Priority scheduling of requests to web portals

Web portals work as a point of access to a large volume of information on the web. This paper focuses on the performance of Web portals in an E-commerce environment which involves the processing of a large number of users’ requests. It proposes a class-based priority scheme which classifies users’ requests into high and low priorities. In E-commerce, some requests (e.g. buy) are generally considered more important than others (e.g. search or browse). We contend that the requests received from a Web portal should generally get higher priority as such requests are more likely to lead to purchases. We believe that assigning such priorities at multiple service levels can improve the performance of Web portals’ requests of higher priority. The proposed scheme is formally specified and implemented, and performance results are obtained and compared to a server that does not prioritise requests. The results show significant performance improvements in the processing of high priority requests.     

A Priority-Based Admission Control Scheme for Commercial Web Servers

This paper investigates into the performance and load management of web servers that are deployed in commercial websites. Such websites offer various services such as flight/hotel booking, online banking, stock trading, and product purchases among others. Customers are increasingly relying on these round-the-clock services which are easier and (generally) cheaper to order. However, such an increasing number of customers’ requests makes a greater demand on the web servers. This leads to web servers’ overload and the consequential provisioning of inadequate level of service. This paper addresses these issues and proposes an admission control scheme which is based on the class-based priority scheme that classifies customer’s requests into different classes. The proposed scheme is formally specified using \(\Pi \) -calculus and is implemented as a Java-based prototype system. The prototype system is used to simulate the behaviour of commercial website servers and to evaluate their performance in terms of response time, throughput, arrival rate, and the percentage of dropped requests. Experimental results demonstrate that the proposed scheme significantly improves the performance of high priority requests but without causing adverse effects on low priority requests.     

Priority scheduling service for E-commerce web servers

Service scheduling is one of the crucial issues in E-commerce environment. E-commerce web servers often get overloaded as they have to deal with a large number of customers’ requests—for example, browse, search, and pay, in order to make purchases or to get product information from E-commerce web sites. In this paper, we propose a new approach in order to effectively handle high traffic load and to improve web server’s performance. Our solution is to exploit networking techniques and to classify customers’ requests into different classes such that some requests are prioritised over others. We contend that such classification is financially beneficial to E-commerce services as in these services some requests are more valuable than others. For instance, the processing of “browse” request should get less priority than “payment” request as the latter is considered to be more valuable to the service provider. Our approach analyses the arrival process of distinct requests and employs a priority scheduling service at the network nodes that gives preferential treatment to high priority requests. The proposed approach is tested through various experiments which show significant decrease in the response time of high priority requests. This also reduces the probability of dropping high priority requests by a web server and thus enabling service providers to generate more revenue.     

Block I/O Scheduling on Storage Servers of Distributed File Systems

This paper presents a new scheme of I/O scheduling on storage servers of distributed/parallel file systems, for yielding better I/O performance. To this end, we first analyze read/write requests in the I/O queue of storage server (we name them block I/Os), by using our proposed technique of horizontal partition. Then, all block requests are supposed to be divided into multiple groups, on the basis of their offsets. This is to say, all requests related to the same chunk file will be grouped together, and then be satisfied within the same time slot between opening and closing the target chunk file on the storage server. As a result, the time resulted by completing block I/O requests can be significantly decreased, because of less file operations on the corresponding chunk files at the low-level file systems of server machines. Furthermore, we introduce an algorithm to rate a priority for each group of block I/O requests, and then the storage server dispatches groups of I/Os by following the priority order. Consequently, the applications having higher I/O priorities, e.g. they have less I/O operations and small size of involved data, can finish at a earlier time. We implement a prototype of this server-side scheduling in the PARTE file system, to demonstrate the feasibility and applicability of the proposed scheme. Experimental results show that the newly proposed scheme can achieve better I/O bandwidth and less I/O time, compared with the strategy of First Come First Served, as well as other server-side I/O scheduling approaches.     

