INTRODUCTION AND OVERVIEW
It is expected that multimedia and data traffic will surpass the traditional voice traffic in mobile networks by the year 2005. High quality streaming multimedia content is likely to form a significant portion of this traffic. It is therefore important that large mobile networks find ways to efficiently manage client traffic and data. Content distribution networks (CDN) have proven effective for managing content-based traffic for large numbers of clients in the Internet.
CDN consist of surrogate servers that replicate the content of the origin servers and serve it to the clients. CDN employ server selection and request redirection methods for selecting an appropriate (surrogate) server and redirecting the client’s request to that server. CDN reduce load on both the network and origin server by localizing the traffic and providing many alternate sources of content.
Although there has been much work on the Internet-wide CDN, CDN for mobile networks have received little attention so far. Mobile CDN proposals, such as, target static WAP or HTML content for mobile clients, while only consider streaming multimedia content distribution for mobile networks. This lack of attention has largely been because of the limited Internet access capabilities of most mobile terminals of recent past. However, this is changing quickly with deployment and acceptability of 3G services that allow mobile terminals to access Internet and other data services at speeds comparable to traditional wired access. In this paper we consider server selection algorithms for streaming multimedia content distribution in networks with mobile users.
The large size of most multimedia content, long-lived nature of typical multimedia sessions, client mobility and the capricious nature of wireless communication medium, put together, present an interesting challenge. A CDN for mobile networks must address all of these issues. has shown that QoS, in terms of delay and jitter, can be significantly improved by changing the server as the client moves so that the client continues to receive content from a nearby server; this process is referred to as server handoff.
But server handoff itself can have adverse effects. It can disrupt the streaming from the server, causing glitches at the client. Moreover, changing the server can be an expensive process for the network. Before a session can be handed over to a new server, sufficient content must be pre-fetched (or placed) at the server to ensure smooth delivery. Random and frequent client movements can cause significant signaling and content placement overhead. One approach to the problem of stream disruption is to mitigate it by sufficient buffering at client equipment at the cost of increased playback delay. Another approach is using make-before-break handoffs. But actually reducing number of handoffs is not trivial .Proposes simply delaying the server handoff process to reduce the number of handoffs and resulting overhead. In this paper we present a more sophisticated server selection scheme. Our scheme reduces the number of server handoffs for mobile clients by using client mobility information and selecting a server with which the client can remain associated for an extended period of time, thus reducing the need for server handoffs. We also cater for traditional server selection criteria such as expected QoS (in terms of delay and jitter) from the server, traffic localization, and server load. The basic trade-off that we explore is how to maintain QoS service differentiation for clients while reducing the network cost due to server handoffs.
MOBILITY BASED SERVER SELECTION
Our approach is to use the client’s mobility information as an additional metric in the server selection process, along with traditional metrics such as client-server proximity, expected QoS, content availability and server load. This allows us to select a server that will remain suitable for content delivery to a client for an extended period of time and thus eliminating the need for frequent server change.
We consider a content distribution network with a relatively large number of servers arranged in a logical tree structure; with servers closest to the access network belonging to the lowest tier (see Figure 1). This hierarchical server layout allows us to maximize the traffic localization and obtain desired trade-off between the number of server handoffs and the QoS to the clients. These advantages become further apparent in proceeding sections.
Each server has a coverage-area defined as the subnets and cells in the underlying network (each subnet comprises one or more cells). Generally, each subnet (and cell) is in the coverage area of the closest server at any given tier, but multiple servers may serve subnets at coverage-area boundaries. We use the term server zone to refer to the coverage area of lowest tier servers. Servers at higher tiers have larger coverage areas and every subnet is covered by multiple tiers. As in a server handoff is performed whenever a client moves from the coverage area of one server to another. However, with the multi tier arrangement of servers, there is an opportunity to trade-off QoS with the number of server handoffs by choosing the tier of the new server. Data delivered from the lower tier servers will suffer less delay (and probably less jitter) than that from the higher tiers but at the same time will result in more server handoffs and may increase the overhead for the network.
We assume that clients have different categories of QoS requirements in terms of delay and jitter. We further assume that each of these categories of QoS can be roughly characterized as the property of a particular server tier. For example, we can assume that highest QoS class, with strict delay and jitter requirement (QoS class 1) maps to the lowest tier server. A fixed mapping is of this type is, however, not necessary. Other QoS metrics such as available bandwidth, response-time, and media quality may be used for server classification. Several dynamic QoS probing and measurement methods, it can be used to dynamically classify servers based on different QoS metrics. We assume that there is a Request Routing function (RR) in each server zone (recall a server zone is the coverage area of lowest-tier servers). We recommend that this function be a proxy based RR that may be co-located with lowest-tier servers. The proxy-based approach provides greater control and flexibility but other RR