A scalable e-Commerce server should be able to provide different levels of quality of service (QoS) to different types of requests according to clients ’ navigation patterns and the server capacity. In this paper, we propose a twodimensional (2D) service differentiation (DiffServ) model for on-line transactions: inter-session and intra-session.The inter-session model aims to provide different levels of QoS to sessions from different customer classes, and the intrasession model aims to provide different levels of QoS to requests in different states of a session. We introduce service slowdown as a QoS metric of e-Commerce servers. It is defined as the weighted sum of request slowdown in different sessions and in different session states. We formulate the problem of 2D DiffServ provisioning as an optimization of processing rate allocation with the objective of minimizing service slowdown. We derive the optimal allocations for an M/G/1 server under various server load conditions and prove that the optimal allocations guarantees requests ’ slowdown to be square-root proportional to their pre-specified differentiation weights in both dimensions. We evaluate the optimal allocation scheme via extensive simulations and compare it with a tailored proportional DiffServ scheme. Simulation results validate that both allocation schemes can achieve predictable, controllable, and fair 2D slowdown differentiation on e-Commerce servers. The optimal allocation scheme guarantees 2D DiffServ at a minimum cost of service slowdown. 1.

There is a growing demand for provisioning of different levels of quality of service (QoS) on Web servers to meet changing resource availability and satisfy different client requirements. The proportional differentiation model is getting momentum because of its fairness and differentiation predictability. It states that QoS of different traffic classes should be kept proportional to their pre-specified differentiation parameters, independent of the class loads. In this paper, we present a processing rate allocation scheme for providing proportional response time differentiation on Web servers. A challenging issue is how to achieve processing rates for different request classes in the implementation. We propose a process allocation strategy, which dynamically and adaptively changes the number of processes allocated for handling different request classes while ensuring the ratio of process allocations specified by the processing rate allocation scheme. We implemented the process allocation strategy at application level on Apache Web servers. Experimental results showed that the processing rate can be achieved by the adaptive process allocation strategy and the Web servers can provide predictable and controllable proportional responsive time differentiation. 1

Slowdown, defined as the ratio of a request’s queueing delay to its service time, is accepted as an important quality of service metric of Internet servers. In this paper, we investigate the problem of providing proportional slowdown differentiation (PSD) services to various applications and clients on cluster-based Internet servers. We extend a closed-form expression of the expected slowdown of a popular Internet workload model with a typical heavy-tailed service time distribution from a single server mode to a server cluster mode. Based on the closed-form expression, we design a two-tier resource allocation approach, which integrates a dispatcher-based node partitioning scheme and a server-based dynamic process allocation scheme. We evaluate the two-tier resource allocation approach via extensive simulations and compare it with an one-tier node partitioning approach. Simulation results show that the two-tier approach can provide fine-grained PSD services on clusterbased Internet servers. We implement the two-tier approach on a cluster testbed. Experimental results further demonstrate the feasibility of the approach in practice. 1

Proportional delay differentiation (PDD) is an important service model for providing relative differentiated services on the Internet. It aims to maintain pre-specified packet queueing-delay ratios between different classes of traffic at each hop. Existing rate-allocation approaches for PDD services assume the average queueing delay of a class is inversely proportional to its service rate. This assumption is not necessarily valid when the system is not heavily loaded. To provide consistent PDD services under various load conditions, in this paper, we propose a novel rate-allocation approach that applies fuzzy control theory to capture the nonlinear relationship between the queueing delay and the service rate. In the approach, a class’s service rate is adjusted according to a set of fuzzy control rules defined over its error (the difference between the target delay ratio and the achieved one), the change of error, and the change of service rate. We prove that the fuzzy control system is stable and the service rate of a class converges to its equilibrium point at steady state. Simulation results demonstrate that, in comparison with other rate-allocation approaches, the fuzzy control approach is able to provide consistent PDD services under wide range load conditions. It is also shown robust under various system conditions, including with multiple classes, changing target delay ratios, changing load conditions, and different traffic patterns.

Proportional slowdown di#erentiation (PSD) aims to maintain slowdown ratios between di#erent classes of clients according to their pre-specified di#erentiation parameters. In this paper, we design a feedback controller to allocate processing rate on Internet servers for PSD. In this approach, the processing rate of a class is adjusted by an integral feedback controller according to the di#erence between the target slowdown ratio and the achieved one. The initial rate class is estimated based on predicted workload using queueing theory. We implement the feedback controller in an Apache Web server. The experimental results under various environments demonstrate the controller's e#ectiveness and robustness.