A two-phase approach to interactivity enhancement for large-scale distributed virtual environments

Distributed virtual environments (DVEs) are distributed systems that allow multiple geographically distributed clients (users) to interact simultaneously in a computer-generated, shared virtual world. Applications of DVEs can be seen in many areas nowadays, such as online games, military simulations, collaborative designs, etc. To support large-scale DVEs with real-time interactions among thousands or even more distributed clients, a geographically distributed server architecture (GDSA) is generally needed, and the virtual world can be partitioned into many distinct zones to distribute the load among the servers. Due to the geographic distributions of clients and servers in such architectures, it is essential to efficiently assign the participating clients to servers to enhance users’ experience in interacting within the DVE. This problem is termed the client assignment problem (CAP) in this paper. We propose a two-phase approach, consisting of an initial assignment phase and a refined assignment phase to address the CAP. Both phases are shown to be NP-hard. Several heuristic assignment algorithms are then devised and evaluated via extensive simulations with realistic settings. We find that, even under heterogeneous environments like the Internet where accurate input data for the assignment algorithms are usually impractical to obtain, the proposed algorithms are still beneficial to the performances of DVE.     

Autonomic microcell assignment in massively distributed online virtual environments

Distributed virtual environments and massively multiplayer online games in particular have been on the rise for several years now. They offer huge digital environments characterized by tens of thousands of users interacting with each other. Efficiently managing these online worlds requires scalable architectures to distribute the load over multiple servers and maintain a high Quality of Experience (QoE). This need will only increase as online virtual worlds become more and more popular. A traditional approach to improve the scalability of this type of system is to statically partition the virtual world in smaller segments called cells, each assigned to a dedicated server.In this paper a novel approach of dividing the virtual world into even smaller parts called microcells is introduced. Critical in this approach are the algorithms that manage the microcell allocation over the available servers. These algorithms must face a number of challenges and have as a central goal to keep the load experienced by the servers below a given threshold. On one hand, clustering interacting microcells on one server allows to limit the overall load by minimizing the communication overhead. On the other hand, locating too many microcells on one server may cause the load to violate the threshold value, resulting in an overload situation. In this paper we present a number of algorithms that determine the microcell allocation and runtime adaptations of the microcell allocation to optimize the deployment. We evaluate the microcell approach by studying the impact of the microcell size and the number of servers. The efficiency of the algorithms in terms of their ability to decrease the maximum server load and their capability to maintain an ideal deployment in dynamic environments is also studied.     

A performance study of robust load sharing strategies fordistributed heterogeneous Web server systems

Replication of information across multiple servers is becoming a common approach to support popular Web sites. A distributed architecture with some mechanisms to assign client requests to Web servers is more scalable than any centralized or mirrored architecture. In this paper, we consider distributed systems in which the Authoritative Domain Name Server (ADNS) of the Web site takes the request dispatcher role by mapping the URL hostname into the IP address of a visible node, that is, a Web server or a Web cluster interface. This architecture can support local and geographical distribution of the Web servers. However, the ADNS controls only a very small fraction of the requests reaching the Web site because the address mapping is not requested for each client access. Indeed, to reduce Internet traffic, address resolution is cached at various name servers for a time-to-live (TTL) period. This opens an entirely new set of problems that traditional centralized schedulers of parallel/distributed systems do not have to face. The heterogeneity assumption on Web node capacity, which is much more likely in practice, increases the order of complexity of the request assignment problem and severely affects the applicability and performance of the existing load sharing algorithms. We propose new assignment strategies, namely adaptive TTL schemes, which tailor the TTL value for each address mapping instead of using a fixed value for all mapping requests. The adaptive TTL schemes are able to address both the nonuniformity of client requests and the heterogeneous capacity of Web server nodes. Extensive simulations show that the proposed algorithms are very effective in avoiding node overload, even for high levels of heterogeneity and limited ADNS control     

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。     

Online Bicriteria Load Balancing Using Object Reallocation

We study the bicriteria load balancing problem on two independent parameters under the allowance of object reallocation. The scenario is a system of $M$ distributed file servers located in a cluster, and we propose three online approximate algorithms for balancing their loads and required storage spaces during document placement. The first algorithm is for heterogeneous servers. Each server has its individual tradeoff of load and storage space under the same rule of selection. The other two algorithms are for homogeneous servers. The second algorithm combines the idea of the first one and the best existing solution for homogeneous servers. Using document reallocation, we obtain a smooth tradeoff curve of the upper bounds of load and storage space. The last one bounds the load and storage space of each server by less than three times of their trivial lower bounds, respectively; and more importantly, for each server, the value of at least one parameter is far from its worst case. The time complexities of these three algorithms are $O(log M)$ plus the cost of document reallocation.     

