Scalable streaming for heterogeneous clients
Periodic broadcast protocols enable the efficient streaming of highly popular media files to large numbers of concurrent clients. Most previous periodic broadcast protocols, however, assume that all clients can receive at the same rate, and also assume that available bandwidth is not time-varying. In this paper, we first develop a new periodic broadcast protocol, Optimized Heterogeneous Periodic Broadcast (OHPB), that can be optimized for a given population of clients with heterogeneous reception bandwidths and quality-of-service requirements. The OHPB protocol utilizes an optimized segment size progression determined by solving a linear optimization model that takes as input the client population characteristics and an objective function such as mean client startup delay. We then propose complementary client protocols employing work-ahead buffering of data during playback, so as to enable more uniform playback quality when the available bandwidth is time-varying.

title: On achieving optimal throughput with network coding
abstract: With the constraints of network topologies and link capacities, achieving the optimal end-to-end throughput in data networks has been known as a fundamental but computationally hard problem. In this paper, we seek efficient solutions to the problem of achieving optimal throughput in data networks, with single or multiple unicast, multicast and broadcast sessions. Although previous approaches lead to solving NP-complete problems, we show the surprising result that, facilitated by the recent advances of network coding, computing the strategies to achieve the optimal end-to-end throughput can be performed in polynomial time. This result holds for one or more communication sessions, as well as in the overlay network model. Supported by empirical studies, we present the surprising observation that in most topologies, applying network coding may not improve the achievable optimal throughput; rather, it facilitates the design of significantly more efficient algorithms to achieve such optimality.

title: Efficient and distributed computation of maximum multicast rates
abstract: The transmission of information within a data network is constrained by network topology and link capacities. In this paper, we study the fundamental upper bound of information multicast rates with these constraints, given the unique replicable and encodable property of information flows. Based on recent information theory advances in coded multicast rates, we are able to formulate the maximum multicast rate problem as a linear network optimization problem, assuming the general undirected network model. We then proceed to apply Lagrangian relaxation techniques to obtain (1) a necessary and sufficient condition for multicast rate feasibility, and (2) a subgradient solution for computing the maximum rate and the optimal routing strategy to achieve it. The condition we give is a generalization of the well-known conditions for the unicast and broadcast cases. Our subgradient solution takes advantage of the underlying network flow structure of the problem, and therefore outperforms general linear programming solving techniques. It also admits a natural intuitive interpretation, and is amenable to fully distributed implementations.

title: A High-Throughput Overlay Multicast Infrastructure with Network Coding
abstract: Network coding has been recently proposed in information theory as a new dimension of the information multicast problem that helps achieve optimal transmission rate or cost. End hosts in overlay networks are natural candidates to perform network coding, due to its available computational capabilities. In this paper, we seek to bring theoretical advances in network coding to the practice of high-throughput multicast in overlay networks. We have completed the first real implementation of network coding in end hosts, as well as decentralized algorithms to construct the routing strategies and to perform random code assignment. Our experiences suggest that approaching maximum throughput with network coding is not only theoretically sound, but also practically promising. We also present a number of unique challenges in designing and realizing coded data dissemination, and corresponding solution techniques to address them.

title: sFlow: towards resource-efficient and agile service federation in service overlay networks
abstract: Existing research work towards the composition of complex federated services has assumed that service requests and deliveries flow through a particular service path or tree. Here, we extend such a service model to a directed acyclic graph, allowing services to be delivered via parallel paths and interleaved with each other. Such an assumption of the service flow model has apparently introduced complexities towards the development of a distributed algorithm to federate existing services, as well as the provisioning of the required quality in the most resource-efficient fashion. To this end, we propose sFlow, a fully distributed algorithm to be executed on all service nodes, such that the federated service flow graph is resource efficient, performs well, and meets the demands of service consumers.

