Anycast Flows Research Articles

A Heuristic Algorithm for Multi-layer Network Optimization in Cloud Computing

Background: One of the most important concepts in cloud computing is modeling the problem as a multi-layer optimization problem which leads to cost savings in designing and operating the networks. Previous researchers have modeled the two-layer network operating problem as an Integer Linear Programming (ILP) problem, and due to the computational complexity of solving it jointly, they suggested a two-stage procedure for solving it by considering one layer at each stage.Aim: In this paper, considering the ILP model and using some properties of it, we propose a heuristic algorithm for solving the model jointly, considering unicast, multicast, and anycast flows simultaneously. Method: We first sort demands in decreasing order and use a greedy method to realize demands in order. Due to the high computational complexity of ILP model, the proposed heuristic algorithm is suitable for networks with a large number of nodes; In this regard, various examples are solved by CPLEX and MATLAB soft wares. Results: Our simulation results show that for small values of M and N CPLEX fails to find the optimal solution, while AGA finds a near-optimal solution quickly.Conclusion: The proposed greedy algorithm could solve the large-scale networks approximately in polynomial time and its approximation is reasonable.

Greedy randomized adaptive search procedure for joint optimization of unicast and anycast traffic in spectrally-spatially flexible optical networks

The volume of data traffic in backbone networks is increasing at a speed that exceeds the growth rates of capacities offered in currently used wavelength division multiplexing (WDM) networks. As a short term solution to the potential capacity crunch, the concept of flexible-grid elastic optical networks is nowadays implemented in many backbone networks. However, in a longer term the space division multiplexing (SDM) technology is the most promising solution for satisfying the capacity requirements in future optical networks. The combination of both technologies allows for realization of spectrally-spatially flexible optical networks (SS-FONs). In this paper, we focus on a network scenario in which two different types of traffic flows are carried over an SS-FON, namely, unicast and anycast flows. Unicast flows are used for basic one-to-one communication, while anycast flows — defined as one-to-one-of-many communication — are related to the network traffic generated in data centers and the fact that data centers placed in different network locations can provide the same service or content to network users. For provisioning of lightpaths for both types of traffic, we address the basic optimization problem in SS-FONs, which is routing, space and spectrum allocation (RSSA). The aim of this study is threefold. First, we propose an effective metaheuristic method based on the greedy randomized adaptive search procedure (GRASP) to solve the RSSA problem. Second, we compare performance of the proposed GRASP method against reference optimization approaches. Finally, we present and discuss a wide range of experiments focused on analysis of SS-FONs with joint unicast and anycast traffic. The main conclusions are: (i) the proposed GRASP algorithm provides results very close to optimal (only 0.5% optimality gap) for smaller problem instances and significantly outperforms other heuristics for larger problem instances (in average around 6.8% better than the reference simulated annealing algorithm); (ii) the use of anycast connections in SS-FONs allows to improve the network performance in terms of spectrum usage.

Integer programming, Constraint programming, and metaheuristic approaches for static optimization of anycast flows in elastic optical networks

Elastic optical network (EON) is a novel optical technology introduced recently to provide flexible and multibitrate data transmission in the optical layer. Since many new network services including cloud computing and content delivery networks are provisioned with the use of specialized data centers located in different network nodes, in place of one‐to‐one unicast transmission, the anycast transmission defined as one‐to‐one‐of‐many gains much popularity as a quite simple way to improve network performance. Therefore, this article focuses on modeling and static optimization of anycast flows in EONs. In particular, a ‐hard Routing and Spectrum Allocation for Restoration of Anycast Flows (RSA/RAF) problem is formulated. Next, various optimization approaches are proposed to solve this problem, namely, integer linear programming (ILP) using branch and bound algorithm, constraint programming (CP), and various heuristic approaches. Extensive numerical experiments are run to evaluate and compare all proposed methods. The main conclusion is that in some cases the CP approach is more efficient than the ILP modeling. Moreover, the results show that the SA algorithm significantly outperforms other heuristic methods. © 2015 Wiley Periodicals, Inc. NETWORKS, Vol. 66(4), 253–266 2015

All capacities modular cost survivable network design problem using genetic algorithm with completely connection encoding

AbstractWe study the survivable network design problem (SNDP) for simultaneous unicast and anycast flows in networks where the link cost follows All Capacities Modular Cost (ACMC) model. Given a network modeled by a connected, undirected graph and a set of flow demands, this problem aims at finding a set of connections with a minimized network cost in order to protect the network against any single failure. This paper proposes a new Genetic Algorithm with an efficient encoding to solve the SNDP in networks with ACMC model (A-SNDP). Our encoding scheme is simple and allows large search space. Extensive simulation results on real large topology instances show that the proposed algorithm is much more efficient than the Tabu Search and other conventional Genetic Algorithm in terms of minimizing the network cost.

