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.

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%.

Optimizing the Migration of Virtual Machines in Cloud Data Centers

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Data Centers ◽  
Bin Packing ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Virtual Machine Migration ◽  
Cloud Data ◽  
Time Simulation ◽  
Cloud Data Centers ◽  
Dynamic Resource

Dynamic resource allocation of cloud data centers is implemented with the use of virtual machine migration. Selected virtual machines (VM) should be migrated on appropriate destination servers. This is a critical step and should be performed according to several criteria. It is proposed to use the criteria of minimum resource wastage and service level agreement violation. The optimization problem of the VM placement according to two criteria is formulated, which is equivalent to the well-known main assignment problem in terms of the structure, necessary conditions, and the nature of variables. It is suggested to use the Hungarian method or to reduce the problem to a closed transport problem. This allows the exact solution to be obtained in real time. Simulation has shown that the proposed approach outperforms widely used bin-packing heuristics in both criteria.

scholarly journals An Efficient Cloud Data Center Allocation to the Source of Requests

2020 ◽  
Vol 32 (3) ◽  
pp. 23-36
Author(s):  
Kanniga Devi R. ◽  
Murugaboopathi Gurusamy ◽  
Vijayakumar P.
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Cloud Service ◽  
Service Level ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Service Consumer ◽  
Cloud Data Centers

A Cloud data center is a network of virtualized resources, namely virtualized servers. They provision on-demand services to the source of requests ranging from virtual machines to virtualized storage and virtualized networks. The cloud data center service requests can come from different sources across the world. It is desirable for enhancing Quality of Service (QoS), which is otherwise known as a service level agreement (SLA), an agreement between cloud service requester and cloud service consumer on QoS, to allocate the cloud data center closest to the source of requests. This article models a Cloud data center network as a graph and proposes an algorithm, modified Breadth First Search where the source of requests assigned to the Cloud data centers based on a cost threshold, which limits the distance between them. Limiting the distance between Cloud data centers and the source of requests leads to faster service provisioning. The proposed algorithm is tested for various graph instances and is compared with modified Voronoi and modified graph-based K-Means algorithms that they assign source of requests to the cloud data centers without limiting the distance between them. The proposed algorithm outperforms two other algorithms in terms of average time taken to allocate the cloud data center to the source of requests, average cost and load distribution.

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.

Energy Efficient, Resource-Aware, Prediction Based VM Provisioning Approach for Cloud Environment

2020 ◽  
Vol 11 (3) ◽  
pp. 22-41
Author(s):  
Akkrabani Bharani Pradeep Kumar ◽  
P. Venkata Nageswara Rao
Keyword(s):  
Energy Consumption ◽  
Electrical Power ◽  
Service Level Agreement ◽  
Service Level ◽  
Management Approach ◽  
Cloud Data ◽  
Proposed Model ◽  
Key Aspects ◽  
Cloud Infrastructures ◽  
Resource Aware

Over the past few decades, computing environments have progressed from a single-user milieu to highly parallel supercomputing environments, network of workstations (NoWs) and distributed systems, to more recently popular systems like grids and clouds. Due to its great advantage of providing large computational capacity at low costs, cloud infrastructures can be employed as a very effective tool, but due to its dynamic nature and heterogeneity, cloud resources consuming enormous amount of electrical power and energy consumption control becomes a major issue in cloud datacenters. This article proposes a comprehensive prediction-based virtual machine management approach that aims to reduce energy consumption by reducing active physical servers in cloud data centers. The proposed model focuses on three key aspects of resource management namely, prediction-based delay provisioning; prediction-based migration, and resource-aware live migration. The comprehensive model minimizes energy consumption without violating the service level agreement and provides the required quality of service. The experiments to validate the efficacy of the proposed model are carried out on a simulated environment, with varying server and user applications and parameter sizes.

scholarly journals An Energy and SLA-Aware Resource Management Strategy in Cloud Data Centers

