PlanetLab Workload Research Articles

Energy-performance aware virtual machines migration in cloud network by using prediction and fuzzy approaches

Energy wastage and service level access are challenges of cloud networks, while efficient migration of virtual machines is a solution for them. Of course, uncontrolled migration increases overhead in cloud comprising: violation in service and high energy consumption. Performance requirements is very important in cloud, generally, it in the form of service level agreements are formalized. The main goal of migration is energy and performance trade-off. Detecting overloaded and under-loaded physical machine as sub-problem of migration, which effects the performance and energy. In this paper, with definition a system modelling, in first for detect overloaded and under-loaded physical machines a predict detection method is proposed. The forecast of the future utilization of the processor by using predict physical machines utilization method is provided. Predict physical machine's utilization module in predict detection method has been called. Unlike the previous physical machine detection methods, the current load of physical machines along with the future load of them is considered, and will lead to more exact results. Then for selection of virtual machines on overloaded physical machines a fuzzy selection method is proposed. Unlike the previous virtual machine-selection methods, all affective variables are considered and will lead to better results. In overall, the predict detection-fuzzy selection algorithm as intelligent approach has proposed and simulated with PlanetLab workload, CloudSim toolkit and Matlab. Simulation results demonstrate that significantly outperforms benchmark algorithms in terms of the energy consumption, migration count, service level agreement violation, energy-service level agreement violation, and energy-service level agreement violation-migration.

Proactive and dynamic load balancing model for workload spike detection in cloud

Dynamic Load Balancing (DLB) has been an ever-green research topic since the beginning of cloud computing. DLB model distributes the available cloud resources (i.e., CPU, RAM, Storage, BW, etc.) among the VMs using a systematic approach to ensure efficient resource utilization. Overloading, an abnormal condition, occurs when the host encounters a resource shortage due to the high workload of Federated Learning. VM migration and consolidation are reliable load-balancing techniques to fix dynamic overloading issues. As part of the migration, the oversubscribed host VMs are moved to the other hosts till the load is regulated. Besides the high workload, the sudden spike in workload also leads the hosts to instant overloading. The former DLB models are efficient in handling general overloading conditions. Still, they are now suffering from limitations in taking the spike overloading due to 1) a sudden spike in workloads, 2) instant overloading, 3) recurrent migrations, and 4) SLA violations. To address these limitations in handling spike overloading, in this paper, we proposed a proactive and dynamic load balancing model (DLBM), which is designed to handle spike overloading with optimal solutions. The offered dynamic load balancing model with a spike detection algorithm helps detect workload spikes in advance to prevent the hosts from overloading and SLA violations. Experimental conducted using the Cloudsim tool and the planet lab workload dataset prove that the proposed DLBM identified the spikes in workload and efficiently controlled the recurrent migrations.

Load Balancing and Server Consolidation for Energy Management in Cloud Data Center

Users worldwide can access utility-oriented computing services through cloud computing. It enables the pay-as-you-go model to host well-known consumer, educational, and business applications. However, cloud data centers encounter significant issues in effectively managing resources, ensuring optimal performance, and reducing energy usage as the demand for cloud computing services is increasing rapidly. A possible strategy to reduce energy consumption and operating expenses is the energy-efficient management of virtual machines (VMs) and server consolidation. In this paper, a VM migration and server consolidation algorithm is proposed, which is energy efficient and SLA aware. The proposed approach is evaluated using the CloudSim simulation kit and PlanetLab workload. The experiment result shows that the proposed approach has reduced energy consumption and SLA violation.

Effective Utilization of Resources Through Optimal Allocation and Opportunistic Migration of Virtual Machines in Cloud Environment

Cloud computing has become more prominent, and it is used in large data centers. Distribution of well-organized resources (bandwidth, CPU, and memory) is the major problem in the data centers. The genetically enhanced shuffling frog leaping algorithm (GESFLA) framework is proposed to select the optimal virtual machines to schedule the tasks and allocate them in physical machines (PMs). The proposed GESFLA-based resource allocation technique is useful in minimizing the wastage of resource usage and also minimizes the power consumption of the data center. The proposed GESFL algorithm is compared with task-based particle swarm optimization (TBPSO) for efficiency. The experimental results show the excellence of GESFLA over TBPSO in terms of resource usage ratio, migration time, and total execution time. The proposed GESFLA framework reduces the energy consumption of data center up to 79%, migration time by 67%, and CPU utilization is improved by 9% for Planet Lab workload traces. For the random workload, the execution time is minimized by 71%, transfer time is reduced up to 99%, and the CPU consumption is improved by 17% when compared to TBPSO.

