Topology Awareness Research Articles

The Top-K QoS-aware Paths Discovery for Source Routing in SDN

Source routing is the routing scheme that arranges the whole path from source to target at the origin node that may suit the requirements from the upper layer applications’ perspective. The centralized control in SDN (Software-Defined Networking) networks enables the awareness of the global topology at the controller. Therefore, augmented source routing schemes can be designed to achieve various purposes. This paper proposes a source routing scheme that conducts the top-K QoS-aware paths discovery in SDN. First, the novel non-invasive QoS over LLDP scheme is designed to collect QoS information based on LLDP in a piggyback fashion. Then, variations of the KSP (K Shortest Paths) algorithm are derived to find the unconstrained/constrained top-K ranked paths with regard to individual/overall path costs, reflecting the Quality of Service. The experiment results show that the proposed scheme can efficiently collect the QoS information and find the top-K paths. Also, the performance of our scheme is applicable in QoS-sensitive application scenarios compared with previous works.

Network Performance Aware MPI Collective Communication Operations in the Cloud

This paper examines the performance of collective communication operations in message passing interfaces (MPI) in the cloud computing environment. The awareness of network topology has been a key factor in performance optimizations for existing MPI implementations. However, virtualization in the cloud environment not only hides the network topology information from the users, but also causes traffic interference and dynamics to network performance. Existing topology-aware optimizations are no longer feasible in the cloud environment. Therefore, we develop novel network performance aware algorithms for a series of collective communication operations including broadcast, reduce, gather and scatter. We further implement two common applications, N-body and conjugate gradient (CG). We have conducted our experiments with two complementary methods (on Amazon EC2 and simulations). Our experimental results show that the network performance awareness results in 25.4 and 28.3 percent performance improvement over MPICH2 on Amazon EC2 and on simulations, respectively. Evaluations on N-body and CG show 41.6 and 14.3 percent respectively on application performance improvement.

International Migration: A Global Complex Network

Migration has become a prominent research theme in geography and regional science and it has been approached from various methodological angles. Nonetheless, a common missing element in most migration studies is the lack of awareness of the overall network topology, which characterizes migration flows. Although gravity models focus on spatial interaction—in this case migration—between pairs of origins and destinations, they do not provide insights into the topology of a migration network. We employ network analysis to address such systemic research questions, in particular: How centralized or dispersed are migration flows and how does this structure evolve over time? And, how is migration activity clustered between specific countries, and if it is clustered, do such patterns change over time? Going a step further than exploratory network analysis, in this paper we estimate international migration models for OECD countries based on a dual approach: gravity models estimated using conventional econometric approaches such as panel data regressions and network-based regression techniques such as multivariate regression quadratic assignment procedures. The empirical results reveal not only the determinants of international migration among OECD countries, but also the value of blending network analysis with more conventional analytic methods.

Mapping to Irregular Torus Topologies and Other Techniques for Petascale Biomolecular Simulation.

Currently deployed petascale supercomputers typically use toroidal network topologies in three or more dimensions. While these networks perform well for topology-agnostic codes on a few thousand nodes, leadership machines with 20,000 nodes require topology awareness to avoid network contention for communication-intensive codes. Topology adaptation is complicated by irregular node allocation shapes and holes due to dedicated input/output nodes or hardware failure. In the context of the popular molecular dynamics program NAMD, we present methods for mapping a periodic 3-D grid of fixed-size spatial decomposition domains to 3-D Cray Gemini and 5-D IBM Blue Gene/Q toroidal networks to enable hundred-million atom full machine simulations, and to similarly partition node allocations into compact domains for smaller simulations using multiple-copy algorithms. Additional enabling techniques are discussed and performance is reported for NCSA Blue Waters, ORNL Titan, ANL Mira, TACC Stampede, and NERSC Edison.

The HdpH DSLs for scalable reliable computation

The statelessness of functional computations facilitates both parallelism and fault recovery. Faults and non-uniform communication topologies are key challenges for emergent large scale parallel architectures. We report on HdpH and HdpH-RS , a pair of Haskell DSLs designed to address these challenges for irregular task-parallel computations on large distributed-memory architectures. Both DSLs share an API combining explicit task placement with sophisticated work stealing. HdpH focuses on scalability by making placement and stealing topology aware whereas HdpH-RS delivers reliability by means of fault tolerant work stealing. We present operational semantics for both DSLs and investigate conditions for semantic equivalence of HdpH and HdpH-RS programs, that is, conditions under which topology awareness can be transparently traded for fault tolerance. We detail how the DSL implementations realise topology awareness and fault tolerance. We report an initial evaluation of scalability and fault tolerance on a 256-core cluster and on up to 32K cores of an HPC platform.

