scholarly journals Cache optimization cloud scheduling (COCS) algorithm based on last level caches

2019 ◽  
Vol 8 (3) ◽  
pp. 184
Author(s):  
K. Vinod Kumar ◽  
Ranvijay Ranvijay
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Average Power ◽  
Cloud Services ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Cache Optimization ◽  
Dynamic Voltage ◽  
Increasing Demand ◽  
And Storage

<p><span>Recently, the utilization of cloud services like storage, various software, networking resources has extremely enhanced due to widespread demand of these cloud services all over the world. On the other hand, it requires huge amount of storage and resource management to accurately cope up with ever-increasing demand. The high demand of these cloud services can lead to high amount of energy consumption in these cloud centers. Therefore, to eliminate these drawbacks and improve energy consumption and storage enhancement in real time for cloud computing devices, we have presented Cache Optimization Cloud Scheduling (COCS) Algorithm Based on Last Level Caches to ensure high cache memory Optimization and to enhance the processing speed of I/O subsystem in a cloud computing environment which rely upon Dynamic Voltage and Frequency Scaling (DVFS). The proposed COCS technique helps to reduce last level cache failures and the latencies of average memory in cloud computing multi-processor devices. This proposed COCS technique provides an efficient mathematical modelling to minimize energy consumption. We have tested our experiment on Cybershake scientific dataset and the experimental results are compared with different conventional techniques in terms of time taken to accomplish task, power consumed in the VMs and average power required to handle tasks.</span></p>

scholarly journals Assessing Risks and Cloud Readiness in PaaS Environments

2019 ◽  
Vol 8 (3S) ◽  
pp. 243-249
Keyword(s):  
Cloud Computing ◽  
Cloud Services ◽  
Risk Minimization ◽  
Cloud Infrastructure ◽  
Computing Environment ◽  
Configurable Computing ◽  
Cloud Computing Environment ◽  
Distributed Models ◽  
Software Vendor ◽  
Complex Architecture

The cloud computing has utilization of pervasive or distributed models on demand access to highly configurable computing devices for fast provision and less management efforts. The complex architecture, multitenant and virtual environment in cloud infrastructure asks for risks identification and mitigation. The cloud computing model business needs reassurances so it’s prime consideration for testing the cloud services. This research primarily identifies various risks, threats, testing models and vulnerabilities in cloud computing environment. This research has implemented the risk assessment and cloud readiness for PaaS environment by scanning its code with a software vendor. The research makes an emphasis on risk minimization strategies and trust evaluation in cloud computing environment.

scholarly journals Adaptive Learning and Automatic Filtering of Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environment

2019 ◽  
Vol 9 (1S3) ◽  
pp. 200-205
Keyword(s):  
Cloud Computing ◽  
Adaptive Learning ◽  
Denial Of Service ◽  
Cloud Services ◽  
Detection Accuracy ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Automatic Filtering

Distributed Denial of Service (DDoS) attacks has become the most powerful cyber weapon to target the businesses that operate on the cloud computing environment. The sophisticated DDoS attack affects the functionalities of the cloud services and affects its core capabilities of cloud such as availability and reliability. The current intrusion detection system (IDS) must cope with the dynamicity and intensity of immense traffic at the cloud hosted applications and the security attack must be inspected based on the attack flow characteristics. Hence, the proposed Adaptive Learning and Automatic Filtering of Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environment is designed to adapt with varying kind of protocol attacks using misuse detection. The system is equipped with custom and threshold techniques that satisfies security requirements and can identify the different DDoS security attacks. The proposed system provides promising results in detecting the DDoS attacks in cloud environment with high detection accuracy and good alert reduction. Threshold method provides 98% detection accuracy with 99.91%, 99.92% and 99.94% alert reduction for ICMP, UDP and TCP SYN flood attack. The defense system filters the attack sources at the target virtual instance and protects the cloud applications from DDoS attacks.

Controlled Intelligent Agents' Security Model for Multi-Tenant Cloud Computing Infrastructures

2017 ◽  
Vol 9 (1) ◽  
pp. 1-13
Author(s):  
Howard Hamilton ◽  
Hadi Alasti
Keyword(s):  
Cloud Computing ◽  
Data Security ◽  
Intelligent Agents ◽  
Cloud Service ◽  
Cloud Services ◽  
Service Users ◽  
Security Model ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Service Models

Data security in the cloud continues to be a huge concern. The adoption of cloud services continues to increase with more businesses transitioning from on premise technology infrastructures to outsourcing cloud-based infrastructures. As the cloud becomes more popular, users are increasingly demanding control over critical security elements of the data and technology assets that are in the cloud. In addition, there are still cries for greater data and security in the cloud. The goal of this paper is to provide cloud service users with greater control over data security in the cloud while at the same time optimizing overall security in the multi-tenant cloud computing environment. This paper introduces cloud-based intelligent agents that are configurable by the users and are expected to give greater compliance for data security in any of the cloud service models.

scholarly journals A File Storage Service on a Cloud Computing Environment for Digital Libraries

2012 ◽  
Vol 31 (4) ◽  
pp. 34 ◽  
Author(s):  
Victor Jesus Sosa-Sosa ◽  
Emigdio M. Hernandez-Ramirez
Keyword(s):  
Cloud Computing ◽  
Digital Libraries ◽  
Data Availability ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Reference Guide ◽  
File Storage ◽  
Evaluation Of Performance ◽  
Storage Service ◽  
And Storage

This paper introduces a file storage service that is implemented on a private/hybrid cloud computing environment. The entire system was implemented using open source software. The characteristic of elasticity is supported by virtualization technologies allowing to increase and to decrease the computing and storage resources based on their demand. An evaluation of performance and resource consumption was made using several levels of data availability and fault tolerance. The set of modules included in this storage environment can be taken as a reference guide for IT staff that wants to have some experience building a modest cloud storage infrastructure.

scholarly journals Minimizing Energy Consumption by Task Consolidation in Cloud Centers with Optimized Resource Utilization

2016 ◽  
Vol 6 (6) ◽  
pp. 3283 ◽  
Author(s):  
Mahendra Kumar Gourisaria ◽  
S. S. Patra ◽  
P. M. Khilar
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Power Consumption ◽  
Resource Utilization ◽  
Data Centers ◽  
Carbon Dioxide Emission ◽  
Computing Environment ◽  
Energy Aware ◽  
Cloud Computing Environment ◽  
Cpu Utilization

<p>Cloud computing is an emerging field of computation. As the data centers consume large amount of power, it increases the system overheads as well as the carbon dioxide emission increases drastically. The main aim is to maximize the resource utilization by minimizing the power consumption. However, the greatest usages of resources does not mean that there has been a right use of energy.  Various resources which are idle, also consumes a significant amount of energy. So we have to keep minimum resources idle. Current studies have shown that the power consumption due to unused computing resources is nearly 1 to 20%. So, the unused resources have been assigned with some of the tasks to utilize the unused period. In the present paper, it has been suggested that the energy saving with task consolidation which has been saved the energy by minimizing the number of idle resources in a cloud computing environment. It has been achieved far-reaching experiments to quantify the performance of the proposed algorithm. The same has also been compared with the FCFSMaxUtil and Energy aware Task Consolidation (ETC) algorithm. The outcomes have shown that the suggested algorithm surpass the FCFSMaxUtil and ETC algorithm in terms of the CPU utilization and energy consumption.</p>

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