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Chi Zhang ◽  
Yuxin Wang ◽  
Yuanchen Lv ◽  
Hao Wu ◽  
He Guo
Keyword(s):  
Energy Consumption ◽  
Resource Management ◽  
Management Strategy ◽  
Data Centers ◽  
Minimum Energy ◽  
Service Level Agreement ◽  
Cloud Data ◽  
Cpu Utilization ◽  
Cloud Data Centers ◽  
Detecting Method

Reducing energy consumption of data centers is an important way for cloud providers to improve their investment yield, but they must also ensure that the services delivered meet the various requirements of consumers. In this paper, we propose a resource management strategy to reduce both energy consumption and Service Level Agreement (SLA) violations in cloud data centers. It contains three improved methods for subproblems in dynamic virtual machine (VM) consolidation. For making hosts detection more effective and improving the VM selection results, first, the overloaded hosts detecting method sets a dynamic independent saturation threshold for each host, respectively, which takes the CPU utilization trend into consideration; second, the underutilized hosts detecting method uses multiple factors besides CPU utilization and the Naive Bayesian classifier to calculate the combined weights of hosts in prioritization step; and third, the VM selection method considers both current CPU usage and future growth space of CPU demand of VMs. To evaluate the performance of the proposed strategy, it is simulated in CloudSim and compared with five existing energy–saving strategies using real-world workload traces. The experimental results show that our strategy outperforms others with minimum energy consumption and SLA violation.

scholarly journals Virtual Machine Consolidation with Minimization of Migration Thrashing for Cloud Data Centers

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xialin Liu ◽  
Junsheng Wu ◽  
Gang Sha ◽  
Shuqin Liu
Keyword(s):  
Virtual Machine ◽  
Carbon Dioxide Emissions ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
High Capacity ◽  
Electrical Energy ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Vm Consolidation

Cloud data centers consume huge amount of electrical energy bringing about in high operating costs and carbon dioxide emissions. Virtual machine (VM) consolidation utilizes live migration of virtual machines (VMs) to transfer a VM among physical servers in order to improve the utilization of resources and energy efficiency in cloud data centers. Most of the current VM consolidation approaches tend to aggressive-migrate for some types of applications such as large capacity application such as speech recognition, image processing, and decision support systems. These approaches generate a high migration thrashing because VMs are consolidated to servers according to VM’s instant resource usage without considering their overall and long-term utilization. The proposed approach, dynamic consolidation with minimization of migration thrashing (DCMMT) which prioritizes VM with high capacity, significantly reduces migration thrashing and the number of migrations to ensure service-level agreement (SLA) since it keeps VMs likely to suffer from migration thrashing in the same physical servers instead of migrating. We have performed experiments using real workload traces compared to existing aggressive-migration-based solutions; through simulations, we show that our approach improves migration thrashing metric by about 28%, number of migrations metric by about 21%, and SLAV metric by about 19%.

scholarly journals Server Consolidation Based on Culture Multiple-Ant-Colony Algorithm in Cloud Computing

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2724 ◽  
Author(s):  
Yuan ◽  
Sun
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Ant Colony Algorithm ◽  
Data Centers ◽  
Virtual Machines ◽  
Ant Colony ◽  
Virtual Machine Migration ◽  
Server Consolidation ◽  
Cloud Data ◽  
Cloud Data Centers

High-energy consumption in data centers has become a critical issue. The dynamic server consolidation has significant effects on saving energy of a data center. An effective way to consolidate virtual machines is to migrate virtual machines in real time so that some light load physical machines can be turned off or switched to low-power mode. The present challenge is to reduce the energy consumption of cloud data centers. In this paper, for the first time, a server consolidation algorithm based on the culture multiple-ant-colony algorithm was proposed for dynamic execution of virtual machine migration, thus reducing the energy consumption of cloud data centers. The server consolidation algorithm based on the culture multiple-ant-colony algorithm (CMACA) finds an approximate optimal solution through a specific target function. The simulation results show that the proposed algorithm not only reduces the energy consumption but also reduces the number of virtual machine migration.

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