Arijit Paul, Quazi Rahman, S. Bandyopadhyay, Y. Aneja
Orthogonal Frequency Division Multiplexing (OFDM) has recently emerged as a promising technology. A network using OFDM based Spectrum-sliced Elastic Optical Path (SLICE) has a higher spectrum efficiency, due to the fine granularity of subcarrier frequencies used. To minimize the utilized spectrum in SLICE networks, the routing and spectrum allocation problem (RSA) has to be efficiently solved. We have solved the RSA problem using a Mixed Integer Linear Programming (MILP) formulation and have compared our approaches with another recent formulation.
{"title":"A fast approach to solve the Route and Spectrum Allocation problem in OFDM networks","authors":"Arijit Paul, Quazi Rahman, S. Bandyopadhyay, Y. Aneja","doi":"10.1145/2684464.2684501","DOIUrl":"https://doi.org/10.1145/2684464.2684501","url":null,"abstract":"Orthogonal Frequency Division Multiplexing (OFDM) has recently emerged as a promising technology. A network using OFDM based Spectrum-sliced Elastic Optical Path (SLICE) has a higher spectrum efficiency, due to the fine granularity of subcarrier frequencies used. To minimize the utilized spectrum in SLICE networks, the routing and spectrum allocation problem (RSA) has to be efficiently solved. We have solved the RSA problem using a Mixed Integer Linear Programming (MILP) formulation and have compared our approaches with another recent formulation.","PeriodicalId":298587,"journal":{"name":"Proceedings of the 16th International Conference on Distributed Computing and Networking","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124880122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The increase in internet coverage and decrease in internet access price has resulted in demand for good internet service. Clients want some guarantee in internet access quality. In this paper, we present a model in which clients are guaranteed connection and bandwidth and if clients do not get the service they request, the service provider pays a penalty to the clients. We consider a system of internet clients with multiple internet service provider (ISP) connections to a set of ISPs. When a client arrives, an ISP has to decide whether to accept the client, and then the price to charge from the client for the duration of its connection. Rejection of a client results in a penalty and delay in getting the requested bandwidth also incurs a penalty. We assume a Poisson arrival process with the rate of arrival sensitive to the price being charged. A client requests bandwidth for a time that is exponentially distributed, then the client is idle for a time that is also exponentially distributed; and then either the client departs or requests bandwidth again after the idle period is over. A service provider tries to maximize its income by charging appropriate prices based on its current state and deciding whether to accept more clients or not. Since penalties are imposed, such solutions also automatically balance load among service providers, and so the quality of service to clients improves. We present solutions that maximize the income of service providers. The solutions are then compared using simulation. Simulation results show that our analysed solution significantly improve quality of service of clients and increase the income of service providers as compared to a simple heuristic based solution that is otherwise could to be used.
{"title":"Pricing with Bandwidth Guarantees for Clients with multi-ISP Connections","authors":"Rohit Tripathi, G. Barua","doi":"10.1145/2684464.2684497","DOIUrl":"https://doi.org/10.1145/2684464.2684497","url":null,"abstract":"The increase in internet coverage and decrease in internet access price has resulted in demand for good internet service. Clients want some guarantee in internet access quality. In this paper, we present a model in which clients are guaranteed connection and bandwidth and if clients do not get the service they request, the service provider pays a penalty to the clients. We consider a system of internet clients with multiple internet service provider (ISP) connections to a set of ISPs. When a client arrives, an ISP has to decide whether to accept the client, and then the price to charge from the client for the duration of its connection. Rejection of a client results in a penalty and delay in getting the requested bandwidth also incurs a penalty. We assume a Poisson arrival process with the rate of arrival sensitive to the price being charged. A client requests bandwidth for a time that is exponentially distributed, then the client is idle for a time that is also exponentially distributed; and then either the client departs or requests bandwidth again after the idle period is over. A service provider tries to maximize its income by charging appropriate prices based on its current state and deciding whether to accept more clients or not. Since penalties are