scholarly journals A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Jing Chen ◽  
Yinglong Wang
Keyword(s):  
Cloud Computing ◽  
Hybrid Method ◽  
Virtual Machines ◽  
Moving Average ◽  
Arima Model ◽  
Cost Effective ◽  
Short Term ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Host Utilization

Dynamic resource scheduling is a critical activity to guarantee quality of service (QoS) in cloud computing. One challenging problem is how to predict future host utilization in real time. By predicting future host utilization, a cloud data center can place virtual machines to suitable hosts or migrate virtual machines in advance from overloaded or underloaded hosts to guarantee QoS or save energy. However, it is very difficult to accurately predict host utilization in a timely manner because host utilization varies very quickly and exhibits strong instability with many bursts. Although machine learning methods can accurately predict host utilization, it usually takes too much time to ensure rapid resource allocation and scheduling. In this paper, we propose a hybrid method, EEMD-RT-ARIMA, for short-term host utilization prediction based on ensemble empirical mode decomposition (EEMD), runs test (RT), and autoregressive integrated moving average (ARIMA). First, the EEMD method is used to decompose the nonstationary host utilization sequence into relatively stable intrinsic mode function (IMF) components and a residual component to improve prediction accuracy. Then, efficient IMF components are selected and then reconstructed into three new components to reduce the prediction time and error accumulation due to too many IMF components. Finally, the overall prediction results are obtained by superposing the prediction results of three new components, each of which is predicted by the ARIMA method. An experiment is conducted on real host utilization traces from a cloud platform. We compare our method with the ARIMA model and the EEMD-ARIMA method in terms of error, effectiveness, and time-cost analysis. The results show that our method is a cost-effective method and is more suitable for short-term host utilization prediction in cloud computing.

LSRU: A Novel Deep Learning based Hybrid Method to Predict the Workload of Virtual Machines in Cloud Data Center

2020 ◽  
Author(s):  
Md. Nahid Hasan Shuvo ◽  
Md. Nahid Hasan Shuvo ◽  
Mirza Mohd Shahriar Maswood ◽  
Mirza Mohd Shahriar Maswood ◽  
Abdullah G. Alharbi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hybrid Method ◽  
Data Center ◽  
Virtual Machines ◽  
Cloud Data Center ◽  
Cloud Data

scholarly journals ENERGY EFFICIENT LOAD BALANCING FOR CLOUD DATA CENTER

2017 ◽  
Vol 10 (13) ◽  
pp. 162
Author(s):  
Amey Rivankar ◽  
Anusooya G
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Energy Efficient ◽  
Large Scale ◽  
Virtual Machines ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Efficient Resource ◽  
Cloud Data Centers ◽  
Near Future

Cloud computing is the latest trend in large-scale distributed computing. It provides diverse services on demand to distributive resources such asservers, software, and databases. One of the challenging problems in cloud data centers is to manage the load of different reconfigurable virtual machines over one another. Thus, in the near future of cloud computing field, providing a mechanism for efficient resource management will be very significant. Many load balancing algorithms have been already implemented and executed to manage the resources efficiently and adequately. The objective of this paper is to analyze shortcomings of existing algorithms and implement a new algorithm which will give optimized load balancingresult.

scholarly journals Adapted Convolutional Neural Networks and Long Short-Term Memory for Host Utilization prediction in Cloud Data Center

2021 ◽  
Author(s):  
ARIF ullah ◽  
Irshad Ahmed Abbasi ◽  
Muhammad Zubair Rehman ◽  
Tanweer Alam ◽  
Hanane Aznaoui
Keyword(s):  
Neural Network ◽  
Resource Allocation ◽  
Cloud Computing ◽  
Data Center ◽  
Short Term Memory ◽  
Short Term ◽  
Cloud Data Center ◽  
Term Memory ◽  
Cloud Data ◽  
Long Short Term Memory

