Replication Policy of Real-Time Distributed System for Cloud Computing

2015 ◽  
Vol 22 (05) ◽  
pp. 1550024
Author(s):  
Mitsutaka Kimura ◽  
Mitsuhiro Imaizumi ◽  
Toshio Nakagawa
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Data Center ◽  
Cloud System ◽  
Next Generation ◽  
Distributed Information ◽  
Intelligent Node ◽  
Client Data ◽  
Information And Communication ◽  
Processing Architecture

Recently, cloud computing has been widely used for the purpose of protecting client data on the Internet [A. Weiss, Computing in the clouds, netWorker11 (2007) 16–25; M. Armbrust et al., Above the clouds: A Berkeley view of cloud computing, Technical Report UCV/EECS-2009-28, University of California at Berkeley (2009)]. But when a client receives network service, response time may be slow because the data center is located in a remote place. In order to solve the problem, real-time distributed systems for cloud computing has been proposed [M. Okuno, D. Ito, H. Miyamoto, H. Aoki, Y. Tsushima and T. Yazaki, A study on distributed information and communication processing architecture for next generation cloud system, IEICE Tech. Report109(A48) (2010) 241–246; M. Okuno, S. Tsutsumi and T. Yazaki, A study of high available distributed network processing technique for next generation cloud system, IEICE Tech. Report111(8) (2011) 25–30; S. Yamada, J. Marukawa, D. Ishii, S. Okamoto and N. Yamanaka, A study of parallel transmission technique with GMPLS in intelligent cloud network, IEICE Tech. Report109(455) (2010) 51–56]. The cloud computing system consists of some intelligent nodes as well as a data center. The data center manages all client data. The intelligent node provides client service near clients. It enables to provide client service at short response time [M. Okuno, D. Ito, H. Miyamoto, H. Aoki, Y. Tsushima and T. Yazaki, A study on distributed information and communication processing architecture for next generation cloud system, IEICE Tech. Report109(448) (2010) 241–246]. We considered the reliability model of distributed information processing for cloud computing, derived cost effectiveness and discussed the optimal replication interval to minimize it [M. Kimura, M. Imaizumi and T. Nakagawa, Reliability modeling of distributed information processing for cloud computing, in Proc. 20th ISSAT Int. Conf. Reliability and Quality in Design (2014), pp. 183–187]. Authors had dealt with the server system with one failure mode. In this paper, we consider the reliability model of a real-time distributed system with n intelligent nodes and formulate a stochastic model of the server system with n intelligent nodes for changing the other normal intelligent node at failure. We derive the expected numbers of the replication and of updating the client data. Further, we derive the expected cost and discuss an optimal replication interval to minimize it. Next, we derive the cost effectiveness and discuss an optimal number of intelligent nodes to minimize it.

Improving Cost for Data Migration in Cloud Computing Using Genetic Algorithm

2020 ◽  
Vol 8 (3) ◽  
pp. 69-81
Author(s):  
Nitin Chawla ◽  
Deepak Kumar ◽  
Dinesh Kumar Sharma
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Data Center ◽  
Data Centers ◽  
Software As A Service ◽  
Data Migration ◽  
Platform As A Service ◽  
Challenging Tasks ◽  
Enterprise Applications ◽  
Client Data

Cloud computing is gradually increasing its popularity in enterprise-wide organizations. Information technology organizations e.g., IBM, Microsoft, and Amazon have already shifted towards Cloud computing. Cloud-based offerings such as Software as a Service, Platform as a Service and Infrastructure as a Service (IAAS) are the most famous offerings. Most of the existing enterprise applications are deployed using an on-premise model. Organizations are looking for Cloud based offerings to deploy or upgrade their existing applications. SAP, Microsoft Dynamics, and Oracle are the most famous ERP or CRM application OEMs. These enterprise applications generate lots of data are hosted in an organization or on client data centers. Moving data from one data center to the Cloud is always a challenging tasks which cost a lot and takes much effort. This study proposes an efficient approach to optimize cost for data migration in cloud computing. This study also proposes the approach to optimize cost for data collection from multiple locations which can be processed centrally and then migrate to Cloud Computing.

Cloud Computing : Security Issues And Resource Allocation Policies

2017 ◽  
Vol 7 (7) ◽  
pp. 176
Author(s):  
Gaurav Sharma ◽  
Urvashi Garg, A.P ◽  
Arun Jain, A.P ◽  
Loveleena Mukhija, A.P
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Service Level Agreement ◽  
Service Level ◽  
Cloud Provider ◽  
New Era ◽  
Security Issues ◽  
Processing Power ◽  
Client Data ◽  
Computing Grid

Cloud computing is the combination of distributed computing, grid computing and parallel technologies which define the shape of a new era. In this technology client data is stored and maintain in the data center of a cloud provider like Google, Amazon and Microsoft etc. It has inherited the legacy technology and including unique ideas. Industries, such as education, banking and healthcare are moving towards the cloud due to the efficiency of services such as transactions carried out, processing power used, bandwidth consumed, data transferred etc. There are various challenges  for adopting cloud computing such as privacy ,interoperability, managed service level agreement (SLA) and reliability. In this paper we survey challenges in resources allocation and the security issues of the cloud environment.