Applying Network Coding to Peer-to-Peer File Sharing

Network coding is a promising enhancement of routing to improve network throughput and provide high reliability. It allows a node to generate output messages by encoding its received messages. Peer-to-peer networks are a perfect place to apply network coding due to two reasons: the topology of a peer-to-peer network is constructed arbitrarily, thus it is easy to tailor the topology to facilitate network coding; the nodes in a peer-to-peer network are end hosts which can perform more complex operations such as decoding and encoding than simply storing and forwarding messages. In this paper, we propose a scheme to apply network coding to peer-to-peer file sharing which employs a peer-to-peer network to distribute files resided in a web server or a file server. The scheme exploits a special type of network topology called combination network. It was proved that combination networks can achieve unbounded network coding gain measured by the ratio of network throughput with network coding to that without network coding. Our scheme encodes a file into multiple messages and divides peers into multiple groups with each group responsible for relaying one of the messages. The encoding scheme is designed to satisfy the property that any subset of the messages can be used to decode the original file as long as the size of the subset is sufficiently large. To meet this requirement, we first define a deterministic linear network coding scheme which satisfies the desired property, then we connect peers in the same group to flood the corresponding message, and connect peers in different groups to distribute messages for decoding. Moreover, the scheme can be readily extended to support link heterogeneity and topology awareness to further improve system performance in terms of throughput, reliability and link stress. Our simulation results show that the new scheme can achieve 15%–20% higher throughput than another peer-to-peer multicast system, Narada, which does not employ network coding. In addition, it achieves good reliability and robustness to link failure or churn.

Topology-Related Metrics and Applications for the Design and Operation of Wireless Sensor Networks

The use of topological features, more specifically, the importance of an element related to its structural position, is a subject widely studied in the literature. For instance, the theory of complex networks provides centrality measures that have been applied to a large variety of fields (e.g., social sciences and biology). In this work, we propose a new topological measure, the Sink Betweenness (SBet), which stems from the theory of complex networks but is adapted to Wireless Sensor Networks (WSNs) to capture relevant information for this kind of network. We also provide a distributed algorithm to calculate it, and show its applicability to two different scenarios. The first one is focused on data fusion applications for event-driven WSNs, where we devise a tree-based data collection algorithm that takes advantage of node centrality to improve the data fusion efficiency. The second scenario is focused on energy balancing problems, more specifically in a problem called energy hole , where nodes closer to the sink are more likely to relay a larger number of packets than those that are further. This phenomenon is strongly related to the topology induced by the deployment of nodes along the sensor field, and it can be effectively captured by the SBet metric. Thus, we devise a data collection algorithm that is able to distribute the relay task more evenly. Simulation results show that the SBet metric can be satisfactorily used in both scenarios. We compare the proposed approach with some of the most efficient available data fusion algorithms, and show that the proposed algorithm generates consistently good-quality data collection infrastructures which require significantly smaller overhead. The use of SBet allows to alleviate the energy-hole effects by evenly balancing the relay load, and thus increasing the network lifetime. These two applications illustrate how the topology awareness can be used to improve different network functions in a WSN.

Information Exchange rather than Topology Awareness: Cooperation between P2P Overlay and Traffic Engineering

Solutions to the routing strategic conflict between noncooperative P2P overlay and ISP underlay go separate ways: hyperselfishness and cooperation. Unpredictable (possibly adverse) impact of the hyperselfish topology awareness, which is adopted in both overlay routing and traffic engineering, has not been sufficiently studied in the literature. Topology-related information exchange in a cooperatively efficient way should be highlighted to alleviate the cross-layer conflict. In this paper, we first illustrate the hyperselfish weakness with two dynamic noncooperative game models in which hyperselfish overlay or underlay has to accept a suboptimal profit. Then we build a synergistic cost-saving (SC) game model to reduce the negative effects of noncooperation. In the SC model, through information exchange, that is, the classified path-delay metrics for P2P overlay and peer locations for underlay, P2P overlay selects proximity as well as saving traffic transit cost for underlay, and ISP underlay adjusts routing to optimize network cost as well as indicating short delay paths for P2P. Simulations based on the real and generated topologies validate cost improvement by SC model and find a proper remote threshold value to limit P2P traffic from remote area, cross-AS, or cross-ISP.

