Yunyue Lin

Complexity analysis and algorithm design for advance bandwidth scheduling in dedicated networks

An increasing number of high-performance networks provision dedicated channels through circuit switching or MPLS/GMPLS techniques to support large data transfer. The link bandwidths in such networks are typically shared by multiple users through advance reservation, resulting in varying bandwidth availability in future time. Developing efficient scheduling algorithms for advance bandwidth reservation has become a critical task to improve the utilization of network resources and meet the transport requirements of application users. We consider an exhaustive combination of different path and bandwidth constraints and formulate four types of advance bandwidth scheduling problems, with the same objective to minimize the data transfer end time for a given transfer request with a prespecified data size: fixed path with fixed bandwidth (FPFB); fixed path with variable bandwidth (FPVB); variable path with fixed bandwidth (VPFB); and variable path with variable bandwidth (VPVB). For VPFB and VPVB, we further consider two subcases where the path switching delay is negligible or nonnegligible. We propose an optimal algorithm for each of these scheduling problems except for FPVB and VPVB with nonnegligible path switching delay, which are proven to be NP-complete and nonapproximable, and then tackled by heuristics. The performance superiority of these heuristics is verified by extensive experimental results in a large set of simulated networks in comparison to optimal and greedy strategies.

Functional Cohesion of Gene Sets Determined by Latent Semantic Indexing of PubMed Abstracts

High-throughput genomic technologies enable researchers to identify genes that are co-regulated with respect to specific experimental conditions. Numerous statistical approaches have been developed to identify differentially expressed genes. Because each approach can produce distinct gene sets, it is difficult for biologists to determine which statistical approach yields biologically relevant gene sets and is appropriate for their study. To address this issue, we implemented Latent Semantic Indexing (LSI) to determine the functional coherence of gene sets. An LSI model was built using over 1 million Medline abstracts for over 20,000 mouse and human genes annotated in Entrez Gene. The gene-to-gene LSI-derived similarities were used to calculate a literature cohesion p-value (LPv) for a given gene set using a Fishers exact test. We tested this method against genes in more than 6,000 functional pathways annotated in Gene Ontology (GO) and found that approximately 75% of gene sets in GO biological process category and 90% of the gene sets in GO molecular function and cellular component categories were functionally cohesive (LPv<0.05). These results indicate that the LPv methodology is both robust and accurate. Application of this method to previously published microarray datasets demonstrated that LPv can be helpful in selecting the appropriate feature extraction methods. To enable real-time calculation of LPv for mouse or human gene sets, we developed a web tool called Gene-set Cohesion Analysis Tool (GCAT). GCAT can complement other gene set enrichment approaches by determining the overall functional cohesion of data sets, taking into account both explicit and implicit gene interactions reported in the biomedical literature. Availability GCAT is freely available at http://binf1.memphis.edu/gcat

Automation and management of scientific workflows in distributed network environments

Large-scale computation-intensive applications in various science fields feature complex DAG-structured workflows comprised of distributed computing modules with intricate inter-module dependencies. Supporting such workflows in heterogeneous network environments and optimizing their end-to-end performance are crucial to the success of large-scale collaborative scientific applications. We design and develop a generic Scientific Workflow Automation and Management Platform (SWAMP), which contains a set of easy-to-use computing and networking toolkits for application scientists to conveniently assemble, execute, monitor, and control complex computing workflows in distributed network environments. The current version of SWAMP integrates the graphical user interface of Kepler to compose abstract workflows and employs Condor DAGMan for workflow dispatch and execution. SWAMP provides a web-based user interface to automate and manage workflow executions and uses a special workflow mapper to optimize the end-to-end workflow performance. A case study of the workflow for Spallation Neutron Source datasets in real networks is presented to show the efficacy of the proposed platform.

On design of bandwidth scheduling algorithms for multiple data transfers in dedicated networks

The significance of high-performance dedicated networks has been well recognized due to the rapidly increasing number of large-scale applications that require high-speed data transfer. Efficient algorithms are needed for path computation and bandwidth scheduling in dedicated networks to improve the utilization of network resources and meet diverse user requests. We consider two periodic bandwidth scheduling problems: multiple data transfer allocation (MDTA) and multiple fixed-slot bandwidth reservation (MFBR), both of which schedule a number of user requests accumulated in a certain period. MDTA is to assign multiple data transfer requests on several pre-specified network paths to minimize the total data transfer end time, while MFBR is to satisfy multiple bandwidth reservation requests, each of which specifies a bandwidth and a time slot. For MDTA, we design an optimal algorithm and provide its correctness proof; for MFBR, we prove it to be NP-complete and propose a heuristic algorithm, Minimal Bandwidth and Distance Product Algorithm (MBDPA). Extensive simulation results illustrate the performance superiority of the proposed MBDPA over a greedy heuristic approach and provide valuable insight into the advantage of periodic bandwidth scheduling over instant bandwidth scheduling.

