关于高端集群式流媒体服务器仿真的研究

流媒体应用在近年来获得了飞速的发展,流媒体服务器在用户并发度和存储容量两个方面的需求日益增高,在这种背景下,集群式流媒体服务器逐渐成为高端流媒体服务器的主流.集群式流媒体服务器的研究工作非常需要科学的仿真工具来协助,一方面能够测试算法的性能和普适性,另一方面可以通过仿真分析对服务器的硬件资源进行优化配置.本文在介绍集群式流媒体服务器的体系结构和性能分析的基础上,设计并实现了一套分布式流媒体仿真工具和集群式流媒体服务器实验床.最后基于仿真实验的数据,得到一些提高集群式流媒体服务器性能和资源优化配置的有益结论.     

Wireless resource management mechanism with green communication for multimedia streaming

In the face of massive parallel multimedia streaming and user access, multimedia servers are often in an overload state, resulting in the delay of service response and the low utilization of wireless resources, which makes it is difficult to satisfy the user experience quality. Aiming at the problems of low utilization rate of multimedia communication resources and large computing load of servers, this paper proposes a self management mechanism and architecture of wireless resources based on multimedia flow green communication. First, based on the combination of multimedia server, relay base station and user cluster, a multimedia green communication system architecture is built based on the comprehensive utilization rate of multimedia communication, and a cluster green communication control algorithm is proposed. Secondly, aiming at the dynamic service demand and asynchronous multimedia communication environment, aiming at ensuring the balance of resource allocation and accelerating the speed of resource allocation, we build a dynamic multimedia wireless resource architecture. Finally, the experimental results of statistics and analysis, from the server in different scale parallel multimedia streams under different scale delay, number of users relay network free resources proportion, user satisfaction, packet loss rate and other performance show that the proposed algorithm is effective and feasible.

     

Performance Analysis of Resource Selection Schemes for a Large Scale Video-on-Demand System

The designers of a large scale video-on-demand system face an optimization problem of deciding how to assign movies to multiple disks (servers) such that the request blocking probability is minimized subject to capacity constraints. To solve this problem, it is essential to develop scalable and accurate analytical means to evaluate the blocking performance of the system for a given file assignment. The performance analysis is made more complicated by the fact that the request blocking probability depends also on how disks are selected to serve user requests for multicopy movies. In this paper, we analyze several efficient resource selection schemes. Numerical results demonstrate that our analysis is scalable and sufficiently accurate to support the task of file assignment optimization in such a system.     

Measuring the capacity of a Web server under realistic loads

The World Wide Web and its related applications place substantial performance demands on network servers. The ability to measure the effect of these demands is important for tuning and optimizing the various software components that make up a Web server. To measure these effects, it is necessary to generate realistic HTTP client requests in a test‐bed environment. Unfortunately, the state‐of‐the‐art approach for benchmarking Web servers is unable to generate client request rates that exceed the capacity of the server being tested, even for short periods of time. Moreover, it fails to model important characteristics of the wide area networks on which most servers are deployed (e.g., delay and packet loss). This paper examines pitfalls that one encounters when measuring Web server capacity using a synthetic workload. We propose and evaluate a new method for Web traffic generation that can generate bursty traffic, with peak loads that exceed the capacity of the server. Our method also models the delay and loss characteristics of WANs. We use the proposed method to measure the performance of widely used Web servers. The results show that actual server performance can be significantly lower than indicated by standard benchmarks under conditions of overload and in the presence of wide area network delays and packet losses. This revised version was published online in August 2006 with corrections to the Cover Date.     

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.     

Cost-effective capacity migration of Peer-to-Peer social media to clouds

Social media streaming has become one of the most popular applications over the Internet. We have witnessed the successful deployment of commercial systems with CDN (Content Delivery Network)- based engines, but they suffer from excessive costs for deploying dedicated servers. And with the further expansions on network traffic of social media streaming, a cost-effective solution remains an illusive goal. The emergence of cloud computing sets out to meet the challenge by dynamically leasing cloud servers. This paper aims to realize the capacity migration of social media systems to clouds at the reduced cost. Firstly, by lowering the capacity requested from clouds to reduce the capacity migration cost. Based on the crawled data from YouTube which is the most representative online social media, we find that with larger than 90% probability, the YouTube user’s all requested videos are within three hops of related videos. Then the three hops of related videos are regarded as a cluster and a user’s request can be partly satisfied by other users who watch videos in the same cluster to lessen the capacity requested from clouds. Therefore the capacity migration for clusters is under the P2P (Peer-to-Peer) paradigm and a cloud-assisted P2P social media system is proposed. Secondly, given the diverse capacities, cost, limited lease size of cloud servers, we formulate an optimization problem about how to lease cloud servers to minimize the leasing cost and a heuristic solution is presented. The evaluation based on the crawled data from a cluster of YouTube videos shows the efficiency of the proposed schemes.     