Online Sparse BLSTM Models for Resource Usage Prediction in Cloud Datacentres

Real time resource usage prediction is an important part of resource provisioning in a cloud data centre. As cloud workloads vary dynamically, effective resource provisioning requires prediction of future resource usage trends. The problem is highly complicated because of highly time varying nature of cloud resource workloads. Training the future resource usage prediction models once, using a fixed set of observations is not sufficient to capture the variability in cloud workloads. In this work, we propose to use gradient descent (GD) and Levenberg-Marquardt (LM) adaptation algorithms for dynamic adaptation of resource utilization prediction models. We also propose a novel sparse framework for fast online adaptation of resource usage prediction models. We propose to analyze different algorithms such as ℓ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> regularization, ℓ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> regularization, optimal brain damage (OBD), optimal brain surgeon (OBS) for introducing sparsity. The proposed sparse framework for online adaptation of multivariate resource usage prediction models is validated for CPU usage prediction in the Google cluster trace and PlanetLab workload trace. A comparative analysis of different sparse frameworks shows that OBD-based LM adaptation algorithm performs better than other frameworks for online multivariate resource usage prediction in a cloud.

Comparative analysis of VM consolidation algorithms for cloud computing

Virtual machine consolidation is a major solution for addressing the issue of increasing energy consumption by cloud computing data centers. A lot of work is done on developing algorithms for detecting underloaded, overloaded hosts, selection of virtual machines and their placement to perform the consolidation. These algorithms are usually tested on publicly available Planet lab workload. There is a need to know how benchmarks algorithms used in consolidation of virtual machines respond to other workloads. This paper is an attempt to evaluate these algorithms on Google workload trace. An importer is made to use this dataset by extending the CloudSim toolkit. The comparison of results using Planet lab and Google workload traces is made which shows the difference of 46.41%, 14.84%, 12.86% and 44.83% respectively in terms of number of virtual machine migrations, service level agreement violation time per active host, number of hosts shutdown, and energy consumption. The objective comparison of results illustrated that there is a need to test the proposed algorithms on multiple datasets in order to be assessed as optimal.

SLA-Aware and Energy-Efficient VM Consolidation in Cloud Data Centers Using Host State Binary Decision Tree Prediction Model

For cloud data center, Virtual Machine (VM) consolidation is an effective way to save energy and improve efficiency. However, inappropriate consolidation of VMs, especially aggressive consolidation, can lead to performance problems, and even more serious Service Level Agreement (SLA) violations. Therefore, it is very important to solve the tradeoff between reduction in energy use and reduction of SLA violation level. In this paper, we propose two Host State Detection algorithms and an improved VM placement algorithm based on our proposed Host State Binary Decision Tree Prediction model for SLA-aware and energy-efficient consolidation of VMs in cloud data centers. We propose two formulas of conditions for host state estimate, and our model uses them to build a Binary Decision Tree manually for host state detection. We extend Cloudsim simulator to evaluate our algorithms by using PlanetLab workload and random workload. The experimental results show that our proposed model can significantly reduce SLA violation rates while keeping energy cost efficient, it can reduce the metric of SLAV by at most 98.12% and the metric of Energy by at most 33.96% for real world workload.

SLA-Aware and Energy-Efficient VM Consolidation in Cloud Data Centers Using Robust Linear Regression Prediction Model

Virtual machine (VM) consolidation provides a promising approach to save energy and to improve resource utilization in data centers. However, the aggressive consolidation of virtual machines may lead to service-level agreements (SLA) violation, which is essential for data centers and their users. Therefore, it is very meaningful to strike a tradeoff between power efficient and reduction of SLA violation level. In this paper, we propose a host overloading/underloading detection algorithm and a new VM placement algorithm based on our proposed robust simple linear regression prediction model for SLA-aware and energy-efficient consolidation of virtual machines in cloud data centers. Different from the native linear regression, our proposed methods amend the prediction and squint toward over-prediction by adding the error to the prediction; in this paper, we propose eight methods to calculate the error. We evaluate our proposed algorithms by extended the CloudSim simulator using PlanetLab workload and random workload. The experimental results show that our proposed model can reduce SLA violation rates by at most 99.16% and energy consumption by at most 25.43% for the real-world workload.

