scholarly journals Efficient Task Scheduling and Fair Load Distribution Among Federated Clouds

2021 ◽  
Vol 15 (3) ◽  
pp. 216-238
Author(s):  
Rajeshwari B S ◽  
M. Dakshayini ◽  
H.S. Guruprasad
Keyword(s):  
Response Time ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Future Generation ◽  
Service Level ◽  
Centralized Control ◽  
Cloud Providers ◽  
Fair Distribution ◽  
User Tasks

The federated cloud is the future generation of cloud computing, allowing sharing of computing and storage resources, and servicing of user tasks among cloud providers through a centralized control mechanism. However, a great challenge lies in the efficient management of such federated clouds and fair distribution of the load among heterogeneous cloud providers. In our proposed approach, called QPFS_MASG, at the federated cloud level, the incoming tasks queue are partitioned in order to achieve a fair distribution of the load among all cloud providers of the federated cloud. Then, at the cloud level, task scheduling using the Modified Activity Selection by Greedy (MASG) technique assigns the tasks to different virtual machines (VMs), considering the task deadline as the key factor in achieving good quality of service (QoS). The proposed approach takes care of servicing tasks within their deadline, reducing service level agreement (SLA) violations, improving the response time of user tasks as well as achieving fair distribution of the load among all participating cloud providers. The QPFS_MASG was implemented using CloudSim and the evaluation result revealed a guaranteed degree of fairness in service distribution among the cloud providers with reduced response time and SLA violations compared to existing approaches. Also, the evaluation results showed that the proposed approach serviced the user tasks with minimum number of VMs.

scholarly journals Deployment of virtual machines for tiered applications in cloud systems with optimized resource allocation based on availability SLAS

2021 ◽  
Author(s):  
Praneeth Sakhamuri
Keyword(s):  
Response Time ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Service Providers ◽  
Service Level Agreement ◽  
Cost Effective ◽  
Service Level ◽  
Single Type ◽  
Application Service Providers ◽  
Application Service

Deploying and managing high availability-tiered application in the cloud is challenging, as it requires providing necessary virtual machines to make the application available. An application is available if it works and responds in a timely manner for varying workloads. Application service providers need to allocate specified number of working virtual machine copies for each server with at least a given minimum computing power, to meet the response time requirement. Otherwise, we may end up with response time failures. This thesis formulates an optimization problem that determines the number and type of virtual machines needed for each server to minimize the cost and at the same time guarantees the availability SLA (Service-Level Agreement) for different workloads. The results demonstrate that a diverse approach is more cost-effective than running on a single type of virtual machine, and buying only the cheapest virtual machines for an application is not always economical.

scholarly journals Deployment of virtual machines for tiered applications in cloud systems with optimized resource allocation based on availability SLAS

2021 ◽  
Author(s):  
Praneeth Sakhamuri
Keyword(s):  
Response Time ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Service Providers ◽  
Service Level Agreement ◽  
Cost Effective ◽  
Service Level ◽  
Single Type ◽  
Application Service Providers ◽  
Application Service

Deploying and managing high availability-tiered application in the cloud is challenging, as it requires providing necessary virtual machines to make the application available. An application is available if it works and responds in a timely manner for varying workloads. Application service providers need to allocate specified number of working virtual machine copies for each server with at least a given minimum computing power, to meet the response time requirement. Otherwise, we may end up with response time failures. This thesis formulates an optimization problem that determines the number and type of virtual machines needed for each server to minimize the cost and at the same time guarantees the availability SLA (Service-Level Agreement) for different workloads. The results demonstrate that a diverse approach is more cost-effective than running on a single type of virtual machine, and buying only the cheapest virtual machines for an application is not always economical.

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.

Optimal Management of Cloud Centers with Different Arrival Modes for Cloud Computing Environment

2012 ◽  
Vol 2 (3) ◽  
pp. 86-97
Author(s):  
Veena Goswami ◽  
Sudhansu Shekhar Patra ◽  
G. B. Mund
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Finite Buffer ◽  
Web Browser ◽  
Computing Paradigm ◽  
Computing Services ◽  
Multi Server

Cloud computing is a new computing paradigm in which information and computing services can be accessed from a Web browser by clients. Understanding of the characteristics of computer service performance has become critical for service applications in cloud computing. For the commercial success of this new computing paradigm, the ability to deliver guaranteed Quality of Services (QoS) is crucial. Based on the Service level agreement, the requests are processed in the cloud centers in different modes. This paper analyzes a finite-buffer multi-server queuing system where client requests have two arrival modes. It is assumed that each arrival mode is serviced by one or more Virtual machines, and both the modes have equal probabilities of receiving service. Various performance measures are obtained and optimal cost policy is presented with numerical results. The genetic algorithm is employed to search the optimal values of various parameters for the system.

