Enabling Robust and Efficient Distributed Computation in Dynamic Peer-to-Peer Networks

Motivated by the need for designing efficient and robust fully-distributed computation in highly dynamic networks such as Peer-to-Peer (P2P) networks, we study distributed protocols for constructing and maintaining dynamic network topologies with good expansion properties. Our goal is to maintain a sparse (bounded degree) expander topology despite heavy {\em churn} (i.e., nodes joining and leaving the network continuously over time). We assume that the churn is controlled by an adversary that has complete knowledge and control of what nodes join and leave and at what time and has unlimited computational power, but is oblivious to the random choices made by the algorithm. Our main contribution is a randomized distributed protocol that guarantees with high probability the maintenance of a {\em constant} degree graph with {\em high expansion} even under {\em continuous high adversarial} churn. Our protocol can tolerate a churn rate of up to $O(n/\poly\log(n))$ per round (where $n$ is the stable network size). Our protocol is efficient, lightweight, and scalable, and it incurs only $O(\poly\log(n))$ overhead for topology maintenance: only polylogarithmic (in $n$) bits needs to be processed and sent by each node per round and any node's computation cost per round is also polylogarithmic. The given protocol is a fundamental ingredient that is needed for the design of efficient fully-distributed algorithms for solving fundamental distributed computing problems such as agreement, leader election, search, and storage in highly dynamic P2P networks and enables fast and scalable algorithms for these problems that can tolerate a large amount of churn.

Efficient Structure Learning of Bayesian Networks using Constraints

This paper addresses the problem of learning Bayesian network structures from data based on score functions that are decomposable. It describes properties that strongly reduce the time and memory costs of many known methods without losing global optimality guarantees. These properties are derived for different score criteria such as Minimum Description Length (or Bayesian Information Criterion), Akaike Information Criterion and Bayesian Dirichlet Criterion. Then a branch-and-bound algorithm is presented that integrates structural constraints with data in a way to guarantee global optimality. As an example, structural constraints are used to map the problem of structure learning in Dynamic Bayesian networks into a corresponding augmented Bayesian network. Finally, we show empirically the benefits of using the properties with state-of-the-art methods and with the new algorithm, which is able to handle larger data sets than before.

The Carboxy-Terminal Modulator Protein (CTMP) Regulates Mitochondrial Dynamics

Background: Mitochondria are central to the metabolism of cells and participate in many regulatory and signaling events. They are looked upon as dynamic tubular networks. We showed recently that the Carboxy-Terminal Modulator Protein (CTMP) is a mitochondrial protein that may be released into the cytosol under apoptotic conditions.

Inscribing value on business model innovations: Insights from industrial projects commercializing disruptive digital innovations

In this paper we seek to show how marketing activities inscribe value on business model innovation, representative of an act, or sequence of socially interconnecting acts. Theoretically we ask two interlinked questions: (1) how can value inscriptions contribute to business model innovations? (2) how can marketing activities support the inscription of value on business model innovations? Semi-structured in-depth interviews were conducted with the thirty-seven members from across four industrial projects commercializing disruptive digital innovations. Various individuals from a diverse range of firms are shown to cast relevant components of their agency and knowledge on business model innovations through negotiation as an ongoing social process. Value inscription is mutually constituted from the marketing activities, interactions and negotiations of multiple project members across firms and functions to counter destabilizing forces and tensions arising from the commercialization of disruptive digital innovations. This contributes to recent conceptual thinking in the industrial marketing literature, which views business models as situated within dynamic business networks and a context-led evolutionary process. A contribution is also made to debate in the marketing literature around marketing's boundary-spanning role, with marketing activities shown to span and navigate across functions and firms in supporting value inscriptions on business model innovations.

Dynamic Buffer Management for Multimedia Services in Beyond 3G Wireless Networks

This paper investigates a dynamic buffer man-agement scheme for QoS control of multimedia services in be-yond 3G wireless systems. The scheme is studied in the context of the state-of-the-art 3.5G system i.e. the High Speed Downlink Packet Access (HSDPA) which enhances 3G UMTS to support high-speed packet switched services. Unlike earlier systems, UMTS-evolved systems from HSDPA and beyond incorporate mechanisms such as packet scheduling and HARQ in the base station necessitating data buffering at the air interface. This introduces a potential bottleneck to end-to-end communication. Hence, buffer management at the air interface is crucial for end-to-end QoS support of multimedia services with multi-plexed parallel diverse flows such as video and data in the same end-user session. The dynamic buffer management scheme for HSDPA multimedia sessions with aggregated real-time and non real-time flows is investigated via extensive HSDPA simulations. The impact of the scheme on end-to-end traffic performance is evaluated with an example multimedia session comprising a real-time streaming flow concurrent with TCP-based non real-time flow. Results demonstrate that the scheme can guar-antee the end-to-end QoS of the real-time streaming flow, whilst simultaneously protecting the non real-time flow from starva-tion resulting in improved end-to-end throughput performance

