Distributed middleware architectures for scalable media services

The fusion of Multimedia and Internet technology has introduced an ever-increasing demand for large-scale reliable media services. This exposes the scalability limitations of current middleware architectures, as they traditionally operate on either very large-server configurations or on tightly coupled distributed systems. On the other hand, the wide availability of high-speed networks and the widespread deployment of powerful personal computing units by end users, has emphasized the advantages of the peer-to-peer (P2P) computing model. In this paper, we evaluate a number of different middleware architectures that facilitate the timely and reliable delivery of media services in P2P networks. Our evaluated architectures exploit features including availability of high-performance links, replication and caching of popular items and finally state-of-the-art search techniques proposed in the context of structured and unstructured P2P overlay networks. Through detailed simulation we investigate the behavior of the suggested P2P architectures for video provision and examine the involved trade-offs. We show that under realistic assumptions, the evaluated architectures are resilient to multiple peer-failures, are scalable with respect to dropped requests when the number of messages in the network increases and provide good response times to the user requests.     

Clustering support and replication management for scalable network services

The ubiquity of the Internet and various intranets has brought about widespread availability of online services and applications accessible through the network. Cluster-based network services have been rapidly emerging due to their cost-effectiveness in achieving high availability and incremental scalability. We present the design and implementation of the Neptune middleware system that provides clustering support and replication management for scalable network services. Neptune employs a loosely connected and functionally symmetric clustering architecture to achieve high scalability and robustness. It shields the clustering complexities from application developers through simple programming interfaces. In addition, Neptune provides replication management with flexible replication consistency support at the clustering middleware level. Such support can be easily applied to a large number of applications with different underlying data management mechanisms or service semantics. The system has been implemented on Linux and Solaris clusters, where a number of applications have been successfully deployed. Our evaluations demonstrate the system performance and smooth failure recovery achieved by proposed techniques.     

Adaptive middleware supporting scalable performance for high-end network services

Network service-based computation is a promising paradigm for both scientific and engineering, and enterprise computing. The network service allows users to focus on their application and obtain services when needed, simply by invoking the service across the network. In this paper, we show that an adaptive, general-purpose run-time infrastructure in support of effective resource management can be built for a wide range of high-end network services running in a single-site cluster and in a Grid. The primary components of the run-time infrastructure are: (1) dynamic performance prediction; (2) adaptive intra-site resource management; and (3) adaptive inter-site resource management. The novel aspect of our approach is that the run-time system is able to dynamically select the most appropriate performance predictor or resource management strategy over time. This capability not only improves the performance, but also makes the infrastructure reusable across different high-end services. To evaluate the effectiveness and applicability of our approach, we have transformed two different classes of high-end applications—data parallel and distributed applications—into network services using the infrastructure. The experimental results show that the network services running on the infrastructure significantly reduce the overall service times under dynamically varying circumstances.     

A grouped P2P network for scalable grid information services

Information services play a crucial role in grid environments in that the state information can be used to facilitate the discovery of resources and the services available to meet user requirements, and also to help tune the performance of a grid system. However, the large size and dynamic nature of the grid brings forth a number of challenges for information services. This paper presents PIndex, a grouped peer-to-peer network that can be used for scalable grid information services. PIndex builds on Globus MDS4, but introduces peer groups to dynamically split the large grid information search space into many small sections to enhance its scalability and resilience. PIndex is subsequently modeled with Colored Petri Nets for performance evaluation. The simulation results show that PIndex is scalable and resilient in dealing with a large number of peer nodes.

On index load balancing in scalable P2P media distribution

Peer-Peer (P2P) technologies have recently been in the limelight for their disruptive power in particular they have emerged as a powerful multimedia content distribution mechanism. However, the widespread deployment of P2P networks are hindered by several issues, especially the ones that influence end-user satisfaction, including reliability. In this paper, we propose a solution for an efficient and user-oriented keyword lookup service on P2P networks. The proposed mechanism has been designed to achieve reliability via index load balancing and address the scalability issues of extremely popular keywords in the index. The system performance have been analytically derived as well implemented using the OpenDHT framework on PlanetLab.

LinuxDirector: A connection director for scalable internet services

LinuxDirector is a connection director that supports load balancing among multiple Internet servers,which can be used to build scalable Internet services based on clusters of servers.LinuxDirector extends the TCP/IP stack of Linux Kernel to support three IP load balancing techniques,VS/NAT,VS/TUN and VS/DR.Four scheduling algorithms have been implemented to assign connections to different servers.Scalability is achieved by transparently adding or removing a node in the cluster.High availability is provided by detecting node or daemon failure and reconfiguring the system appropriately.This paper describes the design and implementation of LinuxDirector and presents serveral of its features including scalability,high availability and connection affinity.     

