Replica Placement and Access Policies in Tree Networks

In this paper, we discuss and compare several policies to place replicas in tree networks, subject to server capacity and Quality of Service (QoS) constraints. The client requests are known beforehand, while the number and location of the servers are to be determined. The standard approach in the literature is to enforce that all requests of a client be served by the closest server in the tree. We introduce and study two new policies. In the first policy, all requests from a given client are still processed by the same server, but this server can be located anywhere in the path from the client to the root. In the second policy, the requests of a given client can be processed by multiple servers. One major contribution of this paper is to assess the impact of these new policies on the total replication cost. Another important goal is to assess the impact of server heterogeneity. In this paper, we establish several new complexity results, and provide several efficient polynomial heuristics for NP-complete instances of the problem. The absolute performance of these heuristics is assessed by comparison with the optimal solution provided by the formulation of the problem in terms of the solution of an integer linear program.     

基于云平台的高并发WebGIS服务

随着Internet技术的发展,传统的WebGIS服务器在面对大用户群高并发访问时会出现服务延迟甚至拒绝服务的现象.针对该问题,本文提出了一种基于云平台的应对高并发的WebGIS服务器架构.架构使用云平台为WebGIS服务器提供弹性计算和存储资源,并从负载均衡、缓存设计、数据库集群三方面缓解高并发瓶颈.选择开源服务器端软件GeoServer作为WebGIS应用实验部署.实验数据表明使用缓存机制明显降低了WebGIS服务响应时间.与单物理服务器相比,云平台WebGIS服务器集群架构能够有效处理高并发请求,且随着集群规模扩展,云平台WebGIS系统能够获得良好的加速比.     

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.     

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.     

移动边缘计算中资源受限的动态服务部署策略

移动边缘计算（MEC）的出现使移动用户能够以低延迟访问部署在边缘服务器上的服务。然而,MEC仍然存在各种挑战,尤其是服务部署问题。边缘服务器的数量和资源通常是有限的,只能部署数量有限的服务;此外,用户的移动性改变了不同服务在不同地区的流行度。在这种情况下,为动态请求部署合适的服务就成为一个关键问题。针对该问题,通过了解动态用户请求来部署适当的服务以最小化交互延迟,将服务部署问题表述为一个全局优化问题,并提出了一种基于集群划分的资源聚合算法,从而在计算、带宽等资源约束下初步部署合适的服务。此外,考虑动态用户请求对服务流行度及边缘服务器负载的影响,开发了动态调整算法来更新现有服务,以确保服务质量（QoS）始终满足用户期望。通过一系列仿真实验验证了所提出策略的性能。仿真结果表明,与现有基准算法相比,所提出的策略可以降低服务交互延迟并实现更稳定的负载均衡。     

A generic tool to federate WAN servers

On a Wide Area Network (WAN), services are more efficient if they are supported by several servers located near their respective clients. In this article, we present a generic tool which facilitates the federation of servers over a WAN. This tool is in charge of both dynamically managing the federation and propagating requests to all the federation’s servers. It may be applied to any kind of widely available service. As far as we know, there is no generic tool that can be used by WAN services to federate their servers. We present the benefits these services could derive from our federation tool. The federation adapts dynamically in case of server addition, server failure and network topology modification. Each server has a global view of the federation which is used especially to propagate requests to the federation. We present the general model and the design of the tool. The tool is built upon group communication objects. And we present some implementation issues in a CORBA (Common Object Request Broker Architecture) environment. The tool is generic in that it may be applied to any service. In this paper we show how a specific service may use the generic federation tool especially to propagate typed requests. Finally, we present an application of this tool for federating CORBA traders.     

