Research on Task Scheduling Method of Mobile Delivery Cloud Computing Based on HPSO Algorithm

2021 ◽  
pp. 670-680
Author(s):  
Jianjun Li ◽  
Junjun Liu ◽  
Yu Yang ◽  
Fangyuan Su
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Method

scholarly journals Multi-objective Prediction based Task Scheduling Method in Cloud Computing

2019 ◽  
Vol 8 (4) ◽  
pp. 9388-9394 ◽  
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Task Scheduling ◽  
Job Scheduling ◽  
Sigmoid Function ◽  
Service Model ◽  
Multi Objective ◽  
Computing Paradigm ◽  
Optimal Node ◽  
Scheduling Method

Cloud Computing is Internet based computing where one can store and access their personal resources from any computer through Internet. Cloud Computing is a simple pay-per-utilize consumer-provider service model. Cloud is nothing but large pool of easily accessible and usable virtual resources. Task (Job) scheduling is always a noteworthy issue in any computing paradigm. Due to the availability of finite resources and time variant nature of incoming tasks it is very challenging to schedule a new task accurately and assign requested resources to cloud user. Traditional task scheduling techniques are improper for cloud computing as cloud computing is based on virtualization technology with disseminated nature. Cloud computing brings in new challenges for task scheduling due to heterogeneity in hardware capabilities, on-demand service model, pay-per-utilize model and guarantee to meet Quality of Service (QoS). This has motivated us to generate multi-objective methods for task scheduling. In this research paper we have presented multi-objective prediction based task scheduling method in cloud computing to improve load balancing in order to satisfy cloud consumers dynamically changing needs and also to benefit cloud providers for effective resource management. Basically our method gives low probability value for not capable and overloaded nodes. To achieve the same we have used sigmoid function and Euclidean distance. Our major goal is to predict optimal node for task scheduling which satisfies objectives like resource utilization and load balancing with accuracy.

scholarly journals Cost Effective Ant Colony Optimization in Cloud Computing

2019 ◽  
Vol 8 (10) ◽  
pp. 3016-3020
Keyword(s):  
Cloud Computing ◽  
Ant Colony Optimization ◽  
Task Scheduling ◽  
Direct Interaction ◽  
Cost Effective ◽  
Cloud Services ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Wide Range ◽  
Scheduling Method

Cloud computing is a term for a wide range of developments possibilities. It is rapidly growing paradigm in software technology that offers different services. Cloud computing has come of age, since Amazon's rollouted the first of its kind of cloud services in 2006. It stores the tremendous amount of data that are being processed every day. Cloud computing is a reliable computing base for data-intensive jobs. Cloud computing provide computing resources as a service. It is on-demand availability of computing resources without direct interaction of user. A major focus area of cloud computing is task scheduling. Task scheduling is one among the many important issues to be dealt with. It means to optimize overall system capabilities and to allocate the right resources. Task scheduling referred to NP-hard problem. The proposed algorithm is Cost Effective ACO for task scheduling, which calculates execution cost of CPU, bandwidth, memory etc. The suggested algorithm is compared with CloudSim with the presented Basic Cost ACO algorithm-based task scheduling method and outcomes clearly shows that the CEACO based task scheduling method clearly outperforms the others techniques which are in use into considerations. The task is allotted to the number of VMs based on the priorities (highest to lowest) given by user. The simulation consequences demonstrate that the suggested scheduling algorithm performs faster than previous Ant Colony Optimization algorithm in reference to the cost. It reduces the overall cost as compare to existing algorithm.

scholarly journals Optimization Algorithms based Task Scheduling Method for Cloud Computing Environment

2019 ◽  
Vol 8 (3) ◽  
pp. 3608-3613
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Large Scale ◽  
Data Centers ◽  
Computing Environment ◽  
Energy Aware ◽  
Cloud Computing Environment ◽  
Scheduling Method ◽  
Consistent Performance ◽  
Computing Structure

There are various enhancements in the world of technology. Among that Cloud computing delivers numerous amenities over the Internet. It employs data centers which comprise hardware and software provision for loading, servers, and systems. The primary reason for the popularity of Cloud computing is consistent performance, economical operation, prompt accessibility, rapid scaling and much more. The chief cause for concern in cloud computing are the errors that happen either in the software or the hardware and energy consumption on a large scale. The clients pay only for resources utilized by them and assets which are accessible during the computing in a cloud setting. In the environment of cloud computing, Task scheduling is significant concepts which can be used to minimize the energy and time spent. The algorithms in Task scheduling might employ various measures toward dispense preference to subtasks that may generate many schedules to the divergent computing structure. Moreover, consumption of energy could be dissimilar for every source which is allocated to a job. This present research explores that the PSO-CA based energy aware task scheduling method can predict with the aim to enhance the resource distribution.

Adaptive task scheduling method in multi-tenant cloud computing

2019 ◽  
Vol 12 (4) ◽  
pp. 1093-1102
Author(s):  
Ashalatha Ramegowda ◽  
Jayashree Agarkhed ◽  
Siddarama R. Patil
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Method

Regressive Whale Optimization for Workflow Scheduling in Cloud Computing

2019 ◽  
Vol 18 (04) ◽  
pp. 1950024 ◽  
Author(s):  
G. Narendrababu Reddy ◽  
S. Phani Kumar
Keyword(s):  
Cloud Computing ◽  
Resource Utilization ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Fitness Function ◽  
Workflow Scheduling ◽  
Whale Optimization ◽  
Cloud Server ◽  
Minimal Rate ◽  
Scheduling Method

Cloud computing is the advancing technology that aims at providing services to the customers with the available resources in the cloud environment. When the multiple users request service from the cloud server, there is a need of the proper scheduling of the resources to attain good customer satisfaction. Therefore, this paper proposes the Regressive Whale Optimization (RWO) algorithm for workflow scheduling in the cloud computing environment. RWO is the meta-heuristic algorithm, which schedules the task depending on a fitness function. Here, the fitness function is defined based on three major constraints, such as resource utilization, energy, and the Quality of Service (QoS). Therefore, the proposed task scheduling requires minimum time and cost for executing the task in the virtual machines. The performance of the proposed method is analyzed using the four experimental setups, and the results of the analysis prove that the proposed multi-objective task scheduling method performs well than the existing methods. The evaluation metrics considered for analyzing the performance of the proposed workflow scheduling method are resource utilization, energy, cost, and time. Resource utilization is the process of making the most of the resources available for performing tasks. Energy is the quantitative property of the resource to perform tasks. The proposed method attains the maximum resource utilization at a rate of 0.0334, minimal rate of energy, scheduling cost, and time as 0.2291, 0.0181, and 0.0007, respectively.

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