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

Service Level Agreement Conformance in Virtual Machine Migration with an Improved Energy and Performance Management Method

2020 ◽  
Vol 17 (9) ◽  
pp. 3904-3906
Author(s):  
Susmita J. A. Nair ◽  
T. R. Gopalakrishnan Nair
Keyword(s):  
Energy Consumption ◽  
Performance Management ◽  
Large Scale ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Performance Degradation ◽  
Virtual Machine Migration ◽  
And Performance

Increasing demand of computing resources and the popularity of cloud computing have led the organizations to establish of large-scale data centers. To handle varying workloads, allocating resources to Virtual Machines, placing the VMs in the most suitable physical machine at data centers without violating the Service Level Agreement remains a big challenge for the cloud providers. The energy consumption and performance degradation are the prime focus for the data centers in providing services by strictly following the SLA. In this paper we are suggesting a model for minimizing the energy consumption and performance degradation without violating SLA. The experiments conducted have shown a reduction in SLA violation by nearly 10%.

Cost- and Time-Based Data Deployment for Improving Scheduling Efficiency in Distributed Clouds

2020 ◽  
Author(s):  
Chunlin Li ◽  
Yihan Zhang ◽  
Xiaomei Qu ◽  
Youlong Luo
Keyword(s):  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Service Level Agreement ◽  
Data Access ◽  
Service Level ◽  
Speculative Execution ◽  
Improved Genetic Algorithm ◽  
Real Time Processing ◽  
Data Intensive ◽  
Access Cost

Abstract In recent years, with the continuous development of internet of things and cloud computing technologies, data intensive applications have gotten more and more attention. In the distributed cloud environment, the access of massive data is often the bottleneck of its performance. It is very significant to propose a suitable data deployment algorithm for improving the utilization of cloud server and the efficiency of task scheduling. In order to reduce data access cost and data deployment time, an optimal data deployment algorithm is proposed in this paper. By modeling and analyzing the data deployment problem, the problem is solved by using the improved genetic algorithm. After the data are well deployed, aiming at improving the efficiency of task scheduling, a task progress aware scheduling algorithm is proposed in this paper in order to make the speculative execution mechanism more accurate. Firstly, the threshold to detect the slow tasks and fast nodes are set. Then, the slow tasks and fast nodes are detected by calculating the remaining time of the tasks and the real-time processing ability of the nodes, respectively. Finally, the backup execution of the slow tasks is performed on the fast nodes. While satisfying the load balancing of the system, the experimental results show that the proposed algorithms can obviously reduce data access cost, service-level agreement (SLA) default rate and the execution time of the system and optimize data deployment for improving scheduling efficiency in distributed clouds.

SLA and Performance Efficient Heuristics for Virtual Machines Placement in Cloud Data Centers

2017 ◽  
Vol 9 (3) ◽  
pp. 17-33 ◽  
Author(s):  
Oshin Sharma ◽  
Hemraj Saini
Keyword(s):  
Energy Consumption ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Consolidation Process ◽  
Cloud Data ◽  
Trade Offs ◽  
And Performance ◽  
High Level

Cloud computing has revolutionized the working models of IT industry and increasing the demand of cloud resources which further leads to increase in energy consumption of data centers. Virtual machines (VMs) are consolidated dynamically to reduce the number of host machines inside data centers by satisfying the customer's requirements and quality of services (QoS). Moreover, for using the services of cloud environment every cloud user has a service level agreement (SLA) that deals with energy and performance trade-offs. As, the excess of consolidation and migration may degrade the performance of system, therefore, this paper focuses the overall performance of the system instead of energy consumption during the consolidation process to maintain a trust level between cloud's users and providers. In addition, the paper proposed three different heuristics for virtual machine (VM) placement based on current and previous usage of resources. The proposed heuristics ensure a high level of service level agreements (SLA) and better performance of ESM metric in comparison to previous research.

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