scholarly journals Dynamic Security Exchange Scheduling Model for Business Workflow Based on Queuing Theory in Cloud Computing

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Rongbin Xu ◽  
Jianguo Wu ◽  
Yongliang Cheng ◽  
Zhiqiang Liu ◽  
Yuanmo Lin ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Dynamic Process ◽  
Queuing Theory ◽  
Business Processes ◽  
Virtual Machines ◽  
Service Discipline ◽  
Security Issue ◽  
Queuing Model ◽  
Scheduling Model ◽  
Cloud Resources

With the rapid development of e-business, large volume of business processes need to be handled in a constrained time. There is always a security issue related to on-time completion in many applications in the economic fields. So, how to effectively manage and organize business processes became very important. By using cloud computing, instance-intensive processes can be handled more effectively by applying just-right virtual machines. Hence, the management of cloud resources became an important issue that many researchers focus on to fully utilize the advantage of cloud. In this paper, we mainly discuss the queuing theory and put forward our novel dynamic process scheduling model based on queuing theory, which is named M/G/k/l-P for business processes. This model can solve the issue of allocating appropriate number of cloud resources based on the number of tasks and execution stages to ensure whether the numbers of cloud resources are sufficient and adequate or not, which can improve the security issue for business process. The service discipline in our model can provide a dynamic process by setting different priorities to improve the experience of users. Evaluations prove that the queuing model of M/G/k/l-P can work very well for business workflow scheduling.

A Secured Real Time Scheduling Model for Cloud Hypervisor

2016 ◽  
Vol 6 (4) ◽  
pp. 97-110
Author(s):  
Rekha Kashyap ◽  
Deo Prakash Vidyarthi
Keyword(s):  
Cloud Computing ◽  
Success Rate ◽  
Real Time ◽  
Virtual Machines ◽  
Resource Provisioning ◽  
Real Time Scheduling ◽  
Scheduling Model ◽  
Physical Resources ◽  
Time Scheduling ◽  
Deadline Constraints

Virtualization is critical to cloud computing and is possible through hypervisors, which maps the Virtual machines((VMs) to physical resources but poses security concerns as users relinquish physical possession of their computation and data. Good amount of research is initiated for resource provisioning on hypervisors, still many issues need to be addressed for security demanding and real time VMs. First work SRT-CreditScheduler (Secured and Real-time), maximizes the success rate by dynamically prioritizing the urgency and the workload of VMs but ensures highest security for all. Another work, SA-RT-CreditScheduler (Security-aware and Real-time) is a dual objective scheduler, which maximizes the success rate of VMs in best possible security range as specified by the VM owner. Though the algorithms can be used by any hypervisor, for the current work they have been implemented on Xen hypervisor. Their effectiveness is validated by comparing it with Xen's, Credit and SEDF scheduler, for security demanding tasks with stringent deadline constraints.

Provisioning of Virtual Machines in the Context of an Auto-Scaling Cloud Computing Environment

2020 ◽  
Vol 17 (6) ◽  
pp. 2430-2434
Author(s):  
R. S. Rajput ◽  
Dinesh Goyal ◽  
Rashid Hussain ◽  
Pratham Singh
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Resource Utilization ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Auto Scaling ◽  
Adequate Resource ◽  
Cloud Resources

The cloud computing environment is accomplishing cloud workload by distributing between several nodes or shift to the higher resource so that no computing resource will be overloaded. However, several techniques are used for the management of computing workload in the cloud environment, but still, it is an exciting domain of investigation and research. Control of the workload and scaling of cloud resources are some essential aspects of the cloud computing environment. A well-organized load balancing plan ensures adequate resource utilization. The auto-scaling is a technique to include or terminate additional computing resources based on the scaling policies without involving humans efforts. In the present paper, we developed a method for optimal use of cloud resources by the implementation of a modified auto-scaling feature. We also incorporated an auto-scaling controller for the optimal use of cloud resources.

A Secured Real Time Scheduling Model for Cloud Hypervisor

2019 ◽  
pp. 507-522
Author(s):  
Rekha Kashyap ◽  
Deo Prakash Vidyarthi
Keyword(s):  
Cloud Computing ◽  
Success Rate ◽  
Real Time ◽  
Virtual Machines ◽  
Resource Provisioning ◽  
Real Time Scheduling ◽  
Scheduling Model ◽  
Physical Resources ◽  
Time Scheduling ◽  
Deadline Constraints