A scalable bandwidth guaranteed distributed continuous media file system using network attached autonomous disks

Repository for continuous media data differs from that of the traditional text-based data both in storage space and streaming bandwidth requirements. The file systems used for continuous media streams need to support large volumes and high bandwidth. We propose a scalable distributed continuous media file system built using autonomous disks. Autonomous disks are attached directly to the network and are able to perform lightweight processing. We discuss different ways to realize the autonomous disk, and describe a prototype implementation on a Linux platform using PC-based hardware. We present the basic requirements of the continuous media file system and present the design methodology and a prototype Linux-based implementation of the distributed file system that supports the requirements. We present experimental results on the performance of the proposed file system prototyped using autonomous disks. We show that the performance of the file system scales linearly with the number of disks and the number of clients. The file system performs much superior to NFS running on the same hardware platform and can deliver higher raw disk bandwidth to the applications. We also present bandwidth and time sensitive read/write procedures for the file system and show that the file system can provide strict bandwidth guarantees for continuous media streams.     

A scalable framework for mobile real-time group communication services

A scalable framework for mobile real-time group communication services is developed in this paper. Examples for possible applications of this framework are mobile social networks, mobile conference calls, mobile instant messaging services, and mobile multi-player on-line games. A key requirement for enabling a real-time group communication service is the tight constraint imposed on the call delivery delay. Since establishing such communication service for a group of independent mobile users under a tight delay constraint is NP-hard, a two-tier architecture is proposed, that can meet the delay constraint imposed by the real-time service requirement for many independent mobile clients in a scalable manner. This goal is achieved by two dimensional partition of the space, first by organization and then geographically. Both the time and memory complexity associated with the location management of N mobile users are O(N) for the location management provided by the proposed framework, while a distributed scheme requires O(N²) for both time and memory complexity.     

A scalable and reliable paradigm for media on demand

The authors describe a system for solving some of the conventional problems associated with traditional streaming protocols by using forward error correction codes to let multiple clients recover from different packet losses using the same redundant data     

Building Web services middleware with predictable execution times

Predictability of execution has seldom been considered important in the design of Web services middleware. However, with the paradigm shift brought by cloud computing and with offerings of Platforms and Infrastructure as services, execution level predictability is mandating an increased importance. Existing Web services middleware are optimised for throughput with unconditional acceptance of requests and execution in a best-effort manner. While achieving perceived levels of throughput, they also result in highly unpredictable execution times. This paper presents a generic set of guidelines, algorithms and software engineering techniques that enable service execution to complete within a given deadline. The proposed algorithms accept requests for execution based on their laxity and executes them to meet requested deadlines. An introduced admission control mechanism results in a large range of laxities, enabling more requests to be scheduled together by phasing out their execution. Specialised development libraries and operating systems empower them with increased control over execution. Two widely used Web services middleware products were enhanced using these techniques. The two systems are compared with their unmodified versions to measure the predictability gain achieved. Empirical evidence confirms that the enhancements made enable these systems to achieve more than 90% of the deadlines under any type of traffic, while the unmodified versions achieve less than 10% of the deadlines in high traffic conditions. Predictability of execution achieved through these techniques, would open up new application areas such as industrial control systems, avionics, robotics and financial trading systems to the use of Web services as a middleware platform.     

基于内容的可缩放性服务的负载分配研究

通过工作站或者PC集群利用客户机内容请求来进行负载分配,这种模式具有很多吸引人的特性,但当前Internet标准去实现以基于内容的负载分配有相当的困难。论述了为达到基于内容的负载分配的可缩放性服务(如WWW服务)的几种方法,并举例说明了这些方法的优缺点,介绍了其中能够实现基于内容的负载分配的一种方法：握手连接。详细介绍了一种由TCP握手连接扩展的向后兼容的协议。     

Time-aware scalable recommendation with clustering-based distributed factorization for edge services

World Wide Web - Joint edge and recommendation have recently drawn much attention, as massive candidate edge applications are available and accessed by end-users. Recommendation technology has been...     

Providing scalable Web services using multicast communication

The recent growth in use of the World-Wide Web in the Internet has caused a significant increase in the demand placed on Web servers. This increased load results in noticeably longer response times for users. We propose an approach to using multicast in the delivery of Web resources that reduces the load on servers as well as the networks that connect them. We analyze the issues involved in using multicast in the Web, especially those related to routing and addressing, and present an alternative approach to multicast routing that is appropriate for this application. We also describe the design and implementation of a system based on the existing WWW client and server architecture and the multicast support provided within IP. Experimental results from this implementation are presented.     

A scalable cost-effective video broadcasting system for on-demand video services

Recent years have seen intensive investigations of Periodic Broadcast, an attractive paradigm for broadcasting popular videos. In this paradigm, the server simply broadcasts segments of a popular video periodically on a number of communication channels. A large number of clients can be served simultaneously by tuning into these channels to receive segments of the requested video. A playback can begin as soon as a client can access the first segment. Periodic Broadcast guarantees a small maximum service delay regardless of the number of concurrent clients. Existing periodic broadcast techniques are typically evaluated through analytical assessment. While these results are good performance indicators, they cannot demonstrate subtle implementation difficulty that can prohibit these techniques from practical deployment. In this paper, we present the design and implementation of a video broadcasting system based on our periodic broadcast scheme called Striping Broadcast. Our experience with the system confirms that the system offers a low service delay close to its analytical guaranteed delay while requiring small storage space and low download bandwidth at a client.     