Abstract Infrastructure service model provides different kinds of virtual computing resources such as networking, storage service, and hardware as per user demands. Host load prediction is an important element in cloud computing for improvement in the resource allocation systems. Hosting initialization issues still exist in cloud computing due to this problem hardware resource allocation takes serval minutes of delay in the response process. To solve this issue prediction techniques are used for proper prediction in the cloud data center to dynamically scale the cloud in order for maintaining a high quality of services. Therefore in this paper, we propose a hybrid convolutional neural network long with short-term memory model for host prediction. In the proposed hybrid model, vector auto regression method is firstly used to input the data for analysis which filters the linear interdependencies among the multivariate data. Then the enduring data are computed and entered into the convolutional neural network layer that extracts complex features for each central processing unit and virtual machine usage components after that long short-term memory is used which is suitable for modeling temporal information of irregular trends in time series components. In all process, the main contribution is that we used scaled polynomial constant unit activation function which is most suitable for this kind of model. Due to the higher inconsistency in data center, accurate prediction is important in cloud systems. For this reason in this paper two real-world load traces were used to evaluate the performance. One is the load trace in the Google data center, while the other is in the traditional distributed system. The experiment results show that our proposed method achieves state-of-the-art performance with higher accuracy in both datasets as compared with ARIMA-LSTM, VAR-GRU, VAR-MLP, and CNN models.

Multidimensional Resource Scheduling in Cloud Data Centers

2014 ◽  
Vol 1008-1009 ◽  
pp. 1513-1516
Author(s):  
Hai Na Song ◽  
Xiao Qing Zhang ◽  
Zhong Tang He
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Bin Packing ◽  
Virtual Machines ◽  
Three Dimensional ◽  
Resource Scheduling ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Cloud Data ◽  
Cloud Data Centers

Cloud computing environment is regarded as a kind of multi-tenant computing mode. With virtulization as a support technology, cloud computing realizes the integration of multiple workloads in one server through the package and seperation of virtual machines. Aiming at the contradiction between the heterogeneous applications and uniform shared resource pool, using the idea of bin packing, the multidimensional resource scheduling problem is analyzed in this paper. We carry out some example analysis in one-dimensional resource scheduling, two-dimensional resource schduling and three-dimensional resource scheduling. The results shows that the resource utilization of cloud data centers will be improved greatly when the resource sheduling is conducted after reorganizing rationally the heterogeneous demands.

scholarly journals Client Aware Scalable Cloudlet to Augment Edge Computing with Mobile Cloud Migration Service

2020 ◽  
Vol 14 (12) ◽  
pp. 165
Author(s):  
Uma Nandhini D ◽  
Udhayakumar S ◽  
Latha Tamilselvan ◽  
Silviya Nancy J
Keyword(s):  
Wireless Lan ◽  
High Speed ◽  
Virtual Machines ◽  
Linear Regression Analysis ◽  
Mobile Cloud ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Battery Lifetime ◽  
Network Bandwidth ◽  
Load Conditions

<p class="0abstract">Computing with mobile is still in its infancy due to its limitations of computational power, battery lifetime and storage capacity. These limitations hinder the growth of mobile computing, which in-turn affects the growth of computationally intensive applications developed for the mobile devices. So in-order to help execute complex applications within the mobile device, mobile cloud computing (MCC) emerged as a feasible solution. The job of offloading the task to the cloud data center for storage and execution from the mobile seems to gain popularity, however, issues related to network bandwidth, loss of mobile data connectivity, and connection setup does not augment well to extend the benefits offered by MCC. Cloudlet servers filled this gab by assisting the mobile cloud environment as an edge device, offering compute power to the connected devices with high speed wireless LAN connectivity. Implementation constraints of cloudlet faces severe challenges in-terms of its storage, network sharing, and VM provisioning. Moreover, the number of connected devices of the cloudlet and its load conditions vary drastically leading to unexpected bottleneck, in which case the availability to server becomes an issue. Therefore, a scalable cloudlet, Client Aware Scalable Cloudlet (CASC) is proposed with linear regression analysis, predicting the knowledge of expected load conditions for provisioning new virtual machines and to perform resource migration.</p>

A Comprehensive Study of Security in Cloud Computing

2017 ◽  
pp. 386-412
Author(s):  
Prasanta K. Manohari ◽  
Niranjan K. Ray
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Emerging Technology ◽  
Security Risk ◽  
Computing Environment ◽  
Cloud Data Center ◽  
Cloud Computing Environment ◽  
Cloud Computing Security ◽  
Cloud Data ◽  
Security Issues

