scholarly journals Kalman filter based prediction and forecasting of cloud server KPIs

2021 ◽  
Author(s):  
Thomas Weripuo Gyeera ◽  
Anthony J.H. Simons ◽  
Mike Stannett
Keyword(s):  
Heterogeneous Computing ◽  
Historical Data ◽  
Cloud Service ◽  
High Volume ◽  
Dynamic Allocation ◽  
Test Bed ◽  
Cloud Data ◽  
Service Failures ◽  
Cloud Server ◽  
Resource Exhaustion

Cloud computing depends on the dynamic allocation and release of resources, on demand, to meet heterogeneous computing needs. This is challenging for cloud data centers, which process huge amounts of data characterised by its high volume, velocity, variety and veracity (4Vs model). Managing such a workload is increasingly difficult using state-of-the-art methods for monitoring and adaptation, which typically react to service failures after the fact. To address this, we seek to develop proactive methods for predicting future resource exhaustion and cloud service failures. Our work uses a realistic test bed in the cloud, which is instrumented to monitor and analyze resource usage. In this paper, we employed the optimal Kalman filtering technique to build a predictive and analytic framework for cloud server KPIs, based on historical data. Our k-step-ahead predictions on historical data yielded a prediction accuracy of 95.59%. The information generated from the framework can best be used for optimal resources provisioning, admission control and cloud SLA management.

scholarly journals Kalman filter based prediction and forecasting of cloud server KPIs

2021 ◽  
Author(s):  
Thomas Weripuo Gyeera ◽  
Anthony J.H. Simons ◽  
Mike Stannett
Keyword(s):  
Heterogeneous Computing ◽  
Historical Data ◽  
Cloud Service ◽  
High Volume ◽  
Dynamic Allocation ◽  
Test Bed ◽  
Cloud Data ◽  
Service Failures ◽  
Cloud Server ◽  
Resource Exhaustion

Cloud computing depends on the dynamic allocation and release of resources, on demand, to meet heterogeneous computing needs. This is challenging for cloud data centers, which process huge amounts of data characterised by its high volume, velocity, variety and veracity (4Vs model). Managing such a workload is increasingly difficult using state-of-the-art methods for monitoring and adaptation, which typically react to service failures after the fact. To address this, we seek to develop proactive methods for predicting future resource exhaustion and cloud service failures. Our work uses a realistic test bed in the cloud, which is instrumented to monitor and analyze resource usage. In this paper, we employed the optimal Kalman filtering technique to build a predictive and analytic framework for cloud server KPIs, based on historical data. Our k-step-ahead predictions on historical data yielded a prediction accuracy of 95.59%. The information generated from the framework can best be used for optimal resources provisioning, admission control and cloud SLA management.

Regression Analysis of Predictions and Forecasts of Cloud Data Centre KPIs Using the Boosted Decision Tree Algorithm

2021 ◽  
Author(s):  
Thomas Weripuo Gyeera
Keyword(s):  
Decision Tree ◽  
Heterogeneous Computing ◽  
Learning Algorithm ◽  
High Volume ◽  
Dynamic Allocation ◽  
Test Bed ◽  
Cloud Data ◽  
On Demand ◽  
Boosted Decision Tree ◽  
Data Centres

<div>The National Institute of Standards and Technology defines the fundamental characteristics of cloud computing as: on-demand computing, offered via the network, using pooled resources, with rapid elastic scaling and metered charging. The rapid dynamic allocation and release of resources on demand to meet heterogeneous computing needs is particularly challenging for data centres, which process a huge amount of data characterised by its high volume, velocity, variety and veracity (4Vs model). Data centres seek to regulate this by monitoring and adaptation, typically reacting to service failures after the fact. We present a real cloud test bed with the capabilities of proactively monitoring and gathering cloud resource information for making predictions and forecasts. This contrasts with the state-of-the-art reactive monitoring of cloud data centres. We argue that the behavioural patterns and Key Performance Indicators (KPIs) characterizing virtualized servers, networks, and database applications can best be studied and analysed with predictive models. Specifically, we applied the Boosted Decision Tree machine learning algorithm in making future predictions on the KPIs of a cloud server and virtual infrastructure network, yielding an R-Square of 0.9991 at a 0.2 learning rate. This predictive framework is beneficial for making short- and long-term predictions for cloud resources.</div>

scholarly journals Regression Analysis of Predictions and Forecasts of Cloud Data Centre KPIs Using the Boosted Decision Tree Algorithm

2021 ◽  
Author(s):  
Thomas Weripuo Gyeera
Keyword(s):  
Decision Tree ◽  
Heterogeneous Computing ◽  
Learning Algorithm ◽  
High Volume ◽  
Dynamic Allocation ◽  
Test Bed ◽  
Cloud Data ◽  
On Demand ◽  
Boosted Decision Tree ◽  
Data Centres

