equilibrium shifts for a while. Due to the increased demands that long-tail traffic places on networks resources, networks need to be carefully provisioned to ensure that quality of service and service level agreements are met. The following subsection deals with the provisioning of standard network resources, and the subsection after that looks at provisioning web servers which carry a significant amount of long-tail traffic. Network provisioning for long-tail traffic For network queues with long-range dependent inputs, the sharp increase in queuing delays at fairly low levels of utilisation and slow decay of queue lengths implies that an incremental improvement in loss performance requires a significant increase in buffer size. While throughput declines gradually as self-similarity increases, queuing delay increases more drastically. When traffic is self-similar, we find that queuing delay grows proportionally to the buffer capacity present in the system. Taken together, these two observations have potentially dire implications for QoS provisions in networks. To achieve a constant level of throughput or packet loss as self-similarity is increased, extremely large buffer capacity is needed. However, increased buffering leads to large queuing delays and thus self-similarity significantly steepens the trade-off curve between throughput/ packet loss and delay. ATM can be employed in telecommunications networks to overcome second order performance measure problems. The short fixed length cell used in ATM reduces the delay and most significantly the jitter for delay-sensitive services such as voice and video. Web site provisioning for long-tail traffic Workload pattern complexities (for example, bursty arrival patterns) can significantly affect resource demands, throughput, and the latency encountered by user requests, in terms of higher average response times and higher response time variance. Without adaptive, optimal management and control of resources, SLAs based on response time are impossible. The capacity requirements on the site are increased while its ability to provide acceptable levels of performance and availability diminishes. Techniques to control and manage long-tail traffic are discussed in the following section. The ability to accurately forecast request patterns is an important requirement of capacity planning. A practical consequence of burstiness and heavy-tailed and correlated arrivals is difficulty in capacity planning. With respect to SLAs, the same level of service for heavy-tailed distributions requires a more powerful set of servers, compared with the case of independent light-tailed request traffic. To guarantee good performance, focus needs to be given to peak traffic duration because it is the huge bursts of requests that