Boleslaw K. Szymanski

Data replication strategies in grid environments

Data grids provide geographically distributed resources for large-scale data-intensive applications that generate large data sets. However, ensuring efficient and fast access to such huge and widely distributed data is hindered by the high latencies of the Internet. To address these problems we introduce a set of replication management services and protocols that offer high data availability, low bandwidth consumption, increased fault tolerance, and improved scalability of the overall system. Replication decisions are made based on a cost model that evaluates data access costs and performance gains of creating each replica. The estimation of costs and gains is based on factors such as run-time accumulated read/write statistics, response time, bandwidth, and replica size. To address scalability, replicas are organized in a combination of hierarchical and flat topologies that represent propagation graphs that minimize inter-replica communication costs. To evaluate our model we use the network simulator NS to study the impact of replication. Our results prove that replication improves the performance of data access on the data grid, and that the gain increases with the size of data used.

SLPA: Uncovering Overlapping Communities in Social Networks via a Speaker-Listener Interaction Dynamic Process

Overlap is one of the characteristics of social networks, in which a person may belong to more than one social group. For this reason, discovering overlapping structures is necessary for realistic social analysis. In this paper, we present a novel, general framework to detect and analyze both individual overlapping nodes and entire communities. In this framework, nodes exchange labels according to dynamic interaction rules. A specific implementation called Speaker-listener Label Propagation Algorithm (SLPA) demonstrates an excellent performance in identifying both overlapping nodes and overlapping communities with different degrees of diversity.

Adaptive Local Refinement with Octree Load Balancing for the Parallel Solution of Three-Dimensional Conservation Laws

Conservation laws are solved by a local Galerkin finite element procedure with adaptive space-time mesh refinement and explicit time integration. The Courant stability condition is used to select smaller time steps on smaller elements of the mesh, thereby greatly increasing efficiency relative to methods having a single global time step. Processor load imbalances, introduced at adaptive enrichment steps, are corrected by using traversals of an octree representing a spatial decomposition of the domain. To accommodate the variable time steps, octree partitioning is extended to use weights derived from element size. Partition boundary smoothing reduces the communications volume of partitioning procedures for a modest cost. Computational results comparing parallel octree and inertial partitioning procedures are presented for the three-dimensional Euler equations of compressible flow solved on an IBM SP2 computer.

Simulation of dynamic data replication strategies in Data Grids

Data Grids provide geographically distributed resources for large-scale data-intensive applications that generate large data sets. However, ensuring efficient access to such huge and widely distributed data is hindered by the high latencies of the Internet. We address these challenges by employing intelligent replication and caching of objects at strategic locations. In our approach, replication decisions are based on a cost-estimation model and driven by the estimation of the data access gains and the replicas creation and maintenance costs. These costs are in turn based on factors such as runtime accumulated read/write statistics, network latency, bandwidth, and replica size. To support large numbers of users who continuously change their data and processing needs, we introduce scalable replica distribution topologies that adapt replica placement to meet these needs. In this paper we present the design of our dynamic memory middleware and replication algorithm. To evaluate the performance of our approach, we developed a Data Grid simulator, called the GridNet. Simulation results demonstrate that replication improves the data access time in Data Grids, and that the gain increases with the size of the datasets involved.

Social consensus through the influence of committed minorities.

We show how the prevailing majority opinion in a population can be rapidly reversed by a small fraction p of randomly distributed committed agents who consistently proselytize the opposing opinion and are immune to influence. Specifically, we show that when the committed fraction grows beyond a critical value p(c) ≈ 10%, there is a dramatic decrease in the time T(c) taken for the entire population to adopt the committed opinion. In particular, for complete graphs we show that when p < pc, T(c) ~ exp [α(p)N], whereas for p>p(c), T(c) ~ ln N. We conclude with simulation results for Erdős-Rényi random graphs and scale-free networks which show qualitatively similar behavior.

