Research on Resource Allocation Strategy of PaaS Platform

2019 ◽  
Vol 12 (1) ◽  
pp. 63-76
Author(s):  
Hongen Peng ◽  
Yabin Xu
Keyword(s):  
Resource Allocation ◽  
Waiting Time ◽  
Ant Colony Algorithm ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Optimal Solution ◽  
Allocation Model ◽  
Heuristic Scheduling ◽  
Local Optimal Solution ◽  
Resource Allocation Strategy

In order to allocate elastic resource to the application of PaaS platform, the authors analyze the key technologies and the particularity of resource scheduling in PaaS platform, and design an application-oriented resource allocation model and heuristic scheduling algorithm based on an ant colony algorithm. Different from the existing resource allocation methods based on virtual machines in IaaS, the scheduling strategy is based on Application in PaaS platform. According to the analysis of the application layout, the heuristic algorithm is used to minimize the number of application migration and reduce the waiting time of the task. In order to avoid falling into the loop or local optimal solution, the authors also used a tabu search technique. The results of comparative experiments show that, this strategy has higher resource utilization and shorter task waiting time.

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

scholarly journals Parameter Selection for Ant Colony Algorithm Based on Bacterial Foraging Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Peng Li ◽  
Hua Zhu
Keyword(s):  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Parameter Selection ◽  
Ant Colony ◽  
Multidimensional Space ◽  
Bacterial Foraging ◽  
Operation Speed ◽  
Local Optimal Solution ◽  
Bacterial Foraging Algorithm ◽  
Selection For

The optimal performance of the ant colony algorithm (ACA) mainly depends on suitable parameters; therefore, parameter selection for ACA is important. We propose a parameter selection method for ACA based on the bacterial foraging algorithm (BFA), considering the effects of coupling between different parameters. Firstly, parameters for ACA are mapped into a multidimensional space, using a chemotactic operator to ensure that each parameter group approaches the optimal value, speeding up the convergence for each parameter set. Secondly, the operation speed for optimizing the entire parameter set is accelerated using a reproduction operator. Finally, the elimination-dispersal operator is used to strengthen the global optimization of the parameters, which avoids falling into a local optimal solution. In order to validate the effectiveness of this method, the results were compared with those using a genetic algorithm (GA) and a particle swarm optimization (PSO), and simulations were conducted using different grid maps for robot path planning. The results indicated that parameter selection for ACA based on BFA was the superior method, able to determine the best parameter combination rapidly, accurately, and effectively.

A Image Segmentation Method of Improved Ant Colony Algorithm for the Manipulator Self-Recognition Target

2011 ◽  
Vol 135-136 ◽  
pp. 50-55
Author(s):  
Yuan Bin Hou ◽  
Yang Meng ◽  
Jin Bo Mao
Keyword(s):  
Image Segmentation ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Immune Algorithm ◽  
Ant Colony ◽  
Segmentation Result ◽  
Segmentation Method ◽  
Self Recognition ◽  
Local Optimal Solution ◽  
Improved Ant Colony Algorithm

According to the requirements of efficient image segmentation for the manipulator self-recognition target, a method of image segmentation based on improved ant colony algorithm is proposed in the paper. In order to avoid segmentation errors by local optimal solution and the stagnation of convergence, ant colony algorithm combined with immune algorithm are taken to traversing the whole image, which uses pheromone as standard. Further, immunization selection through vaccination optimizes the heuristic information, then it improves the efficiency of ergodic process, and shortens the time of segmentation effectively. Simulation and experimental of image segmentation result shows that this algorithm can get better effect than generic ant colony algorithm, at the same condition, segmentation time is shortened by 6.8%.

Application of Ant Colony Algorithm

2014 ◽  
Vol 548-549 ◽  
pp. 1217-1220
Author(s):  
Rui Wang ◽  
Zai Tang Wang
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Pheromone Concentration ◽  
Local Optimal Solution ◽  
Searching Ability ◽  
Excessive Accumulation

This paper mainly considers the application of the ant colony in our life. The principle of ant colony optimization, improves the performance of ant colony algorithm, and the global searching ability of the algorithm. We introduce a new adaptive factor in order to avoid falling into local optimal solution. With the increase the number of interations, this factor will benefit the ant search the edge with lower pheromone concentration and avoid the excessive accumulation of pheromone.