Continuous spatial assignment of moving users

Consider a set of servers and a set of users, where each server has a coverage region (i.e., an area of service) and a capacity (i.e., a maximum number of users it can serve). Our task is to assign every user to one server subject to the coverage and capacity constraints. To offer the highest quality of service, we wish to minimize the average distance between users and their assigned server. This is an instance of a well-studied problem in operations research, termed optimal assignment. Even though there exist several solutions for the static case (where user locations are fixed), there is currently no method for dynamic settings. In this paper, we consider the continuous assignment problem (CAP), where an optimal assignment must be constantly maintained between mobile users and a set of servers. The fact that the users are mobile necessitates real-time reassignment so that the quality of service remains high (i.e., their distance from their assigned servers is minimized). The large scale and the time-critical nature of targeted applications require fast CAP solutions. We propose an algorithm that utilizes the geometric characteristics of the problem and significantly accelerates the initial assignment computation and its subsequent maintenance. Our method applies to different cost functions (e.g., average squared distance) and to any Minkowski distance metric (e.g., Euclidean, L ₁ norm, etc.).     

A unified benchmarking and model-based framework for building QoS-aware streaming media services

A number of technology and workload trends motivate us to consider the appropriate resource allocation mechanisms and policies for streaming media services in shared cluster environments. We present MediaGuard – a model-based infrastructure for building streaming media services – that can efficiently determine the fraction of server resources required to support a particular client request over its expected lifetime. The proposed solution is based on a unified cost function that uses a single value to reflect overall resource requirements such as the CPU, disk, memory, and bandwidth necessary to support a particular media stream based on its bit rate and whether it is likely to be served from memory or disk. We design a novel, time-segment-based memory model of a media server to efficiently determine in linear time whether a request will incur memory or disk access when given the history of previous accesses and the behavior of the server's main memory file buffer cache. Using the MediaGuard framework, we design two media services: (1) an efficient and accurate admission control service for streaming media servers that accounts for the impact of the server's main memory file buffer cache, and (2) a shared streaming media hosting service that can efficiently allocate the predefined shares of server resources to the hosted media services, while providing performance isolation and QoS guarantees among the hosted services. Our evaluation shows that, relative to a pessimistic admission control policy that assumes that all content must be served from disk, MediaGuard (as well as services that are built using it) deliver a factor of two improvement in server throughput.     

Understanding and reducing Web delays

Slow performance costs e-commerce Web sites as much as $4.35 billion annually in lost revenue. Perceived latency-the amount of time between when a user issues a request and receives a response-is a critical issue. Research into improving performance falls into two categories: work on servers and work on networks and protocols. On the server side, previous work has focused on techniques for improving server performance. Such studies show how Web servers behave under a range of loads. These studies often suggest enhancements to application implementations and the operating systems those servers run. On the network side, research has focused on improving network infrastructure performance for Internet applications. Studies focusing on network dynamics have resulted in several enhancements to HTTP, including data compression, persistent connections, and pipelining. These improvements are all part of HTTP 1.1. However, little work has been done on common latency sources that cause the overall delays that frustrate end users. The future of performance improvements lies in developing additional techniques to help implement efficient, scalable, and stable improvements that enhance the end-user experience     

流媒体服务器服务能力基准实验与性能模型

流媒体服务提供商需要了解如何对服务器的服务能力进行测试,如何对系统实时负荷进行估计.本文提出了一组基准实验,测量服务内容为变码率视频时,服务器提供不同质量和方式的视频点播服务的能力,得到与负载相关的服务器性能模型和实时负荷估计方法.实际系统上的验证实验表明,该性能模型可以准确刻画服务器的实时负荷.     

