Evaluation of agent-based architectures in a wireless environment for accessing and manipulating distributed information C. Velicogna, M. Patriarca, G. Stegmayer and O. Chiotti

Abstract—Wireless networks are a changing environment where network nodes have low and variable performance, high latency and limited bandwidth. Also wireless standards impose restrictions on the possible transmission rates. Besides, the channel may not always be available, for example due to bad channel conditions, interference, nodes temporal disconnection, among others. Having this in mind, the use of Mobile Agents in such environment can be a good choice, i.e. in the case where an important quantity of data must be retrieved from remote nodes. Agents can freely move among nodes towards the resources they need to access, and can reduce the traffic load by locally processing the data in each node, and then sending the extracted results or transporting them towards where they are needed. In this paper we evaluate the performance of three mobile agents architectures in a wireless environment, to try to discover the best configuration in an information retrieval scenario. Index Terms—performance evaluation, mobile agents, wireless networks

I. INTRODUCTION

A

ccess and manipulation of distributed data in a wireless environment presents several challenges to a system designer. Wireless networks are a changing environment where network nodes (i.e. desktop PCs, handhelds, laptops, mobile phones, etc.) have variable performance, the bandwidth is limited, there is a high incidence of network errors and the transmission rate is low. Mobility can also result in the loss or degradation of wireless connections. Moreover, the channel may not always be available, for example due to bad channel conditions, interference, nodes temporal disconnection, among others. Therefore, in this kind of environment, the most important resource is the network itself, more than data transmission rate or time response [1]. Several authors have proposed the evaluation of mobile agents, mostly using theoretical models or simulations, and only analyzing one particular scenario for information retrieval in a wireless environment. Most of the time mobile agents are compared against remote evaluation and clientserver message passing [2], trying to answer the question whether mobile agents should be considered for information Manuscript received October 6, 2006. C. Velicogna, M. Patriarca, G. Stegmayer and O. Chiotti are with the R+D Center CIDISI, Universidad Tecnológica Nacional-Regional Santa Fe, Lavaise 610, 3000, Sta. Fe, Argentina (e-mail: [email protected]).

retrieval applications [3] in the scenario of a main node retrieving data from remote nodes and the web, or not. But all of these comparisons are performed on a traditional wired IEEE 802.3 [4], analyzing only one proposed scenario, mostly from a theoretical point of view. Wireless networks are an ideal environment for mobile agents [5], because they can move across unreliable links, searching for data to process. Mobile agents can process the data locally at the remote nodes, and then come back to the main node with the results [6]. They support disconnected operations because they continue to execute after they move, even if they lose network connectivity with their senders. Comparing agents against the traditional message passing paradigm [7] in this environment, several advantages can be highlighted, such as conservation of bandwidth, support for load balancing, reduction in total completion time, among others. Agent-technology can reduce the network load and use in the cases where an important quantity of data must be retrieved from remote nodes; data that needs some kind of processing or filtering [8]. In the last several years, while there has been extensive research going on in the development of mobile agent systems, there are few efforts in the study of their performance in real world applications of wireless networks. As a result, the spread of mobile agent technology in real world wireless applications cannot as yet be seen [9]. There is a lack of evaluation of its performance in practical applications [10], most of the works that propose performance analysis of agent systems [11] make a theoretical analysis or simulations of one configuration of an agent based system [12]. Therefore, the objective of our work is to compare a real implementation of mobile agent-based architectures in a wireless environment, for accessing and manipulating distributed information from remote nodes. To this aim, we have implemented and monitored three mobile agent-based architectures to support a typical information retrieval scenario. The agent-based architectures proposed and evaluated are explained in the next Section. Some implementation details are described in Section 3. Performance measurements designed for comparison among systems appear on Section 4. Finally, tests results and their evaluation can be found on Section 5 and 6.