A multi-objective approach for solving the survivable network design problem with simultaneous unicast and anycast flows

In this paper, we consider the survivable network design problem for simultaneous unicast and anycast flow requests. We assume that the network is modeled by a connected and undirected graph. This problem aims at finding a set of connections with a minimized network cost in order to protect the network against any single failure. The cost is computed using the all capacities modular cost (ACMC) model and a set of flow demands. We name it as ACMC-based survivable network design problem (A-SNDP). It is proved that the problem is NP-hard. We introduce a multi-objective approach to solve A-SNDP. The objectives are to minimize the network cost (NCost) and the network failure (NFail). Extensive simulation results on instances of Polska, Germany and Atlanta networks showed the efficiency of the multi-objective approach for solving A-SNDP.

A heuristic approach to working and spare capacity optimization for survivable anycast streaming protected by p-cycles

Most of previous research in the field of network survivability has been focused on unicast transmissions. However, growing popularity of various concepts following the idea of content-oriented networks has triggered the need to develop new approaches to protect networks with other than unicast flows including multicast and anycast flows. In this paper, we consider the latter case and address the problem of working and spare capacity allocation in networks with anycast streaming, i.e., it is assumed that a set of replica servers is deployed in the network to serve streaming requests. To protect the network we propose to apply p-cycles--a relatively novel survivability approach combining capacity effectiveness of mesh restoration and ring-like restoration speed. To benefit from special properties of anycast flows, we use augmented version of p-cycles called Anycast-Protecting p-Cycles (APpC). We formulate the optimization problem as an ILP (Integer Linear Programming) model further applied to obtain optimal results using the GuRoBi solver. Due to high complexity of this optimization problem, we propose an effective heuristic algorithm based on the Simulated Annealing approach. Several versions of the algorithm are developed and examined--the best method yields on average results only 4.14 % worse than optimal ones. A wide range of experiments is conducted to verify performance of the proposed approach as a function of various network parameters including: p-cycle generator, p-cycle length, number of replica servers, number of clients. Moreover, we evaluate the Anycast-Protecting p-Cycles approach against classical p-cycle.

Simultaneous optimization of unicast and anycast flows and replica location in survivable optical networks

Most of previous works related to survivable networks have been focused on unicast communications. In this paper, we address the problem of network survivability in the context of anycast communications, which has not attracted much attention recently. Anycast transmission is defined as one-to-one-of-many. To assure the network survivability, we use the single backup path approach, i.e., each demand has two node-disjoint paths to provide protection against a single link failure. We formulate new ILP models to find the optimal paths for anycast and unicast connections, as well as to find the optimal location of replica servers. To test the proposed approach, we run extensive numerical experiments using CPLEX solver on four example network topologies with different scenarios of replica server count and the proportion between unicast and anycast traffic. Results prove the efficiency of our solution in terms of various parameters including cost, path length, resource utilization, and time of computation.

Combinatorial Optimization of Capacity and Flow Assignment Problem for Unicast and Anycast Connections with Linear and Convex Objective Functions – Exact and heuristic Algorithms

Abstract In this work we focus on Capacity and Flow Assignment (CFA) problem for simultaneous unicast and anycast flows. Anycast flow is a new technique to deliver packets in computer networks, in which user can download or send packets to any one of several possible servers. In our work we consider two models of CFA problem. In the first model as the criterion we assume the total average delay function with the budget constraint. In the second model as the objective we use the linear function of cost. The considered problems are NP-complete – therefore, to solve larger problem instances we propose new heuristic algorithms that can be applied.

Tabu Search Algorithm for Survivable Network Design Problem with Simultaneous Unicast and Anycast Flows

Tabu Search Algorithm for Survivable Network Design Problem with Simultaneous Unicast and Anycast FlowsIn this work we focus on the problem of survivable network design for simultaneous unicast and anycast flows. This problem follows from the growing popularity of network services applying the anycast paradigm. The anycasting is defined as one-to-one-of-many transmission and is applied in Domain Name Service (DNS), peer-to-peer (P2P) systems, Content Delivery Networks (CDN). In this work we formulate two models that enables joint optimization of network capacity, working and backup connections for both unicast and anycast flows. The goal is to minimize the network cost required to protect the network against failures using the single backup path approach. In the first model we consider modular link cost, in the second we are given a set of link proposal and we must select only one of them. Because these problems are NP-hard, therefore optimal solutions of branch-and-bounds or branch-and-cut methods can be generated for relatively small networks. Consequently, we propose a new heuristic algorithm based on Tabu Search method. We present results showing the effectiveness the proposed heuristic compared against optimal results. Moreover, we report results showing that the use of anycast paradigm can reduce the network cost.