On the Design of Distributed Object Placement and Load Balancing Strategies in Large-Scale Networked Multimedia Storage Systems

In a large-scale multimedia storage system (LMSS) where client requests for different multimedia objects may have different demands, the placement and replication of the objects is an important factor, as it may result in an imbalance in server loading across the system. Since replication management and load balancing are all the more crucial issues in multimedia systems, in the literature, these problems are handled by centralized servers. Each object storage server (OSS) responds to the requests that come from the centralized servers independently and has no communication with other OSSs in the system. In this paper, we design a novel distributed load balancing strategy for LMSS, in which OSSs can cooperate to achieve higher performance. Such OSS modeled as an M/G/m system can replicate the objects to and balance the requests among other servers to achieve a near-optimal average waiting time (AWT) of the requests in the system. We validate the performance of the system via rigorous simulations with respect to several influencing factors and prove that our proposed strategy is scalable, flexible, and efficient for real-life applications.     

Keta中的Socket复用技术

随着信息技术的不断发展,提高Web服务器的性能是个亟待解决的问题。内核级Web Server的实现有效地提高了Web服务器的处理能力。然而,目前的内核级Web服务器通常只处理静态请求,而动态请求需要转发到外部传统Web服务器上进行处理。本文针对一种内核级非对称多线程流水线服务器——Keta,研究并改进了动态请求的转发机制,提出了利用Socket复用技术来实现转发的策略,有效地提高了该服务器的效率。     

Optimizing server placement in hierarchical grid environments

In this paper, we address some problems related to server placement in Grid environments. Given a hierarchical network with requests from clients and constraints on server capability, the minimum server placement problem attempts to place the minimum number of servers that satisfy requests from clients. Instead of using a heuristic approach, we propose an optimal algorithm based on dynamic programming to solve the problem. We also consider the balanced server placement problem, which tries to place a given number of servers appropriately so that their workloads are as balanced as possible. We prove that an optimal server placement can be achieved by combining the above algorithm with a binary search on workloads. This approach can be further extended to deal with constrains on network capability. The simulation results clearly show the improvement in the number of servers and the maximum workload. Furthermore, as the maximum workload is reduced, the waiting time is reduced accordingly.

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     

基于多代理的容忍入侵体系结构

该文提出了一种基于多代理(Agent)技术的容忍入侵体系结构。通过在系统组件中引入一定的冗余度,将冗余和多样性技术相结合,利用门限秘密共享技术将秘密信息分布于多个系统组件上来达到容忍入侵的目的。通过分布在每个服务器上的多个代理,建立一个容忍入侵的安全内核,重要信息通过安全内核来传递,保证系统关键部件为合法用户提供连续和可靠的基本服务。该方案采用系统整体安全策略,将容忍入侵与多代理的入侵检测相结合,使系统具有更好的实用性和可生存性。     

Replication for Load Balancing and Hot-Spot Relief on Proxy Web Caches with Hash Routing

Hash routing is an emerging approach to coordinating a collection of collaborative proxy caches. Hash routing partitions the entire URL space among the proxy caches. Each partition is assigned to a cache server. Duplication of cache contents is eliminated. Client requests to a cache server for non-assigned-partition objects are forwarded to proper sibling caches. In the presence of access skew, the load level of the cache servers can be quite unbalanced, limiting the benefits of hash routing.We examine an adaptable controlled replication (ACR) of non-assigned-partition objects in each cache server to reduce the load imbalance and relieve the problem of hot-spot references. Trace-driven simulations are conducted to study the effectiveness of ACR. The results show that (1) access skew exists, and the load of the cache servers tends to be unbalanced in hash routing; (2) with a relatively small amount of ACR, say 10% of the cachesize, significant improvements in load balance can be achieved; (3) ACR provides a very effective remedy for load imbalance due to hot-spot references; and (4) increasing the cache size does not improve load balance unless replication is allowed.     

基于服务类型的负载平衡中间件模型

数字图书馆会出现用户请求响应时间较长、服务器资源消耗较大以及集群环境中各服务器性能不同导致的网络负载不平衡问题。为解决上述问题,提出基于服务类型的负载平衡中间件模型。该模型考虑不同的服务请求对服务器负载状况的影响,结合各服务器的性能进行动态负载调度。性能测试结果表明,该中间件负载平衡效果良好,可用性与可靠性较高。     

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.     