Towards linear time overlapping community detection in social networks

Membership diversity is a characteristic aspect of social networks in which a person may belong to more than one social group. For this reason, discovering overlapping structures is necessary for realistic social analysis. In this paper, we present a fast algorithm, called SLPA, for overlapping community detection in large-scale networks. SLPA spreads labels according to dynamic interaction rules. It can be applied to both unipartite and bipartite networks. It is also able to uncover overlapping nested hierarchy . The time complexity of SLPA scales linearly with the number of edges in the network. Experiments in both synthetic and real-world networks show that SLPA has an excellent performance in identifying both node and community level overlapping structures.

Efficient and inefficient ant coverage methods

Ant robots are simple creatures with limited sensing and computational capabilities. They have the advantage that they are easy to program and cheap to build. This makes it feasible to deploy groups of ant robots and take advantage of the resulting fault tolerance and parallelism. We study, both theoretically and in simulation, the behavior of ant robots for one-time or repeated coverage of terrain, as required for lawn mowing, mine sweeping, and surveillance. Ant robots cannot use conventional planning methods due to their limited sensing and computational capabilities. To overcome these limitations, we study navigation methods that are based on real-time (heuristic) search and leave markings in the terrain, similar to what real ants do. These markings can be sensed by all ant robots and allow them to cover terrain even if they do not communicate with each other except via the markings, do not have any kind of memory, do not know the terrain, cannot maintain maps of the terrain, nor plan complete paths. The ant robots do not even need to be localized, which completely eliminates solving difficult and time-consuming localization problems. We study two simple real-time search methods that differ only in how the markings are updated. We show experimentally that both real-time search methods robustly cover terrain even if the ant robots are moved without realizing this (say, by people running into them), some ant robots fail, and some markings get destroyed. Both real-time search methods are algorithmically similar, and our experimental results indicate that their cover time is similar in some terrains. Our analysis is therefore surprising. We show that the cover time of ant robots that use one of the real-time search methods is guaranteed to be polynomial in the number of locations, whereas the cover time of ant robots that use the other real-time search method can be exponential in (the square root of) the number of locations even in simple terrains that correspond to (planar) undirected trees.

Intrusion detection: a bioinformatics approach

We address the problem of detecting masquerading, a security attack in which an intruder assumes the identity of a legitimate user. Many approaches based on hidden Markov models and various forms of finite state automata have been proposed to solve this problem. The novelty of our approach results from the application of techniques used in bioinformatics for a pair-wise sequence alignment to compare the monitored session with past user behavior. Our algorithm uses a semiglobal alignment and a unique scoring system to measure similarity between a sequence of commands produced by a potential intruder and the user signature, which is a sequence of commands collected from a legitimate user. We tested this algorithm on the standard intrusion data collection set. As discussed, the results of the test showed that the described algorithm yields a promising combination of intrusion detection rate and false positive rate, when compared to published intrusion detection algorithms.

Exploiting Friendship Relations for Efficient Routing in Mobile Social Networks

Routing in delay tolerant networks is a challenging problem due to the intermittent connectivity between nodes resulting in the frequent absence of end-to-end path for any source-destination pair at any given time. Recently, this problem has attracted a great deal of interest and several approaches have been proposed. Since Mobile Social Networks (MSNs) are increasingly popular type of Delay Tolerant Networks (DTNs), making accurate analysis of social network properties of these networks is essential for designing efficient routing protocols. In this paper, we introduce a new metric that detects the quality of friendships between nodes accurately. Utilizing this metric, we define the community of each node as the set of nodes having close friendship relations with this node either directly or indirectly. We also present Friendship-Based Routing in which periodically differentiated friendship relations are used in forwarding of messages. Extensive simulations on both real and synthetic traces show that the introduced algorithm is more efficient than the existing algorithms.

Measuring behavioral trust in social networks

Trust is an important yet complex and little understood aspect of the dyadic relationship between two entities. Trust plays an important role in the formation of coalitions in social networks and in determining how high value of information flows through the network. We present algorithmically quantifiable measures of trust based on communication behavior. We propose that trust results in likely communication behaviors which are statistically different from random communications; detecting these trust-like behaviors allows us to develop a quantitative measure of who trusts whom in the network. We develop algorithms to efficiently compute such behavioral trust and validate these measures on the Twitter network.