an ant algorithm based dynamic routing strategy for mobile agents

X. Zhou, Y. Zhang, and M.E. Orlowska (Eds.): APWeb 2003, LNCS 2642, pp. 453–464, 2003.© Springer-Verlag Berlin Heidelberg 2003

An Ant Algorithm Based Dynamic Routing Strategy forMobile Agents 1

Dan Wang, Ge Yu, Mingsong Lv, Baoyan Song, Derong Shen, and Guoren Wang

School of Information Science and EngineeringNortheastern University, Shenyang, China 110004{wangdan,yuge }mail.neu.edu.cn

Abstract. Routing strategy is one of the most important aspects in a mobileagent system, which is a complex combinatorial problem. Most of currentmobile agent systems adopt static routing strategies, which don’t considerdynamic network status and host status. This is a hinder to the performance andautonomy of mobile agents. Ant Algorithm is good at solving such kind ofproblems. After analyzing existing routing strategies of typical mobile agentsystems, this paper summarizes factors that may affect routing strategy ofmobile agents, proposes an Ant Algorithm based dynamic routing strategy byusing both experience and network environment such as resource information,network traffic, host workload, presents an acquiring and storing method ofrouting parameters and decision rules according to the major characteristics ofmobile agent migration. The simulation experiment is implemented and theresults show our dynamic routing strategy can effectively improve theperformance and autonomy of mobile agents.

1 Introduction

The mobile agent is a software entity with certain intelligence, which has the ability tomigrate independently in the network environments and can carry out tasks on behalfof their users [1]. Since the mobile agent can migrate dynamically to the host whereresources are available to achieve asynchronous computing, a number of networkconnections as before become unnecessary so that the distributed computingefficiency is greatly improved. A mobile agent can migrate in a range of hosts on anetwork, using the resources and services provided by the hosts to execute the tasksgiven by its user. The migration of a mobile agent is directed by a routing strategy.The routing strategy is based on a routing table, called an itinerary [2], whichcontains the hosts to be visited and the visiting sequence. How to make an efficientitinerary involves many factors such as the network’s traffic, the host workload andthe available resources. The goal of a routing strategy is to execute user’s taskssuccessfully with minimal time and overhead.

1 This work is supported by the National Natural Science Foundation of China (60173051),the Cross Century Excellent Young Teacher Foundation of the Ministry of Education, andthe National 863 High-tech Program (2001AA415210)

454 D. Wang et al.

Generally, a mobile agent’s routing strategy can be categorized as static approachand dynamic approach [3]. The static approach is adopted in most mobile agentsystems, by which the itinerary is predefined by the task designer before an agentmigrates and can not be changed. The major difference among the systems that adoptstatic approach is that some systems such as Aglet[4] implicate their itineraries withinprogram codes, while other systems such as Concordia[5] explicitly keep theiritineraries outside of program codes to reduce the burden of the mobile agent’smigration.

Internet is such a highly dynamic environment that the load of the networks andhosts on it is uncertain, thus mobile agents should have the ability to adjustdynamically the sequence of hosts to be visited according to network situation andtasks to reduce time or the amount of data transmission. With dynamic routingstrategies, the itinerary can be adjusted according to agents’ tasks and network statusduring the process of migration. An initial itinerary is defined by users, and can befrequently revised by migrating agent to adapt to the changing environment. So itsupports well agent’s characteristics such as reactivity, adaptability and autonomy [3].

This paper analyzes existing routing strategies, summarizes factors that affect themigration of mobile agents and proposes an Ant Algorithm based dynamic routingstrategies, for which, the storing and acquiring method of parameters and routingdecision rules is designed according to the major characteristics of mobile agentmigration. Moreover, a simulation experiment is implemented and the systemperformance is evaluated

The reminder of this paper is organized as follows. Section 2 discusses the relatedwork. Section 3 analyzes the factors of routing strategies for mobile agents, and theproblems of applying Ant Algorithm into mobile agent routing strategies. In Section4, an Ant Algorithm based dynamic routing strategy is proposed and theimplementation techniques are presented. In Section 5, the performance evaluation isexplained. Finally, Section 6 concludes the paper and gives the future work.