imposed, such solutions also automatically balance load among service providers, and so the quality of service to clients improves. We present solutions that maximize the income of service providers. The solutions are then compared using simulation. Simulation results show that our analysed solution significantly improve quality of service of clients and increase the income of service providers as compared to a simple heuristic based solution that is otherwise could to be used.","PeriodicalId":298587,"journal":{"name":"Proceedings of the 16th International Conference on Distributed Computing and Networking","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124972426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Power-law graphs model the interconnections in various types of large-scale networks ranging from physical and biological systems to man-made social networks and web graphs. In these graphs, the degree distribution of the nodes obeys the power-law property: the fraction of nodes P(k) having a degree k closely follows the rule P(k) ∞ k−-γ. In the domain of man-made systems, if the topology of a power-law network gets altered due to failures or adversarial attacks, then remedial actions to restore the power-law property are very important. This paper presents self-stabilizing algorithms for maintaining the power-law property in a network of processes. These algorithms allow spontaneous restoration of the power-law property from any initial connected configuration. The algorithms consist of three modular components: a detection component to detect the violation of the power-law property, an interim topology creation component, and a repair component to build the final graph. We propose two different interim topologies, a clique and a linear graph. We then present two different techniques for rebuilding the power-law topology -- a probabilistic approach based on the preferential attachment model, which stabilizes in O(log n) communication rounds with a link complexity of O(n) per process, and a deterministic approach that introduces the novel data structure Bridge Tree and stabilizes in O(n) communication rounds with a much lower link complexity.
{"title":"Self-stabilizing Power-law Networks","authors":"T. Alsulaiman, Andrew Berns, Sukumar Ghosh","doi":"10.1145/2684464.2684485","DOIUrl":"https://doi.org/10.1145/2684464.2684485","url":null,"abstract":"Power-law graphs model the interconnections in various types of large-scale networks ranging from physical and biological systems to man-made social networks and web graphs. In these graphs, the degree distribution of the nodes obeys the power-law property: the fraction of nodes P(k) having a degree k closely follows the rule P(k) ∞ k−-γ. In the domain of man-made systems, if the topology of a power-law network gets altered due to failures or adversarial attacks, then remedial actions to restore the power-law property are very important. This paper presents self-stabilizing algorithms for maintaining the power-law property in a network of processes. These algorithms allow spontaneous restoration of the power-law property from any initial connected configuration. The algorithms consist of three modular components: a detection component to detect the violation of the power-law property, an interim topology creation component, and a repair component to build the final graph. We propose two different interim topologies, a clique and a linear graph. We then present two different techniques for rebuilding the power-law topology -- a probabilistic approach based on the preferential attachment model, which stabilizes in O(log n) communication rounds with a link complexity of O(n) per process, and a deterministic approach that introduces the novel data structure Bridge Tree and stabilizes in O(n) communication rounds with a much lower link complexity.","PeriodicalId":298587,"journal":{"name":"Proceedings of the 16th International Conference on Distributed Computing and Networking","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132391029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Jaekel, S. Bandyopadhyay, Saja Al-Mamoori, Sriharsha Varanasi
In recent years, there is growing recognition of the need to develop suitable mechanisms for reducing the adverse effects of malicious attacks such as high power jamming and tapping attacks. A number of recent papers have proposed static lightpath allocation approaches that take such security issues into consideration. Most of these approaches consider the routing problem separately from the wavelength assignment problem. In this paper we propose a new security-aware ILP formulation, as well as an efficient heuristic for the complete security-aware dynamic routing and wavelength assignment (RWA) problem. To the best of our knowledge, this is the first such work to jointly consider in-band and out-of-band attacks, for either static or dynamic case.
