Cloud Computing Data Centers Research Articles

Cloud Data Center Network Construction Based on Virtualization Technology

With the rapid development of information technology, the IT infrastructure in traditional data centers has high investment costs, low utilization rate, long application deployment period, and the difficulty of operation and maintenance increases with the increase of system and energy consumption. In order to solve the above problems, the article analyzes the current network virtualization technology and systematically studies the application scenarios of network virtualization technology, and proposes a cloud computing data center network construction scheme based on network virtualization technology.

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We present an overview of possibilities for existing Satellite Communication Systems utilization to provide Internet of Things Services. It is shown that existing Satellite Communication Systems provide traffic transmission for IoT Systems with Cloud Architecture. The proposals are explicated on the possibility of using Fog and Edge Computing for Satellite Communication Systems. The implementation of Fog and Edge computing in IoT Systems requires the modernization of the Low-Earth Orbit (LEO) and Geostationary Orbit (GEO) Satellite Communication Systems, and we show the ways of their enhancement. To increase the efficiency of IoT data processing and the reliability of IoT Data Storage, we propose to construct an Orbital Cloud Data Storage in GEO, which consists of several GEO Satellites - Cloud Computing Data Centers. Such a structure would require the development of methods of access providing to the Orbital Cloud Data Storage. For these purposes, our propositions include using GEO High-Throughput Satellites and satellites from the structure of LEO Satellite Communication. The issues of interaction between Orbital Cloud Data Storage and ground-based Cloud Data Processing and Storage Infrastructure are briefly considered. The orbital slots in GEO are proposed for the location of GEO Satellites - Cloud Computing Data Centers.

Application of IOT deep learning in edge computing: a review

With the development of artificial intelligence technology, deep learning is widely used as a method to extract features from complex networks. However, deep learning models often run in cloud computing data centers with powerful computing capabilities. Traditional cloud computing methods rely heavily on the network, which has high latency, and has problems of security and privacy. Edge computing complements cloud computing by performing tasks at the edge of the network, resulting in significant reductions in system operation time, memory cost, and power consumption. At the same time, because it is deployed in an edge computing environment, network performance can be optimized and user privacy can be protected. This review discusses the application of deep learning on the Internet of things(IOT) in the environment of edge computing, compares the results of edge computing and cloud computing in the field of deep learning, shows the superiority of the edge computing. This paper introduces the commonly used method of edge computing, and at the same time puts forward the possible problems of edge computing in the field of deep learning.Finally, we make a prospect for the future in the cross field of edge computing and deep learning.

New approach in the applications and forensics of the networks of the internet of things based on the fog infrastructure using SDN

Ubiquitous computing with smart mobile devices, internet of things, virtualisation, cloud, and fog is changing yesteryears' static networks to dynamic networks of mobile smart devices. Fog computing is a pattern which expands cloud computing and the provision of related services to the network edge. Therefore, fog computing was recently introduced to provide storage and network services between end users and traditional cloud computing data centres. The present study aimed to consider the structure, architecture and applications of fog computing and analyse its differences and similarities with cloud computing, examine forensics in these networks and finally, present a new approach in this regard. This paper describes how the emerging technology - software defined networking (SDN) can be deployed a single infrastructure and leveraged to provide scalable flexible forensic solutions in this diverse and service providers/IT in a deal with the dynamic nature of today's networks attacks.

Optimal Delay-Cost Scheduling Control in Fluid Models of General nxn Input-Queued Switches

Input-queued switch (IQS) architectures are widely used in modern computer/communication networks. The optimal scheduling control of these high-speed, low-latency networks is critical for our understanding of fundamental design and performance issues related to internet routers, cloud computing data centers, and high-performance computing. A large and rich literature exists around optimal scheduling in these systems. This includes the extensive study of IQSs as an important mathematical model for a general class of optimal scheduling control problems of broad interest.