ITA: Innocuous Topology Awareness for Unstructured P2P Networks

One of the most appealing characteristics of unstructured P2P overlays is their enhanced self-* properties, which results from their loose, random structure. In addition, most of the algorithms which make searching in unstructured P2P systems scalable, such as dynamic querying and 1-hop replication, rely on the random nature of the overlay to function efficiently. The underlying communications network (i.e., the Internet), however, is not as randomly constructed. This leads to a mismatch between the distance of two peers on the overlay and the hosts they reside on at the IP layer, which in turn leads to its misuse. The crux of the problem arises from the fact that any effort to provide a better match between the overlay and the IP layer will inevitably lead to a reduction in the random structure of the P2P overlay, with many adverse results. With this in mind, we propose ITA, an algorithm which creates a random overlay of randomly connected neighborhoods providing topology awareness to P2P systems, while at the same time has no negative effect on the self-* properties or the operation of the other P2P algorithms. Using extensive simulations, both at the IP router level and autonomous system level, we show that ITA reduces communication latencies by as much as 50 percent. Furthermore, it not only reduces by 20 percent the number of IP network messages which is critical for ISPs carrying the burden of transporting P2P traffic, but also distributes the traffic load more evenly on the routers of the IP network layer.

On the robustness of in- and out-components in a temporal network.

BackgroundMany networks exhibit time-dependent topologies, where an edge only exists during a certain period of time. The first measurements of such networks are very recent so that a profound theoretical understanding is still lacking. In this work, we focus on the propagation properties of infectious diseases in time-dependent networks. In particular, we analyze a dataset containing livestock trade movements. The corresponding networks are known to be a major route for the spread of animal diseases. In this context chronology is crucial. A disease can only spread if the temporal sequence of trade contacts forms a chain of causality. Therefore, the identification of relevant nodes under time-varying network topologies is of great interest for the implementation of counteractions.Methodology/FindingsWe find that a time-aggregated approach might fail to identify epidemiologically relevant nodes. Hence, we explore the adaptability of the concept of centrality of nodes to temporal networks using a data-driven approach on the example of animal trade. We utilize the size of the in- and out-component of nodes as centrality measures. Both measures are refined to gain full awareness of the time-dependent topology and finite infectious periods. We show that the size of the components exhibit strong temporal heterogeneities. In particular, we find that the size of the components is overestimated in time-aggregated networks. For disease control, however, a risk assessment independent of time and specific disease properties is usually favored. We therefore explore the disease parameter range, in which a time-independent identification of central nodes remains possible.ConclusionsWe find a ranking of nodes according to their component sizes reasonably stable for a wide range of infectious periods. Samples based on this ranking are robust enough against varying disease parameters and hence are promising tools for disease control.

Topology awareness algorithm for virtual network mapping

Network virtualization is recognized as an effective way to overcome the ossification of the Internet. However, the virtual network mapping problem (VNMP) is a critical challenge, focusing on how to map the virtual networks to the substrate network with efficient utilization of infrastructure resources. The problem can be divided into two phases: node mapping phase and link mapping phase. In the node mapping phase, the existing algorithms usually map those virtual nodes with a complete greedy strategy, without considering the topology among these virtual nodes, resulting in too long substrate paths (with multiple hops). Addressing this problem, we propose a topology awareness mapping algorithm, which considers the topology among these virtual nodes. In the link mapping phase, the new algorithm adopts the k-shortest path algorithm. Simulation results show that the new algorithm greatly increases the long-term average revenue, the acceptance ratio, and the long-term revenue-to-cost ratio (R/C).

