scholarly journals Energy-Aware Virtual Machine Clustering for Consolidation in Multi-tenant IaaS Public Clouds

2019 ◽  
pp. 1123-1136
Author(s):  
Kenga Mosoti Derdus ◽  
Vincent Oteke Omwenga ◽  
Patrick Job Ogao
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Service Providers ◽  
Cloud Service ◽  
Resource Usage ◽  
Energy Aware ◽  
Allocation Policy ◽  
Cloud Data ◽  
Vm Allocation ◽  
Vm Consolidation

Cloud computing has gained a lot of interest from both small and big academic and commercial organizations because of its success in delivering service on a pay-as-you-go basis. Moreover, many users (organizations) can share server computing resources, which is made possible by virtualization. However, the amount of energy consumed by cloud data centres is a major concern. One of the major causes of energy wastage is the inefficient utilization of resources. For instance, in IaaS public clouds, users select Virtual Machine (VM) sizes set beforehand by the Cloud Service Providers (CSPs) without the knowledge of the kind of workloads to be executed in the VM. More often, the users overprovision the resources, which go to waste. Additionally, the CSPs do not have control over the types of applications that are executed and thus VM consolidation is performed blindly. There have been efforts to address the problem of energy consumption by efficient resource utilization through VM allocation and migration. However, these techniques lack collection and analysis of active real cloud traces from the IaaS cloud. This paper proposes an architecture for VM consolidation through VM profiling and analysis of VM resource usage and resource usage patterns, and a VM allocation policy. We have implemented our policy on CloudSim Plus cloud simulator and results show that it outperforms Worst Fit, Best Fit and First Fit VM allocation algorithms. Energy consumption is reduced through efficient consolidation that is informed by VM resource consumption.

scholarly journals Energy-Aware and Proactive Host Load Detection in Virtual Machine Consolidation

2021 ◽  
Vol 50 (2) ◽  
pp. 332-341
Author(s):  
Seyed Yahya Zahedi Fard ◽  
Mohammad Karim Sohrabi ◽  
Vahid Ghods
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Power Efficiency ◽  
Data Centers ◽  
Service Providers ◽  
Energy Utilization ◽  
Energy Aware ◽  
Cloud Data ◽  
Server Utilization ◽  
Vm Consolidation

With the expansion and enhancement of cloud data centers in recent years, increasing the energy consumptionand the costs of the users have become the major concerns in the cloud research area. Service quality parametersshould be guaranteed to meet the demands of the users of the cloud, to support cloud service providers,and to reduce the energy consumption of the data centers. Therefore, the data center's resources must be managedefficiently to improve energy utilization. Using the virtual machine (VM) consolidation technique is animportant approach to enhance energy utilization in cloud computing. Since users generally do not use all thepower of a VM, the VM consolidation technique on the physical server improves the energy consumption andresource efficiency of the physical server, and thus improves the quality of service (QoS). In this article, a serverthreshold prediction method is proposed that focuses on the server overload and server underload detectionto improve server utilization and to reduce the number of VM migrations, which consequently improves theVM's QoS. Since the VM integration problem is very complex, the exponential smoothing technique is utilizedfor predicting server utilization. The results of the experiments show that the proposed method goes beyondexisting methods in terms of power efficiency and the number of VM migrations.

scholarly journals Virtual Machine Sizing in Virtualized Public Cloud Data Centres

2019 ◽  
pp. 583-590
Author(s):  
Kenga Mosoti Derdus ◽  
Vincent Oteke Omwenga ◽  
Patrick Job Ogao
Keyword(s):  
Virtual Machine ◽  
Energy Savings ◽  
Service Providers ◽  
Cloud Service ◽  
Public Cloud ◽  
Dynamic Nature ◽  
Cloud Data ◽  
Data Centre ◽  
Data Centres ◽  
Vm Consolidation

Virtual machine (VM) consolidation in data centres is a technique that is used to ensure minimum use of physical servers (hosts) leading to better utilization of computing resources and energy savings. To achieve these goals, this technique requires that the estimated VM size is on the basis of application workload resource demands so as to maximize resources utilization, not only at host-level but also at VM-level. This is challenging especially in Infrastructure as a Service (IaaS) public clouds where customers select VM sizes set beforehand by the Cloud Service Providers (CSPs) without the knowledge of the amount of resources their applications need. More often, the resources are overprovisioned and thus go to waste, yet these resources consume power and are paid for by the customers. In this paper, we propose a technique for determining fixed VM sizes, which satisfy application workload resource demands. Because of the dynamic nature of cloud workloads, we show that any resource demands that exceed fixed VM resources can be addressed via statistical multiplexing. The proposed technique is evaluated using VM usage data obtained from a production data centre consisting of 49 hosts and 520 VMs. The evaluations show that the proposed technique reduces energy consumption, memory wastage and CPU wastage by at least 40%, 61% and 41% respectively.