{"title":"Security-Aware Dynamic Lightpath Allocation Scheme for WDM Networks","authors":"A. Jaekel, S. Bandyopadhyay, Saja Al-Mamoori, Sriharsha Varanasi","doi":"10.1145/2684464.2684502","DOIUrl":"https://doi.org/10.1145/2684464.2684502","url":null,"abstract":"In recent years, there is growing recognition of the need to develop suitable mechanisms for reducing the adverse effects of malicious attacks such as high power jamming and tapping attacks. A number of recent papers have proposed static lightpath allocation approaches that take such security issues into consideration. Most of these approaches consider the routing problem separately from the wavelength assignment problem. In this paper we propose a new security-aware ILP formulation, as well as an efficient heuristic for the complete security-aware dynamic routing and wavelength assignment (RWA) problem. To the best of our knowledge, this is the first such work to jointly consider in-band and out-of-band attacks, for either static or dynamic case.","PeriodicalId":298587,"journal":{"name":"Proceedings of the 16th International Conference on Distributed Computing and Networking","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129198694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present a new lock-free algorithm for concurrent manipulation of a binary search tree in an asynchronous shared memory system that supports search, insert and delete operations. It combines ideas from two recently proposed lock-free algorithms: one of them provides good performance for a read-dominated workload and the other one for a write-dominated workload. Specifically, it uses internal representation of a search tree (as in the first one) and is based on marking edges instead of nodes (as in the second one). Our experiments indicate that our new lock-free algorithm outperforms other lock-free algorithms in most cases providing up to 35% improvement in some cases over the next best algorithm.
{"title":"A Fast Lock-Free Internal Binary Search Tree","authors":"Arunmoezhi Ramachandran, N. Mittal","doi":"10.1145/2684464.2684472","DOIUrl":"https://doi.org/10.1145/2684464.2684472","url":null,"abstract":"We present a new lock-free algorithm for concurrent manipulation of a binary search tree in an asynchronous shared memory system that supports search, insert and delete operations. It combines ideas from two recently proposed lock-free algorithms: one of them provides good performance for a read-dominated workload and the other one for a write-dominated workload. Specifically, it uses internal representation of a search tree (as in the first one) and is based on marking edges instead of nodes (as in the second one). Our experiments indicate that our new lock-free algorithm outperforms other lock-free algorithms in most cases providing up to 35% improvement in some cases over the next best algorithm.","PeriodicalId":298587,"journal":{"name":"Proceedings of the 16th International Conference on Distributed Computing and Networking","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126096138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Kranakis, D. Krizanc, Fraser MacQuarie, S. Shende
We provide randomized rendezvous algorithms for two synchronous robots in a bi-directional ring of length n (n is a real number): the robots are equipped with identical chronometers, execute identical algorithms, but have different speeds u, 1 (where u > 1). In general, neither of the robots are aware of their own speed but in some cases they may be aware either of the magnitude of u or some quantity of time that depends on u, n. The robots start by choosing a direction uniformly and independently at random. Given integer k ≥ 0, we design algorithms that have the two robots alternate for k + 1 rounds between choosing the direction at random followed by walking for a predetermined time. In the last round the robots walk until rendezvous. The first algorithm, RV0, works with one random bit per robot and consists of a single round: after choosing their initial directions the robots never change direction. Rendezvous is established in u·n/2(u2−1) expected time and this is shown to be optimal among all randomized algorithms employing a single random bit during their execution. The second algorithm RV1(k), for k ≥ 1, has the two robots alternate for k + 1 rounds between choosing the direction at random followed by walking for a predetermined time u/u + 1; in the last step the robots walk until rendezvous. Among all algorithms that use k + 1 random bits we establish a sharp threshold; for u ≤ 2, RV1(k) is optimal in terms of expected rendezvous time while for u > 2, RV0 is optimal. Further, we provide new randomized rendezvous algorithms employing more random bits and analyze their expected rendezvous time depending on the knowledge of the robots about the length n of the ring and their speeds (u > 1).