2 Related Work

There are many research works have been done on the routing strategies of mobileagents. In paper [6], the routing strategy is defined as a problem of finding theminimal visiting sequence to complete the tasks in the minimal time with theassumption that network status and directory service are available. Three parametersincluding the latency of two adjacent hosts, the execution time on every host and theprobability of completing the tasks on every host are presented. In paper [7], aheuristic algorithm is presented to realize the minimal execution time and the minimalnumber of agents that accomplish the tasks together. The authors think more than oneagent can accomplish a specific task, and the number of agents to accomplish a taskand the total execution time are two main performance factors. Paper [8] analyzes thebehavior of agents in a network, i.e., whether the agents merely migrate or make theRPC communication with adjacent hosts, or even the combination of the two. Thealgorithm presented only focused on the amount of data that is transmitted acrosshosts. An optimization algorithm is presented in reference [9]. The spending of timeand data transmitted in migration and RPC communication is available in advance. A

An Ant Algorithm Based Dynamic Routing Strategy for Mobile Agents 455

process of alternative migrations and RPC communications characterizes the behaviorof an agent. The concept of itinerary is presented in paper [2].

However, the following problems still are not solved well by the existing works:•ÿ The combination of migration and RPC communication or merely migration

as the working mode for mobile agents. Some algorithms claim that rather thanmerely migration or RPC communication, the combination of the two is the mostefficient way. Agents should decide whether to migrate or to communicateaccording to the cost of each mechanism.

•ÿ Static approach or dynamic approach. The algorithms above are mostly staticstrategies, so agents are less sensitive or adaptive to the environment. SinceInternet is a large-scale network and agents may carry numerous kinds of tasks, it’simpractical to maintain the network status and the host status in advance.

•ÿ Critical Factors. Some algorithms considered only the amount of data that aretransmitted across network; others comprehensively considered network load, hostload and the probability of resources existing on hosts. Few algorithms tookagents’ migration experience into account. Such neglect shouldn’t occur in agentsystems with high intelligence.

•ÿÿÿDifferent optimization targets. Some are focused on minimizing datatransmitted, and others try to minimize total migration time.

3 Routing Strategies for Mobile Agents and Ant Algorithm

This section first analyzes the characteristics and related factors in mobile agentrouting strategies, then introduces Ant algorithm, and discuss how to apply Antalgorithm into the dynamic routing strategy.

3.1 Routing Strategy

The characteristics and critical factors in mobile agent routing strategies include:•ÿ Adopting dynamic approach to have high sensitivity and adaptability

Static approach requires collecting knowledge about network and hosts, but it’simpractical to accomplish such a collecting process on the Internet that has so manyhosts and complex topological structures. At the same time, hosts also changefrequently in load status due to a large amount of information they have to process.Thus routing strategies should be highly sensitive to network status and host statusand can make accordingly adjustment to the changing environment. Therefore, wechoose the dynamic approach.•ÿ Testing and collecting of network information in distributed style

The complexity of Internet topological structure and the huge number of hosts in itmake it unfeasible to introduce a unified or centralized monitoring mode, so it isindispensable to make the distributed collecting of network information and resourceinformation. The information acquired is stored in corresponding hosts instead of asmall set of hosts in charge of preserving information. It’s also impossible for a hostto maintain comprehensive information about the network and all the hosts.

456 D. Wang et al.

• Both resource information and network information should be emphasizedResources drive the migration of agents; hence the nodes in the network monitor

not only status of network and hosts but also information about resources, and theselected routes for an agent should imply resource guidance.• Both the execution time and the amount of data transmitted should be taken

into account as major aspects of the optimization targetThe drawback of network and resource monitoring is excess data transmission,

which exacerbates network burden in some degree. An optimization strategy shouldkeep a balance between the economized time and the excess data transmission.• Previous Experiences should be considered.