Virtual network embedding through topology awareness and optimization

Embedding a sequence of virtual networks (VNs) into a given physical network substrate to accommodate as many VN requests as possible is known to be NP-hard. This paper presents a new approach to studying this problem. In particular, we devise a topology-aware measure on node resources based on random walks and use it to rank a node’s resources and topological attributes. We then devise a greedy algorithm that matches nodes in the VN to nodes in the substrate network according to node ranks. In most situations there exist multiple embedding solutions, and so we want to find the best embedding that increases the possibility of accepting future VN requests and optimizes the revenue for the provider of the substrate network. We present an integer linear programming formulation for this optimization problem when path splitting is not allowed. We then devise a fast-convergent discrete Particle Swarm Optimization algorithm to approximate this problem. Extensive simulation results show that our algorithms produce near optimal solutions and significantly outperform existing algorithms in terms of the ratio of the long-term average revenue over the VN request acceptance.

On-demand base maps on the web generalized according to user profiles

Thematic content in the form of geodata is becoming increasingly available on the web. However, adequate base map information to support appropriate communication of the thematic content is not available in many cases. This base map information might be served through topographic databases available on the web and optimized by means of automated generalization. This article describes an approach based on user profiles, which formally captures the user requirements (preferences) towards the base map and deploys those profiles in a web-based architecture to generate on-demand maps. Those user profiles, which may differ per user, drive the generalization process and produce base maps on demand by taking into account the user requirements as well as the thematic content. The research in this article specifically focuses on the technological environment that generates on-demand base maps incorporating four basic concepts in the user profile: (1) topology awareness between the base map and the thematic content, (2) user-specific base map information at a non-uniform scale to improve the communication of the thematic content, (3) the map specification and (4) inheritance of user profiles. Both existing and new techniques are explored to realize the environment. The approach is evaluated by applying it to a project in the Netherlands dedicated to disseminating physical planning maps through the web.

International Migration: A Global Complex Network

Migration has become a prominent research theme in geography and regional science and it has been approached from various methodological angles. Nonetheless, a common missing element in most migration studies is the lack of awareness of the overall network topology, which characterizes migration flows. Although gravity models focus on spatial interaction - in this case migration - between pairs of origins and destinations, they do not provide insights into the topology of a migration network. In the present paper, we will employ network analysis to address such systemic research questions, in particular: How centralized or dispersed are migration flows and how does this structure evolve over time? And how is migration activity clustered between specific countries, and if so, do such patterns change over time? Going a step further than exploratory network analysis, this paper estimates international migration models for OECD countries based on a dual approach: gravity models estimated using conventional econometric approaches such as panel data regressions as well as network-based regression techniques such as MRQAP. The empirical results reveal not only the determinants of international migration among OECD countries, but also the value of blending network analysis with more conventional analytic methods.

Using Binning for Tree Construction in Application

The application Layer Multicast (ALM) is an alternative and deployable approach to IP multicasting. Topology awareness link stress and delay stretch considerably, therefore it is a very important metric for ALM. This work describes a novel, highly stable and low overhead ALM approach using a binning technique to cluster nearby receivers, referred to as Bincast. Bincast uses a constant number of landmarks to cluster nearby nodes. Then, it constructs a k-ary tree between cluster members. The most stable node is selected as the head of each cluster. Cluster heads are connected to the source through a higher level tree. Detailed performance evaluation revealed that Bincast has a lower delay stretch than similar methods with approximately the same stress. Besides, it is more stable due to the selection of stable nodes as cluster heads.

Navigating Ultrasmall Worlds in Ultrashort Time

Random scale-free networks are ultrasmall worlds. The average length of the shortest paths in networks of size N scales as lnlnN. Here we show that these ultrasmall worlds can be navigated in ultrashort time. Greedy routing on scale-free networks embedded in metric spaces finds paths with the average length scaling also as lnlnN. Greedy routing uses only local information to navigate a network. Nevertheless, it finds asymptotically the shortest paths, a direct computation of which requires global topology knowledge. Our findings imply that the peculiar structure of complex networks ensures that the lack of global topological awareness has asymptotically no impact on the length of communication paths. These results have important consequences for communication systems such as the Internet, where maintaining knowledge of current topology is a major scalability bottleneck.