II. MOBILE AGENT-BASED ARCHITECTURES FOR A WIRELESS ENVIRONMENT We evaluate a typical information retrieval scenario in a wireless environment, comparing three alternative agent system configurations. As explained before, we have chosen monitoring as the technique for the tests execution due to the fact that there are very few works on this area and our intention is to provide a practical experience in the field. The information retrieval problem is the following: in a wireless network, there is a central node that needs to retrieve data from other remote nodes. This data (GPS positioning frames for a certain period of time) must be periodically received at the central point, and have to be processed. This processing involves reading an input file containing all the GPS records, interpreting it line by line, selecting the fields timestamp, latitude and longitude, discarding redundant information i.e. a position that has not changed over time, and putting the results in an output file. Three possible solutions based on agent technology have been defined, where the main node sends mobile agent to the nodes that have the data, to get and process the data. In some cases, the trip (order of nodes to be visited) is predefined; it is a requirement that arrives at the main node, which has to plan how to fulfill it. We have developed an agent-based architecture for the calculation of some performance indexes. The architecture has three components: an Aglets agent server, an Administrator Agent (AA) and the monitored agent system. The server application provides the environment where the aglets can exist and creates the AA which manages the tests and is responsible of tests execution, control and performance indexes generation.

The analyzed cases are shown in the next sub-sections. A. Case I – Itinerant Mobile Agent without Load A Master Agent (MA) is created, with only one Slave Agent (SA) with an itinerary or trip (list of remote nodes) to be followed (Fig. 1). The nodes are PC desktop computers, running concurrent applications. A trip includes also the task/s that must be accomplished at each node. The SA task consists in finding and processing a resident file in the remote node containing node GPS positioning data, and sending the result to the MA with a message. B. Case II - Itinerant Mobile Agent with Load At the main node, an Agent with a trip is created. This Agent follows the trip visiting all the remote nodes. At each node, it processes the local file and carries the results with it the rest of trip up to the end, when it comes back to the main node carrying all the results from all visited nodes (Fig. 2). In this scenario, the size of the results carried with the agent increase the Agent size on each node. Once the Agent finalizes its trip, comes back to the main node. C. Case III – Simultaneous Mobile Agents A MA at the main node creates as many SAs as remote nodes must be visited. On the creation, each one is passed the address of the node where it has to go and the task it has to perfom there. Therefore, they are independent from each other. This case is named simultanous because all the SAs are created and sent at the same time (Fig. 3). Once a SA arrives at a node, it processes the remote file and sends the result to the MA with a message.

Fig. 1 Case I - Itinerant Mobile Agent without Load

Fig. 2 Case II - Itinerant Mobile Agent with Load

Fig. 3 Case III - Simultaneous Mobile Agent

Several technologies for developing Mobile Agents systems have been evaluated and finally Java Aglets®, developed by IBM Research Center in Japan [13] was chosen for the agents implementation. This language extends Java with loose mobility for agents, named Aglets. Aglets model the mobile agent following the idea of applets in Java. An Aglet is defined as a mobile Java object which visits nodes, which are able of receiving Aglets in a context. They run their own execution thread on arrival, and that is why they are said to be autonomous. Using this tool, we have made platform independent applications. The hardware used for the test were PCs Intel® Pentium III® with 256 MB Ram, network cards WirelessLan IEEE 802.11b running on Windows® XP Professional Edition. The software installed in every node, necessary for the tests, was Java Runtime Environment 1.1, Java Development Kit 1.1, ASDK 1.0.3 and the Windows® Performance Monitor.

Monitoring was performed regarding the system usage, the resources utilization and the network load, to obtain significant performance indexes of the different proposed architectures, for comparison among them. Concerning monitoring parameters, “resources” are considered to be the network nodes (PCs) and the wireless network itself. In the case of mobile agents, a “work” is the creation of one or more agents, which afterwards migrate to the remote node/s to process the local file there, and then send a message with the results to the main node, or carry the resulting information with them towards the main node. The time interval for the monitoring (T) is defined as the interval in which the system does a retrieval and processing of the data from all the remote nodes. The quantity of “works” observed is, in the case of the hole system (C), the total number of works processed by the system during the monitoring interval (T); in the case of a

