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Fog computing is an emerging technology to address computing and networking bottlenecks in large scale deployment of IoT applications. It is a promising complementary computing paradigm to cloud computing where computational, networking, storage and acceleration elements are deployed at the edge and network layers in a multi-tier, distributed and possibly cooperative manner. These elements may be virtualized computing functions placed at edge devices or network elements on demand, realizing the ''computing everywhere'' concept. To put the current research in perspective, this paper provides an inclusive taxonomy for architectural, algorithmic and technologic aspects of fog computing. The computing paradigms and their architectural distinctions, including cloud, edge, mobile edge and fog computing are subsequently reviewed. Practical deployment of fog computing includes a number of different aspects such as system design, application design, software implementation, security, computing resource management and networking. A comprehensive survey of all these aspects from the architectural point of view is covered. Current reference architectures and major application-specific architectures describing their salient features and distinctions in the context of fog computing are explored. Base architectures for application, software, security, computing resource management and networking are presented and are evaluated using a proposed maturity model. INDEX TERMS Cloud Computing, edge computing, fog computing, Internet of Things (IoT), advanced internet architecture.

The optimal reactive power dispatch (ORPD) problem is a non-linear mixed-variable optimisation problem. This study employs a new evolutionary algorithm that expands the original shuffled frog leaping algorithm (SFLA) to solve this problem. In order to fully exploit the promising solution region, a local search algorithm known as Nelder -Mead (NM) algorithm is integrated with SFLA. The resultant NM-SFLA is very efficient in solving ORPD problem. The most important benefit of the proposed method is higher speed of convergence to a better solution. The proposed method is applied to ORPD problem on IEEE 30-bus, IEEE 57-bus and IEEE 118-bus power systems and compared with four versions of particle swarm optimisation algorithm, two versions of differential evolutionary algorithm and SFLA. The optimal setting of control variables including generator voltages, transformer taps and shunt VAR compensation devices for active power loss minimisation in a transmission system is determined while all the constraints are satisfied. The simulation results show the efficiency of the proposed method.

A SRLG is a group of network links that share a common physical resource whose failure will cause the failure of all links of the group. To protect a logical connection in a network from a single SRLG failure, two different paths are usually assigned to the connection. The two paths must be disjoint to avoid synchronous failure. In this paper, we extend a highperformance link-disjoint routing algorithm called CoLE to SRLG-disjoint routing. The analysis and the simulation results demonstrate improvement over the previous algorithms.

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SUMMARY Channel assignment in multichannel multiradio wireless mesh networks is a powerful resource management tool to exploit available multiple channels. Channels can be allocated either statically on the basis of long‐term steady state behavior of traffic or dynamically according to actual traffic demands. It is a common belief that dynamic schemes provide better performance; however, these two broad classes of channel allocation schemes have not been compared in detail. In this paper, we quantify the achievable performance gain and fairness improvement through an optimal dynamic channel allocation scheme. We develop optimal algorithms for a dynamic and three static schemes using mixed integer linear programming and compare them in the context of QoS provisioning, where network performance is measured in terms of acceptance rate of QoS sensitive traffic demands. Our extensive simulations show that static schemes should optimize channel allocation for long‐term traffic pattern and maintain max–min fairness to achieve acceptable performances. Although the dynamic and max–min fair static schemes accomplish the same fairness, the dynamic channel allocation outperforms the static scheme about 10% in most cases. In heavily overloaded regimes, especially when network resources are scarce, both have comparable performances, and the max–min fair scheme is preferred because it incurs less overhead. Copyright © 2011 John Wiley & Sons, Ltd.