As e-commerce services are exponentially growing, businesses need quantitative estimates of client-perceived response times to continuously improve the quality of their services. Current server-side nonintrusive measurement techniques are limited to nonsecured HTTP traffic. In this paper, we present the design and evaluation a monitor, namely sMonitor, which is able to measure client-perceived response times for both HTTP and HTTPS traffic. At the heart of sMonitor is a novel size-based analysis method that parses live packets to delimit different webpages and to infer their response times. The method is based on the observation that most HTTP(S)-compatible browsers send significantly larger requests for container objects than those for embedded objects. sMonitor is designed to operate accurately in the presence of complicated browser behaviors, such as parallel downloading of multiple webpages and HTTP pipelining, as well as packet losses and delays. It requires only to passively collect network traffic in and out of the monitored secured services. We conduct comprehensive experiments across a wide range of operating conditions using live secured Internet services, on the PlanetLab, and on controlled networks. The experimental results demonstrate that sMonitor is able to control the estimation error within 6.7 percent, in comparison with the actual measured time at the client side.

It is important to guarantee client-perceived end-to-end quality of service (QoS) under heavy load conditions. Existing work focuses on network transfer time or server-side request processing time. In this paper, we propose a novel framework, eQoS, to monitor and controll client-perceived response time in heavy loaded Web servers. The response time is measured with respect to Web pages that contain multiple embedded objects. Within the framework, we propose an adaptive fuzzy controller, STFC, to allocate server resources. The controller assumes no knowledge of the pageview traffic model. It deals with the effect of process delay in resource allocation by its two-level self-tuning capabilities. We also prove the stability of the STFC. We implement a prototype of eQoS in Linux and conduct comprehensive experiments across wide-range server workload conditions on PlanetLab and simulated networks. Experimental results demonstrate the effectiveness of the framework: It controls the deviation of client-perceived pageview response time to be within 20 percent of a predefined target with both synthetic and real Web traffics. We also compare the STFC with other controllers, including static fuzzy, linear proportional integral (PI), and adaptive PI controllers. Experimental results show that, although the STFC works slightly worse than the static fuzzy controller in the environment where the static fuzzy controller is best tuned, because of its self-tuning capabilities, it has better performance in all other test cases by around 25 percent on average in terms of the deviation from the target response time. In addition, due to its model independence, the STFC outperforms the linear PI and adaptive PI controllers by 50 percent and 75 percent on average, respectively.

A scalable e-commerce server should be able to provide different levels of quality of service (QoS) to different types of requests based on clients' navigation patterns and the server capacity. E-commerce workloads are composed of sessions. In this paper, we propose a session-based two-dimensional (2D) service differentiation model for online transactions: intersession and intrasession. The intersession 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. A primary performance metric of online transactions is slowdown. It measures the waiting time of a request relative to its service time. We present a processing rate allocation scheme for 2D proportional slowdown differentiation. We then introduce service slowdown as a systemwide QoS metric of an e-commerce server. It is defined as the weighted sum of request slowdown in different sessions and in different session states. We formulate the problem of 2D service differentiation as an optimization of processing rate allocation with the objective of minimizing the service slowdown of the server. We prove that the derived rate allocation scheme based on the optimization guarantees client requests' slowdown to be square-root proportional to their prespecified differentiation weights in both intersession and intrasession dimensions. We evaluate this square-root proportional rate allocation scheme and a proportional rate allocation scheme via extensive simulations. Results validate that both schemes can achieve predictable, controllable, and fair 2D service differentiation on e-commerce servers. The square-root proportional rate allocation scheme provides 2D service differentiation at a minimum cost of service slowdown

Proportional delay differentiation (PDD) aims to maintain pre-specified packet queueing-delay ratios between different classes of traffic at each hop. Existing bandwidth-allocation approaches for PDD services rely on the assumption that average queueing delay of a class is inversely proportional to its allocated bandwidth, which is valid only in a heavy-utilized link. Consequently, in a non-heavy utilized link, they are unable to provide consistent PDD services effectively. As a remedy, in this paper, we propose a novel bandwidth-allocation approach that applies fuzzy control theory to reflect the nonlinearity between the queueing delay and the allocated bandwidth. In the approach, the bandwidth of a class is adjusted according to a set of fuzzy control rules defined over its error, the change of error, and the change of bandwidth allocation. Simulation results demonstrate that, in comparison with other bandwidth-allocation approaches, the fuzzy control approach is able to provide consistent PDD services under light, moderate, and heavy utilization conditions. They also show its robustness under various operation environments.

There is a growing demand for provisioning of proportional responsiveness differentiation to various clients on scalable Web servers to meet changing resource availability, and to satisfy different client requirements. Theoretically, a queueing-based processing rate allocation scheme is able to achieve the objective by providing different processing rates to requests of different client classes. However, we find that an implementation of the queueing-theoretical scheme shows weak proportionality with large variance because it does not have fine-grained control over the resources that the kernel consumes and hence the processing rate is not strictly proportional to the number of processes allocated. We design a feedback controller and integrate it with the queueing-theoretical scheme. The integrated application-level approach allocates a certain number of processes to handle requests of different client classes according to the queueing-theoretical scheme. The process allocations are then adjusted according to the difference between target response time and the achieved response time by using proportional integral derivative control. Results demonstrate that this integrated approach can enable Web servers to provide fine-grained response time differentiation. The approach is robust and can be practically deployed on Apache Web servers.