Virtual technology of cache and real-time big data distribution in cloud computing big data center

2020 ◽  
Vol 39 (6) ◽  
pp. 8917-8925
Author(s):  
Bing Zheng ◽  
Xiaoying Zhang ◽  
Dawei Yun
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Real Time ◽  
Data Center ◽  
System Change ◽  
Virtual Technology ◽  
Topology Model ◽  
Network Platform ◽  
Efficient Transmission ◽  
Traditional Understanding

By comparing several cloud computing of big data network center during COVID-19, this paper proposes a new topology model, which realizes two functions of cloud computing big data center caching and big data real-time distribution. In addition, cloud computing network requires higher performance than traditional application big data center, which makes the consideration of network platform construction performance different from the traditional understanding. During COVID-19, we deeply understood the underlying attributes of cloud, combined with the topology model, we can realize the decoupling of cloud computing big data system, change the situation of direct connection between upstream and downstream, and have more reliable and efficient transmission of message and command big data.

Real-Time Demonstration of Homodyne Coherent Bidirectional Transmission for Next-Generation Data Center Interconnects

2021 ◽  
Vol 39 (4) ◽  
pp. 1231-1238
Author(s):  
Tao Gui ◽  
Xuefeng Wang ◽  
Ming Tang ◽  
Yi Yu ◽  
Yanzhao Lu ◽  
...  
Keyword(s):  
Real Time ◽  
Data Center ◽  
Next Generation ◽  
Bidirectional Transmission

scholarly journals Design of Platform for Service Optimization in Cloud System Environment

2019 ◽  
Vol 8 (2S6) ◽  
pp. 163-167
Keyword(s):  
Cloud Computing ◽  
Cloud Services ◽  
Cloud System ◽  
System Environment ◽  
Service Environment ◽  
Computing Services ◽  
Cloud Computing Service ◽  
Information And Communication ◽  
Cloud Computing Services ◽  
Service Optimization

One of the major characteristics of our society in the field of information and communication is its efforts to cope with the rapidly changing IT environment. This is probably because firms are closely related to the survival of the company. As a solution to this problem, researches on cloud computing services are being actively conducted. As cloud computing services have emerged in the marketplace, concepts related to IT assets are changing from being owned to using services. Reflecting this trend, many related companies are providing cloud services. Therefore, in this paper, we design an optimized platform for efficient application of server in cloud system environment. To do this, we designed a system to study various system connections based on the cloud computing service environment and implement an optimized service platform. In this paper, we have implemented optimization and virtualization research in a cloud system environment to design a platform for optimized cloud services. In addition, we designed the optimal platform by studying methodologies such as optimization service environment implementation, compatibility and analysis.

Decentralized Real-Time Optimization of Voltage Reconfigurable Cloud Computing Data Center

2020 ◽  
Vol 4 (2) ◽  
pp. 577-592
Author(s):  
Shoulu Hou ◽  
Wei Ni ◽  
Shuai Zhao ◽  
Bo Cheng ◽  
Shiping Chen ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Data Center ◽  
Time Optimization ◽  
Real Time Optimization

Recent Developments of Ambulatory Assessment Methods

2014 ◽  
Vol 25 (4) ◽  
pp. 279-287 ◽  
Author(s):  
Stefan Hey ◽  
Panagiota Anastasopoulou ◽  
André Bideaux ◽  
Wilhelm Stork
Keyword(s):  
Real Time ◽  
Information And Communication Technologies ◽  
New Technologies ◽  
Psychological Research ◽  
Ambulatory Assessment ◽  
Emotional States ◽  
Recent Developments ◽  
Potential Applications ◽  
Information And Communication ◽  
Physiological Sensors

Ambulatory assessment of emotional states as well as psychophysiological, cognitive and behavioral reactions constitutes an approach, which is increasingly being used in psychological research. Due to new developments in the field of information and communication technologies and an improved application of mobile physiological sensors, various new systems have been introduced. Methods of experience sampling allow to assess dynamic changes of subjective evaluations in real time and new sensor technologies permit a measurement of physiological responses. In addition, new technologies facilitate the interactive assessment of subjective, physiological, and behavioral data in real-time. Here, we describe these recent developments from the perspective of engineering science and discuss potential applications in the field of neuropsychology.

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