A Virtual Organization Based Mobile Agent Computation Model Yong Liu, Cong-fu Xu, Zhao-hui Wu, Wei-dong Chen and Yun-he Pan College of Computer Science, Zhejiang University Hangzhou 310027, P.R. China
[email protected],{xucongfu,wzh,chenwd,yhpan}@zju.edu.cn
Abstract. Mobile agent has developed for decade, and widely implemented in distribute computation. However, traditional mobile agents including strongmigration and weak-migration mobile agents still have some weakness. With the fabric of Grid virtual organization architecture, the mobile agent has gain great advantage comparing to those old mobile agents system. In this paper, a novel formalized mobile agent computation model based on the virtual organization is presented. In this model, all the actions of the mobile agents are treated as states. The process of the mobile agents’ workflow is controlled by a finite-state-machine. This ensures the atomic action for each mobile agent to avoid the abnormal condition of communication mismatch. This model takes full advantages of strong-migration mode agent, such as robustness and intelligence; it can also overcome the serious weakness of large amount of data transmission existing in the strong-migration mode agent systems.
1 Introduction Mobile Agents are programs that can be migrated and executed between different network hosts. They locate for the appropriate computation resources, information resources and network resources, combining these resources in a certain host, to achieve the computing tasks [1]. There are tow types of mobile agents classified by the migration ability of the agents. They are strong migration mobile agents and weak migration mobile agents. The ordinary mobile agents system such as AgentTCL [6], Voyager System, Aglet System [7] etc, can all be ranged into those two types. The AgentTCL used the strong migration policy by which the mobile agent takes not only both executable codes and data used in executing process, but also the states of the executing process. The Voyager and Aglet use weak migration policy by which the mobile agent only takes the executed codes and the states of data. When using the strong migration policy, the mobile agent system needs to record all the states and related data in each position of the agent, which will spend huge time and huge space for the transport, and will lead to low efficiency. When using weak migration policy, the transportation of data will decrease greatly, however, the abilities of adapting the complicated network topology will decrease too. Therefore, how to design a reliable and high-efficiency work pattern for the mobile agent becomes the key problem. In fact, the grid [3] technology has provided a powerful platform for the mobile agent. A
serial of grid protocols [2,3], such as GNSP, GGMP, QDP etc, make the new work pattern available. In this paper, we introduce a finite state machine based mobile agent computation model that the ability of migration is between strong migration and weak migration. In that model, the executing process has been divided into several states controlled by the finite state machine, and only the agent body and the communication data should be transported, by this way, we increase the efficiency of transport and the adaptation of mobile agent.
2 VO Based Fabric Architecture of Computation Model The VO based architecture mentioned in this paper is a structure similar to the fabric layer in [2]. The following part, some definitions are given out: Definition A. Node, the minimized devices that can load and execute the mobile agents in network denoted as Ri. There is a kind of nodes called Key Node, R0i, which deal with the remote communications. Definition B. Group, the set includes one node or several nodes, noted as Gi={ R0, R1, R2,…,Rn}. The group can identify each node in VO, which means each node except key node will only belong to a certain group. Rij means node belongs to group Gj. Group is a comparatively stable organization; the nodes belonging to certain group can leave this group and join another group dynamic. The login and logout of nodes use a GGMP (Grid Group Management Protocol)[2], which is similar to the IGMP. Definition C. Node Distance, the least route number between tow nodes, the Node Distance between node i and j marks as |Ri Rj|. Above all the definitions, we can give out the definition of VO. Definition D. Virtual Organization, VO is a fabric structure that composes by nodes and is established by a serial of protocols. Normally, the group contains resembling and adjacent nodes. There is a Key Node in each group, the Key Node in the group, Gi, marks as R0i. The function of key node in a group is similar to the gateway in a LAN, which communicates with other nodes outside the group. A protocol called GNSP (Gateway Node Selective Protocol) [3] has been used to determine the key node. Among all the nodes and groups, the key nodes constitute a virtual group called Kernel Group, G0. It is the most important portion that server for other nodes, deal with communication, seeking etc between nodes in virtual organization topology.