Directing the next migration by use of previous experiences can improve mobileagent’s execution efficiency.

3.2 Overview of Ant Algorithm

Ant Algorithms, which was inspired by the observation of real ant colonies, were firstproposed in 1991 by Dorigo as an approach to solve difficult combinatorialoptimization problems, such as the traveling salesman problem (TSP) and the JobShop Scheduling problem (JSP) [10]. Research results revealed that ants have theability to find the shortest path between their nests and the food source. The path isacquired by rather the whole ant colony than a single ant. While walking from foodsources to the nest and vice versa, ants deposit on the ground a substance calledpheromone, forming in this way a pheromone trail. Ants can perceive pheromone andtend to choose paths with strong pheromone concentrations. Higher density ofpheromone is deposited on shorter paths, and the tendency makes it possible for acolony of ants to find the shortest path [11-13].

Take TSP problem for example, the topological graph of a network is defined as G= (N, E), and there are arcs between any two nodes. The problem solving process byAnt Algorithm is as follows. At the beginning of the algorithm, m ants are generatedat time point t and are distributed randomly on the nodes in the graph. A tabu listTabuk, which records the nodes that ant k has visited up till now, is introduced toensure that any node cannot be visited more than once by the same ant in a cycle. Theprobability of ant k migrating from node i to node j at time point t is decided by theformula (1):

∑∈

=iNl

ijij

ijijkij t

tP βα

βα

ητητ

)())((

)())(( , )( iTabuNjNj kki −∈∧∈∀

τij(t) represents the density of pheromone on the arcij at time t. At the beginning,every arc with be given a rather small pheromone value, and this value is frequentlyupdated in the consecutive steps. ηij=1/dij is the local heuristic information whichrepresents the expectation of migrating from node i to node j. dij is the Euclid distancebetween node i and node j. α,β are two parameters that control the proportion ofpheromone and local information when calculating probability Pij. N

i is the adjacentnode set of the node i. After |N|-1 steps, each of the ants has finished a journeycovering every nodes and m feasible solutions emerges, this process is called aniteration or a cycle. Then pheromone on arcs is updated according to the quality ofacquired paths.

(1)


Ant Algorithm is categorized into three classes, Ant-Cycle, Ant-Density and Ant-Quantity. They are different in the updating moment and the mechanism. Thepheromone increments of Ant-Cycle, Ant-Density and Ant-Quantity are given in theformula (2),(3),(4), respectively:

∈∀

=∆otherwise0

)(),()(1)(

tTjitLt kkk

ijτ

+

=∆otherwise0

1) t(t,duringj toifrommovesk ant 1Qkijτ

+

=∆otherwise0

1) t(t,duringj toifrommovesk ant 2 ijkij

dQτ

The total increment of pheromone on arc ij is calculated by formula (5):

∑=

∆=∆m

k

kijij tt

1

)()( ττ (5)

A pheromone evaporation mechanism, which reduced the amount of pheromone onevery arc, is introduced to reduce old pheromone information. Let the evaporationproportion is ρ, and then pheromone on arcij is finally updated according to theformula (6):

)()1()( tt ijoldij

newij ττρτ ∆+−= (6)

In Ant-Cycle algorithm, pheromone is updated at the end of period (t0, t+(n-1)),this is called delayed updating; In Ant-Density and Ant-Quantity algorithm,pheromone is updated at the end of every step, and this is called online updating. Dueto slow convergence of the basic Ant Algorithm, many improved algorithms emergeby revising the pheromone updating mechanism. A universal algorithm frameworkcalled Ant Colony Optimization is given in [10].

On one hand, Ant Algorithm has a comprehensive consideration of both historicalexperience information that is represented by pheromone and local heuristicinformation, different values of weighting parameters control the extent of effect ofthe two factors; on the other hand, the optimal solution is worked out by a group ofants and the existence of pheromone provides a special channel for ants’asynchronous communication. Learning mechanism of ants and utilization of the antcolony’s knowledge are also implied since ants need pheromone when deciding thenext migration host.

