key issue for good performance of geographically replicated web services is the efﬁciency of the load

balancing mechanism used to distribute client requests among the replicas. This work revisits the research on

DNS-based load balancing mechanisms considering a SOA (Service-Oriented Architecture) scenario. In this

kind of load balancing solution the Authoritative DNS (ADNS) of the distributed web service performs the

role of the client request scheduler, redirecting the clients to one of the server replicas, according to some load

distribution policy. This paper proposes a new policy that combines client load information and server load

information in order to reduce the negative effects of the DNS caching on the load balancing. We also present

the results obtained through an experimental tesbed built on basis of the TPC-W benchmark.

1 INTRODUCTION

With the increasing adoption of SOA (Service-

Oriented Architecture), a new scenario arises, where

highly accessed web applications are deployed as web

services and clients are not web browsers accessing a

web site, but other enterprises using the services of

other providers.

This new kind of scenario may require higher lev-

els of web service dependability because of the QoS

contracted by the service consumers. Thus, providers

replicate their applications in clusters geographically

distributed linked via the Internet for the sake of fault-

tolerance and performance. A key issue for good per-

formance in these environments is the efﬁciency of

the load balancing mechanism used to distribute client

requests among the replicated services.

This work revisits the research on DNS-based load

balancing mechanisms for gegraphically replicated

web services. In this kind of load balancing solu-

tion the Authoritative DNS (ADNS) of the distributed

Web service performs the role of the client request

scheduler, redirecting the clients to one of the server

replicas, according to some load distribution policy.

Differently from previous works, that considered the

simple browser-server scenario, in our work we con-

sider a SOA scenario.

It is known that large Internet corporations – e.g.

Google (Barroso et al., 2003) and Akamai (Pan et al.,

2003; Su et al., 2006) – use DNS-based load balanc-

ing mechanisms. These mechanisms beneﬁt from the

existing DNS infrastructure, providing transparency

for the Web clients. However, this kind of strategy

has a main limitation: the low control of the ADNS

over the load balancing caused by the DNS caching

system, that prevents name resolution queries to reach

the ADNS.

The main contribution of this paper is the pro-

posal of a new DNS-based load balancing mecha-

nism that uses client load information in order to bet-

ter distribute the load among the replicated servers

– the Current Relative Minimum Load (CRML). Be-

sides, the mechanism reduces the negative effects of

the DNS caching over the load balancing through the

cooperation of the ADNS and the servers.

We also present the evaluation of our load balanc-

ing policy over an experimental testbed implemented

on basis of the TPC-W (TPC, 2002), a well accepted

E-Commerce benchmark. The experiments show that

the CRML policy behaved as good as other policies

in the scenario in which the ADNS had full control of

the name resolution queries and behaved better than

the others in a scenario were the ADNS had partial

control.

The remainder of this text is organized as follows.

Section 2 provides an overview of the DNS system

and the DNS-based load balancing mechanisms. Sec-

tion 3 presents related works. In Section 4 we de-

Nakai A., Madeira E. and Eduardo Buzato L.

DNS-BASED LOAD BALANCING FOR WEB SERVICES.

DOI: 10.5220/0002805000950100

In Proceedings of the 6th International Conference on Web Information Systems and Technology (WEBIST 2010), page

ISBN: 978-989-674-025-2

scribe our new load balancing mechanism. Section 5

presents the testbed used for the evaluation of our pol-

icy and Section 6 shows the experimental results. Sec-

tion 7 concludes the paper with our ﬁnal comments

and future work.

2 BACKGROUND

In the DNS-based load balancing mechanisms, the au-

thoritative nameserver of the distributed Web server

performs the role of the client request scheduler.

When it receives a request for URL resolution, it

replies the IP address of one of the server nodes, ac-

cording to some load distribution policy.

The main advantage of this kind of load balanc-

ing mechanism is that it beneﬁts from the existing

DNS infrastructure. This makes these mechanisms

immediately deployable in today’s Internet (Pan et al.,

2003).

Unfortunately, a limitation of DNS-based load

balancing mechanisms is the weak control of the

ADNS over the load balancing, caused by the DNS

caching, that prevents a portion of DNS queries to

reach the ADNS.

The simplest DNS-based load balancing mecha-

nism is the Round Robin (RR) policy. In this pol-

icy, when a request for name resolution arrives at

the ADNS, it responds with the address of the next

replica of its list, in a rotative way. More sophisti-

cated approaches apply information from server node

utilization and/or client domain information to select

a server replica.