title: Finding roots of unity among quotients of the roots of an integral polynomial
abstract: We present an efficient algorithm for testing whether a given integral polynomial has two distinct roots a, B such that fflp is a root of unity. The test is based on results obtained by investigation of the structure of the splitting field of the polynomial. By this investigate ion, we found also an improved bound for the least common multiple of the orders of roots of unity appearing as quotients of distinct roots.

title: QMBF: a QoS-aware multicast routing protocol
abstract: Many multicast applications, such as video-on-demand and tele-education, desire quality of service (QoS) support from the underlying network. Recently, many QoS-based multicast protocols have been proposed to meet this requirement. However, few of them can achieve high success ratios while maintaining good scalability. In this paper, we propose a new QoS-aware multicast protocol based on a bounded flooding technique (QMBF). In this protocol, every network node maintains the knowledge of a local network cell (LNC) topology as well as QoS state information (collected from the bounded flooding messages). QMBF utilizes this knowledge to increase the probability and accuracy of finding a feasible branch which connects a new member to an existing multicast tree. The protocol exploits two approaches to find feasible branches: repeatedly computing partial feasible branches using the LNC information and searching multiple paths. The design of QMBF allows it to operate on top of any unicast routing protocol or cooperate with a QoS-based unicast routing protocol. Results obtained using NS-2 simulator demonstrate high success ratio for locating feasible branches for multicast paths.

title: Haplotype reconstruction from SNP fragments by minimum error correction
abstract: Haplotype reconstruction based on aligned single nucleotide polymorphism (SNP) fragments is to infer a pair of haplotypes from localized polymorphism data gathered through short genome fragment assembly. An important computational model of this problem is the minimum error correction (MEC) model, which has been mentioned in several literatures. The model retrieves a pair of haplotypes by correcting minimum number of SNPs in given genome fragments coming from an individual's DNA.



In the first part of this paper, an exact algorithm for the MEC model is presented. Owing to the NP-hardness of the MEC model, we also design a genetic algorithm (GA). The designed GA is intended to solve large size problems and has very good performance. The strength and weakness of the MEC model are shown using experimental results on real data and simulation data. In the second part of this paper, to improve the MEC model for haplotype reconstruction, a new computational model is proposed, which simultaneously employs genotype information of an individual in the process of SNP correction, and is called MEC with genotype information (shortly, MEC/GI). Computational results on extensive datasets show that the new model has much higher accuracy in haplotype reconstruction than the pure MEC model.

title: Reducing Energy Consumption of Disk Storage Using Power-Aware Cache Management
abstract: Reducing energy consumption is an important issue for data centers. Among the various components of a data center, storage is one of the biggest consumers of energy. Previous studies have shown that the average idle period for a server disk in a data center is very small compared to the time taken to spin down and spin up. This significantly limits the effectiveness of disk power management schemes. This paper proposes several power-aware storage cache management algorithms that provide more opportunities for the underlying disk power management schemes to save energy. More specifically, we present an off-line power-aware greedy algorithm that is more energy-efficient than Belady’s off-line algorithm (which minimizes cache misses only). We also propose an online power-aware cache replacement algorithm. Our trace-driven simulations show that, compared to LRU, our algorithm saves 16% more disk energy and provides 50% better average response time for OLTP I/O workloads. We have also investigated the effects of four storage cache write policies on disk energy consumption.

title: Position Variance, Recurrence and Perceptual Learning
abstract: Stimulus arrays are inevitably presented at different positions on the retina in visual tasks, even those that nominally require fixation. In particular, this applies to many perceptual learning tasks. We show that perceptual inference or discrimination in the face of positional variance has a structurally different quality from inference about fixed position stimuli, involving a particular, quadratic, non-linearity rather than a purely linear discrimination. We show the advantage taking this non-linearity into account has for discrimination, and suggest it as a role for recurrent connections in area VI, by demonstrating the superior discrimination performance of a recurrent network. We propose that learning the feedforward and recurrent neural connections for these tasks corresponds to the fast and slow components of learning observed in perceptual learning tasks.