Effective Resource Management of Cloud Data Center

Cloud computing is playing an increasingly important role in modern life, especially in the information age. What followed is a growing number of large-scale data centers were established around the world. It is conceivable that so many data centers must consume huge amounts of electrical energy. A considerable part of the consumption of these electrical energy is due to the irrational use of resources. In this study, the proposed adaptive heuristics for dynamic consolidation of VMs is based on an analysis of hybrid historical data from the resource usage by VMs, which significantly reduce energy consumption, while guaranteeing a high level of Service Level Agreements (SLA). Through simulations we validate that our approach reduces power consumption and ensures SLA compared to other algorithms using PlanetLab workload traces.

Energy‐efficient adaptive virtual machine migration mechanism for private Clouds

SummaryPrivate Cloud provides Cloud services with its relatively limited resources compared to public Clouds. Resources in private Clouds should be used energy efficiently. The resource utilization is determined by the assignment of virtual machines (VMs) to hosts. Because of the frequent changes in the resource requests on VMs, the system might become imbalanced, some hosts are overloaded/underloaded. Virtual machine migration is a solution to ease the imbalance problem. Virtual machine migration includes the selection of a VM for migration and decision upon where it should be taken to (mapped). In this work, VM selection and VM mapping are integrated and aimed to ease the imbalance problem for energy efficiency. Moreover, selection and mapping have become adaptive to ease imbalance, while optimizing energy consumption and adaptively responding to changes in the system. Our proposed adaptive mechanism applies Bayesian inference to estimate the likelihood of a VM migration decision, both VM selection for migration and VM mapping, optimizing energy consumption. The proposed mechanism is evaluated on CloudSim using PlanetLab workload on a heterogeneous Cloud. It is demonstrated to reduce energy consumption significantly, (on average) by 116%, while its total execution time is also, (on average) 5.39 times, shorter than the competing state‐of‐the‐art policies.

Virtual machine consolidation enhancement using hybrid regression algorithms

Cloud computing data centers are growing rapidly in both number and capacity to meet the increasing demands for highly-responsive computing and massive storage. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. The reason for this extremely high energy consumption is not just the quantity of computing resources and the power inefficiency of hardware, but rather lies in the inefficient usage of these resources. VM consolidation involves live migration of VMs hence the capability of transferring a VM between physical servers with a close to zero down time. It is an effective way to improve the utilization of resources and increase energy efficiency in cloud data centers. VM consolidation consists of host overload/underload detection, VM selection and VM placement. Most of the current VM consolidation approaches apply either heuristic-based techniques, such as static utilization thresholds, decision-making based on statistical analysis of historical data; or simply periodic adaptation of the VM allocation. Most of those algorithms rely on CPU utilization only for host overload detection. In this paper we propose using hybrid factors to enhance VM consolidation. Specifically we developed a multiple regression algorithm that uses CPU utilization, memory utilization and bandwidth utilization for host overload detection. The proposed algorithm, Multiple Regression Host Overload Detection (MRHOD), significantly reduces energy consumption while ensuring a high level of adherence to Service Level Agreements (SLA) since it gives a real indication of host utilization based on three parameters (CPU, Memory, Bandwidth) utilizations instead of one parameter only (CPU utilization). Through simulations we show that our approach reduces power consumption by 6 times compared to single factor algorithms using random workload. Also using PlanetLab workload traces we show that MRHOD improves the ESV metric by about 24% better than other single factor regression algorithms (LR and LRR). Also we developed Hybrid Local Regression Host Overload Detection algorithm (HLRHOD) that is based on local regression using hybrid factors. It outperforms the single factor algorithms.

Implementation and performance analysis of various VM placement strategies in CloudSim

Infrastructure as a Service (IaaS) has become one of the most dominant features that cloud computing offers nowadays. IaaS enables datacenter's hardware to get virtualized which allows Cloud providers to create multiple Virtual Machine (VM) instances on a single physical machine, thus improving resource utilization and increasing the Return on Investment (ROI). VM consolidation includes issues like choosing appropriate algorithm for selection of VMs for migration and placement of VMs to suitable hosts. VMs need to be migrated from overutilized host to guarantee that demand for computer resources and performance requirements are accomplished. Besides, they need to be migrated from underutilized host to deactivate that host for saving power consumption. In order to solve the problem of energy and performance, efficient dynamic VM consolidation approach is introduced in literature. In this work, we have proposed multiple redesigned VM placement algorithms and introduced a technique by clustering VMs to migrate by taking account both CPU utilization and allocated RAM. We implement and study the performance of our algorithms on a cloud computing simulation toolkit known as CloudSim using PlanetLab workload data. Simulation results demonstrate that our proposed techniques outperform the default VM Placement algorithm designed in CloudSim.