EDBT 2026 Demo / reviewers in the wild / expert
Mahfooz Alam
dblp:187/8355
· DBLP profile ↗
4ranked-venue papers
2as first author
4since 2021 · last 2026
0000-0003-0668-9796ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 3 · 2 first-author · 3 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | LSDMA: Levelized security driven deadline constrained multiple workflow allocation model in cloud computing
Mahfooz Alam, Suhel Mustajab, Mohammad Sajid |
Future Gener. Comput. Syst. | 1 |
| 2026 | A comprehensive review of product recommendation systems using deep learning techniques
Ritu Rajal, Mahfooz Alam |
Knowl. Inf. Syst. | 4 |
| 2024 | Security challenges for workflow allocation model in cloud computing environment: a comprehensive survey, framework, taxonomy, open issues, and future directions
Mahfooz Alam, Suhel Mustajab |
J. Supercomput. | 1 |
| 2022 | A deadline aware load balancing strategy for cloud computingabstractAbstract The load balancing (LB) may be used at different levels to reduce overhead for the decision‐making process. In the past decade, cloud computing has drawn a lot of attention from both the academic and commercial communities to get demanded resources (machines, platforms, data, storage, software, and so forth) as a service on rent economically. Generally, a situation may arise when requests are not meeting their deadlines and the cloud provider wants to finish the running application in minimum time. In this article, a receiver initiated deadline aware LB strategy (RDLBS2) has been proposed which attempts the migration of incoming cloudlets to appropriate virtual machines (VMs) where the deadlines of the cloudlets are met to optimize the turnaround time by exploiting the remaining processing capacities of VMs. A simulation study has been carried out by using Cloud‐Sim as a simulator. A sensitivity analysis has been presented to analyze the effects on performance parameters by varying the number of cloudlets and the number of VMs while keeping the remaining input parameters fixed. The experimental evaluation and analysis suggest that RDLBS2 performs significantly better than its peers on objective parameters almost in all cases under study. Raza Abbas Haidri, Mahfooz Alam, Mohammad Sajid |
Concurr. Comput. Pract. Exp. | 2 |