scholarly journals An Attribute-Oriented Task Scheduling Strategy for Improvement of Quality of Service in Cloud Computing

2021 ◽  
Author(s):  
Jianying Miao
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Simulation Environment ◽  
Attribute Matching ◽  
Resource Allocation Strategy ◽  
And Task

This thesis describes an innovative task scheduling and resource allocation strategy by using thresholds with attributes and amount (TAA) in order to improve the quality of service of cloud computing. In the strategy, attribute-oriented thresholds are set to decide on the acceptance of cloudlets (tasks), and the provisioning of accepted cloudlets on suitable resources represented by virtual machines (VMs,). Experiments are performed in a simulation environment created by Cloudsim that is modified for the experiments. Experimental results indicate that TAA can significantly improve attribute matching between cloudlets and VMs, with average execution time reduced by 30 to 50% compared to a typical non-filtering policy. Moreover, the tradeoff between acceptance rate and task delay, as well as between prioritized and non-prioritized cloudlets, may be adjusted as desired. The filtering type and range and the positioning of thresholds may also be adjusted so as to adapt to the dynamically changing cloud environment.

scholarly journals An Attribute-Oriented Task Scheduling Strategy for Improvement of Quality of Service in Cloud Computing

2021 ◽  
Author(s):  
Jianying Miao
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Simulation Environment ◽  
Attribute Matching ◽  
Resource Allocation Strategy ◽  
And Task

This thesis describes an innovative task scheduling and resource allocation strategy by using thresholds with attributes and amount (TAA) in order to improve the quality of service of cloud computing. In the strategy, attribute-oriented thresholds are set to decide on the acceptance of cloudlets (tasks), and the provisioning of accepted cloudlets on suitable resources represented by virtual machines (VMs,). Experiments are performed in a simulation environment created by Cloudsim that is modified for the experiments. Experimental results indicate that TAA can significantly improve attribute matching between cloudlets and VMs, with average execution time reduced by 30 to 50% compared to a typical non-filtering policy. Moreover, the tradeoff between acceptance rate and task delay, as well as between prioritized and non-prioritized cloudlets, may be adjusted as desired. The filtering type and range and the positioning of thresholds may also be adjusted so as to adapt to the dynamically changing cloud environment.

scholarly journals DELAY DIMINISHED EFFICIENT TASK SCHEDULING AND ALLOCATION FOR HETEROGENEOUS CLOUD ENVIRONMENT

2019 ◽  
Vol 01 (01) ◽  
pp. 51-62 ◽  
Author(s):  
Bhalaji N
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Quality Of Service ◽  
Task Scheduling ◽  
Cloud Environment ◽  
Wide Range ◽  
Heterogeneous Cloud ◽  
The Way

Cloud computing being a promising paradigm has become very prominent among a wide range of applications due to their timely service rendering capability. Attracted to the type of servicing and the way of servicing lots and lots of users, adapt to the cloud computing. This makes the time servicing of the cloud computing a tedious job. So in order to effectively handle the tasks the scheduling approach is entailed in the cloud computing. The paper proposes an efficient task scheduling for the heterogeneous cloud to render service at a minimized delay utilizing the genetic algorithm. The proposed method is validated through the, cloud simulator to understand the efficiency of the same in terms of delay and the quality of service.

scholarly journals Service Level Agreement Violation Preventive Task Scheduling for Quality of Service Delivery in Cloud Computing Environment

2020 ◽  
Vol 178 ◽  
pp. 375-385
Author(s):  
Ismail Zahraddeen Yakubu ◽  
Zainab Aliyu Musa ◽  
Lele Muhammed ◽  
Badamasi Ja’afaru ◽  
Fatima Shittu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Service Delivery ◽  
Task Scheduling ◽  
Service Level Agreement ◽  
Service Level ◽  
Computing Environment ◽  
Cloud Computing Environment

A Survey on Load Balancing Algorithms for Cloud Environment

2019 ◽  
Vol 16 (2) ◽  
pp. 764-767
Author(s):  
P. Chitra ◽  
Karthika D. Renuka ◽  
K. Senathipathi ◽  
S. Deepika ◽  
R. Geethamani
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Comparative Analysis ◽  
Load Balancing ◽  
Resource Utilization ◽  
The Internet ◽  
Cloud Environment ◽  
Web Based ◽  
Qos Metrics

Cloud computing is the cutting edge technology in the information field to provide services to the users over the internet through web–based tools and applications. One of the major aspects of cloud computing is load balancing. Challenges like Quality of service (QoS) metrics and resource utilization can be improved by balancing the load in cloud environment. Specific scheduling criteria can be applied using load balancing for users prioritization. This paper surveys different load balancing algorithms. The approaches that are existing are discussed and analyzed to provide fair load balancing and also a comparative analysis was presented for the performance of the existing different load balancing schemes.

scholarly journals REVIEW OF TASK SCHEDULING METHODS FOR REAL TIME TASKS IN CLOUD ENVIRONMENT

