scholarly journals An Adaptive Deployment Algorithm for IaaS Cloud Virtual Machines Based on Q Learning Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Shuguang Chen
Keyword(s):  
Energy Consumption ◽  
Virtual Machines ◽  
Learning Algorithm ◽  
Service Level Agreement ◽  
Service Level ◽  
High Energy ◽  
Learning Mechanism ◽  
Q Learning ◽  
Sla Violation ◽  
Violation Rate

When deploying infrastructure as a service (IaaS) cloud virtual machines using the existing algorithms, the deployment process cannot be simplified, and the algorithm is difficult to be applied. This leads to the problems of high energy consumption, high number of migrations, and high average service-level agreement (SLA) violation rate. In order to solve the above problems, an adaptive deployment algorithm for IaaS cloud virtual machines based on Q learning mechanism is proposed in this research. Based on the deployment principle, the deployment characteristics of the IaaS cloud virtual machines are analyzed. The virtual machine scheduling problem is replaced with the Markov process. The multistep Q learning algorithm is used to schedule the virtual machines based on the Q learning mechanism to complete the adaptive deployment of the IaaS cloud virtual machines. Experimental results show that the proposed algorithm has low energy consumption, small number of migrations, and low average SLA violation rate.

scholarly journals VM Selection and Allocation Policy to Optimize VM Migration in Cloud Environment

2019 ◽  
Vol 8 (2) ◽  
pp. 3444-3449
Keyword(s):  
Energy Consumption ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Cloud Environment ◽  
Total Energy Consumption ◽  
Vm Migration ◽  
Allocation Policy ◽  
Sla Violation ◽  
Violation Rate

Cloud computing, a metered based technology provides the services using virtualized technology over the internet. In the cloud environment, to improve the performance (such as utilization of the resources, energy minimization) extreme number of virtual machines (VMs) can be installed on the servers as per their resource capacity. In this way, servers can be overloaded. Overloaded servers consume more energythan normal status servers. VM migration (VMM) is an efficient technique to become a server in a normal state. VMM technique is used to consolidate the resources to increase resource utilization (RU) and reduceenergy usage. In the VMM technique, selection of VM such as which VM is migrated from one server to another server and allocation of VM on servers is an important aspect. Appropriate VM selection declines the numeral of VMMs and increasesenergy efficiency. Appropriate VM allocation declines the server to become overloaded. In this paper, the VM selection and allocation strategy is presented. CloudSim toolkit is used to verify the strength of proposed VM selection and allocation algorithm. Proposed VM Selection algorithm (MaMT) performs better than existing MiMT algorithm in terms of total energy consumption, number of hosts shut down, number of VMM, and average Service Level Agreement (SLA) violation rate. MaMT algorithm with resource aware provisioning (RAP) and MiMT+RAP algorithm combines both VM selection and allocation policies. RAP algorithm used both energy and RU parameters while allocating VM to the server.MaMTreduces the energy consumption up to 7.25% and reduces the SLA violation rate up-to 2.6% in comparison to MiMT algorithm. When VM selection and allocation policies combines together than more system performance is improved. MaMT+RAPreduces the energy consumption up to6.76% and reduces the SLA violation rate up-to 0.22% in comparison to MaMT algorithm.MiMT+RAPreduces the energy consumption up to15.23% and reduces the SLA violation rate up-to 0.95% in comparison to MiMT algorithm.

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.

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%.

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.

scholarly journals VM Consolidation Plan for Improving the Energy Efficiency of Cloud

2021 ◽  
Vol 21 (3) ◽  
pp. 145-159
Author(s):  
Satveer ◽  
Mahendra Singh Aswal
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Sla Violation ◽  
Dynamic Consolidation ◽  
Cloud Datacenter ◽  
Cloud Users ◽  
Vm Consolidation

Abstract Achieving energy-efficiency with minimal Service Level Agreement (SLA) violation constraint is a major challenge in cloud datacenters owing to financial and environmental concerns. The static consolidation of Virtual Machines (VMs) is not much significant in recent time and has become outdated because of the unpredicted workload of cloud users. In this paper, a dynamic consolidation plan is proposed to optimize the energy consumption of the cloud datacenter. The proposed plan encompasses algorithms for VM selection and VM placement. The VM selection algorithm estimates power consumption of each VM to select the required VMs for migration from the overloaded Physical Machine (PM). The proposed VM allocation algorithm estimates the net increase in Imbalance Utilization Value (IUV) and power consumption of a PM, in advance before allocating the VM. The analysis of simulation results suggests that the proposed dynamic consolidation plan outperforms other state of arts.

A Novel QoS Aware Shortest Path Algorithm for VSDN

2017 ◽  
Vol 6 (1) ◽  
pp. 1-14
Author(s):  
Amandeep Kaur Sandhu ◽  
Jyoteesh Malhotra
Keyword(s):  
Energy Consumption ◽  
Shortest Path ◽  
Service Providers ◽  
Service Level Agreement ◽  
Service Level ◽  
Software Defined Networks ◽  
Shortest Path Algorithm ◽  
Internet Service ◽  
Decrease Energy Consumption

This article describes how a rapid increase in usage of internet has emerged from last few years. This high usage of internet has occurred due to increase in popularity of multimedia applications. However, there is no guarantee of Quality of Service to the users. To fulfill the desired requirements, Internet Service Providers (ISPs) establish a service level agreement (SLA) with clients including specific parameters like bandwidth, reliability, cost, power consumption, etc. ISPs make maximum SLAs and decrease energy consumption to raise their profit. As a result, users do not get the desired services for which they pay. Virtual Software Defined Networks are flexible and manageable networks which can be used to achieve these goals. This article presents shortest path algorithm which improves the matrices like energy consumption, bandwidth usage, successful allocation of nodes in the network using VSDN approach. The results show a 40% increase in the performance of proposed algorithms with a respect to existing algorithms.

scholarly journals A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

2021 ◽  
Vol 13 (23) ◽  
pp. 13016
Author(s):  
Rami Naimi ◽  
Maroua Nouiri ◽  
Olivier Cardin
Keyword(s):  
Artificial Intelligence ◽  
Energy Consumption ◽  
Job Shop ◽  
Learning Algorithm ◽  
Minimum Energy ◽  
Flexible Job Shop ◽  
Q Learning ◽  
Minimum Energy Consumption ◽  
Machine Failure ◽  
Flexible Job Shop Problem

The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system’s perturbation in an intelligent way and with minimum energy consumption variation is an important matter. Fortunately, thanks to the development of artificial intelligence and machine learning, a lot of researchers are using these new techniques to solve the rescheduling problem in a flexible job shop. Reinforcement learning, which is a popular approach in artificial intelligence, is often used in rescheduling. This article presents a Q-learning rescheduling approach to the flexible job shop problem combining energy and productivity objectives in a context of machine failure. First, a genetic algorithm was adopted to generate the initial predictive schedule, and then rescheduling strategies were developed to handle machine failures. As the system should be capable of reacting quickly to unexpected events, a multi-objective Q-learning algorithm is proposed and trained to select the optimal rescheduling methods that minimize the makespan and the energy consumption variation at the same time. This approach was conducted on benchmark instances to evaluate its performance.

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.

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