2,727 research outputs found
Building Reliable Budget-Based Binary-State Networks
Everyday life is driven by various network, such as supply chains for
distributing raw materials, semi-finished product goods, and final products;
Internet of Things (IoT) for connecting and exchanging data; utility networks
for transmitting fuel, power, water, electricity, and 4G/5G; and social
networks for sharing information and connections. The binary-state network is a
basic network, where the state of each component is either success or failure,
i.e., the binary-state. Network reliability plays an important role in
evaluating the performance of network planning, design, and management. Because
more networks are being set up in the real world currently, there is a need for
their reliability. It is necessary to build a reliable network within a limited
budget. However, existing studies are focused on the budget limit for each
minimal path (MP) in networks without considering the total budget of the
entire network. We propose a novel concept to consider how to build a more
reliable binary-state network under the budget limit. In addition, we propose
an algorithm based on the binary-addition-tree algorithm (BAT) and stepwise
vectors to solve the problem efficiently
QoS-Aware Middleware for Web Services Composition
The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online Business-to-Business (B2B) collaborations. In particular, the creation of value-added services by composition of existing ones is gaining a significant momentum. Since many available Web services provide overlapping or identical functionality, albeit with different Quality of Service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service. Two selection approaches are described and compared: one based on local (task-level) selection of services and the other based on global allocation of tasks to services using integer programming
A Practical Scheme for Wireless Network Operation
In many problems in wireline networks, it is known that achieving capacity on each link or subnetwork is optimal for the entire network operation. In this paper, we present examples of wireless networks in which decoding and achieving capacity on certain links or subnetworks gives us lower rates than other simple schemes, like forwarding. This implies that the separation of channel and network coding that holds for many classes of wireline networks does not, in general, hold for wireless networks. Next, we consider Gaussian and erasure wireless networks where nodes are permitted only two possible operations: nodes can either decode what they receive (and then re-encode and transmit the message) or simply forward it. We present a simple greedy algorithm that returns the optimal scheme from the exponential-sized set of possible schemes. This algorithm will go over each node at most once to determine its operation, and hence, is very efficient. We also present a decentralized algorithm whose performance can approach the optimum arbitrarily closely in an iterative fashion
Practical issues for the implementation of survivability and recovery techniques in optical networks
Network coding via evolutionary algorithms
Network coding (NC) is a relatively recent novel technique that generalises
network operation beyond traditional store-and-forward routing, allowing
intermediate nodes to combine independent data streams linearly. The rapid
integration of bandwidth-hungry applications such as video conferencing and HDTV
means that NC is a decisive future network technology.
NC is gaining popularity since it offers significant benefits, such as throughput
gain, robustness, adaptability and resilience. However, it does this at a potential
complexity cost in terms of both operational complexity and set-up complexity. This
is particularly true of network code construction.
Most NC problems related to these complexities are classified as non
deterministic polynomial hard (NP-hard) and an evolutionary approach is essential to
solve them in polynomial time. This research concentrates on the multicast scenario,
particularly: (a) network code construction with optimum network and coding
resources; (b) optimising network coding resources; (c) optimising network security
with a cost criterion (to combat the unintentionally introduced Byzantine
modification security issue).
The proposed solution identifies minimal configurations for the source to deliver
its multicast traffic whilst allowing intermediate nodes only to perform forwarding
and coding. In the method, a preliminary process first provides unevaluated
individuals to a search space that it creates using two generic algorithms (augmenting
path and linear disjoint path. An initial population is then formed by randomly
picking individuals in the search space. Finally, the Multi-objective Genetic
algorithm (MOGA) and Vector evaluated Genetic algorithm (VEGA) approaches
search the population to identify minimal configurations. Genetic operators
(crossover, mutation) contribute to include optimum features (e.g. lower cost, lower
coding resources) into feasible minimal configurations. A fitness assignment and
individual evaluation process is performed to identify the feasible minimal
configurations. Simulations performed on randomly generated acyclic networks are used to
quantify the performance of MOGA and VEGA
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