{"title":"Randomized Rendezvous Algorithms for Agents on a Ring with Different Speeds","authors":"E. Kranakis, D. Krizanc, Fraser MacQuarie, S. Shende","doi":"10.1145/2684464.2684468","DOIUrl":"https://doi.org/10.1145/2684464.2684468","url":null,"abstract":"We provide randomized rendezvous algorithms for two synchronous robots in a bi-directional ring of length n (n is a real number): the robots are equipped with identical chronometers, execute identical algorithms, but have different speeds u, 1 (where u > 1). In general, neither of the robots are aware of their own speed but in some cases they may be aware either of the magnitude of u or some quantity of time that depends on u, n. The robots start by choosing a direction uniformly and independently at random. Given integer k ≥ 0, we design algorithms that have the two robots alternate for k + 1 rounds between choosing the direction at random followed by walking for a predetermined time. In the last round the robots walk until rendezvous. The first algorithm, RV0, works with one random bit per robot and consists of a single round: after choosing their initial directions the robots never change direction. Rendezvous is established in u·n/2(u2−1) expected time and this is shown to be optimal among all randomized algorithms employing a single random bit during their execution. The second algorithm RV1(k), for k ≥ 1, has the two robots alternate for k + 1 rounds between choosing the direction at random followed by walking for a predetermined time u/u + 1; in the last step the robots walk until rendezvous. Among all algorithms that use k + 1 random bits we establish a sharp threshold; for u ≤ 2, RV1(k) is optimal in terms of expected rendezvous time while for u > 2, RV0 is optimal. Further, we provide new randomized rendezvous algorithms employing more random bits and analyze their expected rendezvous time depending on the knowledge of the robots about the length n of the ring and their speeds (u > 1).","PeriodicalId":298587,"journal":{"name":"Proceedings of the 16th International Conference on Distributed Computing and Networking","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130387717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wireless sensor network is a self-organizing wireless network system which has enabled densely deployment of nodes. These wireless sensors gather and forward data. But finding an efficient route is a challenge while the nodes communicate for data transmission. A routing algorithm, FMCR, is proposed in this paper. A well-known operations research technique, multi-criteria decision analysis, is used in this proposed scheme. Here multiple criteria, such as residual energy, packet transmission frequency and hop count are taken into account. In order to assign the weighted values on each criterion, Fuzzy rules are applied on heuristic properties like node density, dead nodes and delay. The best route is selected using Weighted Product Model (WPM). This scheme has been implemented using TinyOS, an event-driven operating system designed for wireless sensor network.
{"title":"Multi Criteria Decision Analysis assisted Routing in Wireless Sensor Network using Fuzzy rules","authors":"S. Bhunia, Bijoy Das, N. Mukherjee","doi":"10.1145/2684464.2684499","DOIUrl":"https://doi.org/10.1145/2684464.2684499","url":null,"abstract":"Wireless sensor network is a self-organizing wireless network system which has enabled densely deployment of nodes. These wireless sensors gather and forward data. But finding an efficient route is a challenge while the nodes communicate for data transmission. A routing algorithm, FMCR, is proposed in this paper. A well-known operations research technique, multi-criteria decision analysis, is used in this proposed scheme. Here multiple criteria, such as residual energy, packet transmission frequency and hop count are taken into account. In order to assign the weighted values on each criterion, Fuzzy rules are applied on heuristic properties like node density, dead nodes and delay. The best route is selected using Weighted Product Model (WPM). This scheme has been implemented using TinyOS, an event-driven operating system designed for wireless sensor network.","PeriodicalId":298587,"journal":{"name":"Proceedings of the 16th International Conference on Distributed Computing and Networking","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132349932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Given a set of sensors, the strong minimum energy topology (SMET) problem is to assign transmission range to each sensor node so that the sum of the transmission range for all the sensor is minimum subject to the constraint that the network is strongly connected (there is a directed path between every pair of nodes in the Network). This problem is known to be NP-hard. As this problem has lots of practical applications, several approximation algorithms and heuristics have been proposed. In this paper, we propose a new heuristic called Boruvka-incremental power greedy heuristic based on the Boruvka algorithm for the minimum spanning tree (MST) problem for solving the SMET problem. We compare the performance of the Boruvka-incremental power greedy heuristic with Kruskal-incremental power greedy heuristic and Prim-incremental power greedy heuristic. Extensive simulation results illustrate that Boruvka heuristic outperforms the Kruskal-incremental power greedy heuristic and Prim-incremental power greedy heuristic. We have also proved that apart from providing significant improvement in terms of average power savings, Boruvka incremental power greedy heuristic takes O(n) time for planar graphs as compared to O(n log n) time taken by Kruskal-incremental power greedy heuristic and O(n2) time taken by Prim-incremental power greedy heuristic, where n is the number of nodes in the network.
