scholarly journals Simulation and user analysis of BaBar data in a distributed cloud

2011 ◽  
Author(s):  
Colin Leavett-Brown
Keyword(s):  
Babar Data ◽  
Distributed Cloud

Blockchain Distributed Cloud Storage Network Tool(BCCNT)

2018 ◽  
Vol 6 (12) ◽  
pp. 980-983
Author(s):  
Ankita Sharma ◽  
Rishabh Bansal ◽  
Nimish Niret Soren
Keyword(s):  
Cloud Storage ◽  
Storage Network ◽  
Distributed Cloud

Adaptive priority-based data placement and multi-task scheduling in geo-distributed cloud systems

2021 ◽  
Vol 224 ◽  
pp. 107050
Author(s):  
Chunlin Li ◽  
Jun Liu ◽  
Weigang Li ◽  
Youlong Luo
Keyword(s):  
Task Scheduling ◽  
Data Placement ◽  
Cloud Systems ◽  
Distributed Cloud

Consensus-based data replication protocol for distributed cloud

2021 ◽  
Author(s):  
Rashmi Maheshwari ◽  
Naveen Kumar ◽  
Monal Shadi ◽  
Shailesh Tiwari
Keyword(s):  
Data Replication ◽  
Distributed Cloud

scholarly journals Swarm-Like Distributed Algorithm for Scheduling a Microservice-Based Application to the Cloud Servers

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1553
Author(s):  
Marian Rusek ◽  
Grzegorz Dwornicki
Keyword(s):  
Distributed Applications ◽  
Practical Implementation ◽  
Live Migration ◽  
Cloud Infrastructure ◽  
Application Development ◽  
Cloud Management ◽  
Cloud Application ◽  
Cloud Servers ◽  
Distributed Cloud ◽  
Better Than

Introduction of virtualization containers and container orchestrators fundamentally changed the landscape of cloud application development. Containers provide an ideal way for practical implementation of microservice-based architecture, which allows for repeatable, generic patterns that make the development of reliable, distributed applications more approachable and efficient. Orchestrators allow for shifting the accidental complexity from inside of an application into the automated cloud infrastructure. Existing container orchestrators are centralized systems that schedule containers to the cloud servers only at their startup. In this paper, we propose a swarm-like distributed cloud management system that uses live migration of containers to dynamically reassign application components to the different servers. It is based on the idea of “pheromone” robots. An additional mobile agent process is placed inside each application container to control the migration process. The number of parallel container migrations needed to reach an optimal state of the cloud is obtained using models, experiments, and simulations. We show that in the most common scenarios the proposed swarm-like algorithm performs better than existing systems, and due to its architecture it is also more scalable and resilient to container death. It also adapts to the influx of containers and addition of new servers to the cloud automatically.

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