scholarly journals A Service Recommendation Method Based on Requirements for the Cloud Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Liangmin Guo ◽  
Kaixuan Luan ◽  
Xiaoyao Zheng ◽  
Jing Qian
Keyword(s):  
Cloud Services ◽  
Service Recommendation ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Target User ◽  
User Communities ◽  
Matching Degree ◽  
Recommendation Accuracy ◽  
The Difference

In the cloud computing environment, there are huge amounts of functionally similar cloud services. Additionally, user requirements can change. Therefore, it is difficult to recommend services that meet users’ requirements. To overcome the problems, a service recommendation method based on requirements is proposed. First, we form user communities by clustering to reduce the recommended range. Second, we use the reported QoS (Quality of Service) values and the evaluated QoS values to predict the QoS requirements of users. Third, based on the requirements, the matching degree of users to services is obtained. Finally, based on the similarity between the target user and the user’s neighbors and the difference in their matching degree of service and the ratings of services by the neighbors, we can obtain a list of service recommendations for the target user. Compared to the traditional collaborative filtering method and the deviation-based method, our method improves the recommendation accuracy without lowering the efficiency.

Workflow Scheduling for Scientific Application in Homogeneous Cloud Environment

2017 ◽  
Vol 7 (7) ◽  
pp. 137
Author(s):  
. Monika ◽  
Pardeep Kumar ◽  
Sanjay Tyagi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Large Data ◽  
Workflow Scheduling ◽  
Cloud Environment ◽  
Computing Environment ◽  
Scientific Application ◽  
Cloud Computing Environment ◽  
Flow Of Information

In Cloud computing environment QoS i.e. Quality-of-Service and cost is the key element that to be take care of. As, today in the era of big data, the data must be handled properly while satisfying the request. In such case, while handling request of large data or for scientific applications request, flow of information must be sustained. In this paper, a brief introduction of workflow scheduling is given and also a detailed survey of various scheduling algorithms is performed using various parameter.

scholarly journals Service Level Agreement Violation Preventive Task Scheduling for Quality of Service Delivery in Cloud Computing Environment

2020 ◽  
Vol 178 ◽  
pp. 375-385
Author(s):  
Ismail Zahraddeen Yakubu ◽  
Zainab Aliyu Musa ◽  
Lele Muhammed ◽  
Badamasi Ja’afaru ◽  
Fatima Shittu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Service Delivery ◽  
Task Scheduling ◽  
Service Level Agreement ◽  
Service Level ◽  
Computing Environment ◽  
Cloud Computing Environment

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 An Effiecient Approach for Resource Auto-Scaling in Cloud Environments

2016 ◽  
Vol 6 (5) ◽  
pp. 2415
Author(s):  
Bahar Asgari ◽  
Mostafa Ghobaei Arani ◽  
Sam Jabbehdari
Keyword(s):  
Service Level Agreement ◽  
Service Level ◽  
Resource Provisioning ◽  
Cloud Services ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Sla Violation ◽  
Markov Decision ◽  
Cloud Environments ◽  
Auto Scaling

<p>Cloud services have become more popular among users these days. Automatic resource provisioning for cloud services is one of the important challenges in cloud environments. In the cloud computing environment, resource providers shall offer required resources to users automatically without any limitations. It means whenever a user needs more resources, the required resources should be dedicated to the users without any problems. On the other hand, if resources are more than user’s needs extra resources should be turn off temporarily and turn back on whenever they needed. In this paper, we propose an automatic resource provisioning approach based on reinforcement learning for auto-scaling resources according to Markov Decision Process (MDP). Simulation Results show that the rate of Service Level Agreement (SLA) violation and stability that the proposed approach better performance compared to the similar approaches.</p>

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.

Sharing VM Resources With Using Prediction of Future User Requests for an Efficient Load Balancing in Cloud Computing Environment

2021 ◽  
Vol 13 (2) ◽  
pp. 37-64
Author(s):  
Mousa Elrotub ◽  
Ahmed Bali ◽  
Abdelouahed Gherbi
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Performance Metrics ◽  
Virtual Machines ◽  
Machine Learning Techniques ◽  
Data Repository ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Learning Techniques

The problem of balancing user requests in cloud computing is becoming more serious due to the variation of workloads. Load balancing and allocation processes still need more optimizing methodologies and models to improve performance and increase the quality of service. This article describes a solution to balance user workload efficiently by proposing a model that allows each virtual machine (VM) to maximize the serving number of requests based on its capacity. The model measures VMs' capacity as a percentage and maps groups of user requests to appropriate active virtual machines. Finding the expected patterns from a big data repository, such as log data, and using some machine learning techniques can make the prediction more efficiently. The work is implemented and evaluated using some performance metrics, and the results are compared with other research. The evaluation shows the efficiency of the proposed approach in distributing user workload and improving results.

scholarly journals Classification Based on both Attribute Value Weight and Tuple Weight under the Cloud Computing

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Yifeng Zheng ◽  
Zaixiang Huang ◽  
Tianzhong He
Keyword(s):  
Cloud Computing ◽  
Classification Accuracy ◽  
Large Data ◽  
Classification Rule ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Quality Classification ◽  
Rule Set ◽  
Attribute Value

In recent years, more and more people pay attention to cloud computing. Users need to deal with magnanimity data in the cloud computing environment. Classification can predict the need of users from large data in the cloud computing environment. Some traditional classification methods frequently adopt the following two ways. One way is to remove instance after it is covered by a rule, another way is to decrease tuple weight of instance after it is covered by a rule. The quality of these traditional classifiers may be not high. As a result, they cannot achieve high classification accuracy in some data. In this paper, we present a new classification approach, called classification based on both attribute value weight and tuple weight (CATW). CATW is distinguished from some traditional classifiers in two aspects. First, CATW uses both attribute value weight and tuple weight. Second, CATW proposes a new measure to select best attribute values and generate high quality classification rule set. Our experimental results indicate that CATW can achieve higher classification accuracy than some traditional classifiers.

Sign in / Sign up

Export Citation Format

Share Document