Virtualization is critical to cloud computing and is possible through hypervisors, which maps the Virtual machines((VMs) to physical resources but poses security concerns as users relinquish physical possession of their computation and data. Good amount of research is initiated for resource provisioning on hypervisors, still many issues need to be addressed for security demanding and real time VMs. First work SRT-CreditScheduler (Secured and Real-time), maximizes the success rate by dynamically prioritizing the urgency and the workload of VMs but ensures highest security for all. Another work, SA-RT-CreditScheduler (Security-aware and Real-time) is a dual objective scheduler, which maximizes the success rate of VMs in best possible security range as specified by the VM owner. Though the algorithms can be used by any hypervisor, for the current work they have been implemented on Xen hypervisor. Their effectiveness is validated by comparing it with Xen's, Credit and SEDF scheduler, for security demanding tasks with stringent deadline constraints.

scholarly journals Threshold based VM Placement Technique for Load Balanced Resource Provisioning using Priority Scheme in Cloud Computing

2021 ◽  
Vol 13 (5) ◽  
pp. 01-18
Author(s):  
Mayank Sohani ◽  
Dr. S. C. Jain
Keyword(s):  
Cloud Computing ◽  
Resource Sharing ◽  
Virtual Machines ◽  
Resource Scheduling ◽  
Resource Provisioning ◽  
Scheduling Model ◽  
Dynamic Resource Provisioning ◽  
Two Factors ◽  
Cloud Framework ◽  
Resource Cost

The unbalancing load issue is a multi-variation, multi-imperative issue that corrupts the execution and productivity of processing assets. Workload adjusting methods give solutions of load unbalancing circumstances for two bothersome aspects over-burdening and under-stacking. Cloud computing utilizes planning and workload balancing for a virtualized environment, resource partaking in cloud foundation. These two factors must be handled in an improved way in cloud computing to accomplish ideal resource sharing. Henceforth, there requires productive resource, asset reservation for guaranteeing load advancement in the cloud. This work aims to present an incorporated resource, asset reservation, and workload adjusting calculation for effective cloud provisioning. The strategy develops a Priority-based Resource Scheduling Model to acquire the resource, asset reservation with threshold-based load balancing for improving the proficiency in cloud framework. Extending utilization of Virtual Machines through the suitable and sensible outstanding task at hand modifying is then practiced by intensely picking a job from submitting jobs using Priority-based Resource Scheduling Model to acquire resource asset reservation. Experimental evaluations represent, the proposed scheme gives better results by reducing execution time, with minimum resource cost and improved resource utilization in dynamic resource provisioning conditions.

scholarly journals Task Scheduling Management for Load Balancing Using Task Grouping Based on Cloud Computing

2021 ◽  
Vol 9 (3) ◽  
Author(s):  
Ahmad Helmi Abdul Halim ◽  
Asif Iqbal Hajamydeen
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Turnaround Time ◽  
Task Management ◽  
Queuing Model ◽  
Context Switching ◽  
Scheduling Model ◽  
Complex Procedure ◽  
Scheduling Management ◽  
Better Than

Managing task scheduling management in cloud computing is an essential part for the landscape of complex procedure tasks based on various resources in a proficient and scalable path. The aim of this research is to dynamically optimize the aforesaid issue of task scheduling. The task management improvises the imperfection algorithm by pursue on weighted fair queuing model, which is significantly effective compared to the existing method. A task scheduling model has been created to demonstrate the proposed scheduler management. Study shows the improvement in the adaptation of round robin and shortest job first algorithm performing better than the existing algorithm according to the differentiate execution measurements such as, turnaround time, task size and average waiting time. In addition, context switches play an important role in algorithm by sharing between multiple tasks and running task in the scheduler. Altogether, a significant improvement between existing algorithm and proposed studies follows up accordingly to a specific context switching takes place.

Key Performance Information Collection Architecture Based on Cloud Computing

2014 ◽  
Vol 509 ◽  
pp. 182-188
Author(s):  
Bin Chen ◽  
Zhi Jian Wang ◽  
Rong Zhi Qi ◽  
Xin Lv
Keyword(s):  
Cloud Computing ◽  
Queuing Theory ◽  
Virtual Machines ◽  
Cloud Services ◽  
Accurate Information ◽  
Cloud Environment ◽  
Analysis Framework ◽  
Performance Information ◽  
Computing Center ◽  
And Performance

Cloud Computing has become another buzzword in recent years. Follow the popular research and use of the cloud system the performance become the bottleneck of the Newborn. More and more researches are turning their attention to analyze the performance of the cloud services. However, it is hard to extract accurate information from the different type of the cloud components such as datacenter, host, Virtual Machines (VM) in the cloud. Thus, it is significant to collect sufficient row data of the Cloud systems for the performance analysis. In this paper, we described an analysis framework to evaluate comprehensive performance guideline of cloud computing center. The analysis architecture is built based on the performance agent and server interface method (PASI), which consists of performance client (PMC), performance agent (PMA) and performance server (PMS), and we put forward a mathematical model based on the PASI information and queuing theory to forecast the idle rate and availability of the cloud environment. It is proved that the PASI architecture is correctly and effectively evaluates the performance of the cloud component and whole cloud environment.