resource, it is the quantity of works processed at the node i (Cnode i) or in the network (Cnet) during the monitoring interval (T). A parameter that was changed for every case was “size of file to process”. Eight different file size were proved (the file size is due to the amount of GPS frames with positioning data): 1) 236 KB, 2) 471 KB, 3) 707 KB, 4) 943 KB, 5) 1178 KB, 6) 1415 KB, 7) 1649 KB, 8) 1885 KB. The following performance indexes have been calculated: (1) X=C/T, being C total number of works processed by the system, T observed time interval. X is Processing Rate for the system. In particular we calculate Xk = Ck/T, where Xk is the Processing Rate for a resource k. In the case of a node (Xnodei) it is the amount of messages/agents processed by a node i during time T. In the case of the network (Xnet) is the quantity of messages/agents sent through the net during time T. (2) Uk=Bk/T, where Bk is the time that a resource has been busy, T is observation time. Uk is Resource Utilization for the resource k. In the case of a node (Unodei) is the fraction of time T during which a node i has been busy with some work. Concerning the network, (Unet) is the fraction of bandwidth used during time T. (3) System Response Time (R) is the time that the system takes to react to a request. In our case, it is the completion time for each case of study. I. RESULTS A. Performance Index System Processing Rate (X) As can be seen in Fig. 4, in a wireless network the global processing rate X for the mobile agents systems tend to remain constant (near 5% in average) as the size of the file to retrieve increases. Even if this rate is quite low, this is a very positive result because it remains almost constant, no matter the size of the data that has to be transferred. We draw the same conclusions regarding Xnode and Xnet because the results obtained are similar.

B. Performance Index Resource Utilization (Uk) In Fig. 5 and 6 are shown the results for the parameters Unet (network Utilization) and Unode (node utilization). Cases I and II, Itinerant Mobile Agents without and with Load, have a better performance compared to the Case III, Simultaneous Mobile Agents case.

Fig. 5 Wireless Network Utilization (Unet)

It has more agents occupying bandwidth on the network and also sends more messages, therefore it uses more intensively the net bandwidth, even increases the resource use as the files size grows. In any case, the agents make a poor use of the network resources to do their jobs (less than 5% in average of the total bandwidth available is used), which is an excellent result for a wireless environment where bandwidth is very important.

Fig. 6 Nodes Utilization in a Wireless network (Unode) (average results from Unode1 and Unode2)

Fig. 4 Processing rate (X) of the different architectures in a wireless network

As the Fig. 7 shows, for all Cases, the Response Time (R) tends to increment as the files size for the remote file increases, which could be an expected and logical result. Among the mobile agents, the Case III (Simultaneous Mobile Agents) has a better performance with respect to the other cases, almost twice better, and it is the best case, because more agents working help make the retrieval and processing faster.

C. Performance Index Response Time (R)

in the comparisons, because the differences between the response times are maintained as the files size grows. II. CONCLUSIONS

Fig. 7 Response Time (R) in a Wireless Network

D. Coefficients for comparison among cases To be able to do a comparison one-to-one among the three architectures proposed, we have defined some coefficients that allows us to draw more conclusions. They are calculated as the rate “CaseX/CaseY”. The rates have been defined for the parameters that we consider are the most important for a wireless environment, network utilization (Unet) and response time (R). Concerning Network Utilization (Unet), Fig. 8 shows that the relationships remain more or less constant while the files size grows. This happens because the increment in the network utilization for the compared systems grows with a similar rate in all cases. In the first relationship, between Case II and Case I (Itinerant Mobile Agent with and without Load), the rate is always greater than one, no matter the files size, which implies that the Case II makes more use of the wireless network than Case I (as should be expected due to the fact that in Case II the agent carries the results with it). In the rest of the relationships, the values are less than one, which means that the denominator, the Case III (Simultaneous Mobile Agent) makes more use of the network than the other architectures, as should expected from a system with more agents and more messages sent through the network. Finally, in Fig. 9 we have the last evaluated rate among the architectures concerning Response Time (R). The relationships among the agent systems almost remain constant