Cloud computing is one of the emerging technology in the recent times which has varieties of applications at different fields. It is an Internet dependent technology and it store and maintain the data in a cloud data center. Cloud center usually supports more numbers of user, applications and data. In the same time, it also suffered with numerous challenges. Security is a key requirement for cloud data center. Different security mechanisms are proposed for cloud computing environment. In this chapter, we address the background of cloud computing, security risk, requirements, issues, and some of the security techniques are discussed. We discuss different security issues and focus on some existing solutions.

scholarly journals Task Classification Based Energy-Aware Consolidation in Clouds

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
HeeSeok Choi ◽  
JongBeom Lim ◽  
Heonchang Yu ◽  
EunYoung Lee
Keyword(s):  
Resource Utilization ◽  
Data Center ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Energy Aware ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Balance Task ◽  
Task Classification ◽  
Vm Consolidation

We consider a cloud data center, in which the service provider supplies virtual machines (VMs) on hosts or physical machines (PMs) to its subscribers for computation in an on-demand fashion. For the cloud data center, we propose a task consolidation algorithm based on task classification (i.e., computation-intensive and data-intensive) and resource utilization (e.g., CPU and RAM). Furthermore, we design a VM consolidation algorithm to balance task execution time and energy consumption without violating a predefined service level agreement (SLA). Unlike the existing research on VM consolidation or scheduling that applies none or single threshold schemes, we focus on a double threshold (upper and lower) scheme, which is used for VM consolidation. More specifically, when a host operates with resource utilization below the lower threshold, all the VMs on the host will be scheduled to be migrated to other hosts and then the host will be powered down, while when a host operates with resource utilization above the upper threshold, a VM will be migrated to avoid using 100% of resource utilization. Based on experimental performance evaluations with real-world traces, we prove that our task classification based energy-aware consolidation algorithm (TCEA) achieves a significant energy reduction without incurring predefined SLA violations.

Short-term wind speed forecasting using ARIMA model

Energetika ◽  
2016 ◽  
Vol 62 (1-2) ◽  
Author(s):  
Ernesta Grigonytė ◽  
Eglė Butkevičiūtė
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Moving Average ◽  
Arima Model ◽  
Model Structure ◽  
Optimal Model ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Auto Correlation ◽  
Auto Correlation Function

The massive integration of wind power into the power system increasingly calls for better short-term wind speed forecasting which helps transmission system operators to balance the power systems with less reserve capacities. The  time series analysis methods are often used to analyze the  wind speed variability. The  time series are defined as a sequence of observations ordered in time. Statistical methods described in this paper are based on the prediction of future wind speed data depending on the historical observations. This allows us to find a sufficiently good model for the wind speed prediction. The paper addresses a short-term wind speed forecasting ARIMA (Autoregressive Integrated Moving Average) model. This method was applied for a number of different prediction problems, including the short term wind speed forecasts. It is seen as an early time series methodology with well-known limitations in wind speed forecasting, mainly because of insufficient accuracies of the hourly forecasts for the second half of the day-ahead forecasting period. The authors attempt to find the maximum effectiveness of the model aiming to find: (1) how the identification of the optimal model structure improves the forecasting results and (2) what accuracy increase can be gained by reidentification of the structure for a new wind weather season. Both historical and synthetic wind speed data representing the sample locality in the Baltic region were used to run the model. The model structure is defined by rows p, d, q and length of retrospective data period. The structure parameters p (Autoregressive component, AR) and q (Moving Average component, MA) were determined by the Partial Auto-Correlation Function (PACF) and Auto-Correlation Function (ACF), respectively. The model’s forecasting accuracy is based on the root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE). The results allowed to establish the optimal model structure and the length of the input/retrospective period. The  quantitative study revealed that identification of the  optimal model structure gives significant accuracy improvement against casual structures for 6–8 h forecast lead time, but a season-specific structure is not appropriate for the entire year period. Based on the conducted calculations, we propose to couple the ARIMA model with any more effective method into a hybrid model.

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