There are also some shortcomings inherited in the algorithm: (1) the searchingprocess is much too long; (2) an inappropriate choosing of parameters α and β mightcause stasis or convergence to local optimization; and (3) the updating mechanismwill affect the proportion of pheromone and experience information when decidingthe paths. So we claim that, when the Ant Algorithm is chosen to solve someproblems, the following factors must be emphasized:• Appropriate selection of parameters α and β. the values of them are directly

related with the stasis and convergence to the local optimization.• Updating mechanism of the pheromone. This includes the amount of increment,

updating occasion and frequency, which affect the sensitivity of the algorithm.• Adoption of the randomizing scheme. A good randomizing scheme can enable

ants to extend searching space.

(2)

(3)

(4)

Lk(t) is the length of path that ant k find

458 D. Wang et al.

3.3 Applying Ant Algorithm for Dynamic Routing Strategies

We consider it feasible to use Ant Algorithm to solve the mobile agent routingproblems on the Internet, based on the major reasons as follows:• There are essential similarities between problems that Ant Algorithm can

solve and that emerging in mobile agent routing on the InternetWe think the routing problem is a complex combinatorial problem on a weighted

graph, whose weights on arcs change with time and there are numerous hosts on theInternet, on the moment, Ant Algorithm is good at solving this kind of problems.• Mobile agent execution environment (MAE) provides carrier for Ant

AlgorithmPheromone can be stored and collected. On MAE. On the moment, mobile agents,

with certain memory and learning ability are perfect substitutes of artificial ants;• The mobile agent routing problem itself is a process that agents and MAE

learn from circumstance and accumulate experienceWhen first sent to a network, the mobile agent system has little knowledge about

the network and resources information, hence it’s indispensable for the mobile agentsystem to learn from circumstance and store corresponding information on it. Thepheromone storage mechanism is very suitable for mobile agent systems.• The variety of network load and host load requires flexibility and adaptability

of a routing strategyThis requirement is satisfied by local heuristic information and flexible pheromone

storage mechanism of Ant Algorithm.On the other hand, although it is effective and efficient for Ant Algorithm to solve

the TSP problem, the new problems may occur when using it to solve mobile agentrouting problems as follows:• Control Mechanism

The termination of a mobile agent is decided by whether it has accumulatedenough resources or whether the specific tasks are fulfilled, i.e., for informationsearch, thus a mobile agent has no definite target host or definite migrating steps.These factors don’t exist in the process of solving TSP problem by Ant Algorithm. Soa control mechanism of migrating steps or time is needed.• Skip the non-resource host

Resource driven migration may cause that two hosts with certain resource isseparated by a host without such resource. The algorithm should ensure the agent skipthe non-resource host.• Obtaining of major parameters

The approach of obtaining network latency, host load and existence of resources isdifferent. In TSP problem, weights on arcs are explicit before algorithm starts; whilein mobile agent routing problem, major parameters are obtained by distributedmonitoring.• Pheromone updating mechanisms are different

Some work reveal that too many ants in Ant Algorithm may cause descend inperformance by changing the pheromone. In this situation, special pheromoneupdating mechanisms are necessary to avoid such decline in performance.


So, we want design an Ant Algorithm based dynamic routing strategy for mobileagents. By this strategy, when related factors in mobile agent routing strategiesdescribed above is input, a probability value is given as output after calculation,which will direct mobile agent to choose proper hosts to migrate to.

4 Dynamic Routing Strategy Based on Ant Algorithm

The entire mobile agent routing problem is a complex one, so it’s hard to meet all therequirements. To simplify, the following restrictions is made, as shown in Table 1.Besides achieve dynamic routing strategy, we assume that RPC is not taken intoconsideration, and the whole process is made of many migrating steps. At the sametime, one specific task is carried by only one mobile agent.

