Cloud computing demand elasticity algorithm based on ant colony algorithm

2020 ◽  
Vol 13 ◽  
Author(s):  
Chunyu Liu ◽  
Fengrui Mu ◽  
Weilong Zhang
Keyword(s):  
Ant Colony Algorithm ◽  
Theory Of Elasticity ◽  
Ant Colony ◽  
Demand Elasticity ◽  
Network Efficiency ◽  
Elasticity Of Demand ◽  
Operating Profit ◽  
Network Load ◽  
Network Utilization ◽  
The Impact

Background: In recent era of technology, the traditional Ant Colony Algorithm (ACO) is insufficient in solving the problem of network congestion and load balance, and network utilization. Methods: This paper proposes an improved ant colony algorithm, which considers the price factor based on the theory of elasticity of demand. The price factor is denominated in the impact on the network load which means indirect control of network load, congestion or auxiliary solution to calculate the idle resources caused by the low network utilization and reduced profits. Results: Experimental results show that the improved algorithm can balance the overall network load, extend the life of path by nearly 3 hours, greatly reduce the risk of network paralysis, and increase the profit of the manufacturer by 300 million Yuan. Conclusion: Furthermore, results shows that the improved method has a great application value in improving the network efficiency, balancing network load, prolonging network life and increasing network operating profit.

scholarly journals JSSTR: A Joint Server Selection and Traffic Routing Algorithm for the Software-Defined Data Center

2018 ◽  
Vol 8 (9) ◽  
pp. 1478 ◽  
Author(s):  
Yunhe Cui ◽  
Lianshan Yan ◽  
Qing Qian ◽  
Huanlai Xing ◽  
Saifei Li
Keyword(s):  
Load Balancing ◽  
Routing Algorithm ◽  
Network Efficiency ◽  
Network Resources ◽  
Server Selection ◽  
Traffic Routing ◽  
Network Load ◽  
Network Utilization ◽  
Shuffle Frog Leaping Algorithm ◽  
The Impact

Server load balancing technology makes services highly functional by distributing the incoming user requests to different servers. Thus, it plays a key role in data centers. However, most of the current server load balancing schemes are designed without considering the impact on the network. More specifically, when using these schemes, the server selection and routing path calculation are usually executed sequentially, which may result in inefficient use of network resources or even cause some issues in the network. As an emerging architecture, Software-Defined Networking (SDN) provides new solutions to overcome these shortcomings. Therefore, taking advantages of SDN, this paper proposes a Joint Server Selection and Traffic Routing algorithm (JSSTR) based on improving the Shuffle Frog Leaping Algorithm (SFLA) to achieve high network utilization, network load balancing and server load balancing. Evaluation results validate that the proposed algorithm can significantly improve network efficiency and balance the network load and server load.

Ant Colony-Based Load Balancing Algorithm for Multi-Source Multicast Networks

2011 ◽  
Vol 204-210 ◽  
pp. 1399-1402
Author(s):  
Ling Xiu Wang ◽  
Ye Wen Cao
Keyword(s):  
Ant Colony Algorithm ◽  
Minimum Spanning Tree ◽  
Ant Colony ◽  
Multicast Networks ◽  
Multicast Protocol ◽  
Multicast Protocols ◽  
Network Utilization ◽  
Multicast Traffic ◽  
Multicast Scheduling ◽  
Load Balancing Algorithm

IP multicast protocols tend to construct a single minimum spanning tree for a multicast source (i.e., group), in which only a few internal nodes supply multicast traffic. In multicast networks especially with multiple multicast sources where bottleneck effects may occur frequently, frequently used multicast service leads to inefficient network utilization problems. This paper presents a new network utilization algorithm for multicasting called load distribution algorithm (LDA). The LDA algorithm uses selecting candidate path based on ant colony algorithm and multicast scheduling to distribute the contention multicast packets onto their corresponding candidate paths. The numerical results show that a multicast protocol with LDA has higher efficiency of resource utilization and meanwhile maintains less end to end delay compared with the original one without LDA.

scholarly journals Usefulness of the concept of social savings and consumer surplus in studies on the impact of innovation on economic growth