FT‐Grid: a system for achieving fault tolerance in grids

AbstractThe FT‐Grid system introduces a fault‐tolerance framework that allows faults occurring in service‐oriented systems to be tolerated, thus increasing the dependability of such systems. This paper presents the design, development and evaluation of FT‐Grid. We show empirical evidence of the dependability benefits offered by FT‐Grid by performing an experimental dependability analysis using fault‐injection testing performed with the WS‐FIT tool. We then illustrate a potential problem with voting‐based fault‐tolerance schemes in the service‐oriented paradigm—namely that individual channels within a fault‐tolerant system, supposed to be independent of each other, may in fact invoke common services as part of their workflow, thus increasing the potential for common‐mode failure of those channels. We propose a solution to this issue by using the technique of provenance to provide FT‐Grid with topological awareness. We implement a large experimental system, and—with the use of the Provenance Recording for Services system developed as part of the PASOA project at the University of Southampton—perform a large number of experiments that show that a topologically aware FT‐Grid system serves as a much more dependable system than any other configuration tested, while imposing a negligible timing overhead. Copyright © 2007 John Wiley & Sons, Ltd.

Power-Saving Schedulers for a WLAN With Task-Linking Topology Awareness

This paper proposes task-scheduling algorithms designed to save energy in ad hoc networks by using a novel concept of as the scheduling metric. This notion of affinity is used to represent the task-linking topology. For one-hop ad hoc networks, we define three types of affinity measures: node affinity, task affinity, and weighted affinity. Node affinity is defined as the weighted sum of incoming and outgoing task flows at each node, and task affinity as the sum of node affinities of source and destination nodes for each task. Based on these affinity measures, we consider three heuristic scheduling algorithms that are executable in real time: 1) affinity-first shortest task scheduling Qat; 2) task-size-first affinity-based scheduling Qta; and 3) task-size weighted-affinity scheduling Qwa . These algorithms have the advantage of making the total duration of the doze state of all the nodes in the network longer than in other conventional schemes, thereby achieving significant power saving (PS). Our simulation experiments suggest that the PS performance of Qwa is comparable to optimal scheduling (which is not implementable in practice for realistic link topologies). We also present a framework for a PS medium-access-control (MAC) protocol in which our scheduling algorithm can be deployed, and we describe how to enhance the existing 802.11 PS MAC to implement our framework. Simulation results demonstrate that our enhanced PS MAC protocol combined with affinity-based scheduling improves power efficiency and throughput significantly over other existing schemes

Topology awareness of overlay P2P networks

AbstractIn overlay networks, the mechanism of a peer randomly joining and leaving a network, causes a topology mismatch between the overlay and the underlying physical topology. This causes a large volume of redundant traffic in the underlying physical network as well as an extra delay in message delivery in the overlay network. Topology mismatch occurs because overlay networks are not aware of their underlying physical networks. In this paper we present a mathematical model for topology awareness of overlay networks (degree of matching between an overlay and its underlying physical network) and the efficiency of message delivery on them. Then we experimentally show that the model is precise in the real world. Also, after determining the computational complexity of the model, we propose an optimization heuristic algorithm to enhance efficiency of message exchange on overlay networks by increasing their topology awareness. Next, we present the results of running the algorithm on different kinds of random graph and show how we can implement the algorithm on peer‐to‐peer networks; we then simulate the implementation on some unstructured peer‐to‐peer overlay networks. Copyright © 2006 John Wiley & Sons, Ltd.

Eclecticism shrinks even small worlds

We consider small world graphs as defined by Kleinberg (2000), i.e., graphs obtained from a d- dimensional mesh by adding links chosen at random according to the d-harmonic distribution. In these graphs, greedy routing performs in O (log2 n) expected number of steps. We introduce indirect-greedy routing. We show that giving O(log2 n) bits of topological awareness per node enables indirect-greedy routing to perform in O(log1+1/d n) expected number of steps in d-dimensional augmented meshes. We also show that, independently of the amount of topological awareness given to the nodes, indirect-greedy routing performs in Ω(log1+1/d n) expected number of steps. In particular, augmenting the topological awareness above this optimum of O(log2 n) bits would drastically decrease the performance of indirect-greedy routing.Our model demonstrates that the efficiency of indirect-greedy routing is sensitive to the world's dimension, in the sense that high dimensional worlds enjoy faster greedy routing than low dimensional ones. This could not be observed in Kleinberg's routing. In addition to bringing new light to Milgram's experiment, our protocol presents several desirable properties. In particular, it is totally oblivious, i.e., there is no header modification along the path from the source to the target, and the routing decision depends only on the target, and on information stored locally at each node.