A load balancing scheme for massively multiplayer online games

In a distributed MMOG (massively multiplayer online game) server architecture, the server nodes may become easily overloaded by the high demand from the players for state updates. Many works propose algorithms to distribute the load on the server nodes, but this load is usually defined as the number of players on each server, what is not an ideal measure. Also, the possible heterogeneity of the system is frequently overlooked. We propose a balancing scheme with two main goals: allocate load on server nodes proportionally to each one’s power and reduce the inter-server communication overhead, considering the load as the occupied bandwidth of each server. Four algorithms were proposed, from which ProGReGA is the best for overhead reduction and ProGReGA-KF is the most suited for reducing player migrations between servers. We also make a review of related works and some comparisons were made, where our approach performed better.     

服务器负载均衡的应用与实现

讨论了服务器的性能问题，介绍了多台服务器负载均衡的概念和重要性，并详细阐述了多台服务器负载均衡的原理。以一个呼叫中心案例为例描述了多台服务器实现负载均衡的方法。通过多台服务器的负载均衡，提高了应用系统的性能及容错能力，增强了应用系统的稳定性和可靠性。     

分布存储VOD系统的负载均衡设计及其仿真

为了满足日益增长的VOD用户数量,设计了一种多服务器组成的分布式VOD系统,系统中服务器的负载均衡问题成为亟待解决的问题.为了解决负载均衡问题.从用户行为分析入手,建立了用户行为的数学模型.根据用户的节目选择分布以及点播时长分布,制定了媒体文件按时间分块并分布存储在多服务器上的策略,并根据热度信息的变化,每隔一段时间对各服务器上存储的内容进行重发布,以此达到各服务器的负载均衡.实现了仿真系统,并设计出用户行为仿真程序进行用户点播行为仿真.仿真实验结果显示,使用该重发布策略后,各服务器达到很好的负载均衡,说明策略是有效的.     

基于Linux虚拟服务器的负载均衡算法

分析Linux虚拟服务器负载均衡调度过程,提出一种基于Linux虚拟服务器的负载均衡算法。该算法结合系统资源类型和服务器权重系数,依据服务器加权负载标准差,进行集群真实服务器负载状况分析,动态调整服务器权重系数,对改进的最小连接数调度算法进行负载均衡调度。测试结果验证了该算法的有效性。     

基于协作缓存的视频点播系统中调度设计

随着计算机技术和网络技术的不断进步,视频点播服务已经逐渐变成现实,基于协作缓存的视频点播系统是一个分布式结构,中心集群存放影片数据,本地集群缓存数据并提供视频点播服务,具有很好的可扩展性。该文针对VOD系统的协作缓存,提出了静态调度和动态迁移相结合的调度策略,静态调度能够根据影片数据的缓存分布情况,实时调度服务请求,并同时考虑多个VOD服务器的负载动态平衡;动态任务迁移能对服务流分布进行实时分析,并根据服务数据本地化的原则进行服务迁移,进一步提高了协作缓存的命中率。该文阐述了基于协作缓存的视频点播系统的拓扑结构,对静态调度和动态迁移进行了详细设计,并给出了相应的形式化表示。     

一种改进的集群动态负载均衡算法

在集群系统的研究中,负载均衡算法是一个重要的方向,因为它关系到多台服务器在整合成一个集群系统后能否很好地相互协作,以更好地完成用户交予的任务。为实现上述目的,本文在分析已有的负载均衡算法基础上,提出一种改进的能够实时收集服务器负载指标,进而动态地计算出服务器在分配用户连接中的权重的方法。测试结果表明,该方法能够有效地防止服务器倾斜,达到良好的负载均衡效果。     

一种两阶段自适应的IMS-Based IPTV应用服务器负载均衡策略

随着IPTV用户数目的增长,IPTV应用服务器的负担愈加繁重。在这种情况下,集群系统逐渐成为构建高性能和高服务质量的一种有效的应用服务器体系结构。保证应用服务器各节点的负载均衡,可以有效提高系统的吞吐量,提高IPTV服务质量。针对负载均衡问题,提出了一种基于IPTV用户行为特点的,两阶段自适应负载均衡策略,负载均衡器通过实时监控应用服务器集群内各节点的负载情况,动态修改各节点负载分配权值。算法在保持应用服务器各节点负载均衡的同时,维护用户会话的完整性,保证了服务质量QoS（Quality of Services）。仿真结果显示此算法具有较好的性能,能在保证服务质量的前提下有效地提高系统吞吐量。     

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.).     

Video placement and configuration of distributed video servers on cable TV networks

A large-scale, distributed video-on-demand (VOD) system allows geographically dispersed residential and business users to access video services, such as movies and other multimedia programs or documents on demand from video servers on a high-speed network. In this paper, we first demonstrate through analysis and simulation the need for a hierarchical architecture for the VOD distribution network.We then assume a hierarchical architecture, which fits the existing tree topology used in today's cable TV (CATV) hybrid fiber/coaxial (HFC) distribution networks. We develop a model for the video program placement, configuration, and performance evaluation of such systems. Our approach takes into account the user behavior, the fact that the user requests are transmitted over a shared channel before reaching the video server containing the requested program, the fact that the input/output (I/O) capacity of the video servers is the costlier resource, and finally the communication cost. In addition, our model employs batching of user requests at the video servers. We study the effect of batching on the performance of the video servers and on the quality of service (QoS) delivered to the user, and we contribute dynamic batching policies which improve server utilization, user QoS, and lower the servers' cost. The evaluation is based on an extensive analytical and simulation study.     

