Designing Efficient Parallel Algorithms for Graph Problems

Graph algorithms are concerned with the algorithmic aspects of solving graph problems. The problems are motivated from and have application to diverse areas of computer science, engineering and other disciplines. Problems arising from these areas of application are good candidates for parallelization since they often have both intense computational needs and stringent response time requirements. Motivated by these concerns, this thesis investigates parallel algorithms for these kinds of graph problems that have at least one of the following properties: the problems involve some type of dynamic updates; the sparsification technique is applicable; or the problems are closely related to communications network issues. The models of parallel computation used in our studies are the Parallel Random Access Machine (PRAM) model and the practical interconnection network models such as meshes and hypercubes. ¶ ..

Progressive Skyline Query Processing in Wireless Sensor Networks

With the further development of sensor techniques in wireless sensor networks (WSNs), it is becoming urgent that they should be able to support complicated queries like skyline query for multi-preference and decision making. In this paper, we consider skyline query evaluation in WSNs by devising evaluation algorithms for finding skyline points on a dataset progressively. The core techniques adopted are to partition the dataset into several disjoint subsets and output the skyline points by examining each subsequent subset progressively, using some of the skyline points obtained so far to filter out those unlikely skyline points in the current processing subset from transmission. We finally conduct extensive experiments by simulations to evaluate the performance of the proposed algorithms on synthetic and real datasets. The experimental results show that the proposed algorithms outperform existing algorithms significantly in network lifetime prolongation

Efficient Truss Maintenance in Evolving Networks

Truss was proposed to study social network data represented by graphs. A k-truss of a graph is a cohesive subgraph, in which each edge is contained in at least k-2 triangles within the subgraph. While truss has been demonstrated as superior to model the close relationship in social networks and efficient algorithms for finding trusses have been extensively studied, very little attention has been paid to truss maintenance. However, most social networks are evolving networks. It may be infeasible to recompute trusses from scratch from time to time in order to find the up-to-date $k$-trusses in the evolving networks. In this paper, we discuss how to maintain trusses in a graph with dynamic updates. We first discuss a set of properties on maintaining trusses, then propose algorithms on maintaining trusses on edge deletions and insertions, finally, we discuss truss index maintenance. We test the proposed techniques on real datasets. The experiment results show the promise of our work

FACH: Fast algorithm for detecting cohesive hierarchies of communities in large networks

Vertices in a real-world social network can be grouped into densely connected communities that are sparsely connected to other groups. Moreover, these communities can be partitioned into successively more cohesive communities. Despite an ever-growing pile of research on hierarchical community detection, existing methods suffer from either inefficiency or inappropriate modeling. Yet, some cut-based approaches have shown to be effective in finding communities without hierarchies. In this paper, we study the hierarchical community detection problem in large networks and show that it is NP-hard. We then propose an efficient algorithm based on edge-cuts to identify the hierarchy of communities. Since communities at lower levels of the hierarchy are denser than the higher levels, we leverage a fast network sparsification technique to enhance the running time of the algorithm. We further propose a randomized approximation algorithm for information centrality of networks. We finally evaluate the performance of the proposed algorithms by conducting extensive experiments using real datasets. Our experimental results show that the proposed algorithms are promising and outperform the state-of-the-art algorithms by several orders of magnitude.This work is supported by the grant of Australian Research Council Discovery Project No. DP120102627

Remote monitoring cost minimization for an unreliable sensor network with guaranteed network throughput

AbstractIn this paper we consider a link-unreliable remote monitoring scenario where the monitoring center is geographically located far away from the region of the deployed sensor network, and sensing data by the sensors in the network will be transferred to the remote monitoring center through a third party telecommunication service. A cost associated with this service will be incurred, which will be determined by the number of gateways employed and the cumulative volume of data successfully received within a specified monitoring period. For this scenario, we first formulate a novel constrained optimization problem with an objective to minimize the service cost while a pre-defined network throughput is guaranteed. We refer to this problem as the throughput guaranteed service cost minimization problem and prove that it is NP-complete. We then propose a heuristic for it. The key ingredients of the heuristic include identifying gateways and finding an energy-efficient forest of routing trees rooted at the gateways. We also perform theoretical analysis on the solution obtained. Finally, we conduct experiments by simulations to evaluate the performance of the proposed algorithm. Experimental results demonstrate the proposed algorithm outperforms other algorithms in terms of both the service cost and the network lifetime

Approximation Algorithms for Capacitated Minimum Forest Problems in Wireless sensor Networks with a Mobile Sink

To deploy a wireless sensor network for the purpose of large-scale monitoring, in this paper, we propose a heterogeneous and hierarchical wireless sensor network architecture. The architecture consists of sensor nodes, gateway nodes, and mobile sinks. Th

Efficient Virtual Network Embedding Via Exploring Periodic Resource Demands

Cloud computing built on virtualization technologies promises provisioning elastic computing and communication resources to enterprise users. To share cloud resources efficiently, embedding virtual networks of different users to a distributed cloud consisting of multiple data centers (a substrate network) poses great challenges. Motivated by the fact that most enterprise virtual networks usually operate on long-term basics and have the characteristics of periodic resource demands, in this paper we study the virtual network embedding problem by embedding as many virtual networks as possible to a substrate network such that the revenue of the service provider of the substrate network is maximized, while meeting various Service Level Agreements (SLAs) between enterprise users and the cloud service provider. For this problem, we propose an efficient embedding algorithm by exploring periodic resource demands of virtual networks, and employing a novel embedding metric that models the workloads on both substrate nodes and communication links if the periodic resource demands of virtual networks are given; otherwise, we propose a prediction model to predict the periodic resource demands of these virtual networks based on their historic resource demands. We also evaluate the performance of the proposed algorithms by experimental simulation. Experimental results demonstrate that the proposed algorithms outperform existing algorithms, improving the revenue from 10% to 31%