Flexible protein folding by ant colony optimization

Protein structure prediction is one of the most challenging topics in bioinformatics. As the protein structure is found to be closely related to its functions, predicting the folding structure of a protein to judge its functions is meaningful to the humanity. This chapter proposes a flexible ant colony (FAC) algorithm for solving protein folding problems (PFPs) based on the hydrophobic-polar (HP) square lattice model. Different from the previous ant algorithms for PFPs, the pheromones in the proposed algorithm are placed on the arcs connecting adjacent squares in the lattice. Such pheromone placement model is similar to the one used in the traveling salesmen problems (TSPs), where pheromones are released on the arcs connecting the cities. Moreover, the collaboration of effective heuristic and pheromone strategies greatly enhances the performance of the algorithm so that the algorithm can achieve good results without local search methods. By testing some benchmark two-dimensional hydrophobic-polar (2D-HP) protein sequences, the performance shows that the proposed algorithm is quite competitive compared with some other well-known methods for solving the same protein folding problems

Multidimensional Resource Fragmentation-Aware Virtual Network Embedding in MEC Systems Interconnected by Metro Optical Networks

The increasing demand for diverse emerging applications has resulted in the interconnection of multi-access edge computing (MEC) systems via metro optical networks. To cater to these diverse applications, network slicing has become a popular tool for creating specialized virtual networks. However, resource fragmentation caused by uneven utilization of multidimensional resources can lead to reduced utilization of limited edge resources. To tackle this issue, this paper focuses on addressing the multidimensional resource fragmentation problem in virtual network embedding (VNE) in MEC systems with the aim of maximizing the profit of an infrastructure provider (InP). The VNE problem in MEC systems is transformed into a bilevel optimization problem, taking into account the interdependence between virtual node embedding (VNoE) and virtual link embedding (VLiE). To solve this problem, we propose a nested bilevel optimization approach named BiVNE. The VNoE is solved using the ant colony system (ACS) in the upper level, while the VLiE is solved using a combination of a shortest path algorithm and an exact-fit spectrum slot allocation method in the lower level. Evaluation results show that the BiVNE algorithm can effectively enhance the profit of the InP by increasing the acceptance ratio and avoiding resource fragmentation simultaneously

A multi-domain VNE algorithm based on load balancing in the IoT networks

The coordinated development of big data, Internet of Things, cloud computing and other technologies has led to an exponential growth in Internet business. However, the traditional Internet architecture gradually shows a rigid phenomenon due to the binding of the network structure and the hardware. In a high-traffic environment, it has been insufficient to meet people’s increasing service quality requirements. Network virtualization is considered to be an effective method to solve the rigidity of the Internet. Among them, virtual network embedding is one of the key problems of network virtualization. Since virtual network mapping is an NP-hard problem, a large number of research has focused on the evolutionary algorithm’s masterpiece genetic algorithm. However, the parameter setting in the traditional method is too dependent on experience, and its low flexibility makes it unable to adapt to increasingly complex network environments. In addition, link-mapping strategies that do not consider load balancing can easily cause link blocking in high-traffic environments. In the IoT environment involving medical, disaster relief, life support and other equipment, network performance and stability are particularly important. Therefore, how to provide a more flexible virtual network mapping service in a heterogeneous network environment with large traffic is an urgent problem. Aiming at this problem, a virtual network mapping strategy based on hybrid genetic algorithm is proposed. This strategy uses a dynamically calculated cross-probability and pheromone based mutation gene selection strategy to improve the flexibility of the algorithm. In addition, a weight update mechanism based on load balancing is introduced to reduce the probability of mapping failure while balancing the load. Simulation results show that the proposed method performs well in a number of performance metrics including mapping average quotation, link load balancing, mapping cost-benefit ratio, acceptance rate and running time.Peer ReviewedPostprint (published version

QoS multicast tree construction in IP/DWDM optical internet by bio-inspired algorithms

