Trust and deadline aware scheduling algorithm for cloud infrastructure using ant colony optimization

2016 ◽  
Author(s):  
Punit Gupta ◽  
Satya Prakash Ghrera
Keyword(s):  
Ant Colony Optimization ◽  
Scheduling Algorithm ◽  
Ant Colony ◽  
Cloud Infrastructure

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).

scholarly journals Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing

2019 ◽  
Vol 11 (4) ◽  
pp. 90 ◽  
Author(s):  
Gang Li ◽  
Zhijun Wu
Keyword(s):  
Load Balancing ◽  
Ant Colony Optimization ◽  
Task Scheduling ◽  
Large Scale ◽  
Scheduling Algorithm ◽  
Pso Algorithm ◽  
Ant Colony ◽  
Experimental Simulation ◽  
Task Completion ◽  
Task Scheduling Algorithm

This paper focuses on the load imbalance problem in System Wide Information Management (SWIM) task scheduling. In order to meet the quality requirements of users for task completion, we studied large-scale network information system task scheduling methods. Combined with the traditional ant colony optimization (ACO) algorithm, using the hardware performance quality index and load standard deviation function of SWIM resource nodes to update the pheromone, a SWIM ant colony task scheduling algorithm based on load balancing (ACTS-LB) is presented in this paper. The experimental simulation results show that the ACTS-LB algorithm performance is better than the traditional min-min algorithm, ACO algorithm and particle swarm optimization (PSO) algorithm. It not only reduces the task execution time and improves the utilization of system resources, but also can maintain SWIM in a more load balanced state.

scholarly journals Dynamic Load Balancing Ant Colony Optimization (DLBACO) Algorithm for Task Scheduling in Cloud Environment

2019 ◽  
Vol 8 (12) ◽  
pp. 939-946
Keyword(s):  
Load Balancing ◽  
Ant Colony Optimization ◽  
Task Scheduling ◽  
Dynamic Load ◽  
Heuristic Algorithms ◽  
Scheduling Algorithm ◽  
Ant Colony ◽  
Dynamic Load Balancing ◽  
Cloud Environment ◽  
Task Scheduling Algorithm

Cloud computing is a framework which provides on-demand services to the user for scalability, security, and reliability based on pay as used service anytime & anywhere. For load balancing, task scheduling is the most critical issues in the cloud environment. There are so many meta-heuristic algorithms used to solve the load balancing problem. A good task scheduling algorithm should be used for optimum load balancing in cloud environment. Such scheduling algorithm must have some vital characteristic like minimum makespan, maximum throughput, and maximum resource utilization, etc. In this paper, a dynamic load balancing and task scheduling algorithm based on ant colony optimization (DLBACO) has been proposed. This algorithm assigns the task the VM which has highest probability of availability in minimum time. The proposed algorithm balances the whole system by minimizing the makespan of the task and maximizing the throughput. CloudSim simulator is used to simulate the proposed scheduling algorithm and results show that the proposed (DLBACO) algorithm is better than the existing algorithms such as FCFS, LBACO (Load balancing ACO), and primary ACO

Grid Task Scheduling Strategy Based on Particle Swarm Optimizationand Ant Colony Optimization Algorithm

2010 ◽  
Vol 108-111 ◽  
pp. 392-397
Author(s):  
Peng Cheng Wei ◽  
Xi Shi
Keyword(s):  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Task Scheduling ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Scheduling Algorithm ◽  
Particle Swarm ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Swarm Optimization

Based on particle swarm optimization algorithm, this paper presents a grid scheduling optimization algorithm combing the advantages of Ant Colony optimization algorithm. The algorithm processes task scheduling through particle swarm optimization algorithm to get a group of relatively optimal solutions, and then conducts small-area local search with Ant Colony optimization algorithm. Theoretical analysis and results of the simulation experiments show that this scheduling algorithm effectively achieves load balancing of resources with comprehensive advantages in time efficiency and solution accuracy compared to the traditional Ant Colony optimization algorithm and particle swarm optimizationalgorithm, and can be applied to task scheduling in grid computing.

scholarly journals Development And Analysis of Ant Colony Optimization-Based Light Weight Container (ACO-LWC) Algorithm For Efficient Load Balancing

2021 ◽  
Author(s):  
Aruna K ◽  
Pradeep G
Keyword(s):  
Load Balancing ◽  
Response Time ◽  
Ant Colony Optimization ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Round Robin ◽  
Ant Colony ◽  
Light Weight ◽  
Other Information ◽  
Alternate Solution

Abstract Container technology is the latest lightweight virtualization technology which is an alternate solution for virtual machines. Docker is the most popular container technology for creating and managing Linux containers. Containers appear to be the most suitable medium for use in dynamic development, packaging, shipping and many other information technology environments. The portability of the software through the movement of containers is appreciated by businesses and IT professionals. In the docker container, one or more processes may run simultaneously. The main objective of this work is to propose a new algorithm called Ant Colony Optimization-based Light Weight Container (ACO-LWC) load balancing scheduling algorithm for scheduling various process requests. This algorithm is designed such that it shows best performance in terms of load balancing. The proposed algorithm is validated by comparison with two existing load balancing scheduling algorithms namely, least connection algorithm and round robin algorithm. The proposed algorithm is validated using metrics like response time (ms), mean square error (MSE), node load, largest TPS of cluster (fetches/sec), average response time for each request (ms) and run time (s). Quantitative analysis show that the proposed ACO-LWC scheme achieves best performance in terms of all the metrics compared to the existing algorithms. In particular, the response time for least connection, round robin and the proposed ACO-LWC algorithm are 58ms, 60ms and 48ms respectively when 95% requests are finished. Similarly, the error for scheduling 120 requests using least connection, round robin and the proposed ACO-LWC algorithm are 0.15, 0.11 and 0.06 respectively.

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