Performance Of Cloud Data Center Research Articles

Resource optimization using predictive virtual machine consolidation approach in cloud environment

The Proliferation of on-demand usage-based IT services, as well as the diverse range of cloud users, have led to the establishment of energy-hungry hefty cloud data centers. Therefore, cloud service providers are striving to reduce energy consumption for cost-saving and environmental sustainability issues of data centers. In this direction, Virtual Machine (VM) consolidated is a widely used approach to optimize hardware resources at cost of performance degradation due to unnecessary migrations. Hence, the motivation of the proposed approach is to minimize energy consumption while maintaining the performance of cloud data centers. This leads to a reduction in the overall cost and an increase in the reliability of cloud service providers. To achieve this goal Predictive Virtual Machine Consolidation (PVMC) algorithm is proposed using the exponential smoothing moving average (ESMA) method. In the proposed algorithm, the ratio of deviation to utilization is calculated for VM selection and placement. migrating the high CPU using VMs or we can restrict steady resource-consuming VMs from migration. The outcomes of the proposed algorithm are validated on computer-based simulation under a dynamic workload and a variable number of VMs (1–290). The experimental results show an improvement in the mean threshing index (40%, 45%) and instruction energy ratio (15%, 17%) over the existing policies. Hence, the proposed algorithm could be used in real-world data centers for reducing energy consumption while maintaining low service level agreement violations.

Machine learning for energy-resource allocation, workflow scheduling and live migration in cloud computing: State-of-the-art survey

Machine learning and artificial intelligence techniques have been proven helpful when pragmatic to a wide range of complex problems and areas such as energy optimization, workflow scheduling, video gaming, and cloud computing. When machine learning and cloud computing algorithms are combined, they help achieve better outcomes by providing the improved performance of cloud data centers compared to solutions currently employed by various researchers. It is also helpful for migrating the virtual machines based on the current traffic condition and fluctuation due to network congestion and bandwidth availability. The survey aims to present the improvement in dynamic load allocation, task scheduling, energy optimization, live migration, mobile cloud computing, and security on the cloud using machine learning classification. Machine learning algorithms are prevailing analytical approaches that allow machines to identify patterns and simplify the human learning process. The flow of the paper consists of an introduction part, motivation, and background study, including a framework for cloud-machine learning integration, best practices of introducing machine learning in cloud computing, and the objective of the work. The paper also highlights the machine learning-based cloud services and the role of artificial intelligence in different cloud computing platforms. This comprehensive study provides mindfulness and valuable facilities to the researchers by giving thorough studies about various machine learning algorithms and their applicability in cloud computing.

A dynamic VM consolidation approach based on load balancing using Pearson correlation in cloud computing

In recent years, cloud data centers are rapidly growing with a large number of finite heterogeneous resources to meet the ever-growing user demands with respect to the SLA (service level agreement). However, the potential growth in the number of large-scale data centers leads to large amounts of energy consumption, which is constantly a major challenge. In addition to this challenge, intensive number of VM (virtual machine) migrations can decrease the performance of cloud data centers. Thus, how to minimize energy consumption while satisfying SLA and minimizing the number of VM migrations becomes an important challenge classified as NP-hard optimization problem in data centers. Most VM scheduling schemes have been proposed for this problem, such as dynamic VM consolidation. However, most of them failed in low time complexity and optimal solution. Hence, this paper proposes a dynamic VM consolidation approach-based load balancing to minimize the trade-off between energy consumption, SLA violations and VM migrations while keeping minimum host shutdowns and low time complexity in heterogeneous environment. Specifically, the proposed approach consists of four methods which include: BPSO meta-heuristic-based load balancing to impact on energy consumption and number of host shutdowns, overloading host detection and VM placement-based Pearson correlation coefficient to impact on SLA, and VM selection based on imbalance degree to impact on number of VM migration. Moreover, Pearson correlation coefficient and imbalance degree correlate CPU, RAM and bandwidth, respectively, in each host and each VM. Through extensive analysis and simulation experiments using real PlanetLab and random workloads, the performance results demonstrate that the proposed approach exhibits excellent results for the NP-problem.