A paracasting model for concurrent access to replicated Internet content

In this paper, we develop a model to study how to effectively download a document from a set of replicated servers. We propose a generalized application-layer anycasting protocol, known as paracasting, to advocate concurrent access of a subset of replicated servers to cooperatively satisfy a client's request. Each participating server satisfies the request in part by transmitting a subset of the requested file to the client. The client can recover the complete file when different parts of the file sent from the participating servers are received. This model allows us to estimate the average time to download a file from the set of homogeneous replicated servers, and the request blocking probability when each server can accept and serve a finite number of concurrent requests. Our results show that the file download time drops when a request is served concurrently by a larger number of homogeneous replicated servers, although the performance improvement quickly saturates when the number of servers increases. If the total number of requests that a server can handle simultaneously is finite, the request blocking probability increases with the number of replicated servers used to serve a request concurrently. Therefore, paracasting is effective when a small number of servers, say, up to four, are used to serve a request concurrently.     

Video Packet Selection and Scheduling for Multipath Streaming

This paper addresses the problem of choosing the best streaming policy for distortion optimal multipath video delivery, under network bandwidth and playback delay constraints. The streaming policy consists in a joint selection of the network path and of the video packets to be transmitted, along with their sending time. A simple streaming model is introduced, which takes into account the video packet importance, and the dependencies between packets. A careful timing analysis allows to compute the quality perceived by the receiver for a constrained playback delay, as a function of the streaming policy. We derive an optimization problem based on a video abstraction model, under the assumption that the server knows, or can predict accurately the state of the network. A detailed analysis of constrained multipath streaming systems provides helpful insights to design an efficient branch and bound algorithm that finds the optimal streaming strategy. This solution allows to bound the performance of any scheduling strategy, but the complexity of the algorithm becomes rapidly intractable. We therefore propose a fast heuristic-based algorithm, built on load-balancing principles. It allows to reach close to optimal performance with a polynomial time complexity. The algorithm is then adapted to live streaming scenarios, where the server has only a partial knowledge of the packet stream, and the channel bandwidth. Extensive simulations show that the proposed algorithm only induces a negligible distortion penalty compared to the optimal strategy, even when the optimization horizon is limited, or the rate estimation is not perfect. Simulation results also demonstrate that the proposed scheduling solution performs better than common scheduling algorithms, and therefore represents a very efficient low-complexity multipath streaming algorithm, for both stored and live video services     

Harmonic proportional bandwidth allocation and scheduling for service differentiation on streaming servers

To provide ubiquitous access to the proliferating rich media on the Internet, scalable streaming servers must be able to provide differentiated services to various client requests. Recent advances of transcoding technology make network-I/O bandwidth usages at the server communication ports controllable by request schedulers on the fly. In this article, we propose a transcoding-enabled bandwidth allocation scheme for service differentiation on streaming servers. It aims to deliver high bit rate streams to high priority request classes without overcompromising low priority request classes. We investigate the problem of providing differentiated streaming services at application level in two aspects: stream bandwidth allocation and request scheduling. We formulate the bandwidth allocation problem as an optimization of a harmonic utility function of the stream quality factors and derive the optimal streaming bit rates for requests of different classes under various server load conditions. We prove that the optimal allocation, referred to as harmonic proportional allocation, not only maximizes the system utility function, but also guarantees proportional fair sharing between classes with different prespecified differentiation weights. We evaluate the allocation scheme, in combination with two popular request scheduling approaches, via extensive simulations and compare it with an absolute differentiation strategy and a proportional-share strategy tailored from relative differentiation in networking. Simulation results show that the harmonic proportional allocation scheme can meet the objective of relative differentiation in both short and long timescales and greatly enhance the service availability and maintain low queueing delay when the streaming system is highly loaded.     

Double feedback streaming agent for real-time delivery of media over 3G wireless networks

A network agent located at the junction of wired and wireless networks can provide additional feedback information to streaming media servers to supplement feedbacks from clients. Specifically, it has been shown that feedbacks from the network agent have lower latency, and they can be used in conjunction with client feedbacks to effect proper congestion control. In this work, we propose the double-feedback streaming agent (DFSA) which further allows the detection of discrepancies in the transmission constraints of the wired and wireless networks. By working together with the streaming server and client, DFSA reduces overall packet losses by exploiting the excess capacity Of the path with more capacity. We show how DFSA can be used to support three modes of operation tailored for different delay requirements of streaming applications. Simulation results under high wireless latency show significant improvement of media quality using DFSA over non-agent-based and earlier agent-based streaming systems.     

