Receiver synchronization of continuous media streams is required to deal with delay differences and variations resulting from delivery over packet networks such as the Internet. This function is commonly provided using per-stream playout buffers which introduce additional delay in order to produce a playout schedule which meets the synchronization requirements. Packets which arrive after their scheduled playout time are considered late and are discarded. In this paper, we present the Concord algorithm, which provides a delay-sensitive solution for playout buffering. It records historical information and uses it to make short-term predictions about network delay with the aim of not reacting too quickly to short-lived delay variations. This allows an application-controlled tradeoff of packet lateness against buffering delay, suitable for applications which demand low delay but can tolerate or conceal a small amount of late packets. We present a selection of results from an extensive evaluation of Concord using Internet traffic traces. We explore the use of aging techniques to improve the effectiveness of the historical information and hence the delay predictions. The results show that Concord can produce significant reductions in buffering delay and delay variations at the expense of packet lateness values of less than 1%. Keywords--- multimedia stream synchronization, playout buffering. I.

Abstract: In this paper, a new dynamic data allocation algorithm for non-replicated distributed database systems (DDS), namely the threshold algorithm, is proposed. The threshold algorithm reallocates data with respect to changing data access patterns. The algorithm is analyzed for a fragment using simulation. The threshold algorithm is especially suitable for a DDS where data access pattern changes dynamically.

Wide-area sensing services enable users to query data collected from multitudes of widely distributed sensors. In this paper, we consider the novel distributed database workload characteristics of these services, and present IDP, an online, adaptive data placement and replication system tailored to this workload. Given a hierarchical database, IDP automatically partitions it among a set of networked hosts, and replicates portions of it. IDP makes decisions using measurements of access locality within the database, read and write load for individual objects within the database, proximity between queriers and potential replicas, and total load on hosts participating in the database. Our evaluation of IDP under real and synthetic workloads, including flash crowds of queriers, demonstrates that in comparison with previously studied replica placement techniques, IDP reduces average response times for user queries by up to a factor of 3 and reduces network traffic for queries, updates, and data movements by up to an order of magnitude. 1

We present an algorithm to migrate multidimensional data in a non-replicated distributed environment. Our proposed algorithm is intented to improve query performance for mobile objects. Our algorithm automatically detects access pattern changes and migrates portions of the data from current sites of residence to sites recently accessing the data most frequently, thus reducing remote communication costs. We assume the data is indexed by an R-tree multidimensional index and that a global R-tree is used to locate and retrieve portions of the data set. We present a distributed R-tree structure and experimentally explore a specic access detection and migration mechanism. Our experimental results show that when compared to the no-migration case, and depending on network speed, our migration scheme may result in a query time reduction of a few percent to an order of magnitude. 1. INTRODUCTION In this paper we present a new algorithm to automatically migrate multidimensional data among site...

In a distributed system, long-running distributed services are often confronted with changes to the configuration of the underlying hardware. If the service is required to be highly available, the service needs to deal with the problem of adapting to these changes while still providing its service. This problem is increased further if multiple changes can occur concurrently. In this paper, we describe a method that solves this problem by carefully shipping data and forwarding requests to appropriate hosts. Our method specifically enables the distributed service to deal with concurrent changes in a concurrent fashion, thereby promoting the efficiency of the service.

In distributed database systems, tables are frequently fragmented over a number of sites in order to reduce access costs. How to fragment and how to allocate the fragments to the sites is a challenging problem that has previously been solved either by static fragmentation and allocation, or based on query analysis. Many emerging applications of distributed database systems generate very dynamic workloads with frequent changes in access patterns from different sites. In those contexts, continuous refragmentation and reallocation can significantly improve performance. In this paper we present DYTAF, a decentralized approach for dynamic table fragmentation and allocation in distributed database systems based on observation of the access patterns of sites to tables. The approach performs fragmentation and reallocation based on recent access history, aiming at being able to maximize the number local accesses compared to accesses from remote sites. Through simulations we show that the approach gives close to optimal performance for typical access patterns and thus demonstrate the feasibility of our approach. 2 1

the date of receipt and acceptance should be inserted later Abstract In distributed database systems, tables are frequently fragmented and replicated over a number of sites in order to reduce network communication costs. How to fragment, when to replicate and how to allocate the fragments to the sites are challenging problems that has previously been solved either by static fragmentation, replication and allocation, or based on a priori query analysis. Many emerging applications of distributed database systems generate very dynamic workloads with frequent changes in access patterns from different sites. In such contexts, continuous refragmentation and reallocation can significantly improve performance. In this paper we present DYFRAM, a decentralized approach for dynamic table fragmentation and allocation in distributed database systems based on observation of the access patterns of sites to tables. The approach performs fragmentation, replication, and reallocation based on recent access history, aiming at maximizing the number of local accesses compared to accesses from remote sites. We show through simulations and experiments on the DASCOSA distributed database system that the approach significantly reduces communication costs for typical access patterns, thus demonstrating the feasibility of our approach.

Abstract. In a distributed system, long-running distributed services are often confronted with changes to the configuration of the underlying hardware. If the service is required to be highly available, the service needs to deal with the problem of adapting to these changes while still providing its service. This problem is increased further if multiple changes can occur concurrently. In this paper, we describe a method that solves this problem by carefully shipping data and forwarding requests to appropriate hosts. Our method specifically enables the distributed service to deal with concurrent changes in a concurrent fashion, thereby promoting the efficiency of the service. 1.