TPC-W: a benchmark for e-commerce

When choosing an e-commerce site's hardware and software configuration, we need to know how a specific combination of Web servers, commerce servers, database servers, and supporting hardware will handle a desired load level. Benchmarks let us compare competing alternatives. Researchers have extensively studied workloads on Web sites that provide information and have characterized their performance at the level of HTTP requests. My colleagues and I have also conducted studies to understand e-commerce site workloads and to search for invariants that cut across more than one type of e-commerce site. However, there is currently only one available benchmark for e-commerce sites: TPC benchmark W, designed by the Transaction Processing Performance Council. I explore TPC-W's main features, advantages, and limitations     

Workload characterization

Analyzing an e-commerce site's scalability requires a solid understanding of its workload characteristics, but this characterization must occur at multiple levels and time scales. The author and colleagues have performed several workload characterization studies, including auction sites. Here, I present the general method we adopted and a few examples of the workload characterizations we studied.     

Machine learning assisted OSP approach for improved QoS performance on 3D charge-trap based SSDs

Three-dimensional (3D) charge-trap based solid-state-drivers (SSDs) have become an emerging storage solution in recent years. One-shot-programming in 3D charge-trap based SSDs could deliver a maximized system input/output (I/O) throughput at the cost of degraded Quality-of-Service (QoS) performance. This paper proposes reinforcement-learning based one-shot-programming (RLOSP), a reinforcement learning based approach to improve the QoS performance for 3D charge-trap based SSDs. By learning the I/O patterns of the workload environments as well as the device internal status, the proposed approach could properly choose requests in the device queue, and allocate physical addresses for these requests during one-shot-programming. In this manner, the storage device could deliver an improved QoS performance. Experimental results reveal that the proposed approach could reduce the worst-case latency at the 99.9th percentile by 37.5%–59.2%, with an optimal system I/O throughput.     

Performance evaluation of e-commerce requests in wireless cellular networks

Recent technological advances in mobile devices and wireless networks enable mobile users to order goods in an anywhere and anytime fashion. Quality of Service (QoS) provision is one of the most challenging issues in the heterogeneous wireless network-based e-commerce systems. Such e-commerce systems enable users to roam between different wireless networks operators and geographical areas while providing interactive broadband services and seamless connectivity. Due to movement of users during e-commerce requests, one of the most important QoS factors for successful completion of users' requests is related to handover of request from one cell to another. A handover could fail due to unavailability of sufficient bandwidth in the destination cell. Such failure of ongoing e-commerce requests is highly undesirable and can cause serious problems to the e-commerce users and the service providers. This paper proposes an enhanced priority queuing based handover scheme in order to ensure a seamless connectivity of e-commerce requests. It focuses on the performance anaylsis of the proposed scheme. Experimental study demonstrates that the proposed scheme provides QoS with low connection failure and mean response time for handover of e-commerce requests.     

Power and performance control of soft real-time web server clusters

This paper presents a novel way to control power consumption and performance in a multi-tier server cluster designed for e-commerce applications. The requests submitted to these server systems have a soft real-time constraint, given that although some can miss a pre-defined deadline, the system can still meet an agreed upon performance level. Clusters of servers are extensively used nowadays and, with the steep increase in the total power consumption in these systems, economic and environmental problems have been raised. We present ways of decreasing power expenditure, and show the implementation of a SISO (Single Input Single Output) controller that acts on the speed of all server nodes capable of dynamic voltage and frequency scaling (DVFS), with QoS (Quality of Service) being the reference setpoint. For QoS, we use the request tardiness, defined as the ratio of the end-to-end response time to the deadline, rather than the usual metric that counts missed deadlines. We adjust the servers operating frequencies to guarantee that a pre-defined p-quantile of the tardiness probability distribution of the requests meet their deadlines. Doing so we can guarantee that the QoS will be statistically p. We test this technique in a prototype multi-tier cluster, using open software, commodity hardware, and a standardized e-commerce application to generate a workload close to that of the real world. The main contribution of this paper is to empirically show the robustness of the SISO controller, presenting a sensibility analysis of its parameters. Experimental results show that our implementation outperforms other published state-of-the-art cluster implementations.     

Courier: Multi-dimensional QoS guarantees for the consolidated storage system

The increasing cost and complexity of data management is driving data centers to consolidate resource and provide storage service for multiplex applications. Therefore, storage systems must be able to guarantee multi-dimensional Quality of Service (QoS) for various applications. However, satisfying performance targets for each workload is challenging, because that the I/O characteristics of workloads usually varies widely and capability of storage system changes significantly. In this paper, we design and implement a novel QoS scheduler, Courier, to maintain satisfactory performance for applications even in this highly-volatile scenario. Courier dynamically alternates between a feedback-based latency controller and reward budget-based scheduling to achieve per-application performance requirement. The feedback-based controller is employed to estimate request service times and adjust scheduling strategy dynamically. Based on the estimation, it can identify time-critical requests from throughput-sensitive requests and schedule applications with time-critical requests preferentially to avoid latency violations. In addition, Courier rewards well-behavior application with more budget to maintain high storage utilization while providing performance guarantees. We evaluate the effectiveness of our approach using synthetic and real workloads, and the results show that Courier has good ability to achieve per-application performance targets.     