scholarly journals Energy-aware and Carbon-efficient VM Placement Optimization in Cloud Datacenters Using Evolutionary Computing Methods

2022 ◽  
Author(s):  
Tahereh Abbasi-khazaei ◽  
Mohammad Hossein Rezvani
Keyword(s):  
Energy Consumption ◽  
Service Providers ◽  
Cloud Service ◽  
Computing Methods ◽  
Energy Costs ◽  
Vm Placement ◽  
Energy Aware ◽  
Carbon Footprints ◽  
Cloud Data ◽  
Cloud Data Centers

Abstract One of the most important concerns of cloud service providers is balancing renewable and fossil energy consumption. On the other hand, the policy of organizations and governments is to reduce energy consumption and greenhouse gas emissions in cloud data centers. Recently, a lot of research has been conducted to optimize the Virtual Machine (VM) placement on physical machines to minimize energy consumption. Many previous studies have not considered the deadline and scheduling of IoT tasks. Therefore, the previous modelings are mainly not well-suited to the IoT environments where requests are time-constraint. Unfortunately, both the sub-problems of energy consumption minimization and scheduling fall into the category of NP-hard issues. In this study, we propose a multi-objective VM placement to joint minimizing energy costs and scheduling. After presenting a modified memetic algorithm, we compare its performance with baseline methods as well as state-of-the-art ones. The simulation results on the CloudSim platform show that the proposed method can reduce energy costs, carbon footprints, SLA violations, and the total response time of IoT requests.

scholarly journals Energy aware dynamic virtual machine consolidation in cloud data centers

2018 ◽  
Vol 7 (2.8) ◽  
pp. 550 ◽  
Author(s):  
G Anusha ◽  
P Supraja
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Data Centers ◽  
Energy Aware ◽  
Large Power ◽  
Complex Tasks ◽  
Cloud Data ◽  
Network Bandwidth ◽  
Virtual Machine Consolidation ◽  
Cloud Data Centers

Cloud computing is a growing technology now-a-days, which provides various resources to perform complex tasks. These complex tasks can be performed with the help of datacenters. Data centers helps the incoming tasks by providing various resources like CPU, storage, network, bandwidth and memory, which has resulted in the increase of the total number of datacenters in the world. These data centers consume large volume of energy for performing the operations and which leads to high operation costs. Resources are the key cause for the power consumption in data centers along with the air and cooling systems. Energy consumption in data centers is comparative to the resource usage. Excessive amount of energy consumption by datacenters falls out in large power bills. There is a necessity to increase the energy efficiency of such data centers. We have proposed an Energy aware dynamic virtual machine consolidation (EADVMC) model which focuses on pm selection, vm selection, vm placement phases, which results in the reduced energy consumption and the Quality of service (QoS) to a considerable level.

scholarly journals Energy-Aware Autonomic Resource Scheduling Framework for Cloud

2019 ◽  
Vol 4 (1) ◽  
pp. 41-55 ◽  
Author(s):  
Bhupesh Kumar Dewangan ◽  
Amit Agarwal ◽  
Venkatadri M. ◽  
Ashutosh Pasricha
Keyword(s):  
Energy Consumption ◽  
Resource Utilization ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Providers ◽  
Optimal Solution ◽  
Cloud Service ◽  
Cloud Services ◽  
Energy Aware ◽  
Increasing Demand

Cloud computing is a platform where services are provided through the internet either free of cost or rent basis. Many cloud service providers (CSP) offer cloud services on the rental basis. Due to increasing demand for cloud services, the existing infrastructure needs to be scale. However, the scaling comes at the cost of heavy energy consumption due to the inclusion of a number of data centers, and servers. The extraneous power consumption affects the operating costs, which in turn, affects its users. In addition, CO2 emissions affect the environment as well. Moreover, inadequate allocation of resources like servers, data centers, and virtual machines increases operational costs. This may ultimately lead to customer distraction from the cloud service. In all, an optimal usage of the resources is required. This paper proposes to calculate different multi-objective functions to find the optimal solution for resource utilization and their allocation through an improved Antlion (ALO) algorithm. The proposed method simulated in cloudsim environments, and compute energy consumption for different workloads quantity and it increases the performance of different multi-objectives functions to maximize the resource utilization. It compared with existing frameworks and experiment results shows that the proposed framework performs utmost.

