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.

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.

scholarly journals Analysis of the Day-ahead Deviation Plan and Research on the Real-time Scheduling of Photovoltaic Greenhouses Based on Exergy Theory

Energies ◽  
2019 ◽  
Vol 12 (20) ◽  
pp. 3849
Author(s):  
Xiayun Duan ◽  
Yifeng Ding ◽  
Huanna Niu ◽  
Yuzhu Wang
Keyword(s):  
Energy Storage ◽  
Real Time ◽  
Exergy Loss ◽  
Heating Process ◽  
The Real ◽  
Real Time Scheduling ◽  
Scheduling Model ◽  
Time Scheduling ◽  
The Difference ◽  
Electric Loads

For the correction problem of day-ahead plan deviation caused by energy prediction deviation in day-ahead scheduling stage of photovoltaic greenhouses, an exergy analysis method is used to propose the deviation model of heat required for photovoltaic greenhouses. Based on the deviation model, a real-time optimization scheduling model is established. The deviation model not only considers the non-negligible exergy loss during heating process of pipes, but also considers the difference between heat and thermal exergy affected by the actual indoor temperature. The goal of the real-time scheduling model is to minimize the absolute value of the difference between the energy supply and demand prediction deviation to be corrected and the adjustment of multi-form energy storage and electric loads, so that develop the real-time adjustment plan of energy storage and electric loads. The analysis results of the actual photovoltaic greenhouse show that of the heat required by a greenhouse based on the exergy theory calculation, the exergy loss of the heating process accounts for about 10%–20% of the total thermal exergy required and it cannot be ignored, so the calculation results can reflect the actual heat required more accurately and the greenhouse temperature is more suitable for plant growth. Moreover, the proposed real-time scheduling model can correct the deviation of the day-ahead plan and improve local consumption. The promotion ratio can reach 7%. Finally, the farmers’ electricity purchases cost is reduced. Thereby the effectiveness of the proposed heat deviation model and real-time scheduling model is verified.

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 Real-time Multiple-Workflow Scheduling in Cloud Environment

2021 ◽  
Author(s):  
Xiaojin Ma ◽  
Huahu Xu ◽  
Honghao Gao ◽  
Minjie Bian
Keyword(s):  
Real Time ◽  
Arrival Time ◽  
State Of The Art ◽  
Minimum Cost ◽  
Workflow Scheduling ◽  
Start Time ◽  
Real Time Scheduling ◽  
Time Scheduling ◽  
Three Stages ◽  
Deadline Constraints

Abstract With the development of cloud computing, an increasing number of applications in different fields have been deployed to the cloud. In this process, the real-time scheduling of multiple workflows composed of tasks from these different applications must consider various influencing factors which strongly affect scheduling performance. This paper proposes a real-time multiple-workflow scheduling (RMWS) scheme to schedule workflows dynamically with minimum cost under different deadline constraints. Due to the uncertainty of workflow arrival time and specification, RMWS dynamically allocates tasks and divides the scheduling process into three stages. First, when a new workflow arrives, the latest start time and the latest finish time of each task are calculated according to the deadline, and the subdeadline of each task is obtained by probabilistic upward ranking. Then, each ready task is allocated according to its subdeadline and the increased cost of the virtual machine (VM). Meanwhile, only one waiting task can be assigned to each VM to reduce delay fluctuations. Finally, when the task is completed on the assigned VM, all the parameters of the relevant tasks are updated before allocating them to appropriate VMs. The experimental results based on four real-world workflow traces show that the proposed algorithm is superior to two state-of-the-art algorithms in terms of total rental cost, resource utilization, success rate and deadline deviation under different conditions.

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