DISCERNER: Dynamic selection of resource manager in hyper-scale cloud-computing data centres

Data centres constitute the engine of the Internet, and run a major portion of large web and mobile applications, content delivery and sharing platforms, and Cloud-computing business models. The high performance of such infrastructures is therefore critical for their correct functioning. This work focuses on the improvement of data-centre performance by dynamically switching the main data-centre governance software system: the resource manager. Instead of focusing on the development of new resource-managing models as soon as new workloads and patterns appear, we propose DISCERNER, a decision-theory model that can learn from numerous data-centre execution logs to determine which existing resource-managing model may optimise the overall performance for a given time period. Such a decision-theory system employs a classic machine-learning classifier to make real-time decisions based on past execution logs and on the current data-centre operational situation. A set of extensive and industry-guided experiments has been simulated by a validated data-centre simulation tool. The results obtained show that the values of key performance indicators may be improved by at least 20% in realistic scenarios.

Improved chaotic binary grey wolf optimization algorithm for workflow scheduling in green cloud computing

The workflow scheduling in the cloud computing environment is a well-known NP-complete problem, and metaheuristic algorithms are successfully adapted to solve this problem more efficiently. Grey wolf optimization (GWO) is a recently proposed interesting metaheuristic algorithm to deal with continuous optimization problems. In this paper, we proposed IGWO, an improved version of the GWO algorithm which uses the hill-climbing method and chaos theory to achieve better results. The proposed algorithm can increase the convergence speed of the GWO and prevents falling into the local optimum. Afterward, a binary version of the proposed IGWO algorithm, using various S functions and V functions, is introduced to deal with the workflow scheduling problem in cloud computing data centers, aiming to minimize their executions’ cost, makespan, and the power consumption. The proposed workflow scheduling scheme is simulated using the CloudSim simulator and the results show that our scheme can outperform other scheduling approaches in terms of metrics such as power consumption, cost, and makespan.

Multi-Objective Task and Workflow Scheduling Approaches in Cloud Computing: a Comprehensive Review

Efficient task and workflow scheduling are very important for improving resource management and reducing power consumption in cloud computing data centers (DCs). However, regarding numerous tasks, virtual machines, and several objectives which should be taken into account, scheduling is considered to be an NP-Hard problem. Multi-objective optimization is an interesting technique to deal with multiple conflicting goals which have been utilized by various schemes to solve the task and workflow scheduling problems. This paper focuses on the metaheuristic multi-objective optimization context and presents a comprehensive survey and overview of the multi-objective scheduling approaches designed for various cloud computing environments. It classifies the scheduling schemes regarding their applied multi-objective optimization algorithms and describes how they have adapted the optimization algorithms to solve scheduling problems. Furthermore, a comparison of the multi-objective scheduling schemes is provided, which illuminates future research directions, and finally concluding remarks are presented.

RETRACTED ARTICLE: A novel scheduling approach to improve the energy efficiency in cloud computing data centers

Cloud computing is the combination of grid computing, distributed computing and utility computing. Cloud computing provides various types of services (servers and storage facility) in the on demand basis. The main goal of the cloud computing is to preserve and organize the very huge data center or data forms. The data forms are composed of thousands of servers that absorb the giant (ample) amount of electricity in the word of energy. The decreasing the energy consumption in datacenter is the major challenge in cloud computing now a day. This research article is going to address the problem of high energy consumption at datacenter. Concentrate on virtual machine scheduling in cloud datacenter with Dynamic Voltage Frequency Scaling (DVFS) approach. We have combined shortest job first and Round Robin algorithms with Vibrant Quantum. This combination of algorithm is considered as shortest round vibrant queue (SRVQ) algorithm. SRVQ reduces the waiting time of the scheduling process and minimize the starvation. The DVFS and SRVQ worked together and produced fruitful results in the final experiments. This work reduced the server’s energy consumption in the cloud data center. In the final results, our proposed framework exhibits 45% of energy efficiency compare to other previously proposed algorithms. 33% of QoS performance were enhanced by our framework.