The Scheduling Algorithm of Grid Task Based on Cloud Model

2010 ◽  
Vol 439-440 ◽  
pp. 1177-1183
Author(s):  
Shu Tao Gao
Keyword(s):  
Task Scheduling ◽  
Cloud Model ◽  
Excess Entropy ◽  
Scheduling Algorithm ◽  
Optimal Solution ◽  
Simulation Experiments ◽  
Cloud Droplets ◽  
Local Optimal Solution ◽  
Characteristic Values ◽  
Scheduling Optimization Algorithm

In this paper, a kind of grid task scheduling optimization algorithm based on cloud model is proposed with the characteristics of cloud model. With the target being the cloud droplets of the cloud model, this algorithm gets three characteristic values of cloud through the reverse cloud: expectations, entropy and excess entropy, and then obtains cloud droplets using the forward cloud algorithm by adjusting the values of entropy and excess entropy. After several iterations, it achieves the optimal solution of task scheduling. Theoretical analysis and results of simulation experiments show that this scheduling algorithm effectively achieves load balancing of resources and avoids such problems as the local optimal solution of genetic algorithms and premature convergence caused by too much selection pressure with higher accuracy and faster convergence.

scholarly journals Interference resilient stochastic prediction based dynamic resource allocation model for cognitive MANETs

2020 ◽  
Vol 13 (41) ◽  
pp. 4332-4350
Author(s):  
K Shashi Raj ◽  
Keyword(s):  
Resource Allocation ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Dynamic Resource Allocation ◽  
Allocation Strategy ◽  
Secondary Users ◽  
Allocation Model ◽  
Resource Allocation Strategy ◽  
Dynamic Resource ◽  
Mobile Ad Hoc

Background/Objectives: Being dynamic in nature, Mobile Ad-hoc Network (MANET) requires robust resource allocation strategy that can ensure both optimal transmission reliability and resource efficiency to meet Quality of Service (QoS) demands. The objective of this research is to address interference resilience requirement in MANETs which is must due to greedy nature of nodes especially when accessing resource or bandwidth and develop a highly robust stochastic prediction based resource allocation strategy. Methods: The proposed Interference Resilient Stochastic Prediction based Dynamic Resource Allocation model for Cognitive MANET (ISP-DRACM) intends to enable optimal resource allocation under interweave and underlay network setup with instantaneous as well as average interference conditions. It employs a joint power management and resource allocation strategy where it intends to maximize the weighted sum-rate of the secondary users under certain defined conditions like average power and stochastic interference level. Findings/Novelty: Inculcating resource allocation problem as controlled Markov Decision Process using Hidden Markov Model (HMM) and Lagrange relaxation, our proposed model achieves better resource allocation under limited noise or interference condition and hence achieves both costeffectiveness as well as QoS provision. This method has exhibited satisfactory performance towards spectrum allocation to the secondary users without imposing any significant interference for both interweave as well as underlay Cognitive Radio setup. Keywords: Cognitive mobile ad-hoc network; stochastic prediction; interference resilience; channel state information; dynamic resource allocation; underlay and overlay cognitive MANET

Application of Particle Swarm Optimisation-R Factor Optimisation Algorithm for Efficient Resource Allocation and Scheduling in Cloud Environment

2020 ◽  
Vol 17 (4) ◽  
pp. 1990-1998
Author(s):  
R. Valarmathi ◽  
T. Sheela
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Management ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Cost Optimization ◽  
Optimal Solution ◽  
Service Needs ◽  
Cloud Environment ◽  
Efficient Resource

Cloud computing is a powerful technology of computing which renders flexible services anywhere to the user. Resource management and task scheduling are essential perspectives of cloud computing. One of the main problems of cloud computing was task scheduling. Usually task scheduling and resource management in cloud is a tough optimization issue at the time of considering quality of service needs. Huge works under task scheduling focuses only on deadline issues and cost optimization and it avoids the significance of availability, robustness and reliability. The main purpose of this study is to develop an Optimized Algorithm for Efficient Resource Allocation and Scheduling in Cloud Environment. This study uses PSO and R factor algorithm. The main aim of PSO algorithm is that tasks are scheduled to VM (virtual machines) to reduce the time of waiting and throughput of system. PSO is a technique inspired by social and collective behavior of animal swarms in nature and wherein particles search the problem space to predict near optimal or optimal solution. A hybrid algorithm combining PSO and R-factor has been developed with the purpose of reducing the processing time, make span and cost of task execution simultaneously. The test results and simulation reveals that the proposed method offers better efficiency than the previously prevalent approaches.

