Distributed cache memory data migration strategy based on cloud computing

Cloud data migration is the process of moving data, localhost applications, services, and data to the distributed cloud processing framework. The success of this data migration measure is relying upon a few viewpoints like planning and impact analysis of existing enterprise systems. Quite possibly the most widely recognized process is moving locally stored data in a public cloud computing environment. Cloud migration comes along with both challenges and advantages, so there are different academic research and technical applications on data migration to the cloud that will be discussed throughout this paper. By breaking down the research achievement and application status, we divide the existing migration techniques into three strategies as indicated by the cloud service models essentially. Various processes should be considered for different migration techniques, and various tasks will be included accordingly. The similarities and differences between the migration strategies are examined, and the challenges and future work about data migration to the cloud are proposed. This paper, through a research survey, recognizes the key benefits and challenges of migrating data into the cloud. There are different cloud migration procedures and models recommended to assess the presentation, identifying security requirements, choosing a cloud provider, calculating the expense, and making any essential organizational changes. The results of this research paper can give a roadmap for data migration and can help decision-makers towards a secure and productive migration to a cloud computing environment.

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An Optimal Way of VM Placement Strategy in Cloud Computing Platform Using ABCS Algorithm

International Journal of Ambient Computing and Intelligence ◽

10.4018/ijaci.2021070102 ◽

2021 ◽

Vol 12 (3) ◽

pp. 16-38

Author(s):

Pushpa R. ◽

M. Siddappa

Keyword(s):

Cloud Computing ◽

Power Consumption ◽

Virtual Machines ◽

Fitness Function ◽

Vm Placement ◽

Migration Strategy ◽

Swarm Optimization ◽

Bee Colony ◽

Migration Cost ◽

Chicken Swarm Optimization

In this paper, VM replacement strategy is developed using the optimization algorithm, namely artificial bee chicken swarm optimization (ABCSO), in cloud computing model. The ABCSO algorithm is the integration of the artificial bee colony (ABC) in chicken swarm optimization (CSO). This method employed VM placement based on the requirement of the VM for the completion of the particular task using the service provider. Initially, the cloud system is designed, and the proposed ABCSO-based VM placement approach is employed for handling the factors, such as load, CPU usage, memory, and power by moving the virtual machines optimally. The best VM migration strategy is determined using the fitness function by considering the factors, like migration cost, load, and power consumption. The proposed ABCSO method achieved a minimal load of 0.1688, minimal power consumption of 0.0419, and minimal migration cost of 0.0567, respectively.

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Discovery of Society Structure in A Social Network Using Distributed Cache Memory

2019 5th International Conference on Web Research (ICWR) ◽

10.1109/icwr.2019.8765289 ◽

2019 ◽

Cited By ~ 1

Author(s):

Saeideh Fattahi ◽

Reza Yazdani ◽

Seyyed Mehdi Vahidipour

Keyword(s):

Social Network ◽

Cache Memory ◽

Distributed Cache

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Improving Cost for Data Migration in Cloud Computing Using Genetic Algorithm

International Journal of Software Innovation ◽

10.4018/ijsi.2020070105 ◽

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.

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