Anycasting in connection-oriented computer networks: Models, algorithms and results

Anycasting in connection-oriented computer networks: Models, algorithms and resultsOur discussion in this article centers around various issues related to the use of anycasting in connection-oriented computer networks. Anycast is defined as aone-to-one-of-manytransmission to deliver a packet to one of many hosts. Anycasting can be applied if the same content is replicated over many locations in the network. Examples of network techniques that apply anycasting are Content Delivery Networks (CDNs), Domain Name Service (DNS), Peer-to-Peer (P2P) systems. The role of anycasting is growing concurrently with the popularity of electronic music, movies, and other content required by Internet users. In this work we focus on the optimization of anycast flows in connection-oriented networks. We formulate a model of anycast connections and next propose a heuristic algorithm based on the Lagrangean relaxation aimed to optimize jointly routes for anycast and unicast connections. Results of numerical experiments are presented and evaluated. Finally, we analyze briefly problems related to anycasting in dynamic routing and multi-layer networks.

Delay Control and Parallel Admission Algorithms for Real-Time Anycast Flow

An anycast flow is a flow that can be connected to any one of the members in a group of designated (replicated) servers (called anycast group). In this paper, we derive a set of formulas for calculating the end-to-end delay bound for the anycast flows and present novel admission control algorithms for anycast flows with real-time constraints. Given such an anycast group, our algorithms can effectively select the paths for anycast flows' admission and connection based on the least end-to-end delay bounds evaluated. We also present a parallel admission control algorithm that can effectively calculate the available paths with a short delay bound for different destinations in the anycast group so that a best path with the shortest delay bound can be chosen.

Distributed admission control for anycast flows

Anycasting has recently become an important research topic, especially for replicated servers. With anycasting, applications can request the nearest server for provision of desired (multimedia) service. In this paper, we study efficient distributed admission control (DAC) for anycast flows. We focus on algorithms that perform destination selection and efficient path establishment. Taking advantage of anycasting, our distributed algorithms differ from each other in their dependence on system status information. Performance data obtained through mathematical analysis and simulations show that, in terms of admission probabilities, DAC systems that are based on local status information have performance levels close to those that utilize global and dynamic status information. This renders our DAC algorithms useful not only for the network layer, but also for the application layer admission control for anycast flows.

Coal and Conflict: A Study of Industrial Relations at Collieries.W. H. Scott , Enid Mumford , L. C. McGivering , J. M. Kirkby

Previous articleNext article No AccessBook ReviewsCoal and Conflict: A Study of Industrial Relations at Collieries. W. H. Scott , Enid Mumford , L. C. McGivering , J. M. Kirkby Louis KriesbergLouis Kriesberg Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by American Journal of Sociology Volume 70, Number 1Jul., 1964 Article DOIhttps://doi.org/10.1086/223760 Views: 1Total views on this site Citations: 7Citations are reported from Crossref Copyright 1964, 1965, University of ChicagoPDF download Crossref reports the following articles citing this article:Krzysztof Walkowiak, Michal Kucharzak, Pawel Kopec, Andrzej Kasprzak ILP model and algorithms for restoration of anycast flows in Elastic Optical Networks, (Nov 2014): 102–108.https://doi.org/10.1109/RNDM.2014.7014938Xiao-yan Xing, Yi-yong Xiao, Ren-qian Zhang Parameter selection optimization for parametric cost estimation based on Simulated Annealing, (Oct 2010): 231–236.https://doi.org/10.1109/ICIEEM.2010.5646651Zhufang Wang, Donghong Cui A Hybrid Algorithm Based on Genetic Algorithm and Simulated Annealing for Solving Portfolio Problem, (Jul 2009): 106–109.https://doi.org/10.1109/BIFE.2009.34Zhengbing Hu, Rabah Moh'd, Al Shboul The Application of Ant Colony Optimization Technique (ACOT) for Employees Selection and Training, (Apr 2009): 497–502.https://doi.org/10.1109/DBTA.2009.172Jonas C. P. Yu, H. M. Wee, K. J. Wang An integrated three-echelon supply chain model for deteriorating items via simulated annealing method, (Jul 2008): 3921–3926.https://doi.org/10.1109/ICMLC.2008.4621088Humberto Brandao De Oliveira, Germano Vasconcelos, Guilherme Alvarenga A Multi-Start Simulated Annealing Algorithm for the Vehicle Routing Problem with Time Windows, (Oct 2006): 24–24.https://doi.org/10.1109/SBRN.2006.4C. Muller, E.H. Magill, P. Prosser, D.G. Smith Artificial intelligence in telecommunications, (): 883–887.https://doi.org/10.1109/GLOCOM.1993.318205