Dynamic real-time scheduling strategies for interactive continuous media servers

In this paper, we propose and study a dynamic approach to schedule real-time requests in a video-on-demand (VOD) server. Providing quality of service in such servers requires uninterrupted and on-time retrieval of motion video data. VOD services and multimedia applications further require access to the storage devices to be shared among multiple concurrent streams. Most of the previous VOD scheduling approaches use limited run-time,0 information and thus cannot exploit the potential capacity of the system fully. Our approach improves throughput by making use of run-time information to relax admission control. It maintains excellent quality of service under varying playout rates by observing deadlines and by reallocating resources to guarantee continuous service. It also reduces start-up latency by beginning service as soon as it is detected that deadlines of all real-time requests will be met. We establish safe conditions for greedy admission, dynamic control of disk read sizes, fast initial service, and sporadic services. We conduct thorough simulations over a wide range of buffer capacities, load settings, and over varying playout rates to demonstrate the significant improvements in quality of service, throughput and start-up latency of our approach relative to a static approach.     

实时通信集群负载均衡策略研究与应用

实时通信主要传输实时音视频,具有低延时和高带宽消耗的特点.在用户量较大的场景下,单服务器方案无法满足整体需求,此时需搭建分布式集群对外提供服务,而如何将这些访问合理的分配到不同服务器上,均衡集群内服务器的负载就显得尤为重要.本文首先分析单服务器场景下的实时通信流程,然后研究和分析常见的负载均衡算法,同时为满足同群组客户端需转发到相同服务器的一致性要求,提出一种基于一致性哈希算法和遗传算法的自适应负载均衡算法,并对该算法进行应用和实验验证.     

Adaptive video-on-demand broadcasting in ubiquitous computing environment

Video-on-demand (VOD) is a service that allows users to view any video program from a server at the time of their choice, such kind of services are expected to be popular in future ubiquitous computing environment. Lots of broadcasting protocols for VOD services have been proposed, but they usually focus only on the tradeoff between bandwidth and delay, thus they are usually not efficient for the local storage. Since the ubiquitous network is heterogeneous and users will have different resource and communication capability, we need to address the storage issue in VOD systems. In this paper, we present several new effective broadcasting schemes, which can intelligently adjust the solution according to available bandwidth and local storage to achieve an ideal waiting time.     

连续媒体服务器存储系统中的容错算法

连续媒体服务器(如VOD服务器)要对大量连续媒体数据(如声频、视频)进行管理,按一定速率为用户提供连续的媒体服务。因此,在这样的系统中,作为存储设备的磁盘阵列要具有高可靠性和一定的容错能力。文章提出一种基于奇偶检验的数据重构恢复算法,以保证系统中只有一个盘出现故障时,能使服务器及时重构出故障盘上的数据,并且算法充分利用了媒体流内在特性———回放时数据的连续性,与目前使用的标准故障恢复算法相比,大大减少了磁盘在线故障后数据重构过程的系统开销。最后通过分析、比较证明了算法的有效性。     

基于遗传算法的VOD集群负载均衡研究*

VOD集群技术解决了VOD服务器系统容量问题,其核心思想是负载均衡策略和算法。对VOD集群中的负载均衡技术进行了分析和探讨,并提出了一种自适应遗传算法,取得了比较理想的实验结果。     

Optimal Load Distribution for Multiple Heterogeneous Blade Servers in a Cloud Computing Environment

Given a group of heterogeneous blade servers in a cloud computing environment or a data center of a cloud computing provider, each having its own size and speed and its own amount of preloaded special tasks, we are facing the problem of optimal distribution of generic tasks over these blade servers, such that the average response time of generic tasks is minimized. Such performance optimization is important for a cloud computing provider to efficiently utilize all the available resources and to deliver the highest quality of service. We develop a queueing model for a group of heterogeneous blade servers, and formulate and solve the optimal load distribution problem of generic tasks for multiple heterogeneous blade servers in a cloud computing environment in two different situations, namely, special tasks with and without higher priority. Extensive numerical examples and data are demonstrated and some important observations are made. It is found that server sizes, server speeds, task execution requirement, and the arrival rates of special tasks all have significant impact on the average response time of generic tasks, especially when the total arrival rate of generic tasks is large. It is also found that the server size heterogeneity and the server speed heterogeneity do not have much impact on the average response time of generic tasks. Furthermore, larger (smaller, respectively) heterogeneity results in shorter (longer, respectively) average response time of generic tasks.