An approximation-based load-balancing algorithm with admission control for cluster web servers with dynamic workloads

The growth of web-based applications in business and e-commerce is building up demands for high performance web servers for better throughputs and lower user-perceived latency. These demands are leading to a widespread substitution of powerful single servers by robust newcomers, cluster web servers, in many enterprise companies. In this respect the load-balancing algorithms play an important role in boosting the performance of cluster servers. The previous load-balancing algorithms which were designed for the handling of static contents in web services suffer from significant performance degradation under dynamic and database-driven workloads. Regarding this, we propose an approximation-based load-balancing algorithm with admission control for cluster-based web servers in this study. Since it is difficult to accurately determine the loads of web servers through feedbacks from distributed agents in web servers, we propose an analytical model of a web server to estimate the web servers’ loads. To achieve this, the algorithm classifies requests based on their service times and track numbers of outstanding requests for each class of each web server node and also based on their resource demands to dynamically estimate the loads of each node. For the error handling of the model a proportional integral (PI) controller from control theory is used. Then the estimated available capacity of each web server is used for load balancing and admission control decisions. The implementation results with a standard benchmark confirm the effectiveness of the proposed scheme, which improves both the mean response time and the throughput of the cluster compared to rival load-balancing algorithms, and also avoids situations in which the cluster is overloaded, even when the request rates are beyond the cluster capacity.     

Web服务器集群系统QoWS研究

请求负载的增加常常导致Web服务器系统性能降低,用户期望的服务质量得不到保证,这是服务级Web系统所面临和必须解决的问题。文章提出了一种Web服务器集群环境下的负载分配策略,通过对用户请求分类、将不同类别的请求进行响应性能隔离、优先为高级别请求提供服务以及请求许可控制等手段,对不同类别的Web请求提供不同质量的服务,保证了服务级用户的服务质量。同时采用最迟分配原则,改善系统的负载均衡能力,缩短系统平均响应时间。最后通过仿真实验,验证了该策略的正确性和有效性。     

A predictive and probabilistic load-balancing algorithm for cluster-based web servers

The exponential demands for high performance web servers led to use of cluster-based web servers. This increasing trend continues as dynamic contents are changing traditional web environments. Increasing utilization of cluster web servers through effective and fair load balancing is a crucial task specifically when it comes to advent of dynamic contents and database-driven applications on the internet. The proposed load-balancing algorithm classifies requests into different classes. The algorithm dynamically selects a request from a class and assigns the request to a server. For both the scheduling and dispatching, new probabilistic algorithms are proposed. To avoid using unreliable measured utilization in the face of fluctuating loads the proposed load-balancing algorithm benefits from a queuing model to predict the utilization of each server. We also used a control loop feedback to adjust the predicted values periodically based on soft computing techniques. The implementation results, using standard benchmarks confirms the effectiveness of proposed load-balancing algorithm. The algorithm significantly improves both the throughput and mean response time in contrast to two existing load-balancing algorithms.     

Design and implementation of an efficient web cluster with content-based request distribution and file caching

We have implemented an efficient and scalable web cluster named LVS-CAD/FC (i.e. LVS with Content-Aware Dispatching and File Caching). In LVS-CAD/FC, a kernel-level one-way content-aware web switch based on TCP Rebuilding is implemented to examine and distribute the HTTP requests from clients to web servers, and the fast Multiple TCP Rebuilding is implemented to efficiently support persistent connection. Besides, a file-based web cache stores a small set of the most frequently accessed web files in server RAM to reduce disk I/Os and a light-weight redirect method is developed to efficiently redirect requests to this cache. In this paper, we have further proposed new policies related to content-based workload-aware request distribution, in which the web switch considers the content of requests and workload characterization in request dispatching. In particular, web files with more access frequencies would be duplicated in more servers’ file-based caches, such that hot web files can be served by more servers. Our goals are to improve cluster performance by obtaining better memory utilization and increasing the cache hit rates while achieving load balancing among servers. Experimental results of practical implementation on Linux show that LVS-CAD/FC is efficient and scales well. Besides, LVS-CAD/FC with the proposed policies can achieve 66.89% better performance than the Linux Virtual Server with a content-blind web switch.     