Table 1. Considered Parameters

4.1 Routing Parameters

The parameters are defined as follows in our algorithm: (1) dij: Network latencybetween two adjacent hosts i and j; and we assume dij ≠dji. (2) li(j): Record stored onhost i about load information on host j; it’s an estimation of average waiting timecalculated from length of waiting queue. (3) ResItem: Resource content, a stringdescribing the resources that an agent needs. It can be either a specific content or asummarization of similar contents. (4) pi(j): Existing probability of resource j on hosti, p∈[0,1]∨p=-1. p=-1 means that there is no record about the existence of resource jon host i. (5) •R

ij, •L

ij : The former is the value of resource pheromone and the latter isthe value of load pheromone. Similar to Ant Algorithm, both resource pheromone andload pheromone are recorded.

MAE is responsible for storage of parameters. There are two tables on every MAE.Take host i for example, one of the tables is to maintain local resource informationand resource pheromone on adjacent hosts; the other table is to maintain local loadinformation between host i and adjacent hosts and load pheromone. The acquiringapproach of parameters is shown as Table 2.

Table 2. Acquiring Approach of Routing Parameters

460 D. Wang et al.

Since time synchronization is hard on the Internet, it is impossible for agents tocalculate latency between two hosts. Thus MAE maintains dij. MAE will periodicallysend host load information to adjacent hosts and hosts receive and store cominginformation. Another way of transmitting the host load information and latency iswhen an agent has finished executing on a host, it will communicate with adjacenthosts for this information. This approach may be accurate, but the acquiring processmay be more frequent. The storage of resource information depends on the tasks thatagents carry. Only information about frequently used resources will be stored whileothers rarely used is seldom stored to economize storage consumption.

4.2 Pheromone Updating Mechanism

The updating of load pheromone is similar to that of Ant-Quantity algorithm and theupdating of resource pheromone is similar to that of Ant-Cycle algorithm. Whenmigration is terminated, a returning agent is generated and backtracks to updateresource pheromone on visited hosts. The difference between them is the result of thedifferent changing features of the two major factors. Network latency and host loadchange frequently, while resource information seldom change with such a highfrequency. Thus the updating of load pheromone should be more frequent to revealaccurate changes of local load information. We also introduced a cyclic evaporatingprocess that does not exist in traditional Ant Algorithms. In some period, the numberof agents on some part of the network may be small, since the pheromone updating istriggered by agents, the pheromone on such hosts may change much too slow andoriginally stored pheromone may represent out-of-date information. The cyclicevaporation is introduced to replace old information in good time.

[Algorithm 1] Execution and updating pheromoneStep1 Initialization: Set initial value such as maximum migrating steps, maximum

migration time, etc.;Step2 Updating corresponding ResItem and Pi(j) on this host according to

information taken along with the agent;Step3 Turning the agent into sleep mode, add the agent into waiting queue;Step4 Taking out from the waiting queue, and waken up to execute the task;Step5 Calculating Pij(refer to the formula13 in section 4.3), decide the next hosts;Step6 Updating load pheromone on this host: •L

ij

Step7 Sending resource information on this host to the latest visited host;Step8 If (maximum migrating steps or maximum migration time is reached)Then Send a returning agent; Update resource pheromone•R

ij on visited hosts;Terminate migration; Go to Step 9;

Else Migrate to the next host, go to Step 2;Step9 EndThe updating Formulas (7)- (12) are relative to the pheromone updating process:

Load Pheromone Updating: ττρτ ∆+⋅−= LijL

Lij )1( , 0<ρL<1 (7)

Resource Pheromone Updating: τρτρτ ∆⋅+⋅−= RRijR

Rij )1( , 0<ρR<1 (8)

Load Pheromone Cyclic Evaporation: LijL

Lij e ττ ⋅= (9)

eL is load pheromone cyclic evaporation ratio, and 0<eL<1 .