{"title":"Boruvka-Incremental Power Greedy Heuristic for Strong Minimum Energy Topology in Wireless Sensor Networks","authors":"B. S. Panda, B. K. Bhatta, Deepak Mishra, S. De","doi":"10.1145/2684464.2684490","DOIUrl":"https://doi.org/10.1145/2684464.2684490","url":null,"abstract":"Given a set of sensors, the strong minimum energy topology (SMET) problem is to assign transmission range to each sensor node so that the sum of the transmission range for all the sensor is minimum subject to the constraint that the network is strongly connected (there is a directed path between every pair of nodes in the Network). This problem is known to be NP-hard. As this problem has lots of practical applications, several approximation algorithms and heuristics have been proposed. In this paper, we propose a new heuristic called Boruvka-incremental power greedy heuristic based on the Boruvka algorithm for the minimum spanning tree (MST) problem for solving the SMET problem. We compare the performance of the Boruvka-incremental power greedy heuristic with Kruskal-incremental power greedy heuristic and Prim-incremental power greedy heuristic. Extensive simulation results illustrate that Boruvka heuristic outperforms the Kruskal-incremental power greedy heuristic and Prim-incremental power greedy heuristic. We have also proved that apart from providing significant improvement in terms of average power savings, Boruvka incremental power greedy heuristic takes O(n) time for planar graphs as compared to O(n log n) time taken by Kruskal-incremental power greedy heuristic and O(n2) time taken by Prim-incremental power greedy heuristic, where n is the number of nodes in the network.","PeriodicalId":298587,"journal":{"name":"Proceedings of the 16th International Conference on Distributed Computing and Networking","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131621044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A sparse cut of a graph is a partition of the vertices into two disjoint subsets such that the ratio of the number of edges across the two subsets divided by the sum of degrees of vertices in the smaller side is minimum. Finding sparse cuts is an important tool in analyzing large-scale distributed networks such as the Internet and Peer-to-Peer networks, as well as large-scale graphs such as the web graph, online social communities, and VLSI circuits. Sparse cuts are useful in graph clustering and partitioning among numerous other applications. In distributed communication networks, they are useful for topology maintenance and for designing better search and routing algorithms. In this paper, we focus on developing a fast distributed algorithm for computing sparse cuts in networks. Given an undirected n-node network G with conductance φ, the goal is to find a cut set whose conductance is close to φ. We present a distributed algorithm that finds a cut set with sparsity Õ(√φ) (Õ hides polylog n factors). Our algorithm works in the CONGEST distributed computing model and outputs a cut of conductance at most Õ (√φ) with high probability, in Õ(1/b(1/φ + n)log2) rounds, where b is balance of the cut of given conductance. In particular, to find a sparse cut of constant balance, our algorithm takes O((1/φ + n)log2 n) rounds. Our algorithm can also be used to output a local cluster, i.e., a subset of vertices near a given source node, and whose conductance is within a quadratic factor of the best possible cluster around the specified node. Our distributed algorithm can work without knowledge of the optimal φ value (with only a log n factor slowdown) and hence can be used to find approximate conductance values both globally and with respect to a given source node. Our algorithm uses random walks as a key subroutine and is fully decentralized and uses lightweight local computations. We also give a lower bound on the time needed for any distributed algorithm to compute any non-trivial sparse cut --- any distributed approximation algorithm (for any nontrivial approximation ratio) for computing sparsest cut will take Ω (√n + D) rounds, where D is the diameter of the graph. Our algorithm can be used to find sparse cuts (and their conductance values) and to identify well-connected clusters and critical edges in distributed networks. This in turn can be helpful in the design, analysis, and maintenance of topologically-aware networks.