基于Nginx负载均衡的动态改进算法

随着过去几十年互联网服务的指数增长,各大网站的访问量急剧上升。海量的用户请求使得热门网站的网络请求率可能在几秒钟内大规模增加。一旦服务器承受不住这样的高并发请求,由此带来的网络拥塞和延迟会极大地影响用户体验。负载均衡是高可用网络基础架构的关键组件,通过在后端引入一个负载均衡器,将工作负载分布到多个服务器来缓解海量并发请求对服务器造成的巨大压力,提高后端服务器和数据库的性能以及可靠性。而Nginx作为一款高性能的HTTP和反向代理服务器,正越来越多地应用到实践中。文中将分析Nginx服务器负载均衡的体系架构,研究默认的加权轮询算法,并提出一种改进后的动态负载均衡算法,实时收集负载信息,重新计算并分配权值。通过实验测试,对比不同算法下的负载均衡性能,改进后的算法能有效提高服务器集群的性能。     

Learning DFA representations of HTTP for protecting web applications

Intrusion detection is a key technology for self-healing systems designed to prevent or manage damage caused by security threats. Protecting web server-based applications using intrusion detection is challenging, especially when autonomy is required (i.e., without signature updates or extensive administrative overhead). Web applications are difficult to protect because they are large, complex, highly customized, and often created by programmers with little security background. Anomaly-based intrusion detection has been proposed as a strategy to meet these requirements.This paper describes how DFA (Deterministic Finite Automata) induction can be used to detect malicious web requests. The method is used in combination with rules for reducing variability among requests and heuristics for filtering and grouping anomalies. With this setup a wide variety of attacks is detectable with few false-positives, even when the system is trained on data containing benign attacks (e.g., attacks that fail against properly patched servers).     

A High-Performance and Cost-Efficient Interconnection Network for High-Density Servers

The high-density server is featured as low power, low volume, and high computational density. With the rising use of high-density servers in data-intensive and large-scale web applications, it requires a high-performance and cost-efficient intra-server interconnection network. Most of state-of-the-art high-density servers adopt the fully-connected intra-server network to attain high network performance. Unfortunately, this solution costs too much due to the high degree of nodes. In this paper, we exploit the theoretically optimized Moore graph to interconnect the chips within a server. Accounting for the suitable size of applications, a 50-size Moore graph, called Hoffman-Singleton graph, is adopted. In practice, multiple chips should be integrated onto one processor board, which means that the original graph should be partitioned into homogeneous connected subgraphs. However, the existing partition scheme does not consider above problem and thus generates heterogeneous subgraphs. To address this problem, we propose two equivalent-partition schemes for the Hoffman-Singleton graph. In addition, a logic-based and minimal routing mechanism, which is both time and area efficient, is proposed. Finally, we compare the proposed network architecture with its counterparts, namely the fully-connected, Kautz and Torus networks. The results show that our proposed network can achieve competitive performance as fully-connected network and cost close to Torus.     

A neural network proxy cache replacement strategy and its implementation in the Squid proxy server

As the Internet has become a more central aspect for information technology, so have concerns with supplying enough bandwidth and serving web requests to end users in an appropriate time frame. Web caching was introduced in the 1990s to help decrease network traffic, lessen user perceived lag, and reduce loads on origin servers by storing copies of web objects on servers closer to end users as opposed to forwarding all requests to the origin servers. Since web caches have limited space, web caches must effectively decide which objects are worth caching or replacing for other objects. This problem is known as cache replacement. We used neural networks to solve this problem and proposed the Neural Network Proxy Cache Replacement (NNPCR) method. The goal of this research is to implement NNPCR in a real environment like Squid proxy server. In order to do so, we propose an improved strategy of NNPCR referred to as NNPCR-2. We show how the improved model can be trained with up to twelve times more data and gain a 5–10% increase in Correct Classification Ratio (CCR) than NNPCR. We implemented NNPCR-2 in Squid proxy server and compared it with four other cache replacement strategies. In this paper, we use 84 times more data than NNPCR was tested against and present exhaustive test results for NNPCR-2 with different trace files and neural network structures. Our results demonstrate that NNPCR-2 made important, balanced decisions in relation to the hit rate and byte hit rate; the two performance metrics most commonly used to measure the performance of web proxy caches.     

A cost driven disk scheduling algorithm for multimedia object retrieval

This paper describes a novel cost-driven disk scheduling algorithm for environments consisting of multipriority requests. An example application is a video-on-demand (VOD) system that provides high and low quality services, termed priority 2 and 1, respectively. Customers ordering a high quality (priority 2) service pay a higher fee and are assigned a higher priority by the underlying system. Our proposed algorithm minimizes costs by maintaining one-queue and managing requests intelligently in order to meet the deadline of as many priority 1 requests as possible while maximizing the number of priority 2 requests that meet their deadline. Our algorithm is general enough to accommodate an arbitrary number of priority levels. Prior schemes, collectively termed "multiqueue" schemes maintain a separate queue for each priority level in order to optimize the performance of the high priority requests only. When compared with our proposed scheme, in certain cases, our technique provides more than one order of magnitude improvement in total cost.     