2018 ◽  
Vol 2 (4) ◽  
pp. 179
Author(s):  
Janusz Krzysztof Myszczyszyn
Keyword(s):  
Economic Growth ◽  
Design Research ◽  
Consumer Surplus ◽  
Demand Elasticity ◽  
High Price ◽  
Elasticity Of Demand ◽  
The Social ◽  
Alternative Good ◽  
Economic Thinking ◽  
The Impact

Aim: The main objective of the paper was to calculate social savings (and consumer surplus) of innovation on the example of railroads in Germany for 1985. The railways were among the most important innovations in the nineteenth century. Being aware of the limits of the social savings technique, the author included the concept of consumer surplus in his calculation Design / Research methods: For the purpose of the research, the author used the concept of social savings proposed by Robert Fogel and consumer surplus. Conclusions / findings: For the year 1895, social savings amounted to 2.82% (first equation) of GDP and 5.04% of GDP (second equation), taking into account elasticity of demand (-1,38), social savings amounted to 1.27% of GDP and 2.18% of GDP for Germany. The result thus elicited the author referred to the social savings from railroads as made available in literature and the author’s previous research. The author demonstrated that the social savings from the innovation were relatively small. Originality / value of the article: The results of research are useful for examining the impact of innovation, such as railroads, on the level of social savings. The paper fills the gap in the Polish economic thinking on the use of counterfactual methods. Implications of the research: The concept of social savings which takes into account demand elasticity can be applied successfully in evaluating the impact of (various) innovations on economic growth. Limitations of the research: The weakness of the method may be the lack of knowledge about the real level of elasticity of demand for innovation, as well as determining the level of prices of an alternative good, especially if the use of innovation at the initial stage of bringing it to the market involves a relatively high price.

Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining

2012 ◽  
Vol 433-440 ◽  
pp. 3577-3583
Author(s):  
Yan Zhang ◽  
Hao Wang ◽  
Yong Hua Zhang ◽  
Yun Chen ◽  
Xu Li
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Computing Power ◽  
Slow Convergence ◽  
The Impact

To overcome the defect of the classical ant colony algorithm’s slow convergence speed, and its vulnerability to local optimization, the authors propose Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining to solve Traveling Salesman Problem according to the characteristics of natural ant colony multi-group and pheromone updating features of ant colony algorithm, combined with OpenMP parallel programming idea. The new algorithm combines three different pheromone updating methods to make a new declining pheromone updating method. It effectively reduces the impact of pheromone on the non-optimal path in the ants parade loop to subsequent ants and improves the parade quality of subsequent ants. It makes full use of multi-core CPU's computing power and improves the efficiency significantly. The new algorithm is compared with ACO through experiments. The results show that the new algorithm has faster convergence rate and better ability of global optimization than ACO.

scholarly journals Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results

2016 ◽  
Vol 24 (3) ◽  
pp. 385-409 ◽  
Author(s):  
Fernando E. B. Otero ◽  
Alex A. Freitas
Keyword(s):  
Ant Colony Algorithm ◽  
Predictive Accuracy ◽  
Ant Colony ◽  
Support Vector ◽  
Classification Rules ◽  
Class Prediction ◽  
Vector Machines ◽  
Size Measure ◽  
The Impact

Most ant colony optimization (ACO) algorithms for inducing classification rules use a ACO-based procedure to create a rule in a one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-Miner[Formula: see text] algorithm, where an ACO-based procedure is used to create a complete list of rules (ordered rules), i.e., the ACO search is guided by the quality of a list of rules instead of an individual rule. In this paper we propose an extension of the cAnt-Miner[Formula: see text] algorithm to discover a set of rules (unordered rules). The main motivations for this work are to improve the interpretation of individual rules by discovering a set of rules and to evaluate the impact on the predictive accuracy of the algorithm. We also propose a new measure to evaluate the interpretability of the discovered rules to mitigate the fact that the commonly used model size measure ignores how the rules are used to make a class prediction. Comparisons with state-of-the-art rule induction algorithms, support vector machines, and the cAnt-Miner[Formula: see text] producing ordered rules are also presented.