Dynamic Buffer Management for Multimedia Services in 3.5G Wireless Networks

This paper presents and investigates a dynamic
buffer management scheme for QoS control of multimedia
services in a 3.5G wireless system i.e. the High Speed Downlink
Packet Access (HSDPA). HSDPA was introduced to enhance
UMTS for high-speed packet switched services. With HSDPA,
packet scheduling and HARQ mechanisms in the base station
require data buffering at the air interface thus introducing a
potential bottleneck to end-to-end communication. Hence, for
multimedia services with multiplexed parallel diverse flows
such as video and data in the same end-user session, buffer
management schemes in the base station are essential to support
end-to-end QoS provision. We propose a dynamic buffer management
scheme for HSDPA multimedia sessions with aggregated real-time and non real-time flows in the paper. The end-to-end performance impact of the scheme is evaluated with an example multimedia session comprising a real-time streaming
flow concurrent with TCP-based non real-time flow via extensive HSDPA simulations. Results demonstrate that the scheme can guarantee the end-to-end QoS of the real-time streaming flow, whilst simultaneously protecting non real-time flow from starvation resulting in improved end-to-end throughput performance

Interfacing cellular networks of S. cerevisiae and E. coli: Connecting dynamic and genetic information

Background: In recent years, various types of cellular networks have penetrated biology and are nowadays used omnipresently for studying eukaryote and prokaryote organisms. Still, the relation and the biological overlap among phenomenological and inferential gene networks, e.g., between the protein interaction network and the gene regulatory network inferred from large-scale transcriptomic data, is largely unexplored.

Results: We provide in this study an in-depth analysis of the structural, functional and chromosomal relationship between a protein-protein network, a transcriptional regulatory network and an inferred gene regulatory network, for S. cerevisiae and E. coli. Further, we study global and local aspects of these networks and their biological information overlap by comparing, e.g., the functional co-occurrence of Gene Ontology terms by exploiting the available interaction structure among the genes.

Conclusions: Although the individual networks represent different levels of cellular interactions with global structural and functional dissimilarities, we observe crucial functions of their network interfaces for the assembly of protein complexes, proteolysis, transcription, translation, metabolic and regulatory interactions. Overall, our results shed light on the integrability of these networks and their interfacing biological processes.

Dense subgraphs on dynamic networks

In distributed networks, it is often useful for the nodes to be aware of dense subgraphs, e.g., such a dense subgraph could reveal dense substructures in otherwise sparse graphs (e.g. the World Wide Web or social networks); these might reveal community clusters or dense regions for possibly maintaining good communication infrastructure. In this work, we address the problem of self-awareness of nodes in a dynamic network with regards to graph density, i.e., we give distributed algorithms for maintaining dense subgraphs that the member nodes are aware of. The only knowledge that the nodes need is that of the dynamic diameter D, i.e., the maximum number of rounds it takes for a message to traverse the dynamic network. For our work, we consider a model where the number of nodes are fixed, but a powerful adversary can add or remove a limited number of edges from the network at each time step. The communication is by broadcast only and follows the CONGEST model. Our algorithms are continuously executed on the network, and at any time (after some initialization) each node will be aware if it is part (or not) of a particular dense subgraph. We give algorithms that (2 + e)-approximate the densest subgraph and (3 + e)-approximate the at-least-k-densest subgraph (for a given parameter k). Our algorithms work for a wide range of parameter values and run in O(D log n) time. Further, a special case of our results also gives the first fully decentralized approximation algorithms for densest and at-least-k-densest subgraph problems for static distributed graphs. © 2012 Springer-Verlag.

Brief announcement: Maintaining large dense subgraphs on dynamic networks

In distributed networks, some groups of nodes may have more inter-connections, perhaps due to their larger bandwidth availability or communication requirements. In many scenarios, it may be useful for the nodes to know if they form part of a dense subgraph, e.g., such a dense subgraph could form a high bandwidth backbone for the network. In this work, we address the problem of self-awareness of nodes in a dynamic network with regards to graph density, i.e., we give distributed algorithms for maintaining dense subgraphs (subgraphs that the member nodes are aware of). The only knowledge that the nodes need is that of the dynamic diameter D, i.e., the maximum number of rounds it takes for a message to traverse the dynamic network. For our work, we consider a model where the number of nodes are fixed, but a powerful adversary can add or remove a limited number of edges from the network at each time step. The communication is by broadcast only and follows the CONGEST model in the sense that only messages of O(log n) size are permitted, where n is the number of nodes in the network. Our algorithms are continuously executed on the network, and at any time (after some initialization) each node will be aware if it is part (or not) of a particular dense subgraph. We give algorithms that approximate both the densest subgraph, i.e., the subgraph of the highest density in the network, and the at-least-k-densest subgraph (for a given parameter k), i.e., the densest subgraph of size at least k. We give a (2 + e)-approximation algorithm for the densest subgraph problem. The at-least-k-densest subgraph is known to be NP-hard for the general case in the centralized setting and the best known algorithm gives a 2-approximation. We present an algorithm that maintains a (3+e)-approximation in our distributed, dynamic setting. Our algorithms run in O(Dlog n) time. © 2012 Authors.