Predictive Data Center Selection Scheme for Response Time Optimization in Cloud Computing

2021 ◽  
Vol 11 (1) ◽  
pp. 93-111
Author(s):  
Deepak Kapgate
Keyword(s):  
Cloud Computing ◽  
Response Time ◽  
Data Center ◽  
Queuing Theory ◽  
Performance Metrics ◽  
Forecasting Model ◽  
Selection Algorithm ◽  
Queuing Model ◽  
Selection Scheme ◽  
Selection Algorithms

The quality of cloud computing services is evaluated based on various performance metrics out of which response time (RT) is most important. Nearly all cloud users demand its application's RT as minimum as possible, so to minimize overall system RT, the authors have proposed request response time prediction-based data center (DC) selection algorithm in this work. Proposed DC selection algorithm uses results of optimization function for DC selection formulated based on M/M/m queuing theory, as present cloud scenario roughly obeys M/M/m queuing model. In cloud environment, DC selection algorithms are assessed based on their performance in practice, rather than how they are supposed to be used. Hence, explained DC selection algorithm with various forecasting models is evaluated for minimum user application RT and RT prediction accuracy on various job arrival rates, real parallel workload types, and forecasting model training set length. Finally, performance of proposed DC selection algorithm with optimal forecasting model is compared with other DC selection algorithms on various cloud configurations.

An QPSL Queuing Model for Load Balancing in Cloud Computing

2020 ◽  
Vol 16 (3) ◽  
pp. 33-48
Author(s):  
Shadab Siddiqui ◽  
Manuj Darbari ◽  
Diwakar Yagyasen
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Queuing Theory ◽  
Service Rate ◽  
Queuing Model ◽  
Queuing Models ◽  
Fifo Queue ◽  
Queue Discipline ◽  
Service Rates ◽  
Waiting Lines

Load balancing is the process of distributing a workload among various servers. Queuing is the most common scenario for day-to-day applications. Queuing theory is used to study the problem of waiting lines. Queuing theory bridges the gap between service demands and the delay in replies given to users. The proposed QPSL Queuing Model makes use of M/M/k queue with FIFO queue discipline for load balancing in cloud computing. The model makes use of exponential distribution for calculating service rates and Poisson distribution for calculating waiting lines. The proposed QPSL queuing model is also compared with other existing queuing models for load balancing on various parameters. The experimental analysis depicts that QPSL model performed better in terms of service rate and response time.

scholarly journals Performance Analysis of a Cloud Computing System using Queuing Model with Correlated Task Reneging

2021 ◽  
Vol 2091 (1) ◽  
pp. 012003
Author(s):  
Rakesh Kumar ◽  
Bhavneet Singh Soodan ◽  
Godlove Suila Kuaban ◽  
Piotr Czekalski ◽  
Sapana Sharma
Keyword(s):  
Cloud Computing ◽  
Performance Analysis ◽  
Queuing Theory ◽  
Computing System ◽  
Queuing Model ◽  
Average Delay ◽  
Computing Systems ◽  
Cloud Computing System ◽  
Request Queue ◽  
And Performance

Abstract Queuing theory has been extensively used in the modelling and performance analysis of cloud computing systems. The phenomenon of the task (or request) reneging, that is, the dropping of requests from the request queue often occur in cloud computing systems, and it is important to consider it when developing performance evaluations models for cloud computing infrastructures. Majority of studies in the performance evaluation of cloud computing data centres with the use of queuing theory do not consider the fact that the tasks could be removed from queue without being serviced. The removal of tasks from the queue could be due to the user impatience, execution deadline expiration, security reasons, or as an active queue management strategy. The reneging could be correlated in nature, that is, if a request is dropped (or reneged) at any time epoch, and then there is a probability that a request may or may not be dropped at the next time epoch. This kind of dropping (or reneging) of requests is referred to as correlated request reneging. In this paper we have modelled a cloud computing infrastructure with correlated request reneging using queuing theory. An M/M/1/N queuing model with correlated reneging has been used to study the performance analysis of the load balancing server of a cloud computing system. The steady-state as well as the transient performance analyses have been carried out. Important measures of performance like average queue size, average delay, probability of task blocking, and the probability of no waiting in the queue are studied. Finally, some comparisons are performed which describe the effect of correlated task reneging over simple exponential reneging.

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

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