3
VO Based Mobile Agent Computation Model
To implement the VO based computation model, the first requirement is that the mobile agents can be executed in different nodes, so the finite-state mobile agent has been given out, in that model the data and resource have been distinguished. The definitions present as follow:
3.1 Finite-state Mobile Agent Definition E. Finite-state mobile agent is a resource driven mobile agent system. In fact, the mobile agent whose migration ability is between strong migration and weak migration can be seen as a finite-state machine auto motioning and driven by the resource and data. Definition F. Data are all the local data that will affect the mobile agent’s state changing. Definition G. Resource is a general designation of all the data, device and software in VO nodes. In our model, all the runtime parameters and devices in remote nodes are called resources, which is different from the data affecting the mobile agent’s state-change. Therefore, we can ignore the influence of network topology to the executing of the mobile agents; we can only care for the state-change of the mobile agents when they are executing, migrating, in other words, this mobile agent system need not to care for which node does the mobile agent move from or move to, the current state of mobile agent is the only parameter that should be recorded in that mobile agent, be migrated with mobile agent and be update in time. There are several parts, finite states, transition relation, exterior input symbols (data or resource) etc, which constitute a finite-state mobile agent. The finite-state mobile agent marks as follow: LSM_Agent = {Ar, Sr, Ur, Fr} Ar is the identity of the mobile agent. It will retain the identity value during the runtime, and the VO architecture can locate the right mobile agent by Ar. It can adopt a universal finite state set and common transition relation in practice service, such as virtual experiment device sharing service mobile agent, while the Ar. can be seemed as an instance handle. Sr is the finite state set, including request state S0, suspend state S1, block state S2, and service state Y1, Y2, … , Yn. The input symbols Ur include tow condition: one is the resource input symbol ri for service state Yi, the other one is the service time ti, Fr is the transition relation. 3.2 VO based Mobile Agent Computation Model (MACM) After defining the finite-state mobile agent, we can give out the computation model for VO based mobile agent: Definition H. VO based mobile agent computation model is a six-tuple MACM = (R, S, M, Φ , v, E), where, R is the node set. S is the finite state set of the mobile agent. S does not include the state of the agent migration Λ and the null state ε . Here, migration state Λ means that the mobile agent starts move to another node to executing new state; null state ε means the mobile agent does not perform any action (executing and migration), M ⊂ S is the set of all the message operation states for mobile agent. M = {Ms, Ma}, Ms is the state of sending message, Ma is the state of receive message, v ∈R is the initial node that the mobile agent has been produced, a mobile agent’s
service firstly comes from the node v, and then cycles driven by the finite states, E ⊂ R is the set of final node for the mobile agent, only in the final node the mobile agent can be destroyed and the service ends, Φ , The transition relation, is a finite subset of (R X (S U {Λ, ε } )) → R:
Φ : R X (S U {Λ, ε } ) → R, where (1) To all the Ri, Rj ∈ R, if Φ (Ri, ε ) = Rj, then Ri = Rj, (2) To all the Ri, Rj ∈ R, if Φ (Ri, Λ ) = Rj, then Ri ≠ Rj, (3) To all the Ri, Rj ∈ R, Sk ∈ S, if Φ (Ri, Sk) = Rj, then Ri = Rj, (4) To all the Ri ∈ R, if Φ (Ri, Ma) = Ri, then the next transition state relation is Φ (Ri, Ms) = Ri. In this computation model the migration state Λ is established by the
communication of the nodes in VO. From the definition of the computation model, all the migration, communication (message method) and executing remote have been regard as a state in computation model. There is commonly a communication invalidation problem [4] in traditional message passing mobile agent system, which is caused by the asynchronies of the mobile agent’ migration and the mobile agent’ message. After the mobile agent sends out its message, it moves to another nodes, and then there may be a problem that the return message cannot find the position of the original agent. Analogously, a broadcast based mode [5] for the mobile agent’s seeking and communication has the problems such as huge transmitted data, easy to be block in finite bandwidth, low reliability etc. In our VO based computation model, the communication has been treated as states, and a serious of rules have been defined to ensure the symmetry of the sending message operation and receiving message operation, so the communication process and the migration process can be a atomic operation. This will ensure the communication and migration to be sequence logic; there will not be the communication invalidation problem. 3.3 Transition Forecast Algorithm in MACM In VO based MACM, data and resource are unified, so a minimize distance transition policy can be implemented, which can decrease the migration cost much. The transition problem can be described as, in a node set {R0, R1, R2, …, Rn}, when a mobile agent arrives at node Ri, which node will be the next migration position for this mobile agent. Here the algorithm is given as follow. Algorithm. Minimal distance transition forecast algorithm Step 1. The mobile agent serving in node Ri finds a resource miss problem, which is say there is not enough resource for the service continuing. Step 2. Node Ri broadcasts for the resource, only the node who has the resource that agent needs will reply this broadcast. Step 3. When Ri receives the reply messages, there will be possible-transition node set, {R’0, R’1, …,R’m}, the node contained in possible-transition node set has the resource that the mobile agent needs. Calculating min{| RiR’0| , |RiR’1 |,…,| RiR’m|}, getting the minimal node distance |RiR’k| , the node R’k is the next migration node. Step 4. Node move to node R’k , algorithm ends.
4 Analysis China ministry of education began to carry on a resource-sharing project among universities of China from 1999. The aim of this CSCR (Computer Support Cooperation Research) project is to fully utilize the device, data distributing in each university. The most difficulty is the smart, high efficient, stable, reliable CSCR platform. We implement the MACM in the platform. We propose a concept of Service Availability to evaluate the performance of the MACM. Service Availability (SrvAvl) is the ratio of the agent’s service time in node with the general time (service time and the migration time) of the mobile agent. SrvAvl =
T service T service + T migration
(1)
Here, Tservice is the service time in nodes, Tmigration is the migration time of mobile agent, from the equation, we can conclude that decrease the migration time can efficiently increase the service availability when the service time keep stable. In our MACM, the migration time Tmigration can calculate by the following equation. ij T migration =
M agent B
=
| Ri R j |
M agent
q =1
Bq
∑
(2)
Magent is the size of the mobile agent; Bq is the transfer velocity from node Ri to node Rj of section q. commonly, we use an average transfer velocity, B, between Ri and Rj, the relation between the mobile agent’s size, migration times and the service availability show in figure 1. From figure1, when the mobile agents have different size, the corresponding service availabilities distinguish. The service availability of the smaller size agent is higher than the bigger size agent. So decreasing the size of mobile agent can greatly increase the system performance.
5 Conclusion In this paper, we propose a virtual organization based mobile agent computation model to solving the problems in traditional mobile agent system. This model integrates the advantages of the strong-migration agent and the weak-migration agent, so that it can provide a more intelligence and robust mobile agent computation model which can avoid much frequently data transmit. With the aid of the virtual organization architecture, this computation model can effectually avoid the communication invalidation problem. However, this model needs high performance of the key node and the average bandwidth of the kernel group will affect the system capabilities greatly.
Fig. 1. Relation between the agent size, migration times and service availability, here the red curve presents the smallest size of agent with M, blue curve presents the middle size of agent with 2M, black curve presents the biggest size of agent with 3M.
Acknowledgement. This paper is supported by the projects of Zhejiang Provincial Natural Science Foundation of China (No. 602045, and No. 601110), and it is also supported by the advanced research project sponsored by China Defense Ministry & Education Ministry.
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