Comparison of scheduling schemes for on-demand IaaS requests

Infrastructure-as-a-service (IaaS) is one of emerging powerful cloud computing services provided by IT industry at present. This paper considers the interaction aspects between on-demand requests and the allocation of virtual machines in a server farm operated by a specific infrastructure owner. We formulate an analytic performance model of the server farm taking into account the quality of service (QoS) guaranteed to users and the operational energy consumption in the server farm. We compare several scheduling algorithms from the aspect of the average energy consumption and heat emission of servers as well as the blocking probabilities of on-demand requests. Based on numerical results of a comparison of different allocation strategies, a saving on the energy consumption is possible in the operational range (where on-demand requests do not face unpleasant blocking probability) with the allocation of virtual machines to physical servers based on the priority.     

On the efficiency of secure XML broadcasting

Secure broadcasting of web documents is becoming a crucial requirement for many web-based applications. Under the broadcast document dissemination strategy, a web document source periodically broadcasts (portions of) its documents to a potentially large community of users, without the need for explicit requests. By secure broadcasting, we mean that the delivery of information to users must obey the access control policies of the document source. Traditional access control mechanisms that have been adapted for XML documents, however, do not address the performance issues inherent in access control. In this paper, a labeling scheme is proposed to support rapid reconstruction of XML documents in the context of a well-known method, called XML pool encryption. The proposed labeling scheme supports the speedy inference of structure information in all portions of the document. The binary representation of the proposed labeling scheme is also investigated. In the experimental results, the proposed labeling scheme is efficient in searching for the location of decrypted information.     

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.     

Towards green data centers: A comparison of x86 and ARM architectures power efficiency

Servers and clusters are fundamental building blocks of high performance computing systems and the IT infrastructure of many companies and institutions. This paper analyzes the feasibility of building servers based on low power computers through an experimental comparison of server applications running on x86 and ARM computer architectures. The comparison executed on web and database servers includes power usage, CPU load, temperature, request latencies and the number of requests handled by each tested system. Floating point performance and power usage are also evaluated. The use of ARM based systems has shown to be a good choice when power efficiency is needed without losing performance.     

SIP服务器系统

随着会话初始协议SIP(Session Initiation Protocol)的完善和发展,基于SIP协议的服务器广泛应用于通信系统中.然而在硬件不变的前提下,单一SIP服务器的负载能力有限,无法满足大规模用户数量的增长.本文在充分研究和分析SIP协议的基础上,设计实现了一个SIP服务器系统.该系统中,使用多个服务器实例协调工作,并发的增加了处理请求的数量,从而提升对SIP服务的处理能力.该设计具有易于实现和扩展性强的特点.实践证明,本文设计的方案能够解决当前的不足,为未来的业务的扩展提供了支撑.     

ContextGrid: A contextual mashup-based collaborative browsing system

Due to a large amount of resources (i.e., information and knowledge) available on world wide web, it has been more difficult for users to effectively find relevant web resources. Most of the current web browsing methods and systems have been investigated to apply adaptive approaches which can extract personal contexts (e.g., interests and preferences) of the users. In this paper, we propose a contextual mashup-based collaborative browsing (co-browsing) platform, called ContextGrid, for providing online users with various knowledge sharing services. Particularly, the proposed mashup scheme can integrate heterogeneous pieces of information collected by various Open APIs, and assist the users to decide which partners should be selected for mutual collaborations. In order to evaluate the proposed mashup-based method, we have implemented a co-browsing platform which can exchange bookmarks, and measured whether the contextual mashup scheme makes a meaningful influence on improving the performance of the co-browsing process with multiple users.     

Web集群服务器的分离式调度策略

主要用排队论方法讨论了Web集群整体性能与请求调度策略之间的关系，所获得的结论是：在Web集群非过载情况下，一部分后端服务器仅处理静态请求而另一部分后端服务器仅处理动态请求的分离式调度策略要好于所有后端服务器既处理静态请求又处理动态请求的混合式调度策略。用SPECweb99测试工具所做的实际测试更进一步证明：当负载参数为120个连接时，采用分离式调度策略的Web集群服务器可完成63个连接，而采用混合式调度策略的Web集群服务器仅能完成36个连接，性能提高了22.5%。