puting load but also monitor the disparate network's performance. When the performance of the service group meets a level that will break the agreed qual-.
Server, Network and User Adaptivity in Multimedia Networks
Yoshikuni Onozato , Ushio Yamamoto , Julie A. McCann , Geo R. Dowling Michael Schroeder , Andrew Tuson , Natawut Nupairoj Konosuke Kawashima and Masaki Aida 1
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1 Dept. of Computer Science, Gunma University, Japan Dept. of Computing, School of Informatics, City University, U.K. 3 Dept. of Computer Engineering, Chulalongkorn University, Thailand 4 Trac Research Center, NTT Advanced Technology Corp., Japan 2
Abstract
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This paper presents a integrated multimedia information network which consists of a set of servers belonging to a service group which cooperate in an adaptive fashion to satisfy user requests. To do this, the system must not only monitor the service group's computing load but also monitor the disparate network's performance. When the performance of the service group meets a level that will break the agreed quality of service, elements of the service group will adapt to overcome this problem. Adaptability can thus be introduced in the form of server-adaptivity, networkadaptivity and user-adaptivity; and methods in arti cial intelligence have signi cant potential in this regard. I. Introduction
In multimedia networks, the volume of information is huge and dynamically changing. Due to the unpredictability of network performance, the Quality of Service (QoS) provided by a distributed multimedia application can be very variable. Yet, within these bounds there are methods to make best use of the network while maintaining an agreed QoS. This can be done using adaptive systems. In this paper we focus on adaptive techniques to cope with the realization of a multimedia information network which provides improved processing and delivery of multimedia using the following three characteristics: 1. server-adaptivity 2. network-adaptivity 3. user-adaptivity Each server has an ability to handle the user's requirements as well as many diverse data formats. This work is supported in part by Research for the Future Program at Japan Society for the Promotion of Science.
The server must cope with di ering user requirements through its adaptive capabilities to deliver multimedia content. In a server-adaptive system, the server aims to provide multimedia information to the best of its ability depending on the performance required against server load and its perception of network performance. Properties of communication networks, such as traf c signal propagation, are dynamically changing. This may cause time delays in delivering the information to the receiver side, which is a serious problem for timerigorous multimedia information. Network-adaptive technology can radically enhance the required level of transmission. Users have various expectations regarding multimedia environments. User-adaptive technology can help to satisfy the user requirements of the content, quality and quantity of information, response time and so on. The study of adaptivity concerns the following three issues related to the adaptive performance of the system: 1.
variation handling, in trac and network load so that the rate of data delivery is kept steady;
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anticipation of potential diculties so that the system can adapt in a timely fashion;
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optimality, in that if possible, the system should select the best possible response at the present time.
Regarding server-adaptivity, we introduce the notion of a meta-site, which allows the most suitable server to be selected, which will provide the agreed QoS [3]. With network-adaptivity, we present a mechanism that will handle trac variations in the communication network, such as store-and-forward transmission [1] and Burst-transmission [2]. These systems average out the high peaks and the troughs therefore providing improved data delivery.
Network
Server-adaptive Providing Multimedia Information
Network-adaptive Transmission for Multimedia Information
User-adaptive Multimedia Information Retrieval
Figure 1: Server-adaptive, Network-adaptive, User-adaptive Multimedia Information Network We also consider applying these ideas to a wireless network. The power control in a variable rate CDMA cellular system is investigated for various propagation models including fading, shadowing and so on. Other research topics that are being considered are: 1. allocation of multimedia information in a network of distributed multimedia servers. 2. adaptive Internet data delivery system [4]. 3. application and performance of web cashing policies in adaptive multimedia networks [4]. II. Adaptivity in Multimedia Networks
Adaptivity in multimedia networks can be divided into three main types, each of which is covered in more detail below. A. Server Adaptivity
In a Multimedia Information Network there is a many to many relationship between the servers and the clients which have subscribed to the service. Through replication or the distribution of data, such a network can help balance the load on the servers which are part of the network to provide the appropriate level of service. An example of this approach is the media-scaling of multimedia data. For instance, in a situation where there is high trac levels in the network we could have the server adopt a more aggressive data compression scheme, while accepting additional computational overhead on the server. An alternative would of course be to store multiple copies of the data each using a different compression scheme, though this would trade o server computational load for server storage.
We term this server adaptive approach as "cooperative service provision". With this approach, if a server cannot maintain a guaranteed QoS, it requests that other servers help it. E ectively, some of the work is o -loaded to another server. For example, we assume that a server is transmitting the multimedia information to a user. In this case, if the load on the server is increased and the quality of service cannot be kept in the required state, the server nds another server which has a replicated part of the multimedia information to be transmitted and asks it to send that information to the user. As a result, di erent parts of multimedia information are transmitted from di erent servers but integrated data is presented to the user. By this approach, the server load is more balanced whilst maintaining a high quality of the service. B. Network Adaptivity
In a mobile network, the environment is changing dynamically because of signal fading. This may be due to the receiving device moving into a network shadow where it cannot receive the signal as clearly. Morinaga, [5], suggested the necessity of having more active schemes to continuously monitor and predict fading conditions more precisely, and dynamically change the transmission rate itself according to the conditions detected. This environment is basically an intelligent multimedia communication system [5]. Future network applications will comprise both wired and wireless technologies, such as ber optic networks, satellite networks, low-earth orbiting satellite networks, and ground-radio networks. An intelligent system would adaptively select the most appropriate media for the particular application and the current network characteristics perceived at that moment in time [5].