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.     

Load balancing to adjust for proximity in some network topologies

Each job scheduler in large decentralized load balancing systems generally must consider whether it is advantageous to offload jobs to remote computation servers when the local load is too high. Although processing power may appear to be available at a very distant server, two problems arise due to the transmission delay between the scheduler and server. Predictably, the response time of the job is adversely affected as the job spends valuable time in transit, but a more subtle problem involves the value, or reliability, of the state information regarding job queues. The longer the delay between scheduler and server, the less a scheduler should value the state information of the server (given that the state changes over time). We examine the performance of schedulers in topologies with different average proximity and show a probabilistic algorithm that allows schedulers to dynamically form efficient clusters in the network.     

基于请求类型的数字图书馆负载平衡模型

对于具有海量信息的数字图书馆,文章针对不同请求服务时间变化大的特性,提出基于请求类型的负载平衡模型,该模型根据各种不同请求对服务器负载状况的影响并结合各服务器的性能进行动态负载调度。避免占用服务器资源较多的请求同时分配到同一台服务器上。仿真结果表明该模型具有良好的负载平衡效果。有效地缩短了用户请求的响应时间。     

An efficient location reporting and indexing framework for urban road moving objects

The tracking of moving objects consists of two critical operations: location reporting, in which moving objects (or clients) send their locations to centralized servers, and index maintenance, through which centralized servers update the locations of moving objects. In existing location reporting techniques, each moving object reports its locations to servers by utilizing long-distance links such as 3G/4G. Corresponding to this location reporting strategy, servers need to respond to all the location updating requests from individual moving objects. Such techniques suffer from very high communication cost (due to the individual reporting using long-distance links) and high index update I/Os (due to the massive amount of location updating requests). In this paper, we present a novel Group-movement based location Reporting and Indexing (GRI) framework for location reporting (at moving object side) and index maintenance (at server side). In the GRI framework, we introduce a novel location reporting strategy which allows moving objects to report their locations to servers in a group (instead of individually) by aggregating the moving objects that share similar movement patterns through wireless local links (such as WiFi). At the server side, we present a dual-index, Hash-GTPR-tree (H-GTPR), to index objects sharing similar movement patterns. Our experimental results on synthetic and real data sets demonstrate the effectiveness and efficiency of our new GRI framework, as well as the location reporting strategy and the H-GTPR tree index technique.     

Scheduling to maximize participation

We study a problem of scheduling client requests to servers. Each client has a particular latency requirement at each server and may choose either to be assigned to some server in order to get serviced provided that her latency requirement is met, or not to participate in the assignment at all. From a global perspective, in order to optimize the performance of such a system, one would aim to maximize the number of clients that participate in the assignment. However, clients may behave selfishly in the sense that, each of them simply aims to participate in an assignment and get serviced by some server where her latency requirement is met with no regard to overall system performance. We model this selfish behavior as a strategic game, show how to compute pure Nash equilibria efficiently, and assess the impact of selfishness on system performance. We also show that the problem of optimizing performance is computationally hard to solve, even in a coordinated way, and present efficient approximation and online algorithms.     

File distribution using a peer-to-peer network––a simulation study

As the Internet became widespread, people saw it as a way of distributing content. But while the average bandwidth capacity is increasing, users around the world are trying to share more data. Although the servers are able to acquire more bandwidth, they cannot keep up with the rapidly increasing requests of the users. Several systems appeared that alleviate the server from the dissemination process. But such systems are not always suitable for disseminating highly anticipated files of considerable size. The evolution of peer-to-peer systems gave a new way of attacking this problem. Each user can assist the dissemination process by acting as a server as well. In this paper the way traditional systems meet users' demands is demonstrated, and simulation results of a peer-to-peer approach based on a mobile agent platform are presented. Our focus is on how different parameters can affect the dissemination process of a highly anticipated file in this network.