Resource Pheromone Cyclic Evaporation: RijR

Rij e ττ ⋅= (10)

eR is resource pheromone cyclic evaporation ratio, and 0<eR<1Load Pheromone Increment:

LLij Q=∆τ (11)

Resource Pheromone Increment ∑−

=++ +⋅=∆=∆

1

1,11, )1(

n

iiR

Rii

Rii ipQττ (12)

QL is adjustment constant and QR is the adjustment constant.Resource pheromone updating is designed similar to that of Ant-Cycle algorithm in

order to reflex the resource-distributing situation on a relative long path. The updatingof resource pheromone is symmetric, R

jiRij ττ ∆=∆ .

4.3 Routing Decision Rules

Routing Decision Rules is to calculate a probability value using the value ofpheromone and the local heuristic information according to a specific ratio, whichdirectly affect the choosing of the next host to migrate to. The load status of networkalways changes very fast, so the algorithm needs to converge in a high speed. Weadopt the decision rules in ACS Algorithm [10]:• A tunable parameter q0 is predefined with range 0<q0<1;• When making decision, a random parameter q, which is evenly distributed within

[0, 1], is generated. Decision is made according to the formula (13):

> −∈=

=≤

)(y probabilitof value toaccordingdecisionmakeen thqq

otherwise0

)(l)(argmaxAj1 Pen thqq

0

iij0

jAif

itabuNlif

i

i

∑−∈

⋅+⋅⋅⋅+⋅⋅

=

ii tabuNl

iiijRij

Lij

iiijR

ijLij

i jpjld

jpjldjA

]))(()))(/(1()()[(

))(())(/(1()()()( ηγβα

ηγβα

ττττ

[Example 1] Assume that an agent is on host i now, the candidates of the next hostare l, m, n, we also assume that Ai(l)=0.5, Ai(m)=0.3, Ai(n)=0.2.• If q•q0, the host with the largest value of Ai(j) will be chosen, i.e., Pil=1, Pim=0,

Pin=0, so the next host to migrate to is l.• If q>q0, then Pil = Ai(l), Pim = Ai(m), Pin =Ai(n). The next host to be visited is not

definite, and is decided according to the probabilities above. This mechanismallows searching in broader space, and the probability of stasis is reduced.

5 Performance Evaluation Experiment and Results

The experiment model is shown as Fig.1, which maintains five modules: Simulator:The entrance of the program, initialize global data structure, start monitors; IMap:The map on which the agents migrate; AgentGenerator: Generate agents insuccession, and send the agents to the simulating network; ResMonitor: Record theresource information into documents for latter analysis; Host Set: Manage all the

(13)

462 D. Wang et al.

hosts in the simulating network. Each of the hosts has an agent queue, a load monitor,a resource information table and a load information table.

Major data structures are initialized and monitors are started when program starts.Simulating hosts collect resource and load information cyclically and updatecorresponding tables. Agents are generated and sent to the simulating network. If thetask is completed, an agent will terminate by itself. Resource information is written todocuments cyclically for latter analysis. We simulated a network with 20 hosts. Tosimplify the simulation, we only tested the performance under static environments,i.e., parameters about all the hosts didn’t change while simulating. Hence cyclicevaporation is canceled. The whole simulation went on for 2 hours, which simulated a20-hour situation in the network.

In Fig.2 and Fig.3, only the performance about the agents that started at host 1 andhost 11 is illustrated since agents that started from other hosts have similarperformance.

The execution time of agents from the same host are stable, and there is anincrease trend in the resource that an agent gets after approximately 300 agents havestarted from each host. The result implies that with the procession of the algorithm,agents are directed to the route on which there is more targeted resource, and can get alarger amount of resource within the same time.

