Overlay Architectures Enabling Cloud Computing for Multi-level Security Environments

Author(s):  
Christopher C. Lamb ◽  
Gregory L. Heileman
Keyword(s):  
2020 ◽  
Vol 30 (5) ◽  
pp. 1503-1520
Author(s):  
Taohua Ouyang ◽  
Xin Cao ◽  
Jun Wang ◽  
Sixuan Zhang

PurposeIn this study, the authors aim to address the following two research questions: (1) How do technology innovation paradoxes manifest themselves in technological changes? (2) How do incumbent firms manage technology innovation paradoxes through multi-level organizational ambidexterity? To do so, the authors examine technology innovation in cloud computing, which has taken shape and brought about changes to the information technology industry. Specifically, the authors examine how a traditional software company, China Standard Software Co., Ltd. (CS2C), successfully navigated the technological transition to cloud computing from its existing operating systems business by managing innovation paradoxes through multi-level ambidexterity capabilities.Design/methodology/approachThis study examines a single exploratory case and conducts an in-depth analysis of how technology innovation paradoxes manifest themselves in technological changes and how incumbent firms manage technology innovation paradoxes through multi-level organizational ambidexterity. The data collection and analysis occurred simultaneously through three phases. In Phase 1, one of the authors who had worked at CS2C for many years enabled the authors to obtain access to the company. The data analysis during this phase provided the authors with the history and current situation of CS2C, enabling them to understand the external circumstances, such as particular historical period, and internal conditions, such as cultural and technological changes, that would be relevant throughout the course of their study. It also helped the authors identify organizational ambidexterity capability as the guiding theoretical concept for their research. In Phase 2, the authors engaged in site visits and conducted detailed interviews with employees working at CS2C. In Phase 3, most of the data analysis was conducted. When the interview data were not sufficient to support the theoretical analysis, additional data were collected via phone calls and emails, to assure data-theory-model alignment.FindingsThe authors’ findings show that technology innovation paradoxes manifest themselves as contradictory relationships and mutual support relationships between exploitative and exploratory innovation. In addition, the authors identify three integration mechanisms as key to multi-level organizational ambidexterity capabilities in managing technology innovation paradoxes in technological changes.Originality/valueThree important theoretical implications can be drawn from our case analysis. First, this research contributes to the knowledge of innovation paradoxes during technological changes. Second, this research provides a model of multi-level organizational ambidexterity capability in technological changes. Third, this research proposes three integration mechanisms driven by three types of ambidexterity capability at different organizational levels.


Author(s):  
S. Rekha ◽  
C. Kalaiselvi

This paper studies the delay-optimal virtual machine (VM) scheduling problem in cloud computing systems, which have a constant amount of infrastructure resources such as CPU, memory and storage in the resource pool. The cloud computing system provides VMs as services to users. Cloud users request various types of VMs randomly over time and the requested VM-hosting durations vary vastly. A multi-level queue scheduling algorithm partitions the ready queue into several separate queues. The processes are permanently assigned to one queue, generally based on some property of the process, such as memory size, process priority or process type. Each queue has its own scheduling algorithm. Similarly, a process that waits too long in a lower-priority queue may be moved to a higher-priority queue. Multi-level queue scheduling is performed via the use of the Particle Swarm Optimization algorithm (MQPSO). It checks both Shortest-Job-First (SJF) buffering and Min-Min Best Fit (MMBF) scheduling algorithms, i.e., SJF-MMBF, is proposed to determine the solutions. Another scheme that combines the SJF buffering and Extreme Learning Machine (ELM)-based scheduling algorithms, i.e., SJF- ELM, is further proposed to avoid the potential of job starva¬tion in SJF-MMBF. In addition, there must be scheduling among the queues, which is commonly implemented as fixed-priority preemptive scheduling. The simulation results also illustrate that SJF- ELM is optimal in a heavy-loaded and highly dynamic environment and it is efficient in provisioning the average job hosting rate.


2017 ◽  
Vol 865 ◽  
pp. 636-641
Author(s):  
Wei Chen ◽  
Yu Ting Shang

This article discusses the disaster recovery technology of online system based on cloud computing, mainly starting from planning a backup strategy to restore the transaction log, pages, files and file groups by page and data restore from a snapshot database. Timely data recovery and fault exercises with a holistic, multi-level data backup and disaster recovery technology could protect the security of the online system.


2020 ◽  
Vol 25 (6) ◽  
pp. 771-782
Author(s):  
Gutta Sridevi ◽  
Midhunchakkravarthy

As the size of the cloud-based applications and its tasks are increasing exponentially, it is necessary to estimate the load balancing metrics in the real-time cloud computing environments. Hybrid load balancing framework play a vital role in the cloud-based applications and tasks monitoring and resource allocation. Most of the conventional load balancing metrics are dependent on limited number of cloud metrics and type of virtual machines. Also, these models require high computational memory and time on large number of tasks. In this paper, an advanced multi-level statistical load balancer-based parameters estimation model is designed and implemented on the real-time cloud computing environment. In this model, a novel statistical load balancing data collector is used to find the best metrics for the load balance computation. In this model, different types of tasks are simulated under different virtual machine types such as small, medium and large instances. Experimental results show that the proposed multi-level based statistical load balancing collector has better efficiency than the conventional models in terms of memory utilization, CPU utilization, runtime and reliability are concerned.


2018 ◽  
Vol 7 (4.36) ◽  
pp. 773
Author(s):  
M. J. Balachandran ◽  
P. Sujatha

Last couple of decades, internet usage has been changed each and every technology domain. This has lead to implementation and accommodation of cloud computing. As the Cloud has the nature of sharing the data, it led to various types of security attacks. Hence security mechanisms which has various features/types are needed and hence security breaches can be prevented. Authentication is one of the vital techniques playing a major role in security part. Cloud Computing verifies the identification of a user during the process of accessing the services from cloud servers. Different authentication techniques are used to verify the user’s identity before granting the access to them.  This paper  analyze the performance of AES and  3DES algorithms and find out the best suit for implementing multi level authentication in cloud. Based on execution time, request and response time against the concurrent user load and file size, AES is faster, more secure and safer than 3DES.     


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