In order to introduce network-adaptivity an instance con ict scheme has been proposed in [6, 7]. This is a demand-assigned multiple-access scheme. The instance con ict scheme consists of rule groups and instance groups which are speci c to a given control station on the network; where the term `control station' refers to a part of the network that is able to implement decisions concerning network functions (eg. routers). Instance groups thus represent the possible actions available to the network, which are then selected according to a given rule. In the context of a multiple-access system, the control system can generate, operate or cancel the instance in the slot of a control station by applying the rule group based on channel information, ie. load, error rate, etc. Channel allocation is thus decided in the fully distributed manner such that the action (instance) used is decided solely by the local rule group and local performance information, without interference from a central decisionmaker [6, 7]. Representing network functions by instances allows the control system to dynamically determine which network functions will be performed. Selection under complex and compound conditions, for example in a satellite communications system [7], can also be performed a straightforward fashion. Speci cally, when a number of candidate network functions are able to be used in a given situation, the adaptive system must weigh up the reasons supporting the use of each function, using the rule group and select the most promising action based on this information. In this way a high-quality, or even optimal, network route can be selected from a large number of alternatives so that network-adaptivity can be achieved with high exibility. C. User Adaptivity
On the user side, multimedia information is integrated into meaningful information for the user by collating or synchronizing the input stream. During a given presentation, data is being received from many servers distributed around the world. For example, video may be streamed from the USA while text related to the video, may be retrieved from a server in Japan. The user may then observe a delay in the delivery of one component of this information, which will impair the usefulness of the whole document. To solve this problem, we propose the use of data replication. When such a delay is detected in the delivery of one component, an alternative replicated version is sought from another server. The other server is the rst one to respond saying it has a copy of the required component. Using this approach, it is expected that the delay observed by the user will be decreased and the user can get a smooth stream of combined multimedia information.
Another example of using this approach is related to the user's preference. Each user has his/her own preferences regarding the content, media, and quality of service and so on. If the default service does not provide some information that the user requires, then alternatives can be sought and sent to the user as a substitute. To realize the above, an agent approach may be employed. Intelligent agents deployed within the multimedia network have a learning capability. Observing user behaviour characteristics, the agent system can build a user pro le that combines with their pre-de ned preferences. Further, agents can support and negotiate with other agents. That is, they can give/get knowledge to/from the other agents to e ectively help the endusers. An example of this could be negotiating the best price for video delivery over the Internet after probing the di ering costs and taking into account the user preferences and the user's pro le. D. Advanced Strategies
Implementations of adaptivity in multimedia networks have largely been realized by the use of hard-coded heuristic rules. Thus the opportunity remains that the principled use of arti cial intelligence (AI) methods may e ect better designed and more e ective adaptive multimedia networks. To make this point we will now suggest a number of avenues for future research, one for each of the three adaptive characteristics given in the introduction.
D.1. AI for Server Adaptivity In section A.we discussed media-scaling of the data according to network trac situations and suggested two strategies for data compression to overcome this: compression-on-demand (which is CPUintensive), or redundant storage under di erent compression schemes. In practice, the appropriate strategy will be a mixed one which allows an acceptable tradeo to be made with respect to CPU and storage constraints. However, nding such a strategy is nontrivial and can be framed as an combinatorial optimisation problem (that of allocation) which is NP-hard. Recent work in AI has focused on neighbourhood search methods [8], such as genetic algorithms [9] which use the metaphor of evolution by natural selection to nd high quality solutions to demanding real-world combinatorial optimisation problems. We suggest that this would be an e ective approach to nding a suitable mix of strategies given a prediction of the demand for data. D.2. AI for Network Adaptivity One topic of research in applying AI to network adaptability would be to replace the hard-coded rules groups used in the instance con ict scheme with something
more appropriate. One approach would to view the selection of network functions (instances) as a problem of intelligent control. This is justi ed because the relationship between the information sources used by the rule groups and the appropriate choice of instance is non-linear and thus dicult to capture fully by simple rules. Two strategies for intelligent control are suggested here. The rst is to convert the rules being used into fuzzy logic rules | controllers based on this approach have proved successful for a number of non-linear control tasks [10]. Similarly, neural networks can also be successfully used for this task [11], though at the loss of being able to readily interpret the workings of the controller. D.3. AI for User Adaptivity Apart from the use of data replication in section C.to ensure good variation handling, one can also use caches to locally store frequently-used items of data, so that one does not have to repeatedly ask the network for the same data item. Of course the question then becomes one of selecting/designing the most appropriate cache architecture. Given that we can evaluate a candidate architecture by simulation, we can again use a genetic algorithm to nd a near-optimal cache architecture for a given usage distribution. This approach has, in fact already been used for the design of caches and their replacement polices in [12]. III. Conclusion
This paper presents a integrated multimedia information network which consists of a set of servers belonging to a service group which cooperate in an adaptive fashion to satisfy user requests. To do this, the system must not only monitor the service group's computing load but also monitor the disparate network's performance. When the performance of the service group meets a level that will break the agreed quality of service, elements of the service group will adapt to overcome this problem. Adaptability can thus be introduced in the form of server-adaptivity, networkadaptivity and user-adaptivity; and methods in arti cial intelligence have signi cant potential in this regard. References
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