Операторна форма постановки, аналізу і розв’язання задач управління ІТ- інфраструктурою хмарного ЦОД

The cloud data center IT infrastructure management tasks belongs to the management task class of nonlinear non-stationary discrete objects with variable structure. The quality of IT services and the performance of cloud data centers are directly dependent on the performance of the infrastructure layer. The non-stationarity, alteration of the structure and parameters of the management object does not allow the use of traditional management methods to ensure the provision of cloud services with specified quality. In order to manage the IT infrastructure resources of the cloud data center, the special operator form based on the definition, implementation and evaluation of management strategies in the defined mode of the data center is proposed in the article. Management strategy is determined by assessing the state of IT infrastructure using metrics. The implementation of the defined strategy is accomplished through planning and subsequent applying of basic management methods. The evaluation of the strategy's suitability is determined by the special operator based on the criterion of costs for supporting the operation of the physical infrastructure in order to further refine the choice of management strategy. Management strategies and strategies selection model for cloud data center resources management have been developed. Ref. 12, pic. 2

Cloud Load Balancing Algorithms: A Comparative Assessment

Nowadays, Cloud computing is developing quickly and customers are requesting more administrations and superior outcomes. In the cloud domain, load balancing has turned into an extremely intriguing and crucial research area. Numbers of algorithms were recommended to give proficient mechanism for distributing the cloud user’s requests for accessing pool cloud resources. Also load balancing in cloud should provide notable functional benefits to cloud users and at the same time should prove out to be eminent for cloud services providers. In this paper, the pre-existing load balancing techniques are explored. The paper intends to provide landscape for classification of distinct load balancing algorithms based upon the several parameters and also address performance assessment bound to various load balancing algorithms. The comparative assessment of various load balancing algorithms will helps in proposing a competent load balancing technique for intensify the performance of cloud data centers.

Load monitoring and system-traffic-aware live VM migration-based load balancing in cloud data center using graph theoretic solutions

This paper proposes solutions to monitor the load and to balance the load of cloud data center. The proposed solutions work in two phases and graph theoretical concepts are applied in both phases. In the first phase, cloud data center is modeled as a network graph. This network graph is augmented with minimum dominating set concept of graph theory for monitoring its load. For constructing minimum dominating set, this paper proposes a new variant of minimum dominating set (V-MDS) algorithm and is compared with existing construction algorithms proposed by Rooji and Fomin. The V-MDS approach of querying cloud data center load information is compared with Central monitor approach. The second phase focuses on system and network-aware live virtual machine migration for load balancing cloud data center. For this, a new system and traffic-aware live VM migration for load balancing (ST-LVM-LB) algorithm is proposed and is compared with existing benchmarked algorithms dynamic management algorithm (DMA) and Sandpiper. To study the performance of the proposed algorithms, CloudSim3.0.3 simulator is used. The experimental results show that, V-MDS algorithm takes quadratic time complexity, whereas Rooji and Fomin algorithms take exponential time complexity. Then the V-MDS approach for querying Cloud Data Center load information is compared with the Central monitor approach and the experimental result shows that the proposed approach reduces the number of message updates by half than the Central monitor approach. The experimental results show on load balancing that the developed ST-LVM-LB algorithm triggers lesser Virtual Machine migrations, takes lesser time and migration cost to migrate with minimum network overhead. Thus the proposed algorithms improve the service delivery performance of cloud data center by incorporating graph theoretical solutions in monitoring and balancing the load.

A combined forecast-based virtual machine migration in cloud data centers

Live virtual machine (VM) migration improves the performance of cloud data center in terms of energy efficiency, fault tolerance, and availability. The workload handled by cloud data center is dynamic in nature. This increases the resource requirement of either the migrated virtual machine or collocated virtual machine at any time leading to further migration. Inappropriately handled live VM migration imposes severe application performance degradation. In this paper, a combined forecasting technique to predict the resource requirement of any virtual machine is proposed. Based on the current and predicted resource utilization, live migration is performed by Combined Forecast Load-Aware technique. Experiments were carried out to evaluate the performance of the proposed technique on live VM migration. The outcomes indicate that the proposed approach has minimized the number of migrations, energy usage, and the message overhead when compared with the existing state-of-art technique.

