Zegura, Ellen W.

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Selecting Among Replicated Adaptive Multicast Servers

2000 , Ammar, Mostafa H. , Zegura, Ellen W. , Fei, Zongming

Server replication and multicasting are well-established techniques for increasing capacity of a networked service and improving client performance. In this paper, we consider the combination of these two techniques. Specifically, we investigate the problem of selecting amongst rate-adaptive multicast servers, which adjust their sending rate based on network conditions and/or feedback from clients. Effective server rate adaptation can lead to efficient utilization of network resources and performance improvement perceived by clients. In this initial study of adaptive multicast server selection, we explore some fundamental issues and study the implications of different selection strategies on the performance perceived by clients. We first define the Static Multicast Selection Problem, in which there are static sets of clients and servers, and one needs to establish a set of multicast trees with one tree for each server. We explore several optimization problems based on different performance measures. We prove that the general problem is NP-hard and then present two interesting special cases with an optimal polynomial-time solution in each case. We design a heuristic for the general case and show that it can improve the performance over some simple strategies. We also consider the Dynamic Multicast Selection Problem, in which clients may join and leave multicast trees already established. We design a heuristic for this dynamic case by which clients can select a tree to join. We investigate the performance of the heuristic through simulation.

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Application-Layer Anycasting

1996 , Ammar, Mostafa H. , Zegura, Ellen W. , Shah, Viren , Fei, Zongming , Bhattacharjee, Samrat

Server replication is a key approach for maintaining user-perceived quality of service within a geographically wide-spread network. The anycasting communication paradigm is designed to support server replication by allowing applications to easily select and communicate with the "best" server, according to some performance or policy criteria, in a group of content- equivalent servers. We examine the definition and support of the anycasting paradigm at the application layer, providing a service that maps anycast domain names into one or more IP addresses using anycast resolvers. In addition to being independent from network-layer support, our definition includes the notion of filters, functions that are applied to groups of addresses to affect the selection process. We consider both metric-based filters (e.g., server response time) and policy-based filters; we further allow filtering both at the anycast resolver and local to the anycast client. A key input to the filtering process is metric information describing the relative performance of replicated servers. We examine the use of various techniques for maintaining this information at anycast resolvers.

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Optimal Allocation of Clients to Replicated Multicast Servers

1999 , Ammar, Mostafa H. , Zegura, Ellen W. , Fei, Zongming

Server replication is an approach that is often used to improve the scalability of a service. One of the important factors in the efficient utilization of replicated servers is the ability to direct client requests to the `best" server, according to some optimality criteria. Recently, there have been several proposals for multicast services in which a server delivers information to multiple clients simultaneously. Such proposals include multicasting of web content, multicast-based video services (on-demand and pay-per-view style services), multicasting of database content and broadcast disks. The goal of many of these proposals is to use multicast to enhance the ability of the service to handle a large number of clients economically. Multicast servers may be replicated for several reasons: to distribute the load among on-demand multicast servers, to balance the `feedback" load on the servers or on entities along the multicast tree from the servers, or to select the server that is at the root of the `best" multicast routing tree. In this paper we first give a definition of the static multicast server selection problem, in which we assume a set of static clients and multicast servers and consider how one might produce an optimalallocation of the clients to the servers. We propose a transformation method for deriving multicast server selection algorithms from the traditional multicast routing algorithms. To investigate the dynamic behavior of client join and leave and the cost incurred during the process, we next define the dynamic multicast server selection problem, in which the potential clients join and leave the multicast session dynamically, and the goal is to produce an optimal allocation of clients to servers with an emphasis on how this allocation behaves over time. We formulate the problem as a Markovian Decision Process (MDP) and analyze the tradeoff between the cost of the multicast tree(s) and the transition cost of establishing and removing links from the tree(s). We also explore the effect of join/leave frequency on optimal policy. Our analysis leads to two heuristics which we use to propose a selection algorithm. The algorithm uses a very simple join and leave strategy yet still can generate low cost trees. Our simulation compares the performance of our proposed algorithm with various other multicast server selection algorithms.

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A Novel Server Selection Technique for Improving the Response Time of a Replicated Service

1997 , Bhattacharjee, Samrat , Zegura, Ellen W. , Ammar, Mostafa H. , Fei, Zongming

Server replication is an approach often used to improve the ability of a service to handle a large number of clients. One of the important factors in the efficient utilization of replicated servers is the ability to direct client requests to the best server, according to some optimality criteria. In this paper we target an environment in which servers are distributed across the Internet, and clients identify servers using our application-layer anycasting service. Our goal is to allocate servers to clients in a way that minimizes a client's response time. To that end, we develop an approach for estimating the performance that a client would experience when accessing particular servers. Such information is maintained in a resolver that clients can query to obtain the identity of the server with the best response time. Our performance collection technique combines server push with client probes to estimate the expected response time. A set of experiments is used to demonstrate the properties of our performance determination approach and to show its advantages when used within the application-layer anycasting architecture.