2020 ◽  
Vol 5 (1) ◽  
pp. 85-89
Author(s):  
Dinkan Patel ◽  
Anjuman Ranavadiya
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Dynamic Scheduling ◽  
Scheduling Algorithms ◽  
Minimum Time ◽  
Cloud Environment ◽  
Static Scheduling ◽  
On Demand

Cloud Computing is a type of Internet model that enables convenient, on-demand resources that can be used rapidly and with minimum effort. Cloud Computing can be IaaS, PaaS or SaaS. Scheduling of these tasks is important so that resources can be utilized efficiently with minimum time which in turn gives better performance. Real time tasks require dynamic scheduling as tasks cannot be known in advance as in static scheduling approach. There are different task scheduling algorithms that can be utilized to increase the performance in real time and performing these on virtual machines can prove to be useful. Here a review of various task scheduling algorithms is done which can be used to perform the task and allocate resources so that performance can be increased.

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.

An Improved Task Scheduling Algorithm Based on Multi-QoS in Cloud Computing

2014 ◽  
Vol 538 ◽  
pp. 512-515
Author(s):  
Feng Song Li ◽  
Yuan Sheng Lou
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Priority Scheduling ◽  
Task Scheduling Algorithm ◽  
Priority Function

For the issues of quality of service (QoS) in the cloud computing raised by users, this paper proposes a strategy for QoS classification and builds tasks' priority function modeling and through priority scheduling tasks, assigning tasks to the reasonable resources, and finally to complete the task efficiently, improve the utilization of resources.

scholarly journals Priority-Based Virtual Machine Selection Algorithm in Cloud Computing

2019 ◽  
Vol 8 (3) ◽  
pp. 1457-1462
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Cloud Service ◽  
Service Level ◽  
Cloud Services ◽  
Cloud Service Provider

Cloud computing technology has gained the attention of researchers in recent years. Almost every application is using cloud computing in one way or another. Virtualization allows running many virtual machines on a single physical computer by sharing its resources. Users can store their data on datacenter and run their applications from anywhere using the internet and pay as per service level agreement documents accordingly. It leads to an increase in demand for cloud services and may decrease the quality of service. This paper presents a priority-based selection of virtual machines by cloud service provider. The virtual machines in the cloud datacenter are configured as Amazon EC2 and algorithm is simulated in cloud-sim simulator. The results justify that proposed priority-based virtual machine algorithm shortens the makespan, by 11.43 % and 5.81 %, average waiting time by 28.80 % and 24.50%, and cost of using the virtual machine by 21.24% and 11.54% as compared to FCFS and ACO respectively, hence improving quality of service.

scholarly journals Randomized routing of virtual machines in IaaS data centers

2019 ◽  
Vol 5 ◽  
pp. e211
Author(s):  
Hadi Khani ◽  
Hamed Khanmirza
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Power Consumption ◽  
Analytical Model ◽  
Data Center ◽  
Large Scale ◽  
Data Centers ◽  
Virtual Machines ◽  
General Distribution

Cloud computing technology has been a game changer in recent years. Cloud computing providers promise cost-effective and on-demand resource computing for their users. Cloud computing providers are running the workloads of users as virtual machines (VMs) in a large-scale data center consisting a few thousands physical servers. Cloud data centers face highly dynamic workloads varying over time and many short tasks that demand quick resource management decisions. These data centers are large scale and the behavior of workload is unpredictable. The incoming VM must be assigned onto the proper physical machine (PM) in order to keep a balance between power consumption and quality of service. The scale and agility of cloud computing data centers are unprecedented so the previous approaches are fruitless. We suggest an analytical model for cloud computing data centers when the number of PMs in the data center is large. In particular, we focus on the assignment of VM onto PMs regardless of their current load. For exponential VM arrival with general distribution sojourn time, the mean power consumption is calculated. Then, we show the minimum power consumption under quality of service constraint will be achieved with randomize assignment of incoming VMs onto PMs. Extensive simulation supports the validity of our analytical model.

scholarly journals Efficient Task Scheduling for Quality of Service in Cloud Computing Network

2020 ◽  
Vol 8 (5) ◽  
pp. 3193-3196
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Response Time ◽  
Task Scheduling ◽  
Waiting Time ◽  
Threshold Values ◽  
Specific Time ◽  
Required Quality ◽  
Cloud Resources

Task scheduling in cloud is the process of allocating a resource to a task at specific time. The allocation of limited cloud resources to large number of tasks to satisfy the required quality of service is the key challenge in cloud. Allocation of a resource with less capability to a task increases the response time, makespan of the task and waiting time of the entire tasks in the waiting queue. This problem will result to an unsatisfied Quality of Service. In this paper we proposed an efficient task scheduling that uses three threshold values to specify the resource to be allocated to a task at a given time. This method ensures that a capable resource is allocated to task such that the response time and makespan of the all task are minimized. The proposed method was simulated using CloudSim and the result shows a better response time and makespan than the well known Min-Min and Max-Min Method.

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