<div>The National Institute of Standards and Technology defines the fundamental characteristics of cloud computing as: on-demand computing, offered via the network, using pooled resources, with rapid elastic scaling and metered charging. The rapid dynamic allocation and release of resources on demand to meet heterogeneous computing needs is particularly challenging for data centres, which process a huge amount of data characterised by its high volume, velocity, variety and veracity (4Vs model). Data centres seek to regulate this by monitoring and adaptation, typically reacting to service failures after the fact. We present a real cloud test bed with the capabilities of proactively monitoring and gathering cloud resource information for making predictions and forecasts. This contrasts with the state-of-the-art reactive monitoring of cloud data centres. We argue that the behavioural patterns and Key Performance Indicators (KPIs) characterizing virtualized servers, networks, and database applications can best be studied and analysed with predictive models. Specifically, we applied the Boosted Decision Tree machine learning algorithm in making future predictions on the KPIs of a cloud server and virtual infrastructure network, yielding an R-Square of 0.9991 at a 0.2 learning rate. This predictive framework is beneficial for making short- and long-term predictions for cloud resources.</div>

scholarly journals Regression Analysis of Predictions and Forecasts of Cloud Data centre KPIs using the Boosted Tree Decision Algorithm

2021 ◽  
Author(s):  
Thomas Weripuo Gyeera ◽  
Anthony J.H. Simons ◽  
Mike Stannett
Keyword(s):  
Heterogeneous Computing ◽  
Learning Algorithm ◽  
High Volume ◽  
Dynamic Allocation ◽  
Test Bed ◽  
Decision Algorithm ◽  
Cloud Data ◽  
Resource Information ◽  
On Demand ◽  
Data Centres

<div>The National Institute of Standards and Technology defines the fundamental characteristics of cloud computing as: on-demand computing, offered via the network, using pooled resources, with rapid elastic scaling and metered charging. The rapid dynamic allocation and release of resources on demand to meet heterogeneous computing needs is particularly challenging for data centres, which process a huge amount of data characterised by its high volume, velocity, variety and veracity (4Vs model). Data centres seek to regulate this by monitoring and adaptation, typically reacting to service failures after the fact. We present a real cloud test bed with the capabilities of proactively monitoring and gathering cloud resource information for making predictions and forecasts. This contrasts with the state-of-the-art reactive monitoring of cloud data centres. We argue that the behavioural patterns and Key Performance Indicators (KPIs) characterizing virtualized servers, networks, and database applications can best be studied and analysed with predictive models. Specifically, we applied the Boosted Decision Tree machine learning algorithm in making future predictions on the KPIs of a cloud server and virtual infrastructure network, yielding an R-Square of 0.9991 at a 0.2 learning rate. This predictive framework is beneficial for making short- and long-term predictions for cloud resources.</div>

scholarly journals Regression Analysis of Predictions and Forecasts of Cloud Data centre KPIs using the Boosted Tree Decision Algorithm

2021 ◽  
Author(s):  
Thomas Weripuo Gyeera ◽  
Anthony J.H. Simons ◽  
Mike Stannett
Keyword(s):  
Heterogeneous Computing ◽  
Learning Algorithm ◽  
High Volume ◽  
Dynamic Allocation ◽  
Test Bed ◽  
Decision Algorithm ◽  
Cloud Data ◽  
Resource Information ◽  
On Demand ◽  
Data Centres

<div>The National Institute of Standards and Technology defines the fundamental characteristics of cloud computing as: on-demand computing, offered via the network, using pooled resources, with rapid elastic scaling and metered charging. The rapid dynamic allocation and release of resources on demand to meet heterogeneous computing needs is particularly challenging for data centres, which process a huge amount of data characterised by its high volume, velocity, variety and veracity (4Vs model). Data centres seek to regulate this by monitoring and adaptation, typically reacting to service failures after the fact. We present a real cloud test bed with the capabilities of proactively monitoring and gathering cloud resource information for making predictions and forecasts. This contrasts with the state-of-the-art reactive monitoring of cloud data centres. We argue that the behavioural patterns and Key Performance Indicators (KPIs) characterizing virtualized servers, networks, and database applications can best be studied and analysed with predictive models. Specifically, we applied the Boosted Decision Tree machine learning algorithm in making future predictions on the KPIs of a cloud server and virtual infrastructure network, yielding an R-Square of 0.9991 at a 0.2 learning rate. This predictive framework is beneficial for making short- and long-term predictions for cloud resources.</div>

scholarly journals Secured Document Sharing Using Visual Cryptography in Cloud Data Storage