3 RELATED WORK

The use of information about server node utilization

in DNS-based load balancing mechanisms is exem-

pliﬁed in (Colajanni et al., 1998; Yokota et al., 2004;

Moon and Kim, 2005). In these works, an agent mon-

itors the states of the servers and reports this infor-

mation to the ADNS. When a name resolution query

arrives, the ADNS uses the utilization information of

the server nodes to assign one of the replicas to the

client. Many kinds of information can be used in the

ADNS decision, such as request queue length, CPU,

network, or memory usage.

There are two types of client domain information

that can be used in the load balancing: client proxim-

ity and client domain load. In the ﬁrst case, the ADNS

tries to assign the nearest server to the client (Barroso

et al., 2003; Pan et al., 2003; Su et al., 2006). A main

concern in this kind of load balancing mechanism is

to estimate the proximity between clients and servers.

Since the ADNS does not have information about the

client host, a solution for this problem is to assume

that clients are located near to their local nameservers

and estimate the distance between servers and local

nameservers.

The capacity of estimating the load generated by

client domains may be very useful for load balanc-

ing mechanisms. This information allows the ADNS

to treat differently domains that generate high request

rates (hot domains) from the others (cold domains).

The works (Colajanni et al., 1998), (Colajanni and Yu,

2002), and (Chatterjee et al., 2005) present promis-

ing results using information about client domain load

for: (i) avoiding the assignment of hot domains to

the same servers; (ii) estimating the real load of each

server; and/or (iii) applying different TTLs for name

resolutions addressed to hot and cold domains.

In this work, we present a new DNS-based

load balancing mechanism that combines information

from clients and servers to alleviate the effects of the

DNS caching on the load balancing.

4 THE CRML POLICY

This section presents our proposed load balancing

policy, the Current Relative Minimum Load (CRML).

4.1 Rationale

The idea for the algorithm was motivated by the anal-

ysis of the three load balancing policies described in

(Colajanni et al., 1998; Colajanni and Yu, 2002). Here

is a summary of them:

• Least Utilized Node (LUN): the ADNS assigns a

request to the less utilized node, based on the most

recent server load information;

• Two Tier Round Robin (RR2): the ADNS divides

client domains into two groups, hot and cold domains.

Hot domains are those that generate high number of

requests. The policy applies the round robin strategy

separately to each group, trying to avoid the assign-

ment of requests proceeding from hot domains to the

same server nodes;

• Minimum Residual Load (MRL): the ADNS

maintains a table containing all the assignments and

their times of occurrence. Based on this table and on

estimates of the request rate of each client domain, the

policy calculates the load of each web server replica

and assigns an incoming name resolution request to

the least loaded one;

WEBIST 2010 - 6th International Conference on Web Information Systems and Technologies

A deﬁciency of LUN is that the information used

by the algorithm in decision making is often outdated.

This happen because the algorithm considers only the

last utilization information received from the servers,

however, the state of the servers may have changed

at the moment of a new assignment. In order to deal

with this deﬁciency it should be necessary to make de-

cisions based not only on the last utilization informa-

tion but also considering the assignments performed

after this information has arrived.

In our experiments, the RR2 policy presented a

signiﬁcative improvement in comparison to RR. This

result shows us that to handle differently hot and cold

domains is a good strategy. A deﬁciency of RR2 is

that, even if a server is overloaded, the algorithm con-

tinues to assign new name resolution requests to that

server because of the round robin strategy.

The MRL works quite ﬁne because of its ability of

estimating the total load of the servers based on pre-

vious name resolution assignments. Nevertheless, if

the control of the ADNS decreases, by the reason of

the caching of intermediary DNS servers, the MRL

assignment table becomes incomplete and leads the

ADNS to make wrong decisions. Moreover, if the

number of clients is very high, it could be very ex-

pensive to maintain the assignment table.

4.2 Policy Description

In the CRML policy, we follow the hypothesis that the

distribution of hot domains among the server replicas

dictates the success of the load balancing mechanism.

Thus, as well as RR2, the CRML policy divides the

clients into two groups, hot and cold domains. Cold

domains are distributed among the server replicas us-

ing the ordinary round robin strategy.