Fog Based Three Layer Architecture for Privacy Preserving

Fog computing is one of the most latest technology used by the cloud providers to safe guard the user data and service provider’s data servers. Fog computing acts as mediator between hardware and remote servers or cloud servers. Cloud computing still has the lot of vulnerabilities. Privacy to the users data is main issue in the present cloud computing. Whenever users uploads data into cloud server then user will lose their right on their own data because users don’t know about, what cloud providers do with users data, they can sell the users data for their own profit without knowing to users. Fog computing provides lot of services like operation of computer, storage and networking services between users and cloud computing data centers. With the networking services users can lose their data privacy or leakage without knowing to user. Because public clouds are not secure enough and users doesn’t know where data is storing in cloud servers. Breaking the data into small parts can lead to loss of data and which it can create way to attackers to steal data. Even data might be changed instated of one data with another. Intelligence can be applied in the fog computing technology to use of computing resources and security reasons. Applying multiple layers of security features by using kubernets can improve better service to user and user’s data can be safe from the attackers. Whenever user lost connection with the server kubernets establishes reconnection between user and server. RSA256 encryption is applied to users data with this we can provide better security between cloud server and users.

Application of network virtual cloud computing data center based on fuzzy algorithm

Application of network virtual cloud computing data center based on fuzzy algorithm

Load Predicting Model of Mobile Cloud Computing Based on Glowworm Swarm Optimization LSTM Network

Focusing on the issue of host load estimating in mobile cloud computing, the Long Short Term Memory networks (LSTM)is introduced, which is appropriate for the intricate and long-term arrangement information of the cloud condition and a heap determining calculation dependent on Glowworm Swarm Optimization LSTM neural system is proposed. Specifically, we build a mobile cloud load forecasting model using LSTM neural network, and the Glowworm Swarm Optimization Algorithm (GSO) is used to search for the optimal LSTM parameters based on the research and analysis of host load data in the mobile cloud computing data center. Finally, the simulation experiments are implemented and similar prediction algorithms are compared. The experimental results show that the prediction algorithms proposed in this paper are in prediction accuracy higher than equivalent prediction algorithms.

Bullfighting extreme scenarios in efficient hyper-scale cluster computing

Data centres are quickly evolving to support new demands for Cloud-Computing services. Extreme workload peaks represent a challenge for the maintenance of the performance and service level agreements, even more when operation costs need to be minimised. In this paper, we first present an extensive analysis of the impact of extreme workloads in large-scale realistic Cloud-Computing data centres, including a comparison between the most relevant centralised resource-managing models. Moreover, we extend our previous works by proposing a new energy-efficiency policy called Bullfighter which is able to keep performance key performance indicators while reducing energy consumption in extreme scenarios. This policy employs queue-theory distributions to foresee workload demands and adapt automatically to workload fluctuations even in extreme environments, while avoiding the fine-tuning required for other energy policies. Finally, it is shown through extensive simulation that Bullfighter can save more than 40% of energy in the aforementioned scenarios without exerting any noticeable impact on data-centre performance.

Load Balancing Scheduling of Power Network in Cloud Computing Environment

In the traditional cloud computing environment, the power network load balancing scheduling method cannot adaptively track the time-varying coupling characteristics of load data, and the power data center is not efficient in processing massive data, so it is increasingly unable to meet the multi-service quality requirements of power users. The purpose of this paper is to improve the load balancing of power network, improve the efficiency of power network balance scheduling and power resource balance allocation in cloud computing environment. At the research method level, this paper analyzes the application status of data center and cloud computing task scheduling, and starts from the task scheduling demand analysis of cloud computing data center to determine load balancing and QoS as the technical goals of task scheduling. According to the parallel model framework of MapReduce, the task execution process is studied, and the design scheme of power cloud data center is given in view of the problems of low resource utilization, poor scalability and high energy consumption cost faced by the existing power data center. The experimental results show that the network load balancing scheduling method proposed in this paper can meet the multi-qos demand of power users to the maximum extent, effectively improve the efficiency of power data center operation, and achieve better load balancing effect than the traditional scheme.

A Study on Energy Consumption of DVFS and Simple VM Consolidation Policies in Cloud Computing Data Centers Using CloudSim Toolkit

The exponential growth in cloud services has led to an all-time high use and expansion of cloud computing frameworks. The frameworks operating in data centers, reportedly account for about two-hundredths of total energy consumed around the globe. Numerous methods have been proposed to curb or reduce the consumption. One such method is Virtual Machine consolidation. In this paper, we simulate the operation of a data center with varying number of hosts across different operating hours with support of CloudSim and estimate the energy consumption of non-power aware hosts, dynamic voltage and frequency scaling—enabled hosts, and two popular VM consolidation policies, namely—local regression minimum utilization and static threshold random selection. We then compare the above techniques to show the various levels of energy consumption. We also have a brief look at the SLA violation rates of the two consolidation policies.

