Muhammad Abdullah

Orcid: 0000-0002-3151-558X

Affiliations:
  • University of the Punjab, Punjab University College of Information and Technology, Lahore, Pakistan


According to our database1, Muhammad Abdullah authored at least 13 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

Online presence:

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Bibliography

2024
Cdascaler: a cost-effective dynamic autoscaling approach for containerized microservices.
Clust. Comput., July, 2024

2023
Efficient job placement using two-way offloading technique over fog-cloud architectures.
Clust. Comput., December, 2023

Automatic Migration-Enabled Dynamic Resource Management for Containerized Workload.
IEEE Syst. J., June, 2023

2022
Burst-Aware Predictive Autoscaling for Containerized Microservices.
IEEE Trans. Serv. Comput., 2022

Predictive Auto-Scaling of Multi-Tier Applications Using Performance Varying Cloud Resources.
IEEE Trans. Cloud Comput., 2022

Scalable Containerized Pipeline for Real-time Big Data Analytics.
Proceedings of the IEEE International Conference on Cloud Computing Technology and Science, 2022

2021
Predictive Autoscaling of Microservices Hosted in Fog Microdata Center.
IEEE Syst. J., 2021

2020
Diminishing Returns and Deep Learning for Adaptive CPU Resource Allocation of Containers.
IEEE Trans. Netw. Serv. Manag., 2020

Automatic NoSQL to Relational Database Transformation with Dynamic Schema Mapping.
Sci. Program., 2020

Online Workload Burst Detection for Efficient Predictive Autoscaling of Applications.
IEEE Access, 2020

2019
Unsupervised learning approach for web application auto-decomposition into microservices.
J. Syst. Softw., 2019

Learning Predictive Autoscaling Policies for Cloud-Hosted Microservices Using Trace-Driven Modeling.
Proceedings of the 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), 2019

2018
Containers vs Virtual Machines for Auto-scaling Multi-tier Applications Under Dynamically Increasing Workloads.
Proceedings of the Intelligent Technologies and Applications, 2018


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