A Heuristic Clustering-Based Task Deployment Approach for Load Balancing Using Bayes Theorem in Cloud Environment

Aiming at the current problems that most physical hosts in the cloud data center are so overloaded that it makes the whole cloud data center’ load imbalanced and that existing load balancing approaches have relatively high complexity, this paper has focused on the selection problem of physical hosts for deploying requested tasks and proposed a novel heuristic approach called Load Balancing based on Bayes and Clustering (LB-BC). Most previous works, generally, utilize a series of algorithms through optimizing the candidate target hosts within an algorithm cycle and then picking out the optimal target hosts to achieve the immediate load balancing effect. However, the immediate effect doesn’t guarantee high execution efficiency for the next task although it has abilities in achieving high resource utilization. Based on this argument, LB-BC introduces the concept of achieving the overall load balancing in a long-term process in contrast to the immediate load balancing approaches in the current literature. LB-BC makes a limited constraint about all physical hosts aiming to achieve a task deployment approach with global search capability in terms of the performance function of computing resource. The Bayes theorem is combined with the clustering process to obtain the optimal clustering set of physical hosts finally. Simulation results show that compared with the existing works, the proposed approach has reduced the failure number of task deployment events obviously, improved the throughput, and optimized the external services performance of cloud data centers.

I Q R based Approach for Energy Efficient Dynamic VM Consolidation for Green Cloud Data Centers

the advent of cloud computing in the arena of IT field energy consumption and service level agreement (SLA) violation emerge as a major problem, which reduces the profit of cloud service providers (CSP) and affect the cloud customers by fencing the reusability and scalability of the cloud data center services. This problem needs to be eradicate for the efficient resource provisioning in cloud data center. To satisfy the customer need virtual machine (VM) migration technique is required to balance the load of entire data center. Therefore we need to transfer the virtual machine of the overloaded host to the light weighted host using virtual machine migration technique. Due to frequent load balancing of cloud data center enormous amount of energy consumption takes place. This enhances the overall energy cost and degrades the performance of cloud data center. This paper proposes an Energy Efficient Dynamic VM Consolidation algorithm for reducing energy consumption. Keywordscomputing, Cloud service provider (CSP), Energy consumption, SLA violation, Load balancing, VM migration.

Virtual machine placement optimizing to improve network performance in cloud data centers

With the wide application of virtualization technology in cloud data centers, how to effectively place virtual machine (VM) is becoming a major issue for cloud providers. The existing virtual machine placement (VMP) solutions are mainly to optimize server resources. However, they pay little consideration on network resources optimization, and they do not concern the impact of the network topology and the current network traffic. A multi-resource constraints VMP scheme is proposed. Firstly, the authors attempt to reduce the total communication traffic in the data center network, which is abstracted as a quadratic assignment problem; and then aim at optimizing network maximum link utilization (MLU). On the condition of slight variation of the total traffic, minimizing MLU can balance network traffic distribution and reduce network congestion hotspots, a classic combinatorial optimization problem as well as NP-hard problem. Ant colony optimization and 2-opt local search are combined to solve the problem. Simulation shows that MLU is decreased by 20%, and the number of hot links is decreased by 37%.

Implementation of User-End Broker Policy to Improve the Reliability of Cloud Services

Outage in cloud computing services is a critical issue and is primarily attributed to the single data center connectivity. To address the cloud outage, this work proposes a model for the subscription and selection of more than one data center. Selection of data center can be determined by the usage of broker at the user ends itself. Provision of broker at user's end reduces the overhead at provider's end; as a result performance of cloud data center improves. For the selection of appropriate data center, broker takes the feedback from the available data centers, and select one of them. During the selection of cloud, their status (up/down) at that particular time is also considered. In case of outage at one data center, other can be selected from the available list. Broker also facilitates the homogeneous use of cloud by allotting the load to less congested data centers. Experimental results revealed that multiple data center approach is not only helpful in countering the outage (as other data center can be selected from the broker) but also the usage cost.

Netshare and stochastic netshare

Application performance in cloud data centers often depends crucially on network bandwidth, not just the aggregate data transmitted as in typical SLAs. We describe a mechanism for data center networks called NetShare that requires no hardware changes to routers but allows bandwidth to be allocated predictably across services based on weights. The weights are either specified by a manager, or automatically assigned at each switch port based on a virtual machine heuristic for isolation. Bandwidth unused by a service is shared proportionately by other services, providing weighted hierarchical max-min fair sharing. On a testbed of Fulcrum switches, we demonstrate that NetShare provides bandwidth isolation in various settings, including multipath networks.