Stream of traffic balance in active cloud infrastructure service virtual machines using ant colony

2020 ◽  
Vol 9 (4) ◽  
pp. 373
Author(s):  
Ankita Taneja ◽  
Hari Singh ◽  
Suresh Chand Gupta
Keyword(s):  
Virtual Machines ◽  
Ant Colony ◽  
Cloud Infrastructure ◽  
Traffic Balance

Cost-Aware Ant Colony Optimization Based model for Load Balancing in Cloud Computing

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Malini Alagarsamy ◽  
Ajitha Sundarji ◽  
Aparna Arunachalapandi ◽  
Keerthanaa Kalyanasundaram
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Ant Colony Optimization ◽  
Carbon Footprint ◽  
Data Center ◽  
Virtual Machines ◽  
Dynamic Environment ◽  
Ant Colony ◽  
Cloud Infrastructure ◽  
Server Utilization

: Balancing the incoming data traffic across the servers is termed as Load balancing. In cloud computing, Load balancing means distributing loads across the cloud infrastructure. The performance of cloud computing depends on the different factors which include balancing the loads at the data center which increase the server utilization. Proper utilization of resources is termed as server utilization. The power consumption decreases with an increase in server utilization which in turn reduces the carbon footprint of the virtual machines at the data center. In this paper, the cost-aware ant colony optimization based load balancing model is proposed to minimize the execution time, response time and cost in a dynamic environment. This model enables to balance the load across the virtual machines in the data center and evaluate the overall performance with various load balancing models. As an average, the proposed model reduces carbon footprint by 45% than existing methods.

A Multi-Objective Fuzzy Ant Colony Optimization Algorithm for Virtual Machine Placement

2016 ◽  
Vol 5 (4) ◽  
pp. 165-191 ◽  
Author(s):  
Boominathan Perumal ◽  
Aramudhan M.
Keyword(s):  
Ant Colony Optimization ◽  
Virtual Machine ◽  
Bin Packing ◽  
Virtual Machines ◽  
Transition Probability ◽  
Virtual Machine Placement ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Placement Problem ◽  
Multi Objective

In cloud computing, the most important challenge is to enforce proper utilization of physical resources. To accomplish the mentioned challenge, the cloud providers need to take care of optimal mapping of virtual machines to a set of physical machines. In this paper, the authors address the mapping problem as a multi-objective virtual machine placement problem (VMP) and propose to apply multi-objective fuzzy ant colony optimization (F-ACO) technique for optimal placing of virtual machines in the physical servers. VMP-F-ACO is a combination of fuzzy logic and ACO, where we use fuzzy transition probability rule to simulate the behaviour of the ants and the authors apply the same for virtual machine placement problem. The results of fuzzy ACO techniques are compared with five variants of classical ACO, three bin packing heuristics and two evolutionary algorithms. The results show that the fuzzy ACO techniques are better than the other optimization and heuristic techniques considered.

scholarly journals Server Consolidation Based on Culture Multiple-Ant-Colony Algorithm in Cloud Computing

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2724 ◽  
Author(s):  
Yuan ◽  
Sun
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Ant Colony Algorithm ◽  
Data Centers ◽  
Virtual Machines ◽  
Ant Colony ◽  
Virtual Machine Migration ◽  
Server Consolidation ◽  
Cloud Data ◽  
Cloud Data Centers

High-energy consumption in data centers has become a critical issue. The dynamic server consolidation has significant effects on saving energy of a data center. An effective way to consolidate virtual machines is to migrate virtual machines in real time so that some light load physical machines can be turned off or switched to low-power mode. The present challenge is to reduce the energy consumption of cloud data centers. In this paper, for the first time, a server consolidation algorithm based on the culture multiple-ant-colony algorithm was proposed for dynamic execution of virtual machine migration, thus reducing the energy consumption of cloud data centers. The server consolidation algorithm based on the culture multiple-ant-colony algorithm (CMACA) finds an approximate optimal solution through a specific target function. The simulation results show that the proposed algorithm not only reduces the energy consumption but also reduces the number of virtual machine migration.

scholarly journals A STOCHASTIC DEVELOPMENT OF CLOUD COMPUTING BASED TASK SCHEDULING ALGORITHM

2019 ◽  
Vol 2019 (1) ◽  
pp. 41-48 ◽  
Author(s):  
Karunakaran V
Keyword(s):  
Virtual Machines ◽  
Cost Minimization ◽  
Scheduling Algorithm ◽  
Cloud Infrastructure ◽  
Solution Quality ◽  
Task Scheduling Algorithm ◽  
Simulation Results ◽  
Selection For ◽  
Stochastic Development ◽  
Data Centres

Due to diversity of services with respect to technology and resources, it is challenging to choose virtual machines (VM) from various data centres with varied features like cost minimization, reduced energy consumption, optimal response time and so on in cloud Infrastructure as a Service (IaaS) environment. The solutions available in the market are exhaustive computationally and aggregates multiple objectives to procure single trade-off that affects the solution quality inversely. This paper describes a hybrid algorithm that facilitates VM selection for scheduling applications based on Gravitational Search and Non-dominated Sorting Genetic Algorithm (GSA and NSGA). The efficiency of the proposed algorithm is verified by the simulation results.

Cost-Aware Ant Colony Optimization for Resource Allocation in Cloud Infrastructure

2020 ◽  
Vol 13 (3) ◽  
pp. 326-335
Author(s):  
Punit Gupta ◽  
Ujjwal Goyal ◽  
Vaishali Verma
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Fitness Function ◽  
Low Cost ◽  
Scheduling Algorithm ◽  
Scheduling Algorithms ◽  
Ant Colony ◽  
Cloud Infrastructure ◽  
Network Cost

Background: Cloud Computing is a growing industry for secure and low cost pay per use resources. Efficient resource allocation is the challenging issue in cloud computing environment. Many task scheduling algorithms used to improve the performance of system. It includes ant colony, genetic algorithm & Round Robin improve the performance but these are not cost efficient at the same time. Objective: In early proven task scheduling algorithms network cost are not included but in this proposed ACO network overhead or cost is taken into consideration which thus improves the efficiency of the algorithm as compared to the previous algorithm. Proposed algorithm aims to improve in term of cost and execution time and reduces network cost. Methods: The proposed task scheduling algorithm in cloud uses ACO with network cost and execution cost as a fitness function. This work tries to improve the existing ACO that will give improved result in terms of performance and execution cost for cloud architecture. Our study includes a comparison between various other algorithms with our proposed ACO model. Results: Performance is measured using an optimization criteria tasks completion time and resource operational cost in the duration of execution. The network cost and user requests measures the performance of the proposed model. Conclusion: The simulation shows that the proposed cost and time aware technique outperforms using performance measurement parameters (average finish time, resource cost, network cost).

CSCLab

2015 ◽  
Vol 5 (4) ◽  
pp. 36-55
Author(s):  
Paula Prata ◽  
Samuel Alves
Keyword(s):  
Virtual Machines ◽  
Cloud Infrastructure ◽  
Virtual Computing ◽  
The Creation ◽  
Cloud Resources

This paper presents a platform to create and manage virtual computing laboratories using Cloud resources. Using this platform a professor can create a customized laboratory according to the class needs. The laboratory is composed of a set of virtual machines that students may use to get access to the necessary computing resources to attend the class. The platform aims at the creation of a solution to avoid proprietary lock in's, and it was designed to be agnostic to the cloud infrastructure. The machines of the lab can be accessed using some remote desktop protocol and managed by non-experts users.

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