{"title":"Distributed Computation of Sparse Cuts via Random Walks","authors":"Atish Das Sarma, A. R. Molla, Gopal Pandurangan","doi":"10.1145/2684464.2684474","DOIUrl":"https://doi.org/10.1145/2684464.2684474","url":null,"abstract":"A sparse cut of a graph is a partition of the vertices into two disjoint subsets such that the ratio of the number of edges across the two subsets divided by the sum of degrees of vertices in the smaller side is minimum. Finding sparse cuts is an important tool in analyzing large-scale distributed networks such as the Internet and Peer-to-Peer networks, as well as large-scale graphs such as the web graph, online social communities, and VLSI circuits. Sparse cuts are useful in graph clustering and partitioning among numerous other applications. In distributed communication networks, they are useful for topology maintenance and for designing better search and routing algorithms. In this paper, we focus on developing a fast distributed algorithm for computing sparse cuts in networks. Given an undirected n-node network G with conductance φ, the goal is to find a cut set whose conductance is close to φ. We present a distributed algorithm that finds a cut set with sparsity Õ(√φ) (Õ hides polylog n factors). Our algorithm works in the CONGEST distributed computing model and outputs a cut of conductance at most Õ (√φ) with high probability, in Õ(1/b(1/φ + n)log2) rounds, where b is balance of the cut of given conductance. In particular, to find a sparse cut of constant balance, our algorithm takes O((1/φ + n)log2 n) rounds. Our algorithm can also be used to output a local cluster, i.e., a subset of vertices near a given source node, and whose conductance is within a quadratic factor of the best possible cluster around the specified node. Our distributed algorithm can work without knowledge of the optimal φ value (with only a log n factor slowdown) and hence can be used to find approximate conductance values both globally and with respect to a given source node. Our algorithm uses random walks as a key subroutine and is fully decentralized and uses lightweight local computations. We also give a lower bound on the time needed for any distributed algorithm to compute any non-trivial sparse cut --- any distributed approximation algorithm (for any nontrivial approximation ratio) for computing sparsest cut will take Ω (√n + D) rounds, where D is the diameter of the graph. Our algorithm can be used to find sparse cuts (and their conductance values) and to identify well-connected clusters and critical edges in distributed networks. This in turn can be helpful in the design, analysis, and maintenance of topologically-aware networks.","PeriodicalId":298587,"journal":{"name":"Proceedings of the 16th International Conference on Distributed Computing and Networking","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114707881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Opportunistic networks are partially connected wireless ad hoc networks, in which pairwise unpredicted transient contacts between mobile devices are the only opportunities for these devices to exchange information or services. Ensuring the coordination of multiple parts of a distributed application in such conditions is a challenge. This paper presents a system that can solve consensus problems in an opportunistic network. This system combines an implementation of the One-Third Rule (OTR) algorithm with a communication layer that supports network-wide, content-driven message dissemination based on controlled epidemic routing. Experimental results obtained with a small flotilla of smartphones are also presented, that validate the system and demonstrate that consensus can be solved effectively in an opportunistic network.
{"title":"Solving Consensus in Opportunistic Networks","authors":"Abdulkader Benchi, P. Launay, F. Guidec","doi":"10.1145/2684464.2684479","DOIUrl":"https://doi.org/10.1145/2684464.2684479","url":null,"abstract":"Opportunistic networks are partially connected wireless ad hoc networks, in which pairwise unpredicted transient contacts between mobile devices are the only opportunities for these devices to exchange information or services. Ensuring the coordination of multiple parts of a distributed application in such conditions is a challenge. This paper presents a system that can solve consensus problems in an opportunistic network. This system combines an implementation of the One-Third Rule (OTR) algorithm with a communication layer that supports network-wide, content-driven message dissemination based on controlled epidemic routing. Experimental results obtained with a small flotilla of smartphones are also presented, that validate the system and demonstrate that consensus can be solved effectively in an opportunistic network.","PeriodicalId":298587,"journal":{"name":"Proceedings of the 16th International Conference on Distributed Computing and Networking","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116695631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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