An Improved Decision Strategy of Network Routing Based on Ant Colony Algorithm

2014 ◽  
Vol 599-601 ◽  
pp. 1378-1382
Author(s):  
Yu Bo Jia ◽  
Qian Qian Ding ◽  
Dan Li Liu ◽  
Yun Long Zhang ◽  
Jian Feng Zhang
Keyword(s):  
Convergence Rate ◽  
Ant Colony Algorithm ◽  
Memory Device ◽  
Network Routing ◽  
Ant Colony ◽  
Utilization Rate ◽  
Decision Strategy ◽  
Slow Convergence ◽  
Network Utilization ◽  
High Efficient

-Aiming at getting a high efficient network routing decision strategy, to settle problems of slow convergence speed and easily to fall into local optimal, this paper proposes a new decision strategy based on ant colony algorithm. Memory device to record each time pheromone value and pheromone differences value of the adjacent times to decide follow the former route or find a new one are the focus of this paper. The new decision technology accelerates the convergence rate, improves network utilization rate and accuracy of network routing.

scholarly journals Scaling complexity comparison of an ACO-based routing algorithm used as an IoT network core

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Goran Djukanovic ◽  
Goran Popovic ◽  
Dimitris Kanellopoulos
Keyword(s):  
Ant Colony Algorithm ◽  
Routing Algorithm ◽  
Sensor Nodes ◽  
Critical Design ◽  
Network Load ◽  
Routing Method ◽  
Network Load Balancing ◽  
Sensor Density ◽  
The Impact ◽  
The Internet Of Things

This paper proposes a routing method that is based on an Ant Colony Algorithm (ACO) for minimizing energy consumption in Wireless Sensor Networks (WSNs). The routing method is used as the backbone of the Internet of Things (IoT) platform. It also considers the critical design issues of a WSN, such as the energy constraint of sensor nodes, network load balancing, and sensor density in the field. Special attention is paid to the impact of network scaling on the performance of the ACO-based routing algorithm.

Study of Parameters Configuration in Ant Colony Algorithm

2014 ◽  
Vol 678 ◽  
pp. 51-54
Author(s):  
Yan Rong Cui
Keyword(s):  
Combinatorial Optimization ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Optimal Parameter ◽  
Ant Colony ◽  
Combinatorial Optimization Problems ◽  
Parameter Combination ◽  
The Impact

It has obtained a better result to use ant colony algorithm to solve complex combinatorial optimization problems, but different value of the parameters in ant colony algorithm affects the performance of the algorithm. This paper studies the configuration of parameters in ant colony algorithm, and analyses the impact of the key parameters of the algorithm, and obtains the optimal parameter combination of using ant colony algorithm to solve TSP problems by using EIL51TSP data to simulate.

scholarly journals Reconfiguration Performance of the Urban Power Distribution System Based on the Genetic-Ant Colony Fusion Algorithm

2021 ◽  
Vol 257 ◽  
pp. 02062
Author(s):  
Longju Bai
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Ant Colony Algorithm ◽  
Optimal Algorithm ◽  
Ant Colony ◽  
Local Optimum ◽  
Power Distribution System ◽  
Short Period ◽  
Urban Distribution ◽  
The Impact

This study aims to enhance the reliability of the urban power grid system and decrease the economic loss due to power network faults. Based on the analysis of the traditional algorithms for restructuring the urban distribution system after faults, this study proposes an upgraded genetic algorithm (GA) and ant colony algorithm (ACA) and combines these two to overcome the limitations of the local optimum of GAs and low convergence speed of ACAs. Taking the IEEE33-node system as the research object, the network loss, maximum recovery of the power-loss load, and the number of switching operations as the objective function, the impact of different algorithms on the restoration and reconfiguration of the distribution system was examined according to MATLAB system simulation and the optimal algorithm for the reconfiguration of the urban distribution system failure recovery. The experimental results revealed that compared with the current distribution system reconfiguration algorithm, the genetic-ant colony algorithm (GACA) has higher algorithm time efficiency and solution accuracy and can markedly decrease the recovery time and improve the impact of the distribution system in a short period. Overall, the proposed GACA is an efficient self-healing algorithm of urban distribution systems and useful for augmenting the reliability of the urban power system.

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