scholarly journals Virtual Machine Customization Using Resource Using Prediction for Efficient Utilization of Resources in IaaS Public Clouds

2021 ◽  
Vol 6 (2) ◽  
pp. 170-182
Author(s):  
Derdus Kenga ◽  
Vincent Omwenga ◽  
Patrick Ogao
Keyword(s):  
Energy Consumption ◽  
Resource Utilization ◽  
Virtual Machines ◽  
Service Providers ◽  
Cloud Service ◽  
Cloud Data ◽  
Efficient Resource ◽  
Server Utilization ◽  
Auto Scaling ◽  
Cloud Users

The main cause of energy wastage in cloud data centres is the low level of server utilization. Low server utilization is a consequence of allocating more resources than required for running applications. For instance, in Infrastructure as a Service (IaaS) public clouds, cloud service providers (CSPs) deliver computing resources in the form of virtual machines (VMs) templates, which the cloud users have to choose from. More often, inexperienced cloud users tend to choose bigger VMs than their application requirements. To address the problem of inefficient resources utilization, the existing approaches focus on VM allocation and migration, which only leads to physical machine (PM) level optimization. Other approaches use horizontal auto-scaling, which is not a visible solution in the case of IaaS public cloud. In this paper, we propose an approach of customizing user VM’s size to match the resources requirements of their application workloads based on an analysis of real backend traces collected from a VM in a production data centre. In this approach, a VM is given fixed size resources that match applications workload demands and any demand that exceeds the fixed resource allocation is predicted and handled through vertical VM auto-scaling. In this approach, energy consumption by PMs is reduced through efficient resource utilization. Experimental results obtained from a simulation on CloudSim Plus using GWA-T-13 Materna real backend traces shows that data center energy consumption can be reduced via efficient resource utilization

scholarly journals Energy-Efficient Cloud Service Selection and Recommendation Based on QoS for Sustainable Smart Cities

2021 ◽  
Vol 11 (20) ◽  
pp. 9394
Author(s):  
Preeti Sirohi ◽  
Fahd N. Al-Wesabi ◽  
Haya Mesfer Alshahrani ◽  
Piyush Maheshwari ◽  
Amit Agarwal ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Recommendation System ◽  
Service Providers ◽  
Smart Cities ◽  
Service Selection ◽  
Cloud Service ◽  
Heat Emission ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Cloud Servers

The growing demand for cloud technology brings several cloud service providers and their diverse list of services in the market, putting a challenge for the user to select the best service from the inventory of available services. Therefore, a system that understands the user requirements and finds a suitable service according to user-customized requirements is a challenge. In this paper, we propose a new cloud service selection and recommendation system (CS-SR) for finding the optimal service by considering the user’s customized requirements. In addition, the service selection and recommendation system will consider both quantitative and qualitative quality of service (QoS) attributes in service selection. The comparison is made between proposed CS-SR with three existing approaches analytical hierarchy process (A.H.P.), efficient non-dominated sorting-sequential search (ENS-SS), and best-worst method (B.W.M.) shows that CR-SR outperforms the above approaches in two ways (i) reduce the total execution time and (ii) energy consumption to find the best service for the user. The proposed cloud service selection mechanism facilitates reduced energy consumption at cloud servers, thereby reducing the overall heat emission from a cloud data center.

scholarly journals An Energy-Aware and Under-SLA-Constraints VM Consolidation Strategy Based on the Optimal Matching Method

2017 ◽  
Vol 14 (4) ◽  
pp. 75-89 ◽  
Author(s):  
WeiLing Li ◽  
Yongbo Wang ◽  
Yuandou Wang ◽  
YunNi Xia ◽  
Xin Luo ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Resource Selection ◽  
Service Level Agreement ◽  
Energy Reduction ◽  
Utilization Rate ◽  
Energy Aware ◽  
Optimal Matching ◽  
Sla Violation ◽  
Vm Consolidation