The increasing popularity of high-volume performance-critical Internet applications is a challenge for servers to provide individual response-time guarantees. Considering the fact that most Internet applications can tolerate a small percentage of deadline misses, we define delay constraint as a statistical guarantee to relax server resource requirements. A recent decay function model characterizes the relationship between the request delay constraint, deadline misses, and server capacity in a transfer function based filter system. A time-invariant scheduler was proposed to minimize system load variances in support of requests with the same delay constraints. This paper extends the model to support requests with different deadlines and describes an optimal time-variant scheduling policy that minimizes load variances and capacity requirement. The resultant capacity bound is further tightened by utilizing the information of request arrival distribution. Simulation results validate the extended decay function model and show the superiority of the scheduler in comparison with other scheduling algorithms.

A desirable behavior of an Internet server is that a request's queuing delay depends on its service time in a linear fashion. Measuring the quality of service in terms of slowdown, the ratio of a request's queuing delay to its service time, provides a simple way to attain the objective. Moreover, it treats client requests equally regardless of their service time, whereas response time favors requests that need more processing resources. In this paper, we propose a proportional slowdown differentiation (PSD) service model on Internet servers. It aims to maintain prespecified slowdown ratios between different classes of client requests. To provide PSD services, we first derive a closed-form expression of the expected slowdown in an M/G/1 FCFS queuing system with a typical heavy-tailed service time distribution, the bounded Pareto distribution. Based on the closed-form expression, we design a queuing-theoretic strategy of processing-rate allocation. The rate allocation is realized by deploying a virtual server for each class. Simulation results show that the strategy can provide controllable PSD services on Internet servers. It, however, comes along with large variance and weak predictability due to the dynamics of Internet traffic. To address these issues, we design an integral feedback controller and integrate it into the queuing-theoretic strategy. Simulation results demonstrate that the integrated strategy is robust and can deliver predictable PSD services at a superior fine-grained level. We modified the Apache Web server with an implementation of the integrated processing-rate allocation strategy. Experimental results further demonstrate its effectiveness and feasibility in practice.

The proportional delay differentiation (PDD) model is an important approach for relative differentiated services provisioning on the Internet. It aims to maintain pre-specified packet queueing-delay ratios between different classes of traffic at each hop. Existing PDD packet scheduling algorithms are able to achieve the goal in long time-scales when the system is highly utilized. The paper presents a new PDD scheduling algorithm, called Little's average delay (LAD), based on a proof of Little's law. It monitors the arrival rate and the cumulative delays of the packets from each traffic class, and schedules the packets according to their transient queueing properties so as to achieve the desired class delay ratios in both short and long time-scales. Simulation results show that, in comparison with other PDD scheduling algorithms, LAD can provide no worse level of service quality in long time-scales and more accurate and robust control over the delay ratio in short time-scales. In particular, LAD outperforms its main competitors significantly when the desired delay ratio is large.

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 two-dimensional (2D) service differentiation (DiffServ) model for online 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 intra-session 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.

Summary form only given. A proportional differentiation model states that quality of service of different classes of Internet traffic should be kept proportional to their prespecified differentiation parameters, independent of the class loads. The model has been applied in the proportional queueing delay differentiation (FDD) in both network core and network edges. However, in the server side, an important and interesting performance metric is slowdown, the ratio of a request's queueing delay to its service time. Slowdown is important because it is desirable that a request's delay be proportional to its processing requirement. We investigate the problem of processing rate allocation for proportional slowdown differentiation (PSD) on Internet servers. Existing algorithms for FDD provisioning in the network side are not applicable to PSD provisioning in the server side because slowdown is not only dependent on a job's queueing delay but also on its service time, which varies significantly depending on the requested services. We first derive a closed form expression of the expected slowdown in an M/Gp/1 FCFS queue, which is an M/G/l FCFS queue with a typical heavy-tailed service time distribution (bounded Pareto distribution). PSD provisioning is realized by deploying a task server for handling each request class in a FCFS way. We then develop a strategy of processing rate allocation for the task servers for PSD provisioning. Simulation results have showed that the proposed rate allocation strategy can provide predictable and controllable PSD services on the servers.

One of the important issues in building differentiated services is how to support relative differentiated queueing delay services for packets from different classes. This paper presents a new packet scheduling algorithm, called virtual-length, to address this issue. It provides effective control over the relative average queueing delay ratio between two classes of traffic by monitoring their average arrival rates and cumulative queue lengths. The packets of different classes are forwarded according to their weighted queue lengths with respect to predefined proportional delay parameters. The simulation results show that virtualLength is capable of providing controllable and predictable services for classes with different load distributions under various load conditions. In particular, it can provide more accurate control over classes of traffic in short timescales than other packet scheduling algorithms, and comparable service in long timescales. The results also show that the performance of virtualLength is independent of the distributions of packet arrivals and sizes.