An Approach for Optimizing Resource Allocation and Usage in Cloud Computing Systems by Predicting Traffic Flow

The cloud provides computing resources as a service (scalable and cost-effective storage, management, and accessibility of data and applications) through the Internet. Even though cloud computing offers many opportunities for ICT (information and communication technology), many issues still remain, and the increasing demand for resource management and traffic flow is also becoming increasingly problematic. The amount of data in the cloud computing environment is increasing on a daily basis, which increases data traffic flow. Due to this problem, clients complained about the network speed. Autoregressive Integrated Moving Average (ARIMA), Monte Carlo, Extreme gradient boosting regression (XGBoost), is used in this paper for predicting traffic flow. A Monte Carlo prediction of 84% outperformed ARIMA's prediction of 79.8% and XGBoost's prediction of 71.5%, indicating that Monte Carlo is more accurate than other models when predicting traffic flow in organizational cloud computing systems. A machine learning model will be used for future studies, along with hourly monitoring and resource allocation.The cloud provides computing resources as a service (scalable and cost-effective storage, management, and accessibility of data and applications) through the Internet. Even though cloud computing offers many opportunities for ICT (information and communication technology), many issues still remain, and the increasing demand for resource management and traffic flow is also becoming increasingly problematic. The amount of data in the cloud computing environment is increasing on a daily basis, which increases data traffic flow. Due to this problem, clients complained about the network speed. Autoregressive Integrated Moving Average (ARIMA), Monte Carlo, Extreme gradient boosting regression (XGBoost), is used in this paper for predicting traffic flow. A Monte Carlo prediction of 84% outperformed ARIMA's prediction of 79.8% and XGBoost's prediction of 71.5%, indicating that Monte Carlo is more accurate than other models when predicting traffic flow in organizational cloud computing systems. A machine learning model will be used for future studies, along with hourly monitoring and resource allocation

Applications of Fog Computing in Video Streaming

The purpose of this paper is to show the viability of fog computing in the area of video streaming in vehicles. With the rise of autonomous vehicles, there needs to be a viable entertainment option for users. The cloud fails to address these options due to latency problems experienced during high internet traffic. To improve video streaming speeds, fog computing seems to be the best option. Fog computing brings the cloud closer to the user through the use of intermediary devices known as fog nodes. It does not attempt to replace the cloud but improve the cloud by allowing faster upload and download of information. This paper explores two algorithms that would work well with vehicles and video streaming. This is simulated using a Java application, and then graphically represented. The results showed that the simulation was an accurate model and that the best algorithm for request history maintenance was the variable model

Network traffic analyzer and simulator for cloud ecosystem

Master's thesis in Computer scienceCloud computing is the computing service that is access through the Internet. The term cloud refers to the Internet and computing refers to the services that is provided through the Internet. The cloud provides the enormous services to the user providing the entire infrastructure in the cloud server with reducing the cost of infrastructure development. The cloud provides the service in request of customer to fulfill their business needs. Actually the performance of cloud depends on various factors which depends the reliability of customer towards the Internet. Among those various affecting factors, one of the major factors is the network traffic which makes delay in response of the request which ultimately increases the customer dissatisfaction. In this paper, we analyze the network traffic and sampling of network traffic in the cloud ecosystem. The data is migrated to cloud and the network traffic is analyzed and sampled during the process. Our main aim is to analyze the network traffic and traffic sampling of the cloud ecosystem. For this process the simulator tool record the start and end time of data transfer to cloud environment. Then from this record, the total time taken in different network traffic scenario can be visualized. It helps to compare the traffic rate while varying the data size on migrating to cloud. Furthermore, the rate of transfer of data packet is calculated. It also calculates the average rate of transfer of data under different network condition. In simple the tool migrates the data to cloud platform, analyze the network traffic and also does sampling of traffic when there is heavy network traffic during the migrating process. Monitoring the performance of CPU and memory is another important feature which provides the information of the performance of the system. We hope this work helps the user or any cloud migration analyst to study the network traffic and to provide satisfaction over cloud services. They can estimate their total time taken to migrate their data in cloud on the basis of data size. It also provides information about other network parameter like the transfer rate of packet, average rate of flow of data in different network condition, relation of data size and time, the bytes of data transfer to and from the network in each time interval