ReDAL: An Efficient and Practical Request Distribution Technique for Application Server Clusters

Modern Web-based application infrastructures are based on clustered multitiered architectures, where request distribution occurs in two sequential stages: over a cluster of Web servers and over a cluster of application servers. Much work has focused on strategies for distributing requests across a Web server cluster in order to improve the overall throughput across the cluster. The strategies applied at the application layer are the same as those at the Web server layer because it is assumed that they transfer directly. In this paper, we argue that the problem of distributing requests across an application server cluster is fundamentally different from the Web server request distribution problem due to core differences in request processing in Web and application servers. We devise an approach for distributing requests across a cluster of application servers such that the overall system throughput is enhanced, and load across the application servers is balanced.     

Efficient algorithms of video replication and placement on a cluster of streaming servers

A cost-effective approach to building up scalable video streaming servers is to couple a number of streaming servers together in a cluster so as to alleviate the inherent storage and networking constraints of streaming services. In this article, we investigate a crucial problem of video replication and placement on a distributed storage cluster of streaming servers for high quality and high availability services. We formulate it as a combinatorial optimization problem with objectives of maximizing the encoding bit rate and the number of replicas of each video and balancing the workload of the servers. The objectives are subject to the constraints of the storage capacity and the outgoing network-I/O bandwidth of the servers. Under the assumption of single fixed encoding bit rate for all video objects with different popularity values, we give an optimal replication algorithm and a bounded placement algorithm, respectively. We further present an efficient replication algorithm that utilizes the Zipf-like video popularity distributions to approximate the optimal solutions, which can reduce the complexity of the optimal replication algorithm. For video objects with scalable encoding bit rates, we propose a heuristic algorithm based on simulated annealing. We conduct a comprehensive performance evaluation of the algorithms and demonstrate their effectiveness via simulations over a synthetic workload set.     

Energy-Efficient Redundant Execution of Processes in a Fault-Tolerant Cluster of Servers

This paper investigates into fault tolerance of cluster of servers and their energy efficiency to realize a reliable and energy aware server cluster system. A client issues a request to one server in a server cluster and the server sends a reply to the client in information systems. Once the server stops by fault, the client does not receive a reply of the request. Even if the request is performed on another server on detection of fault of the server, some QoS requirements like response time may not be satisfied. Hence, each request has to be redundantly performed on multiple servers to be tolerant of server faults. The redundant power consumption laxity-based (RPCLB) algorithm is discussed where multiple servers are selected to redundantly and energy-efficiently perform a request process in our previous studies. Since each application process is redundantly performed on more than one server, the larger amount of electric power is consumed. In this paper, we propose a novel and improved RPCLB (IRPCLB) algorithm to reduce the power consumption of servers, where once a process successfully terminates on one server, meaningless redundant processes are forced to terminate on the other servers. In the evaluation, we show the total power consumption of servers and total execution time of processes are reduced in homogeneous and heterogeneous types of clusters by the IRPCLB algorithm than the RPCLB and RR algorithms.     

Using eager strategies to improve NFS I/O performance

Typical network file system (NFS) clients write lazily: they leave dirty pages in the page cache and defer writing to the server. This reduces network traffic when applications repeatedly modify the same set of pages. However, this approach can lead to memory pressure, when the number of available pages on the client system is so low that the system must work harder to reclaim dirty pages. We show that NFS performance is poor under memory pressure and present two mechanisms to solve it: eager writeback and eager page laundering. These mechanisms change the client's data management policy from lazy to eager, in which dirty pages are written back proactively, resulting in higher throughput for sequential writes. In addition, we show that NFS servers suffer from out-of-order file operations, which further reduce performance. We introduce request ordering, a server mechanism to process operations, as much as possible, in the order they were sent by the client, which improves read performance substantially. We have implemented these techniques in the Linux operating system. I/O performance is improved, with the most pronounced improvement visible for sequential access to large files. We see 33% improvement in the performance of streaming write workloads and more than triple the performance of streaming read workloads. We evaluate several non-sequential workloads and show that these techniques do not degrade performance, and can sometimes improve performance.     

一种基于网络地址转换的负载均衡算法

本文探讨集群服务器使用的负载均衡技术及负载分配的策略,并将网络地址转换应用于VOD集群,将负载分给多个服务器分担,以解决VOD集群服务器面临的大量并发访问造成的CPU或I/O的高负载问题。为了达到最佳的负载均衡效果,负载均衡器需要根据各个服务器的当前CPU和I/O状态来分配负载,这就需要动态监视服务器的负载,并应用优化的负载分配策略,达到平均分配负载的目的。