Network QoS is essential for supporting real time interactive services such as VoIP. In the differentiated services framework QoS is provided by marking packets to receive preferential forwarding treatment, at each network node. This per hop forwarding treatment at each node may however be ineffective in providing the delay and jitter guarantees unless bounds are also imposed on the number of hops in the service routing paths. Interactive services also require minimal interruptions from network disruptions. Routing on hop limited paths may however have an adverse impact on the proportion of network capacity dedicated for protection to guarantee recovery from failures. In this paper we analyze this tradeoff in the context of MPLS fast reroute when protection capacities are pre-provisioned. We show that the underlying optimization problem is NP hard. We design almost best possible approximation algorithms for the problem and show that they work well in practice as well.

1 Introduction The “How much information?” study produced by the school of information management and systems at the University of California at Berkeley [10], estimates that about 5 exabytes of new information was produced in 2002. It estimates that the amount of stored information doubled in the period between 1999 and 2002. It is believed that more data will be created in the next five years than in the history of the world. Clearly we live in an era of data explosion. This data explosion necessitates the use of large storage systems. Storage Area Networks (or SANs) are the leading [13] infrastructure for enterprise storage.

Broadcasting and multicasting are fundamental operations. In this work we develop algorithms for performing broadcast and multicast in clusters of workstations. In this model, sending a message from one machine to another machine in the same cluster takes 1 time unit, and sending a message to a machine in a different cluster takes C time units. Lowekamp and Beguelin proposed heuristics for this model, but their algorithms may produce broadcast times that are arbitrarily worse than optimal. We develop the first constant factor approximation algorithms for this model. Algorithm LCF (Largest Cluster First) for the basic model is simple, efficient and has a worst case approximation guarantee of 2. We then extend these models to more complex models where we remove the assumption that an unbounded amount of simultaneous communication may happen using the global network. The algorithms for these models build on the LCF method developed for the basic problem. Finally, we develop broadcasting algorithms for the postal model where the sending processor does not block for C time units when the message is in transit. Moreover, we assume that the messages are small and so the bottleneck is the latency.

We consider the problem of collecting a large amount of data from several different hosts to a single destination in a wide-area network. This problem is important since improvements in data collection times in many applications such as wide-area upload applications, high-performance computing applications, and data mining applications are crucial to performance of those applications. Often, due to congestion conditions, the paths chosen by the network may have poor throughput. By choosing an alternate route at the application level, we may be able to obtain substantially faster completion time. This data collection problem is a nontrivial one because the issue is not only to avoid congested link(s), but to devise a coordinated transfer schedule which would afford maximum possible utilization of available network resources. Our approach for computing coordinated data collection schedules makes no assumptions about knowledge of the topology of the network or the capacity available on individual links of the network. This approach provides significant performance improvements under various degrees and types of network congestions. To show this, we give a comprehensive comparison study of the various approaches to the data collection problem which considers performance, robustness, and adaptation characteristics of the different data collection methods. The adaptation to network conditions characteristics are important as the above applications are long lasting, i.e., it is likely changes in network conditions will occur during the data transfer process. In general, our approach can be used for solving arbitrary data movement problems over the Internet. We use the Bistro platform to illustrate one application of our techniques.

Our work is motivated by the problem of managing data on storage devices, typically a set of disks. Such high-demand storage servers are used as Web servers or as multimedia servers for handling high demand for data. As the system is running, it needs to dynamically respond to changes in demand for different data items. In this work we study the data migration problem, which arises when we need to quickly change one storage configuration into another. We show that this problem is NP-hard. In addition, we develop polynomial-time approximation algorithms for this problem and prove a worst-case bound of 9.5 on the approximation factor achieved by our algorithm.

In this paper we consider the problem of collecting a large amount of data from several different hosts to a single destination in a wide-area network. Often, due to congestion conditions, the paths chosen by the network may have poor throughput. By choosing an alternate route at the application level, we may be able to obtain substantially faster completion time. This data collection problem is a nontrivial one because the issue is not only to avoid congested link(s), but to devise a coordinated transfer schedule which would afford maximum possible utilization of available network resources. In this paper we present an approach for computing coordinated data collection schedules, which can result in significant performance improvements. We make no assumptions about knowledge of the topology of the network or the capacity available on individual links of the network, i.e., we only use end-to-end information. Finally, we also study the shortcomings of this approach in terms of the gap between the theoretical formulation and the resulting data transfers in wide-area networks. In general, our approach can be used for solving arbitrary data movement problems over the Internet. We use the Bistro platform to illustrate one application of our techniques.

Our work is motivated by the problem of managing data on storage devices, typically a set of disks. Such high demand storage servers are used as web servers, or multimedia servers for handling high demand for data. As the system is running, it needs to dynamically respond to changes in demand for different data items. In this work we study the data migration problem, which arises when we need to quickly change one storage configuration into another. We show that this problem is NP-hard. In addition, we develop polynomial-time approximation algorithms for this problem and prove a worst case bound of 9.5 on the approximation factor achieved by our algorithm. We also compare the algorithm to several heuristics for this problem.