scholarly journals Improved ant colony algorithm for path planning of fixed wing unmanned aerial vehicle

2022 ◽  
Vol 355 ◽  
pp. 03002
Author(s):  
Hongchao Zhao ◽  
Jianzhong Zhao
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Ant Colony Algorithm ◽  
Search Time ◽  
Optimal Solution ◽  
Ant Colony ◽  
Local Optimal Solution ◽  
Search Speed ◽  
Aerial Vehicle ◽  
Improved Ant Colony Algorithm

Aiming at the problems of long search time and local optimal solution of ant colony algorithm (ACA) in the path planning of unmanned aerial vehicle (UAV), an improved ant colony algorithm (IACA) was proposed from the aspects of simplicity and effectiveness. The flight performance constraints of fixed wing UAVs were treated as conditions of judging whether the candidate expanded nodes are feasible, thus the feasible nodes’ number was reduced and the search efficiency was effectively raised. In order to overcome the problem of local optimal solution, the pheromone update rule is improved by combining local pheromone update and global pheromone update. The heuristic function was improved by integrating the distance heuristic factor with the safety heuristic factor, and it enhanced the UAV flight safety performance. The transfer probability was improved to increase the IACA search speed. Simulation results show that the proposed IACA possesses stronger global search ability and higher practicability than the former IACA.

Scheduling with Deadline Constraint of Healthcare Applications on Cloud-Based Workflow

2020 ◽  
Vol 10 (10) ◽  
pp. 2430-2438
Author(s):  
Haiying Che ◽  
Xiaolong Wang ◽  
Hong Wang ◽  
Zixing Bai ◽  
Honglei Li
Keyword(s):  
Large Scale ◽  
Ant Colony Algorithm ◽  
Scheduling Algorithm ◽  
Healthcare Applications ◽  
Start Time ◽  
Workflow Technology ◽  
Factors Affecting ◽  
Heuristic Scheduling ◽  
Tasks Scheduling ◽  
High Flexibility

Cloud-based workflow technology has played an important role in the development of large scale healthcare applications with high flexibility to meet variety of healthcare process requirements. Among all the factors affecting the healthcare applications on cloud-based workflow, the tasks scheduling is the crucial one. This paper aims at the cloud-based workflow tasks scheduling with deadline constraints and its implementation in two approaches: heuristic scheduling algorithm (HSA) and meta heuristic scheduling algorithm (HSA-ACO). HSA decomposes the workflow according to its structure and divide the deadline into the level deadlines. Tasks in each level get scheduling priority according to the earliest start time under the constraint of level deadline. In another method, HSA-ACO integrates HSA with ant colony algorithm to achieve better performance. In the last part, we launch the experiment to compare HSA and HSA-ACO with algorithms like Prolis, LACO and ICPCP in three types of workflow with different scales. The experiment results show that HSA-ACO is better than the other algorithms.

A Resource Allocation Model of Logistics Services Supply Chain for Multi-Agent Based on Ant Colony Algorithm

2012 ◽  
Vol 157-158 ◽  
pp. 189-192
Author(s):  
Bin Yang ◽  
Chun Xiao Li ◽  
Yi Bing Zhang ◽  
Wei Kong
Keyword(s):  
Resource Allocation ◽  
Supply Chain ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Multi Agent System ◽  
Allocation Model ◽  
Logistics Service ◽  
Service Supply Chain ◽  
Logistics Services ◽  
Multi Agent

In the logistics service supply chain based on Multi-Agent system, it is a combinatorial optimization problem possessing highly complexity and dynamic uncertainty to allocate the resources of various logistics companies. Therefore, in this paper, ant colony algorithm is introduced to the Multi-Agent System, and we establish a resource allocation model based on ant colony algorithm for logistics service supply chain in view of Multi-Agent. In the model, ants are replaced by agents. At the end, the paper realizes the optimization of logistics services supply chain.

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