2015 ◽  
Vol 15 (4) ◽  
pp. 111-123 ◽  
Author(s):  
K. Brindha ◽  
N. Jeyanthi
Keyword(s):  
Data Storage ◽  
Visual Cryptography ◽  
Cloud Service ◽  
Secret Data ◽  
Cloud Data ◽  
Cloud Server ◽  
Cloud Data Storage ◽  
Cloud Framework ◽  
And Storage ◽  
Cloud Users

Abstract Security has emerged as the most concerned aspect of cloud computing environment and a prime challenge for the cloud users. The stored data can be retrieved by the user whenever and wherever required. But there is no guarantee that the data stored in the cloud server has not been accessed by any unauthorized user. The current cloud framework does not allow encrypted data to be stored due to the space and storage cost. Storing secret data in an unencrypted form is vulnerable to external attacks by both illegitimate customers and a Cloud Service Provider (CSP). Traditional encryption techniques require more computation and storage space. Hence, protecting cloud data with minimal computations is the prime task. Secured Document Sharing Using Visual Cryptography (SDSUVC) technique proposes an efficient storage scheme in a cloud for storing and retrieving a document file without any mathematical computations and also ensures data confidentiality and integrity.

A Group Collaboratable Proof of Retrievability Scheme for Cloud Data Storage

2014 ◽  
Vol 13 (7) ◽  
pp. 4625-4632
Author(s):  
Jyh-Shyan Lin ◽  
Kuo-Hsiung Liao ◽  
Chao-Hsing Hsu
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Cloud Service ◽  
Third Party ◽  
Group Collaboration ◽  
Cloud Data ◽  
Cloud Data Storage ◽  
Important Trend ◽  
Data Update ◽  
Proof Of Retrievability

Cloud computing and cloud data storage have become important applications on the Internet. An important trend in cloud computing and cloud data storage is group collaboration since it is a great inducement for an entity to use a cloud service, especially for an international enterprise. In this paper we propose a cloud data storage scheme with some protocols to support group collaboration. A group of users can operate on a set of data collaboratively with dynamic data update supported. Every member of the group can access, update and verify the data independently. The verification can also be authorized to a third-party auditor for convenience.

An examination on data integrity auditing patterns in cloud computing

2018 ◽  
Vol 7 (1.9) ◽  
pp. 200
Author(s):  
T A.Mohanaprakash ◽  
J Andrews
Keyword(s):  
Cloud Computing ◽  
Information Security ◽  
Data Integrity ◽  
Cloud Service ◽  
New Approach ◽  
Computing Services ◽  
Security Challenges ◽  
Cloud Server ◽  
State Of Affairs ◽  
Dynamic Updates

Cloud computing is associate inclusive new approach on however computing services square measure made and utilized. Cloud computing is associate accomplishment of assorted styles of services that has attracted several users in today’s state of affairs. The foremost enticing service of cloud computing is information outsourcing, because of this the information homeowners will host any size of information on the cloud server and users will access the information from cloud server once needed. A dynamic outsourced auditing theme that cannot solely defend against any dishonest entity and collision, however conjointly support verifiable dynamic updates to outsourced information. The new epitome of information outsourcing conjointly faces the new security challenges. However, users might not totally trust the cloud service suppliers (CSPs) as a result of typically they may be dishonest. It's tough to work out whether or not the CSPs meet the customer’s expectations for information security. Therefore, to with success maintain the integrity of cloud information, several auditing schemes are projected. Some existing integrity ways will solely serve for statically archived information and a few auditing techniques is used for the dynamically updated information. The analyzed numerous existing information integrity auditing schemes together with their consequences.

High-volume point cloud data simplification based on decomposed graph filtering

2021 ◽  
Vol 129 ◽  
pp. 103815
Author(s):  
Zhou Wu ◽  
Yan Zeng ◽  
DongSheng Li ◽  
Jiepeng Liu ◽  
Liang Feng
Keyword(s):  
Point Cloud ◽  
High Volume ◽  
Point Cloud Data ◽  
Cloud Data

Implementation of Smart Home System Based on Internet of Things

2013 ◽  
Vol 475-476 ◽  
pp. 1150-1153 ◽  
Author(s):  
Yan Zeng Gao ◽  
Ling Yan Wei
Keyword(s):  
Internet Of Things ◽  
Embedded System ◽  
Smart Home ◽  
Smart Phone ◽  
Cloud Service ◽  
Control Center ◽  
Cloud Server ◽  
Novel Method ◽  
Smart Phone Application

Smart home can apply new internet of things concepts along cloud service technologies. This paper introduces a novel method for smart home system building. The system is driven by use case and it is composed of home control center, zigbee end devices, smart phone applications and cloud server. The home control center is based on arm-linux embedded system, it is the relay of cloud server and home devices. Wireless network of smart home devices was designed according to zigbee. A smart phone application was developed as the role of the user interface.

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