On design of scheduling algorithms for advance bandwidth reservation in dedicated networks

There are an increasing number of high- performance networks that provision dedicated channels through circuit-switching or MPLS/GMPLS techniques to support large- scale data transfer. The available bandwidths on these dedicated links vary over time and therefore efficient bandwidth scheduling algorithms are needed to improve the utilization of network resources and satisfy diverse user requirements. Based on different path and bandwidth constraints, we formulate four instant scheduling problems for a data transfer request: (i) variable path with variable bandwidth (VPVB), (ii) fixed path with variable bandwidth (FPVB), (iii) variable path with fixed bandwidth (VPFB), and (iv) fixed path with fixed bandwidth (FPFB), with the common objective to minimize transfer end time for a given data size. We design an optimal algorithm for each of these scheduling problems with polynomial- or pseudo- polynomial-time complexity with respect to the network size and total number of time slots in a bandwidth reservation table.

Path Computation with Variable Bandwidth for Bulk Data Transfer in High-Performance Networks

There are an increasing number of high-performance networks that provision dedicated channels through circuit-switching or MPLS/GMPLS techniques to support bulk data transfer in large-scale science or e-commerce applications. These dedicated links are typically shared by multiple users through advance reservations, resulting in varying bandwidth availability in future time periods. Therefore, efficient advance bandwidth reservation algorithms are needed to improve the utilization of network resources and meet the transport requirements of application users. We investigate the bandwidth-oriented path computation problem for two types of data transfer: (i) fixed path with variable bandwidth and (ii) variable path with variable bandwidth to minimize the transfer end time of a given data size. We prove that both problems are NP-complete and propose a heuristic algorithm for each of them. Extensive simulation results illustrate the performance superiority of the proposed heuristics over methods based on greedy strategies.

Monitoring security events using integrated correlation-based techniques

We propose an adaptive cyber security monitoring system that integrates a number of component techniques to collect time-series situation information, perform intrusion detection, and characterize and identify security events so corresponding defense actions can be taken in a timely and effective manner. We employ a decision fusion algorithm with analytically proven performance guarantee for intrusion detection based on local votes from distributed sensors. The security events in the proposed system are represented as forms of correlation networks using random matrix theory and identified through the computation of network similarity measurement. Extensive simulation results on event identification illustrate the efficacy of the proposed system.

On Tree Construction of Super Peers for Hybrid P2P Live Media Streaming

This paper considers a hybrid hierarchical P2P overlay network structure that consists of both super and normal peers. The media streaming architecture is built upon a tree-structured network of super peers and the tree construction process has a significant impact on the overall system performance. We build network cost models and formulate a specific type of problem to maximize the minimum node throughput in Tree Construction (max-minTC), which aims at optimizing the systems stream rate by constructing an efficient spanning tree among super peers. We consider two scenarios: (i) When the overlay network has an arbitrary topology, we prove max-minTC to be NP-complete by reducing from the Degree Constrained Spanning Tree problem and propose an efficient heuristic algorithm. The performance superiority of the proposed algorithm is justified by experimental results collected by a live media streaming system deployed in real networks and is also illustrated by extensive simulations performed on a large set of simulated networks of various sizes from small to large scales in comparison with other methods, (ii) When the topology of the overlay network is complete, we rigorously prove that the same heuristic algorithm yields an optimal solution.

Visualization of security events using an efficient correlation technique

The timely and reliable data transfer required by many networked applications necessitates the development of comprehensive security solutions to monitor and protect against an increasing number of malicious attacks. However, providing complete cyber space situation awareness is extremely challenging because of the lack of effective translation mechanisms from low-level situation information to high-level human cognition for decision making and action support. We propose an adaptive cyber security monitoring system that integrates a number of component techniques to collect time-series situation information, perform intrusion detection, keep track of event evolution, characterize and identify security events, and present a visual representation in order to provide comprehensive situational view so that corresponding defense actions can be taken in a timely and effective manner. We explore the principles of designing and applying appropriate visualization techniques for situation monitoring by defining graphical representations of security events. This differs from the traditional rule-based pattern matching techniques in that security events in the proposed system are represented as forms of correlation networks using random matrix theory and identified through the computation of network similarity measurement. The events and corresponding event types are visualized using a stemplot to show location and quantity. Extensive simulation results on event identification illustrate the efficacy of the proposed system.

Energy-Conserving Dynamic Routing in Multi-sink Heterogeneous Sensor Networks

The success of various sensor network applications highly depends on the use of energy-conserving routing methods to prolong network lifetime. We present a heterogeneous sensor network architecture consisting of a small number of high-end sensors serving as sink nodes and a large number of low-end sensors responsible for environmental sensing. We propose a path bottleneck-oriented and energy cost-based routing scheme that uses a multi-objective function to optimize the balance of network-wide energy consumption. Within this routing scheme, we design (i) a centralized version for sensor networks with global topology information, (ii) a semi-distributed version to further improve energy efficiency, and (iii) a fully distributed version to support large-scale sensor networks. Extensive simulation results show that the proposed routing scheme outperforms existing routing algorithms in prolonging the lifetime of sensor networks.