Copyright @ Elsevier Ltd. All rights reserved.In this paper, two bio-inspired Quality of Service (QoS) multicast algorithms are proposed in IP over dense wavelength division multiplexing (DWDM) optical Internet. Given a QoS multicast request and the delay interval required by the application, both algorithms are able to find a flexible QoS-based cost suboptimal routing tree. They first construct the multicast trees based on ant colony optimization and artificial immune algorithm, respectively. Then a dedicated wavelength assignment algorithm is proposed to assign wavelengths to the trees aiming to minimize the delay of the wavelength conversion. In both algorithms, multicast routing and wavelength assignment are integrated into a single process. Therefore, they can find the multicast trees on which the least wavelength conversion delay is achieved. Load balance is also considered in both algorithms. Simulation results show that these two bio-inspired algorithms can construct high performance QoS routing trees for multicast applications in IP/DWDM optical Internet.This work was supported in part ny the Program for New Century Excellent Talents in University, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1, the National Natural Science Foundation of China under Grant no. 60673159 and 70671020, the National High-Tech Reasearch and Development Plan of China under Grant no. 2007AA041201, and the Specialized Research Fund for the Doctoral Program of Higher Education under Grant no. 20070145017

HIGH CAPACITY AND OPTIMIZED IMAGE STEGANOGRAPHY TECHNIQUE BASED ON ANT COLONY OPTIMIZATION ALGORITHM

The tremendous development of digital technology, it is mandatory to address the security while transmitting information over network in a way that observer couldn’t depict it. Measures to be taken to provide the security by establishing hidden communication using steganography principle which is help to camouflage the secret information in some carrier file such as text, image, audio and video. In this era of hidden data communication, image becoming an effective tool on account of their frequency, capability and accuracy. Image steganography uses an image as a carrier medium to hide the secret data. The main motive of this article is that the uses the combination of frequency domain and optimization method inorder to increasing in robustness. In this article, Integer Wavelet transform is performed into the host image and coefficients have been transformed. ACO optimization algorithm is used to find the optimal coefficients where to hide the data. Furthermore, sample images and information having been demonstrated which proved the increased robustness as well as high level of data embedding capacity

Geo-distributed Multi-tier Workload Migration Over Multi-timescale Electricity Markets

Virtual machine (VM) migration enables cloud service providers (CSPs) to balance workload, perform zero-downtime maintenance, and reduce applications\u27 power consumption and response time. Migrating a VM consumes energy at the source, destination, and backbone networks, i.e., intermediate routers and switches, especially in a Geo-distributed setting. In this context, we propose a VM migration model called Low Energy Application Workload Migration (LEAWM) aimed at reducing the per-bit migration cost in migrating VMs over Geo-distributed clouds. With a Geo-distributed cloud connected through multiple Internet Service Providers (ISPs), we develop an approach to find out the migration path across ISPs leading to the most feasible destination. For this, we use the variation in the electricity price at the ISPs to decide the migration paths. However, reduced power consumption at the expense of higher migration time is intolerable for real-time applications. As finding an optimal relocation is $\mathcal {NP}$-Hard, we propose an Ant Colony Optimization (ACO) based bi-objective optimization technique to strike a balance between migration delay and migration power. A thorough simulation analysis of the proposed approach shows that the proposed model can reduce the migration time by $25\%$–$30\%$ and electricity cost by approximately $25\%$ compared to the baseline

Coordinated node and link mapping VNE using a new paths algebra strategy

AbstractThe main resource allocation research challenge in network virtualization is the Virtual Network Embedding (VNE) Problem. It consists of two stages, usually performed separately: node mapping and link mapping. A new mathematical multi-constraint routing framework for linear and non-linear metrics called “paths algebra” has already been proposed to solve the second stage, providing good results thanks to its flexibility. Unlike existing approaches, paths algebra is able to include any kind of network parameters (linear and non-linear) to solve VNE in reasonable runtime. While paths algebra had only been used to solve one stage (link mapping) of the VNE, this paper suggests an improvement to solve VNE using the paths algebra-based strategy by coordinating, in a single stage, the mapping of nodes and links based on a ranking made of the bi-directional pair of nodes of the substrate network, ordered by their available resources. Simulation results show that the New Paths Algebra-based strategy shows significant performance improvements when compared against the uncoordinated paths Algebra-based link mapping approach