The purpose of this work is to make a comparison among three agent-based architectures to find out the best mobile agent-based architecture, in a wireless environment, for mobile data management of distributed information in remote nodes. This new technology is proposed as a suitable option in the new mobile era, where existing information retrieval techniques for distributed data access and sharing are no longer useful. With our work we want to make a contribution concerning practical performance evaluation of agent-based systems, as was said in the introduction, a field where very few practical applications have been evaluated. The implementation of three agent-based architectures for distributed data retrieval and processing has allowed us to do a practical evaluation of a mobile agents system in a wireless environment. With our work we want to make a contribution concerning performance evaluation of agent-based systems, as was said in the introduction, a field where few practical applications have been evaluated. If the important parameter is System Response Time (R), then all the three architectures behave almost in the same way as the files size grows, being Case III (Simultaneous Mobile Agents) the best choice. If the critical parameter is network utilization (Unet), as it is probably the case in wireless networks, the conclusion is that the best architecture should be the use of Mobile Agents without or with Load, Cases I and II. In these cases there is only one agent in the network, that follows a trip visiting remote nodes and does some kind of processing locally that helps not to overload the network bandwidth, which is most of the time the critical resource in a wireless environment. The results obtained from the performance indexes showed expected results. There is no predominant agent-based architecture which is the best for all the indexes. It depends on which resource is the most important. However, the results show that it would be interesting having a mobile agent-based architecture, to be customized according to the information retrieval task to be done and the user requirements.

Fig. 8 Comparison among the Mobile Agents architectures, for the network Utilization (Unet) index in a Wireless Network

Fig. 9 Comparison among the Mobile Agents architectures, for the Response Time (R) index in a Wireless Network

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L. Vasiu, Q.H. Mahmoud, “Mobile Agents in Wireless Devices”, Computers Magazine, vol. 37, no. 2, 2004, pp. 104-105. A. Fuggetta, G.P. Picco, G. Vigna, “Understanding Code Mobility”, IEEE Transactions on Software Engineering, vol. 24, no. 5, 1998, pp. 342-361. R.S. Gray, G. Cybenko, D. Kotz, R.A. Peterson, D. Rus, “D'Agents: Applications and Performance of a Mobile-Agent System”, SoftwarePractice and Experience, vol. 32, no. 6, 2002, pp. 543-573. ANSI/IEEE Std 802.11, 1999 Edition - IEEE Standard for Information technology. Telecommunications and information exchange between systems. Local and metropolitan area networks. Specific requirements Part 11: Wireless LAN Medium, Access Control (MAC) and Physical Layer (PHY) Specifications. D. Kotz, G. Cybenko, R.S. Gray, G. Jiang, R.A. Peterson, M.A. Hofmann, D.A. Chacón, K.R. Whitebread, J. Hendler, “Performance Analysis of Mobile Agents for Filtering Data Streams on Wireless Networks”, Mobile Networks and Applications, vol. 7, no. 2, 2002, pp. 163-174. T. Magedanz, A. Karmouch, “Mobile Software Agents for Telecommunication Applications”, Computer Communications, vol. 23, no. 8, 2000, pp. 705-707. R. Gray, D. Kotz, G. Cybenko, D. Rus, “Mobile agents: Motivations and state-of-the-art systems”, Technical Report: TR2000-365. Dartmouth College, 2000. G.P. Picco, “Mobile Agents: An Introduction”, Journal of Microprocessors and Microsystems, vol. 25, no. 2, 2001, pp. 65-74. L. Osborne, K. Shah, “Performance Analysis of Mobile Agents in Wireless Internet Applications using Simulation”, In Proc. Parallel and Distributed Computing and Networks, pp. 66-79, 2004. A. Puliafito, S. Riccobene, M. Scarpa, “Which paradigm should I use? An analytical comparison of the client-server, remote evaluation and mobile agents paradigm”, Concurrency and Computation: ptactice and experiencies, vol. 13, no. 1, 2001, pp. 71-94. M. Laukkanen, H. Helin, “Web Services in Wireless Networks - What Happened to the Performance?”, in Proc. Int. Conf. on Web Services, pp. 278-284, 2003. K. Shah, “Performance Analysis Of Mobile Agents In Wireless. Internet Applications Using Simulation”, Master Thesis, Faculty of the College of Graduate Studies, Lamar University, 2003. Tokyo Research Laboratory Homepage, http://www.research.ibm.com

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