In order to better distribute the load generated by

hot domains, the ADNS maintains an assignment ta-

ble containing the assignments of servers to hot do-

mains and their time of occurrence. Note that the set

of assignments in this table is potentially incomplete,

because many clients might have received name res-

olutions from intermediary DNS servers. Hence, the

ADNS cooperates with the servers to compensate the

effect of the DNS caching. Each server tracks the cur-

rent set of hot domains from which it is receiving re-

quests and report this information to the ADNS. Be-

sides, servers also report estimates of the load (request

rates) that hot domains are generating. Combining the

information of the assignment table and the informa-

tion proceeding from the servers, the ADNS can esti-

mate the request rate each server is receiving from hot

domains.

Let S be the set of web servers; let l

(a) be the load

generated by the assignment a to the server i; let A

be the set of assignments to the server i, known by

the ADNS, and whose the TTL has not expired; and

let A

be the assignments reported by the server i.

When the ADNS receives a name resolution request

from a hot client it computes:

CRML = min

i∈S

(

∑

a∈{A

∪A

}

(a)

)

and assigns it to the the corresponding server.

The efﬁciency of the CRML depends on how the

set of assignments that the ADNS knows ({A

∪

}) is close to the reality. The ADNS knowledge

is limited by the staleness of the information reported

by the servers. The last sets of assignments (A

) re-

ceived from the servers may lack assignmets that were

established after the information were sent and may

contain assigments that are not valid anymore.

In a certain limit, the use of the ADNS assign-

ment table reduces the staleness of the server-side in-

formation. Another way to reduce the effects of stale

server-side information is to exclude any assignment

of the client that is requesting a name-resolution from

the CRML calculation. Previous assignments related

to this client is obviously not valid anymore, since it

is requesting a new one.

Since the assignment table of the CRML does not

store the state of all clients, only the states of hot

clients, the cost for maintaining the table is smaller

than using MRL.

4.3 Software Architecture

This section presents the software architecture that

supports the proposed load balancing policy. The ar-

chitecture is illustrated in Figure 1.

Figure 1: CRML Architecture.

In this architecture, the server replicas are com-

posed of two modules: (i) the web server module,

that processes the incoming HTTP requests, and (ii)

the monitor module, that collects information about

hot client domains and reports it to the ADNS.

DNS-BASED LOAD BALANCING FOR WEB SERVICES

The ADNS is a DNS server extended with two

modules, the domain mapper and the scheduler. The

former is responsible for identifying if an incoming

request comes from a hot or a cold domain. The lat-

ter uses the information reported by the server repli-

cas and the information stored in the DNS assignment

table to decide the better replica to which redirect a

client.

5 EXPERIMENTAL TESTBED

In order to evaluate our load balancing mecha-

nism, we implemented an experimental testbed, the

Lab4WS (Lab for Web Services). This section

presents this testbed. More details about the Lab4WS

Testbed can be found in (Nakai et al., 2009).

The testbed includes the implementation of a

SOAP Web service based on the TPC-W bench-

mark (TPC, 2002), a transactional benchmark for E-

Commerce web sites that is well accepted by the re-

search community. The service consists of a set of 20

operations that allow clients to search and buy prod-

ucts. A proxy placed in front of each service replica

collects client information and reports to the DNS the

list of hot clients that are accessing the replica and the

estimative of load generated by those clients.

The load generation of our testbed is performed

by a set of client emulators. Each load generator emu-

lates the load of an E-Commerce Web site that serves

an entire Web domain and generates requests to the

TPC-W Web Service. The generated load follows the

TPC-W speciﬁcation, which speciﬁes three kinds of

workloads that vary according to the percentage of

read and write operations. As in previous works –

e.g. (Colajanni and Yu, 2002) – the load generation

is divided between different clients according to the

Zipf’s distribution, where the probability of a client to

belong to the ith domain is proportional to 1/i

. The

testbed user can vary the skewness of the distribution

changing the exponent x of the function. This solution

was motivated by previous works that demonstrated

that if one ranks the popularity of client domains by

the frequency of their accesses to a Web site, the size

of each domain is a function with a big head and a

very long tail.

The testbed also contains a DNS emulator that ma-

terializes the ADNS of the web system. This emulator

allows the testbed user to deploy new load balancing

policies in a friendly way. The effect of the DNS

caching is implemented as a mechanism that con-

trols the percentage of name resolution requests that

reach the ADNS. The testbed user can deﬁne this per-

centage. The client emulators randomly decide what

name resolution requests are sent to the ADNS. When

a name resolution request is not sent to the ADNS,

the client emulator reuses the last name resolution it

received, emulating a caching effect.