A multi-objective trade-off framework for cloud resource scheduling based on the Deep Q-network algorithm

Cloud computing, now a mature computing service, provides an efficient and economical solution for processing big data. As such, it attracts a lot of attention in academia and plays an important role in industrial applications. With the recent increase in the scale of cloud computing data centers, and improvements in user service quality requirements, the structure of the whole cloud system has become more complex, which has also made the resource scheduling management of these systems more challenging. Thus, the goal of this research was to resolve the conflict between cloud service providers (CSPs) who aim to minimize energy costs and those who seek to optimize service quality. Based on the excellent environmental awareness and online adaptive decision-making ability of deep reinforcement learning (DRL), we proposed an online resource scheduling framework based on the Deep Q-network (DQN) algorithm. The framework could make a trade-off of the two optimization objectives of energy consumption and task makespan by adjusting the proportion of the reward of different optimization objectives. Experimental results showed that this framework could effectively be used to make a trade-off of the two optimization objectives of energy consumption and task makespan, and exhibited obvious optimization effects compared with the baseline algorithm. Therefore, our proposed framework can dynamically adjust the optimization objective of the system according to the different requirements of the cloud system.

Comparative analysis of VM consolidation algorithms for cloud computing

Virtual machine consolidation is a major solution for addressing the issue of increasing energy consumption by cloud computing data centers. A lot of work is done on developing algorithms for detecting underloaded, overloaded hosts, selection of virtual machines and their placement to perform the consolidation. These algorithms are usually tested on publicly available Planet lab workload. There is a need to know how benchmarks algorithms used in consolidation of virtual machines respond to other workloads. This paper is an attempt to evaluate these algorithms on Google workload trace. An importer is made to use this dataset by extending the CloudSim toolkit. The comparison of results using Planet lab and Google workload traces is made which shows the difference of 46.41%, 14.84%, 12.86% and 44.83% respectively in terms of number of virtual machine migrations, service level agreement violation time per active host, number of hosts shutdown, and energy consumption. The objective comparison of results illustrated that there is a need to test the proposed algorithms on multiple datasets in order to be assessed as optimal.

Research on Software Architecture Optimization of Cloud Computing Data Center Based on Hadoop

This paper introduces the architecture optimization design of high performance cloud computing data center (HCDC), and proposes an efficient parallel algorithm based on Hadoop system and MapReduce framework. The performance of the algorithm in big data analysis and processing, and the impact of the algorithm on energy efficiency are studied. Finally, the challenges of future work in this area of research are discussed.

Role of Different Integer Virtual Machine in Cloud Data Center

In modern data centres of cloud computing contains virtualization system. In order to improve network stability, energy efficiency, and makespan proper virtualization need. The virtual machine is one of the examples of virtualizations. Cloud computing data centres consist of millions of virtual machine to manage load balancing. In this study check the different number of virtual machine role in data centres, for that purpose, we established a network with the help of cloudsim and compare different data centres at each zones taking a different number of the virtual machine with different paramater and network banwith.After the simulation the result shows that increasning in the number of VM can affect the netwok accuracy in term of energy ,processing time ,coast and network stabality .Â

Research on Internet of Things security architecture based on fog computing

The traditional data processing of the Internet of Things is concentrated in cloud computing, and its huge number of devices and massive real-time data transmission are extremely stressful on network bandwidth and cloud computing data centers. Fog computing is the infrastructure that can use processing power anywhere in the cloud. Virtual computing extends the power of cloud computing to the edge of the network, enabling any computing device to host and process software services, analyzing and storing data closer to where data are generated. The architecture of the fog computing brings enormous processing power. Since its processing power is often located near the required equipment, the distance of data transmission is reduced and the delay is reduced. This article explores how to use the fog computing layer between the cloud data center and the end node layer to store and process large amounts of local data in a timely manner, speeding decision making and enabling Internet of Things manufacturers and software developers to limit their ability to send data. They reduced cloud computing costs and built a reasonable security architecture.