41,641 research outputs found
QoS routing in ad-hoc networks using GA and multi-objective optimization
Much work has been done on routing in Ad-hoc networks, but the proposed routing solutions only deal with the best effort data traffic. Connections with Quality of Service (QoS) requirements, such as voice channels with delay and bandwidth constraints, are not supported. The QoS routing has been receiving increasingly intensive attention, but searching for the shortest path with many metrics is an NP-complete problem. For this reason, approximated solutions and heuristic algorithms should be developed for multi-path constraints QoS routing. Also, the routing methods should be adaptive, flexible, and intelligent. In this paper, we use Genetic Algorithms (GAs) and multi-objective optimization for QoS routing in Ad-hoc Networks. In order to reduce the search space of GA, we implemented a search space reduction algorithm, which reduces the search space for GAMAN (GA-based routing algorithm for Mobile Ad-hoc Networks) to find a new route. We evaluate the performance of GAMAN by computer simulations and show that GAMAN has better behaviour than GLBR (Genetic Load Balancing Routing).Peer ReviewedPostprint (published version
Quality-of-service provisioning in high speed networks : routing perspectives
The continuous growth in both commercial and public network traffic with various quality-of-service (QoS) requirements is calling for better service than the current Internet\u27s best effort mechanism. One of the challenging issues is to select feasible paths that satisfy the different requirements of various applications. This problem is known as QoS routing. In general, two issues are related to QoS routing: state distribution and routing strategy. Routing strategy is used to find a feasible path that meets the QoS requirements. State distribution addresses the issue of exchanging the state information throughout the network, and can be further divided into two sub-problems: when to update and how to disseminate the state information.
In this dissertation, the issue of when to update link state information from the perspective of information theory is addressed. Based on the rate-distortion analysis, an efficient scheme, which outperforms the state of the art in terms of both protocol overhead and accuracy of link state information, is presented. Second, a reliable scheme is proposed so that, when a link is broken, link state information is still reachable to all network nodes as long as the network is connected. Meanwhile, the protocol overhead is low enough to be implemented in real networks. Third, QoS routing is NP-complete. Hence, tackling this problem requires heuristics. A common approach is to convert this problem into a shortest path or k-shortest path problem and solve it by using existing algorithms such as Bellman-Ford and Dijkstra algorithms. However, this approach suffers from either high computational complexity or low success ratio in finding the feasible paths. Hence, a new problem, All Hops k-shortest Path (AHKP), is introduced and investigated. Based on the solution to AHKP, an efficient self-adaptive routing algorithm is presented, which can guarantee in finding feasible paths with fairly low average computational complexity. One of its most distinguished properties is its progressive property, which is very useful in practice: it can self-adaptively minimize its computational complexity without sacrificing its performance. In addition, routing without considering the staleness of link state information may generate a significant percentage of false routing. Our proposed routing algorithm is capable of minimizing the impact of stale link state information without stochastic link state knowledge. Fourth, the computational complexities of existing s-approximation algorithms are linearly proportional to the adopted linear scaling factors. Therefore, two efficient algorithms are proposed for finding the optimal (the smallest) linear scaling factor such that the computational complexities are reduced. Finally, an efficient algorithm is proposed for finding the least hop(s) multiple additive constrained path for the purpose of saving network resources
An Experimental Investigation of Hyperbolic Routing with a Smart Forwarding Plane in NDN
Routing in NDN networks must scale in terms of forwarding table size and
routing protocol overhead. Hyperbolic routing (HR) presents a potential
solution to address the routing scalability problem, because it does not use
traditional forwarding tables or exchange routing updates upon changes in
network topologies. Although HR has the drawbacks of producing sub-optimal
routes or local minima for some destinations, these issues can be mitigated by
NDN's intelligent data forwarding plane. However, HR's viability still depends
on both the quality of the routes HR provides and the overhead incurred at the
forwarding plane due to HR's sub-optimal behavior. We designed a new forwarding
strategy called Adaptive Smoothed RTT-based Forwarding (ASF) to mitigate HR's
sub-optimal path selection. This paper describes our experimental investigation
into the packet delivery delay and overhead under HR as compared with
Named-Data Link State Routing (NLSR), which calculates shortest paths. We run
emulation experiments using various topologies with different failure
scenarios, probing intervals, and maximum number of next hops for a name
prefix. Our results show that HR's delay stretch has a median close to 1 and a
95th-percentile around or below 2, which does not grow with the network size.
HR's message overhead in dynamic topologies is nearly independent of the
network size, while NLSR's overhead grows polynomially at least. These results
suggest that HR offers a more scalable routing solution with little impact on
the optimality of routing paths
SCOR: Software-defined Constrained Optimal Routing Platform for SDN
A Software-defined Constrained Optimal Routing (SCOR) platform is introduced
as a Northbound interface in SDN architecture. It is based on constraint
programming techniques and is implemented in MiniZinc modelling language. Using
constraint programming techniques in this Northbound interface has created an
efficient tool for implementing complex Quality of Service routing applications
in a few lines of code. The code includes only the problem statement and the
solution is found by a general solver program. A routing framework is
introduced based on SDN's architecture model which uses SCOR as its Northbound
interface and an upper layer of applications implemented in SCOR. Performance
of a few implemented routing applications are evaluated in different network
topologies, network sizes and various number of concurrent flows.Comment: 19 pages, 11 figures, 11 algorithms, 3 table
On Diagnosis of Forwarding Plane via Static Forwarding Rules in Software Defined Networks
Software Defined Networks (SDN) decouple the forwarding and control planes
from each other. The control plane is assumed to have a global knowledge of the
underlying physical and/or logical network topology so that it can monitor,
abstract and control the forwarding plane. In our paper, we present solutions
that install an optimal or near-optimal (i.e., within 14% of the optimal)
number of static forwarding rules on switches/routers so that any controller
can verify the topology connectivity and detect/locate link failures at data
plane speeds without relying on state updates from other controllers. Our upper
bounds on performance indicate that sub-second link failure localization is
possible even at data-center scale networks. For networks with hundreds or few
thousand links, tens of milliseconds of latency is achievable.Comment: Submitted to Infocom'14, 9 page
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
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