6 CRML EVALUATION

In our experiments, we consider a scenario in which

a set of retailers form partnerships with a large E-

Commerce enterprise to outsource the application

logic of their e-store Web sites. Each e-store is vis-

ited by a great number of end customers, and ac-

cesses the E-Commerce enterprise services via Web

Services. The E-Commerce enterprise needs to dis-

tribute the load incoming from the e-stores among its

geographically distributed replicas of servers.

6.1 Methodology

For these experiments, our testbed was deployed on

the Emulab

network testbed. Emulab is a user-

conﬁgurable lab environment that allows users to

model and emulate network topologies on a cluster,

varying parameters such as latency and bandwidth.

We ran the experiments using 5 TPC-W Web Service

replicas and 16 machines running Emulated Clients

with a load equivalent to 300 requests/second (75 re-

quests/second for each secondary replica). All ma-

chines were Pentium Xeon 64, 3GHz, with 2GB of

memory. A latency of 100 ms was introduced into the

links between the machines, to emulate a wide area

network latency.

In order to evaluate the different load balancing

mechanisms, we adopted two main metrics: the max-

imum system queue length and the response time ob-

served by the clients. The maximum system queue

length is the largest number of requests waiting to be

answered on a service replica, observed in a given in-

stant, among all service replicas.

If the incoming load exceeds the service replica

capacity, the response rate becomes lower than the

request rate and the incoming requests tend to accu-

mulate at the server. Thus, the request queue grows.

Since the requests take longer to be served, the re-

sponse times observed by the client increase, and the

system throughput goes down.

6.2 Experimental Results

We have evaluated the CRML policy on two scenar-

ios. In the ﬁrst, we compare ﬁve load balancing mech-

anisms (RR, LUN, MRL, RR2, and CRML) with

https://www.emulab.net/

WEBIST 2010 - 6th International Conference on Web Information Systems and Technologies

(a) Cumulative frequency of the maximum system queue

length.

(b) Cumulative frequency of response times.

Figure 2: Experimental results: 100% of ADNS control.

(a) Cumulative frequency of the maximum system queue

length.

(b) Cumulative frequency of response times.

Figure 3: Experimental results: 30% of ADNS control.

a uniform distribution, which represents the “ideal”

load balancing, assuming that the ADNS has total

control over the name resolution requests. In the sec-

ond scenario, we compare MRL, RR2, and CRML,

which presented the better performances in the ﬁrst

scenario, assuming that only 30% of the name resolu-

tion requests reach the ADNS.

In these experiments we used the DNS TTL=60s

and interval for server information dissemination of

10s. The threshold adopted to identify hot clients

was a load equal to 4 requests/second for MRL and

CRML, and a load equal to 10 requests/second for

RR2. Load was generated using Zipf distribution with

x = 1.0.

Figure 2(a) shows the cumulative frequency of

the maximum system queue length for the ﬁrst sce-

nario. The MRL and CRML mechanisms presented

good performances, showing maximum system queue

lengths close to the uniform distribution. This re-

sult was expected because in this scenario, where the

ADNS has full control over the name resolution re-

quests, these two policies are able to compute the

states of the servers with a high precision. The RR2

does not performed as good as CRML and MRL, but

its performance was better than RR, showing that the

use of client load information improved the round

robin strategy. The LUN policy presented the worst

performance, showing that the simple use of informa-

tion about server utilization is not effective for load

balancing.

The response times obtained in the experiments

of the ﬁrst scenario reﬂected the results of the maxi-

mum system queue length measurement. Figure 2(b)

shows the cumulative frequency of response times for

the subjectSearch operation, which performs a search

for items by the subject in the book store. While, us-

ing CRML and MRL, 95% of the operation requests

were answered in less then 3s, using RR2, about 25%

of the requests were answered in more than 3s. Us-

ing RR, about 35% of the operation requests were an-

swered in at least 3s, and, using LUN, more than 80%

of the requests were answered in at least 3s.

DNS-BASED LOAD BALANCING FOR WEB SERVICES

The results obtained in the experiments of the sec-

ond scenario are shown in Figure 3. As expected, due

to the low control of the ADNS, the RR2 and MRL

policies presented worse results than in the ﬁrst sce-

nario. In more than 50% of the time, these policies

showed maximum system lengths larger than 1500

(Figure 3(a)). Differently from the others, CRML

worked quite ﬁne in the second scenario, presenting

maximum system lengths smaller than 250 in 95% of

the time.