CARR: a scalable solution for network packet classification

Modern Internet routers have to handle a large number of packet classification rules, which requires classification schemes to be scalable both in time and space. In this paper, we present a scalable packet classification algorithm that is developed by combining two new concepts to the well‐known bit vector (BV) scheme. We propose a range search method based on a cache‐aware tree (CATree) which makes full use of processor's cache line to reduce the number of dynamic random access memory (DRAM) accesses. Theoretically, the number of DRAM accesses of CATree is about log(m+1) times lower than that of the widely used binary search algorithm, where m is the number of keys in a single cache line. Based on our computational results on a set of 1024 keys, the CATree algorithm is 36% faster than binary search algorithm and the performance is better when applied to a larger set of keys. In addition, we develop a rule re‐arrangement algorithm to reduce the bitmap space of BV. With this re‐arrangement, the rules for the same action may be assigned an identical priority. This reduces the number of priorities as well as the memory space of the bitmap. Furthermore, this also reduces the number of memory accesses and hence, increases the CPU cache utilization. With CATree and rule re‐arrangement, the cache‐aware bit vector with rule re‐arrangement algorithm achieves better performance in comparison with the regular BV scheme, both in space and time. In our experiments, the proposed algorithm reduces the bitmap memory space of a practical set of firewall rules by two orders of magnitude and is 91% faster than the regular BV.     

A scalable network forensics mechanism for stealthy self-propagating attacks

Network forensics supports capabilities such as attacker identification and attack reconstruction, which complement the traditional intrusion detection and perimeter defense techniques in building a robust security mechanism. Attacker identification pinpoints attack origin to deter future attackers, while attack reconstruction reveals attack causality and network vulnerabilities. In this paper, we discuss the problem and feasibility of back tracking the origin of a self-propagating stealth attack when given a network traffic trace for a sufficiently long period of time. We propose a network forensics mechanism that is scalable in computation time and space while maintaining high accuracy in the identification of the attack origin. We further develop a data reduction method to filter out attack-irrelevant data and only retain evidence relevant to potential attacks for a post-mortem investigation. Using real-world trace driven experiments, we evaluate the performance of the proposed mechanism and show that we can trim down up to 97% of attack-irrelevant network traffic and successfully identify attack origin.     

Modeling discrete event scalable network systems

Scalability in simulation tools is one of the most important traits to measure performance of software. The reason is that today’s Internet is the main instance of a large-scale and highly complex system. Simulation of Internet-scale network systems has to be supported by any simulation tool. Despite this fact, many network simulators lacks support for building large models. In this work, in order to propose a new approach for scalability issue in network simulation tools, a network simulator is developed based on behavior of honeybees and high performance DEVS, modular and hierarchical system theoretic approach. A biologically-inspired discrete event modeling approach is described for studying networks’ scalability and performance traits. Since natural systems can offer important concepts for modeling network systems, key adaptive and emergent attributes of honeybees and their societal properties are incorporated into a set of simulation models that are developed using the discrete event system specification approach. Large-scale network models are simulated and evaluated to show the benefits of nature-inspired network models.     

Mechanisms for building autonomically scalable services on cooperatively shared computing platforms

We present here a system architecture and its underlying mechanisms for building autonomically scalable and resilient services on cooperatively shared computing platforms. Specifically, our focus is on utilizing computing platforms exhibiting the following characteristics. The resources at a node in such platforms are allocated to competing users on fair‐share basis, without any reserved resource capacities for any user. There is no platform‐wide resource manager for the placement of users on different nodes. The users independently select nodes for their applications. Moreover, a node can become unavailable at any time due to crashes or shutdowns. Building scalable services in such environments poses unique challenges due to node‐level fluctuations in the available resource capacities and node crashes. The service load may surge in a short time due to flash crowds. Autonomic scaling of service capacity is performed by dynamic control of the degree of service replication based on the estimated service capacity and the observed load. We present here models for estimating the service capacity at a node under fluctuating operating conditions. Furthermore, we develop adaptive and agile load distribution mechanisms for distributing load among replicas based on their time‐varying service capacities. We present the results of our evaluations of these mechanisms on PlanetLab, which exemplifies the platform level characteristics considered here.Copyright © 2013 John Wiley & Sons, Ltd.     

基于H.264可伸缩视频流的自适应媒体播放算法

为了应对H.264可伸缩视频编码（SVC）应用中网络特性的波动,提出了一种预测播放中断与缓冲区溢出风险进行及早调节的自适应媒体播放(AMP)算法。该算法估算网络流量与视频图像组（GOP）结构中各帧长度用于风险预测,通过K步调节过程实现良好的调节平滑性与速度,并利用SVC的可伸缩性尽量减少溢出带来的质量损失。仿真结果表明,该算法在抑制播放中断、处理缓冲区溢出与抖动性能等方面,优于现行的平滑AMP与常规AMP算法。