运用JAVA处理Web信息流

Web信息流量的变化,反映了Web用户对Web服务器的访问,具有长期的、周的、日的、时的和瞬间的随机变化特性。该文用快速Fourier变换(FFT)和统计分析结合的方法来处理Web流量变化的情况。FFT用于分析Web流量变化的确定性(即周期性)部分,统计理论用于分析Web流量变化的随机部分。整个处理系统通过JAVA编程实现。这里采用从实际的Web服务器采集的一列数据(“每秒用户请求数”),作为这个系统的输入,处理结果则通过系统的图形界面输出。该分析处理方法可以广泛地用于Web信息流量的预测、流量负载的动态分流、Web服务器的调配、网络容量的设计和最优规划、服务质量(QoS)性能保证和服务水平协商(SLAs)。该文提出并实现的分析方法和技术也可以应用到其它类型的数据处理。     

Differentiated Real-Time Data Services for E-Commerce Applications

The demand for real-time e-commerce data services has been increasing recently. In many e-commerce applications, it is essential to process user requests within their deadlines, i.e., before the market status changes, using fresh data reflecting the current market status. However, current data services are poor at processing user requests in a timely manner using fresh data. To address this problem, we present a differentiated real-time data service framework for e-commerce applications. User requests are classified into several service classes according to their importance, and they receive differentiated real-time performance guarantees in terms of deadline miss ratio. At the same time, a certain data freshness is guaranteed for all transactions that commit within their deadlines. A feedback-based approach is applied to differentiate the deadline miss ratio among service classes. Admission control and adaptable update schemes are applied to manage potential overload. A simulation study, which reflects the e-commerce data semantics, shows that our approach can achieve a significant performance improvement compared to baseline approaches. Our approach can support the specified per-class deadline miss ratios maintaining the required data freshness even in the presence of unpredictable workloads and data access patterns, whereas baseline approaches fail.     

Cost-based admission control for Internet Commerce QoS enhancement

In many e-commerce systems, preserving Quality of Service (QoS) is crucial to keep a competitive edge. Poor QoS translates into poor system resource utilisation, customer dissatisfaction and profit loss. In this paper, a cost-based admission control (CBAC) approach is described which is a novel approach to preserve QoS in Internet Commerce systems. CBAC is a dynamic mechanism which uses a congestion control technique to maintain QoS while the system is online. Rather than rejecting customer requests in a high-load situation, a discount-charge model which is sensitive to system current load and navigational structure is used to encourage customers to postpone their requests. A scheduling mechanism with load forecasting is used to schedule user requests in more lightly loaded time periods. Experimental results showed that the use of CBAC at high load achieves higher profit, better utilisation of system resources and service times competitive with those which are achievable during lightly loaded periods. Throughput is sustained at reasonable levels and request failure at high load is dramatically reduced.     

Resource Allocation for Session-Based Two-Dimensional Service Differentiation on e-Commerce Servers

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.     

Extending Proxy Caching Capability: Issues and Performance

Proxy caching is an effective approach to reduce the response latency to client requests, web server load, and network traffic. Recently there has been a major shift in the usage of the Web. Emerging web applications require increasing amount of server-side processing. Current proxy protocols do not support caching and execution of web processing units. In this paper, we present a weblet environment, in which, processing units on web servers are implemented as weblets. These weblets can migrate from web servers to proxy servers to perform required computation and provide faster responses. Weblet engine is developed to provide the execution environment on proxy servers as well as web servers to facilitate uniform weblet execution. We have conducted thorough experimental studies to investigate the performance of the weblet approach. We modify the industrial standard e-commerce benchmark TPC-W to fit the weblet model and use its workload model for performance comparisons. The experimental results show that the weblet environment significantly improves system performance in terms of client response latency, web server throughput, and workload. Our prototype weblet system also demonstrates the feasibility of integrating weblet environment with current web/proxy infrastructure.     

Performance vs. freshness in web database applications

In this work, we present a novel approach for the efficient materialization of dynamic web pages in e-commerce applications such as an online retail store with millions of items, hundreds of HTTP requests per second and tens of dynamic web page types. In such applications, user satisfaction, as measured in terms of response time (QoS) and content freshness (QoD), determines their success especially under heavy workload. The novelty of our materialization approach over existing ones is that, it considers the data dependencies between content fragments of a dynamic web page. We introduce two new semantic-based data freshness metrics that capture the content dependencies and propose two materialization algorithms that balance QoS and QoD. In our evaluation, we use a real-world experimental system that resembles an online bookstore and show that our approach outperforms existing QoS-QoD balancing approaches in terms of server-side response time (throughput), data freshness and scalability.     

Building and using application utility models to dynamically choose thread counts

On today’s multiprocessor systems, simultaneously executing multi-threaded applications contend for cache space and CPU time. This contention can be managed by changing application thread count. In this paper, we describe a technique to configure thread count using utility models. A utility model predicts application performance given its thread count and other workload thread counts. Built offline with linear regression, utility models are used online by a system policy to dynamically configure applications’ thread counts. We present a policy which uses the models to maximize throughput while maintaining QoS. Our approach improves system throughput by 6 % and meets QoS 22 % more often than the best evaluated traditional policy.     