Experiments show that different sets of parameters can lead to different results,even in networks with the same scale. At the same time, networks with differentscales require adjusting parameters. Some sets of parameters cannot even give anyperformance improvement. We think: It’s apparent that the determinative factor inselecting routes is the value of Pij. In calculation, the four values in it may counteractwith each other. For example, a high value of resource pheromone and a relativelylow value of the load local information don’t guarantee a high calculative value of Pij.it’s quite difficult to design a set of parameters that possesses both high adaptabilityand high resource directivity. In practical applications, parameters should be setcarefully by adjusting the four weighting parameters in formula (13) to achieve onlysome of the targets but not all.

The disseminating speed of resource information is directly related to the scale ofthe network, and the larger networks require the longer period.

Routing Simulator

ResourceInformation

MAE Monitor

AgentQueue

AgentGenerator ResMonitorSimulatorIMap Host Set

LoadInformation

MAE 1 MAE nMA1

Program

Entrance

Generating

MA

Monitoring

Hosts

Monitoring

Resources

Migration

Map

Fig. 1. Experiment Model

MA i…… ……


6 Conclusions and Future Work

This paper analyzed the feasibility and new problems of using Ant Algorithm to solvemobile agent routing problem and developed corresponding algorithm, which candirect agents to select routes sensibly considering network status, host status andresource information. Experiments reveal that this routing strategy for mobile agentshas many advantages, some aspects of the algorithm have satisfied the requirement

Fig. 3. The acquired resources and average execution time departing from Host 1 and Host 11

the execution time of Mobile Agents departing from Host 11

ÿ

þÿ

ýÿ

üÿ

ûÿ

úÿÿ

úþÿ

ú ùú úÿú úùú þÿú þùú øÿú øùú ýÿú ýùú ùÿú ùùú

the nth Mobile Agent

exec

utio

n tim

e (s

ec.)

the execution time of Mobile Agents departing from Host 1

ÿ

þÿ

ýÿ

üÿ

ûÿ

úÿÿ

úþÿ

ú ùú úÿú úùú þÿú þùú øÿú øùú ýÿú ýùú ùÿú ùùú üÿú


exec

utio

n tim

e(s

ec.)

the resources acquired by Mobile Agents

departing from Host 11

ÿ

ÿþý

ÿþü

ÿþû

ÿþú

ù

ùþý

ùþü

ù øù ùÿù ùøù ýÿù ýøù ÷ÿù ÷øù üÿù üøù øÿù øøù


Acq

uire

d R

esou

rces

the resources acquired by Mobile Agents

departing from Host 11

ÿ

ÿþý

ÿþü

ÿþû

ÿþú

ù

ùþý

ùþü

ù øù ùÿù ùøù ýÿù ýøù ÷ÿù ÷øù üÿù üøù øÿù øøù


Acq

uire

d R

esou

rces

Fig. 2. Acquired resources

A c q u ire d res o u rc e s b y e ac h mo b ile a g e n t

ÿ

ÿþý

ÿþü

ÿþû

ÿþú

ù

ùþý

ùþü

ù ùÿÿù ýÿÿù øÿÿù üÿÿù ÷ÿÿù ûÿÿù öÿÿù úÿÿù õÿÿù ùÿÿÿù ùùÿÿù ùýÿÿù

T he sequence of m obi le agen ts

reso

urce

s ac

quir

ed b

y ea

ch M

A

464 D. Wang et al.

for designing mobile agents’ routing strategies•such as: (1) Adopting dynamicrouting strategy. Agents can decide the next host by calculation using the informationabout load and resources stored on the host. (2) Resources information can bedisseminated quickly between adjacent hosts and only information about usedresources instead of that about all the resources on hosts is spread; thus reduce thestorage consumption on hosts. (3) Distributed acquiring of resource information andload information is achieved by cyclical collecting mechanism.(4) Pheromoneconstructs a special communication channel and enables agents to study theexperience of former agents.

There are several issues that are to be addressed in future research. One is to adoptan accurate method to evaluate the host load and to monitor network effectively; theothers includes implementing an effective resource information directory service,considering the situation more than one mobile agent, and so on.

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