The performance of the three policies is also

perceived in the response time graph (Figure 3(b)).

While the CRML presented response times lower than

3s in more than 95% of the requests, RR2 and MRL

presented response times higher than 3s in 35% of the

requests.

The experimental results show that, in the scenario

where the ADNS had full control of the name reso-

lution queries, CRML performed as well as the best

policy (MRL) considered, presenting response times

close to the ideal distribution. Moreover, CRML pre-

sented a better performance than RR2 and MRL in the

scenario where the ADNS had partial control. This

result shows that the cooperation between the ADNS

and the servers compensated the effect of the DNS

caching on the calculation of the server load states,

allowing a good load balancing.

7 CONCLUSIONS

In this paper, we have presented a new DNS-based

load balancing policy, the CRML. This policy com-

bines information from clients and servers to alleviate

the negative effect of the DNS caching over the load

balancing mechanism.

The experimental results showed that our load bal-

ancing policy worked as good as other DNS-based

load balancing policies in the scenario where the

ADNS had full control over name resolution requests.

Furthermore, CRML outperformed RR2 and MRL in

the scenario where the ADNS control was limited.

Future work includes: (i) the evaluation of the

sensitivity of our policy to different combinations of

DNS TTLs, time interval of server information prop-

agation, and the threshold for identifying hot do-

mains; and (ii) the evaluation of the combination of

CRML with server redirection mechanisms and dy-

namic TTL policies.

ACKNOWLEDGEMENTS

The authors would like to thank Fapesp (n. 07/56423-

6), CAPES, and CNPQ for the ﬁnancial support, and

Schooner, Emulab, PlanetLab, and RNP (National

Education and Research Network) for the infrastruc-

ture support.

REFERENCES

Barroso, L. A., Dean, J., and H

olzle, U. (2003). Web search

for a planet: The google cluster architecture. IEEE

Micro, 23(2):22–28.

Chatterjee, D., Tari, Z., and Zomaya, A. Y. (2005). A task-

based adaptive ttl approach for web server load bal-

ancing. In Proceedings. 10th IEEE Symposium on

Computers and Communications, 2005. ISCC 2005.,

pages 877–884. IEEE Computer Society.

Colajanni, M. and Yu, P. S. (2002). A performance study of

robust load sharing strategies for distributed hetero-

geneous web server systems. IEEE Transactions on

Knowledge and Data Engineering, 14(2):398–414.

Colajanni, M., Yu, P. S., and Dias, D. (1998). Analysis of

task assignment policies in scalable distributed web-

server systems. IEEE Transactions on Parallel and

Distributed Systems, 9(6):585–600.

Moon, J.-B. and Kim, M. H. (2005). Dynamic load bal-

ancing method based on dns for distributed web sys-

tems. In E-Commerce and Web Technologies. EC-

Web, volume 3590 of Lecture Notes in Computer Sci-

ence, pages 238–247. Springer.

Nakai, A. M., Madeira, E., and Buzato, L. E. (2009).

Lab4ws: A testbed for web services. In Proceedings

of the 2nd International Conference on Computer Sci-

ence and its Applications (CSA’09)/2nd IEEE Inter-

national Workshop on Internet and Distributed Com-

puting Systems (IDCS’09), pages 647–652.

Pan, J., Hou, Y. T., and Li, B. (2003). An overview of

dns-based server selections in content distribution net-

works. Computer Networks, 43(6):695–711.

Su, A.-J., Choffnes, D. R., Kuzmanovic, A., and Bus-

tamante, F. E. (2006). Drafting behind akamai

(travelocity-based detouring). SIGCOMM Comput.

Commun. Rev., 36(4):435–446.

TPC (2002). TPC Benchmark W - Speciﬁcation 1.8.

http://www.tpc.org/tpcw/spec/tpcw V1.8.pdf.

Yokota, H., Kimura, S., and Ebihara, Y. (2004). A proposal

of dns-based adaptive load balancing method for mir-

ror server systems and its implementation. In AINA

’04: Proceedings of the 18th International Confer-

ence on Advanced Information Networking and Ap-

plications, page 208, Washington, DC, USA. IEEE

Computer Society.

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