Enhanced IPFIX flow monitoring for VXLAN based cloud overlay networks

The demands for cloud computing services is rapidly growing due to its fast adoption and the migration of workloads from private data centers to cloud data centers. Many companies, small and large, prefer switching their data to the enterprise cloud environment rather than expanding their own data centers. As a result, the network traffic in cloud data centers is increasing rapidly. However, due to the dynamic resource provisioning and high-speed virtualized cloud networks, the traditional flow-monitoring systems is unable to provide detail visibility and information of traffic traversing the cloud overlay network environment. Hence, it does not fulfill the monitoring requirement of cloud overlay traffic. As the growth of cloud network traffic causes difficulties for the service providers and end-users to manage the traffic efficiently, an enhanced IPFIX flow monitoring mechanism for cloud overlay networks was proposed to address this problem. The monitoring mechanism provided detail visibility and information of overlay network traffic that traversed the cloud environment, which is not available in the current network monitoring systems. The experimental results showed that the proposed monitoring system able to capture overlay network traffic and segregated the tenant traffic based on virtual machines as compare to the standard monitoring system

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

A Survey on Intrusion Detection Systems for Fog and Cloud Computing

The rapid advancement of internet technologies has dramatically increased the number of connected devices. This has created a huge attack surface that requires the deployment of effective and practical countermeasures to protect network infrastructures from the harm that cyber-attacks can cause. Hence, there is an absolute need to differentiate boundaries in personal information and cloud and fog computing globally and the adoption of specific information security policies and regulations. The goal of the security policy and framework for cloud and fog computing is to protect the end-users and their information, reduce task-based operations, aid in compliance, and create standards for expected user actions, all of which are based on the use of established rules for cloud computing. Moreover, intrusion detection systems are widely adopted solutions to monitor and analyze network traffic and detect anomalies that can help identify ongoing adversarial activities, trigger alerts, and automatically block traffic from hostile sources. This survey paper analyzes factors, including the application of technologies and techniques, which can enable the deployment of security policy on fog and cloud computing successfully. The paper focuses on a Software-as-a-Service (SaaS) and intrusion detection, which provides an effective and resilient system structure for users and organizations. Our survey aims to provide a framework for a cloud and fog computing security policy, while addressing the required security tools, policies, and services, particularly for cloud and fog environments for organizational adoption. While developing the essential linkage between requirements, legal aspects, analyzing techniques and systems to reduce intrusion detection, we recommend the strategies for cloud and fog computing security policies. The paper develops structured guidelines for ways in which organizations can adopt and audit the security of their systems as security is an essential component of their systems and presents an agile current state-of-the-art review of intrusion detection systems and their principles. Functionalities and techniques for developing these defense mechanisms are considered, along with concrete products utilized in operational systems. Finally, we discuss evaluation criteria and open-ended challenges in this area

Cloud Computing Security Framework - Privacy Security

Cloud computing is an emerging style of IT delivery that intends to make the Internet the ultimate home of all computing resources- storage, computations, and accessibility. It has an important aspect for the companies and organization to build and deploy their infrastructure and application. It changed the IT roadmap essential from service seeking infrastructure to infrastructure seeking services. It holds the promise of helping organizations because of its performance, high availability, least cost and many others. But the promise of the cloud cannot be fulfilled until IT professionals have more confidence in the security and safety of the cloud. Data Storage service in the cloud computing is easy as compare to the other data storage services. At the same time, cloud security in the cloud environment is challenging task. Security issues such as service availability, massive traffic handling, application security and authentication, ranging from missing system configuration, lack of proper updates, or unwise user actions from remote data storage. It can expose user’s private data and information to unwanted access. It consider to be biggest problem in a cloud computing. The focus of this research consist on the secure cloud framework and to define a methodology for cloud that will protect user’s data and highly important information from malicious insider as well as outsider attacks by using Kerberos, and LDAP identification