Jungseul Ok

Embedding of virtual network requests over static wireless multihop networks

Network virtualization is a technology of running multiple heterogeneous network architecture on a shared substrate network. One of the crucial components in network virtualization is virtual network embedding, which provides a way to allocate physical network resources (e.g., CPU and link bandwidth) to virtual network requests. Despite significant research efforts on virtual network embedding in wired and cellular networks, little attention has been paid to that in wireless multi-hop networks, which is becoming more important due to its rapid growth and the need to share these networks among different business sectors and users. In this paper, we first study the root causes of new challenges of virtual network embedding in wireless multi-hop networks, and propose a new embedding algorithm that efficiently uses the resources of the physical substrate network. We examine our algorithms performance through extensive simulations under various scenarios. Due to lack of competitive algorithms, we compare the proposed algorithm to five other algorithms, mainly borrowed from wired embedding or made by us, partially with or without the key algorithmic ideas to assess their impacts.

Optimal Rate Sampling in 802.11 systems

Rate Adaptation (RA) is a fundamental mechanism in 802.11 systems. It allows transmitters to adapt the coding and modulation scheme as well as the MIMO transmission mode to the radio channel conditions, and in turn, to learn and track the (mode, rate) pair providing the highest throughput. So far, the design of RA mechanisms has been mainly driven by heuristics. In contrast, in this paper, we rigorously formulate such design as an online stochastic optimisation problem. We solve this problem and present ORS (Optimal Rate Sampling), a family of (mode, rate) pair adaptation algorithms that provably learn as fast as it is possible the best pair for transmission. We study the performance of ORS algorithms in stationary radio environments where the successful packet transmission probabilities at the various (mode, rate) pairs do not vary over time, and in non-stationary environments where these probabilities evolve. We show that under ORS algorithms, the throughput loss due to the need to explore sub-optimal (mode, rate) pairs does not depend on the number of available pairs. This is a crucial advantage as evolving 802.11 standards offer an increasingly large number of (mode, rate) pairs. We illustrate the efficiency of ORS algorithms (compared to the state-of-the-art algorithms) using simulations and traces extracted from 802.11 test-beds.

On Maximizing Diffusion Speed Over Social Networks With Strategic Users

A variety of models have been proposed and analyzed to understand how a new innovation (e.g., a technology, a product, or even a behavior) diffuses over a social network, broadly classified into either of epidemic-based or game-based ones. In this paper, we consider a game-based model, where each individual makes a selfish, rational choice in terms of its payoff in adopting the new innovation, but with some noise. We address the following two questions on the diffusion speed of a new innovation under the game-based model: (1) what is a good subset of individuals to seed for reducing the diffusion time significantly, i.e., convincing them to preadopt a new innovation and (2) how much diffusion time can be reduced by such a good seeding. For (1), we design near-optimal polynomial-time seeding algorithms for three representative classes of social network models, Erdös-Rényi, planted partition and geometrically structured graphs, and provide their performance guarantees in terms of approximation and complexity. For (2), we asymptotically quantify the diffusion time for these graph topologies; further derive the seed budget threshold above which the diffusion time is dramatically reduced, i.e., phase transition of diffusion time. Furthermore, based on our theoretical findings, we propose a practical seeding algorithm, called Practical Partitioning and Seeding (PrPaS) and demonstrate that PrPaS outperforms other baseline algorithms in terms of the diffusion speed over a real social network topology. We believe that our results provide new insights on how to seed over a social network depending on its connectivity structure, where individuals rationally adopt a new innovation.

Incentivizing strategic users for social diffusion: Quantity or quality?

We consider a problem of how to effectively diffuse a new product over social networks by incentivizing selfish users. Traditionally, this problem has been studied in the form of influence maximization via seeding, where most prior work assumes that seeded users unconditionally and immediately start by adopting the new product and they stay at the new product throughout their lifetime. However, in practice, seeded users often adjust the degree of their willingness to diffuse, depending on how much incentive is given. To address such diffusion willingness, we propose a new incentive model and characterize the speed of diffusion as the value of a combinatorial optimization. Then, we apply the characterization to popular network graph topologies (Erdos-Renyi, planted partition and power law graphs) as well as general ones, for asymptotically computing the diffusion time for those graphs. Our analysis shows that the diffusion time undergoes two levels of order-wise reduction, where the first and second one are solely contributed by the number of seeded users, i.e., quantity, and the amount of incentives, i.e., quality, respectively. In other words, it implies that the best strategy given budget is (a) first identify the minimum seed set depending on the underlying graph topology, and (b) then assign largest possible incentives to users in the set. We believe that our theoretical results provide useful implications and guidelines for designing successful advertising strategies in various practical applications.

Influence maximization over strategic diffusion in social networks

We study the problem of diffusion speed maximization over strategic diffusion, where individuals decide to adopt a new behavior or not based on a networked coordination game with their neighbors. For a variety of topological structures of social networks, we design polynomial-time algorithms that provide provable approximation guarantees. By analyzing three graph classes, i.e., Erdös-Rényi, planted partition and geometrically structured graphs, we obtain new topological insights, which does not exists in the literature for popular epidemic-based models. Our results first imply that for globally well-connected graphs, a careful seeding is not necessary. On the other hand, for locally well-connected graphs, their clustering characteristics should be intelligently exploited for good seeding, where seeding inside and intersection of clusters are important for such graphs having big and small clusters, respectively. We believe that these new insights will provide useful tools to understand and control the sociological evolution of innovations spread over large-scale social networks.