Scaling Web sites through caching

A large jump in a Web site's traffic may indicate success, but this increased interest could turn into a nightmare if the site is not prepared to handle the bigger load. Site resources (processors, storage boxes, load balancers, LANs, and the like) could see their utilization rise to levels that generate increasingly long response times. Eventually, one of these resources - the so-called bottleneck resource - will reach 100 percent utilization, pushing the site's throughput to its maximum point. When a Web site becomes overloaded, customers grow frustrated with long waits and rejected requests. This situation can lead to an undesirable loss of site-generated revenue and may even tarnish the reputation of organizations relying on Web sites to support mission-critical applications. In this article, I discuss how caching technologies can improve Web site performance and scalability. I'll provide some simple quantitative expressions to let designers understand the most important trade-offs.     

Enabling cost-aware and adaptive elasticity of multi-tier cloud applications

Elasticity (on-demand scaling) of applications is one of the most important features of cloud computing. This elasticity is the ability to adaptively scale resources up and down in order to meet varying application demands. To date, most existing scaling techniques can maintain applications’ Quality of Service (QoS) but do not adequately address issues relating to minimizing the costs of using the service. In this paper, we propose an elastic scaling approach that makes use of cost-aware criteria to detect and analyse the bottlenecks within multi-tier cloud-based applications. We present an adaptive scaling algorithm that reduces the costs incurred by users of cloud infrastructure services, allowing them to scale their applications only at bottleneck tiers, and present the design of an intelligent platform that automates the scaling process. Our approach is generic for a wide class of multi-tier applications, and we demonstrate its effectiveness against other approaches by studying the behaviour of an example e-commerce application using a standard workload benchmark.     

Analyzing the performance of deferred reservations

This paper studies the performance of deferred resource reservation in data networks. Conventional resource reservation protocols, such as PNNI and RSVP adopt an all-or-nothing approach, where partially acquired resources must be released if resources are not available at all links on the chosen path. During periods of high network load, this leads users to retry requests repeatedly, adding control traffic at exactly the time when the network’s capacity to process that control traffic is exhausted. Deferred REServation (DRES) can significantly improve performance by reducing the overall call rejection probability, allowing more traffic to be carried, using the same resources. Call admissibility is increased by deferring requests at routers for a limited period of time until resources become available. The paper includes analytical models that predict the blocking probability on a DRES multiplexor and on a multi-hop path, and simulation results for substantial network configurations, using several QoS routing methods. The results show that DRES can provide substantial performance gains over traditional reservations (upto 50% with QoS routing enabled and upto an order of magnitude for non-QoS traditional routing).     

Workfile Disk Management for Concurrent Mergesorts in a Multiprocessor Database System

This paper studies workfile disk management for concurrent mergesorts ina multiprocessor database system. Specifically, we examine the impacts of workfile disk allocation and data striping on the average mergesort response time. Concurrent mergesorts in a multiprocessor system can creat severe I/O interference in which a large number of sequential write requests are continuously issued to the same workfile disk and block other read requests for a long period of time. We examine through detailed simulations a logical partitioning approach to workfile disk management and evaluate the effectiveness of datastriping. The results show that (1) without data striping, the best performance is achieved by using the entire workfile disks as a single partition if there are abundant workfile disks (or system workload is light); (2) however, if there are limited workfile disks (or system workload is heavy), the workfile disks should be partitioned into multiple groups and the optimal partition size is workload dependent; (3) data striping is beneficial only if the striping unit size is properly chosen; and (4) with a proper striping size, the best performance is generally achieved by using the entire disks as a single logical partition.     

Web server software architectures

Web site scalability depends on several things - workload characteristics, security mechanisms, Web cluster architectures as we discussed previously. Another important item that can affect a site's performance and scalability is the Web server software architecture. We provide a classification of Web server architectures, offer a quantitative analysis of some possible software architectural options, and discuss the importance of software contention on overall response time.     

Failure-aware resource provisioning for hybrid Cloud infrastructure

Hybrid Cloud computing is receiving increasing attention in recent days. In order to realize the full potential of the hybrid Cloud platform, an architectural framework for efficiently coupling public and private Clouds is necessary. As resource failures due to the increasing functionality and complexity of hybrid Cloud computing are inevitable, a failure-aware resource provisioning algorithm that is capable of attending to the end-users quality of service (QoS) requirements is paramount. In this paper, we propose a scalable hybrid Cloud infrastructure as well as resource provisioning policies to assure QoS targets of the users. The proposed policies take into account the workload model and the failure correlations to redirect users’ requests to the appropriate Cloud providers. Using real failure traces and a workload model, we evaluate the proposed resource provisioning policies to demonstrate their performance, cost as well as performance–cost efficiency. Simulation results reveal that in a realistic working condition while adopting user estimates for the requests in the provisioning policies, we are able to improve the users’ QoS about 32% in terms of deadline violation rate and 57% in terms of slowdown with a limited cost on a public Cloud.