Growing demand of computational power brings increasing scale and complexity of cloud datacenters. However, such increase also generates growing energy consumption and related cost incurred for cooling and maintenance. With concerns of cost and energy saving by both industry and academy, the reduction of energy consumption of cloud datacenters becomes a hotspot issue. Recently, virtual-machine-consolidation-based strategies are proposed as promising methods for reduction of cloud energy consumption. Virtual machine (VM) consolidation effectively increases the resource utilization rate. However, it remains a great challenge how to reduce energy consumption while maintaining the quality of service (QoS) at a satisfactory level. In this work, a comprehensive framework is presented for the above-mentioned problem, which aims at maximizing the number of physical machines (PMs) to be turned off within a consolidation period following the constraints of QoS, in terms of Service-Level-Agreement (SLA) violation rate. In comparison with most existing related works which consider invariant utilization rate of PMs in computing energy reduction of candidate migration plans, propose framework considers time-varying utilization rate and employs the number of PMs to be turned off within a consolidation period (NPTCP for simple) as the optimization objective. The proposed framework consists of a resource selection algorithm taking the predicted migration overhead (derived by the Pareto distribution) as inputs and another algorithm generating optimal matching plans based on preference scores of candidate VMs. For the model validation purpose, a case study is conducted on the CloudSim simulation platform and it shows that the proposed method achieves better energy reduction and less SLA violation.

scholarly journals Energy-aware load balancing in distributed data centers

2020 ◽  
Author(s):  
Rodrigo A. C. Da Silva ◽  
Nelson L. S. Da Fonseca
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Virtual Machine ◽  
Data Center ◽  
Data Centers ◽  
Energy Savings ◽  
Virtual Machines ◽  
Distributed Data ◽  
Energy Aware ◽  
Cloud Data

This paper summarizes the dissertation ”Energy-aware load balancing in distributed data centers”, which proposed two new algorithms for minimizing energy consumption in cloud data centers. Both algorithms consider hierarchical data center network topologies and requests for the allocation of groups of virtual machines (VMs). The Topology-aware Virtual Machine Placement (TAVMP) algorithm deals with the placement of virtual machines in a single data center. It reduces the blocking of requests and yet maintains acceptable levels of energy consumption. The Topology-aware Virtual Machine Selection (TAVMS) algorithm chooses sets of VM groups for migration between different data centers. Its employment leads to relevant overall energy savings.

A Secure Virtual Machine Allocation Strategy Against Co-Resident Attacks

2019 ◽  
Vol 23 (5) ◽  
pp. 898-908
Author(s):  
Hefei Jia ◽  
Xu Liu ◽  
Xiaoqiang Di ◽  
Hui Qi ◽  
Binbin Cai ◽  
...  
Keyword(s):  
Virtual Machine ◽  
Service Providers ◽  
Cloud Service ◽  
Allocation Strategy ◽  
Side Channels ◽  
Vm Allocation ◽  
Private Data ◽  
Physical Resources ◽  
Balancing Energy ◽  
Machine Allocation

In the area of network development, especially cloud computing, security has been a long-standing issue. In order to better utilize physical resources, cloud service providers usually allocate different tenants on the same physical machine, i.e., physical resources such as CPU, memory, and network devices are shared among multiple tenants on the same host. Virtual machine (VM) co-resident attack, a serious threat in this sharing methodology, includes malicious tenants who tend to steal private data. Currently, most solutions focus on how to eliminate known specific side channels, but they have little effect on unknown side channels. Compared to eliminating side channels, developing a VM allocation strategy is an effective countermeasure against VM co-resident attack as it reduces the probability of VM co-residency, but research on this topic is still in its infancy. In this study, firstly, a novel, efficient, and secure VM allocation strategy named Against VM Co-resident attack based on Multi-objective Optimization Best Fit Decreasing (AC-MOBFD) is proposed, which simultaneously optimizes load balancing, energy consumption, and host resource utilization during VM placement. Subsequently, security of the proposed allocation strategy is measured using two metrics – VM attack efficiency and VM attack coverage. Extensive experiments on simulated and real cloud platforms, CloudSim and OpenStack, respectively, demonstrate that using our strategy, the attack efficiency of VM co-residency is reduced by 37.3% and VM coverage rate is reduced by 24.4% when compared to existing strategies. Finally, we compare the number of co-resident hosts with that of hosts in a real cloud platform. Experimental results show that the deviation is below 9.4%, which validates the feasibility and effectiveness of the presented strategy.

Sign in / Sign up

Export Citation Format

Share Document