% Shajulin Benedict
% Encoding: utf-8
@InCollection{Prodan2021,
  author = {Shajulin Benedict and Prateek Agrawal and Radu Prodan},
  booktitle = {Communications in Computer and Information Science},
  publisher = {Springer Singapore},
  title = {{Energy Consumption Analysis of R-Based Machine Learning Algorithms for Pandemic Predictions}},
  year = {2021},
  month = jun,
  pages = {192--204},
  volume = {1393},
  abstract = {The push for agile pandemic analytic solutions has attained development-stage software modules of applications instead of functioning as full-fledged production-stage applications – i.e., performance, scalability, and energy-related concerns are not optimized for the underlying computing domains. And while the research continues to support the idea that reducing the energy consumption of algorithms improves the lifetime of battery-operated machines, advisable tools in almost any developer setting, an energy analysis report for R-based analytic programs is indeed a valuable suggestion. This article proposes an energy analysis framework for R-programs that enables data analytic developers, including pandemic-related application developers, to analyze the programs. It reveals an energy analysis report for R programs written to predict the new cases of 215 countries using random forest variants. Experiments were carried out at the IoT cloud research lab and the energy efficiency aspects were discussed in the article. In the experiments, ranger-based prediction program consumed 95.8 J.},
  doi = {10.1007/978-981-16-3660-8_18},
  keywords = {Analysis, Energy consumption, Machine learning, R-program, Tools},
  url = {https://link.springer.com/chapter/10.1007/978-981-16-3660-8_18}
}
@Article{Torre2020,
  author = {Ennio Torre and Juan J. Durillo and Vincenzo de Maio and Prateek Agrawal and Shajulin Benedict and Nishant Saurabh and Radu Prodan},
  journal = {Information and Software Technology},
  title = {{A dynamic evolutionary multi-objective virtual machine placement heuristic for cloud data centers}},
  year = {2020},
  issn = {0950-5849},
  month = {dec},
  pages = {106390},
  volume = {128},
  abstract = {Minimizing the resource wastage reduces the energy cost of operating a data center, but may also lead to a considerably high resource overcommitment affecting the Quality of Service (QoS) of the running applications. The effective tradeoff between resource wastage and overcommitment is a challenging task in virtualized Clouds and depends on the allocation of virtual machines (VMs) to physical resources. We propose in this paper a multi-objective method for dynamic VM placement, which exploits live migration mechanisms to simultaneously optimize the resource wastage, overcommitment ratio and migration energy. Our optimization algorithm uses a novel evolutionary meta-heuristic based on an island population model to approximate the Pareto optimal set of VM placements with good accuracy and diversity. Simulation results using traces collected from a real Google cluster demonstrate that our method outperforms related approaches by reducing the migration energy by up to 57% with a QoS increase below 6%.},
  doi = {10.1016/j.infsof.2020.106390},
  keywords = {VM placement, Multi-objective optimisation, Resource overcommitment, Resource wastage, Live migration, Energy consumption, Pareto optimal set, Genetic algorithm, Data center simulation},
  publisher = {Elsevier BV},
  url = {https://www.sciencedirect.com/science/article/pii/S0950584919302101}
}
@Article{Saurabh2020,
  author = {Nishant Saurabh and Shajulin Benedict and Jorge G. Barbosa and Radu Prodan},
  journal = {Journal of Parallel and Distributed Computing},
  title = {{Expelliarmus: Semantic-centric virtual machine image management in IaaS Clouds}},
  year = {2020},
  issn = {0743-7315},
  month = {dec},
  pages = {107--121},
  volume = {146},
  abstract = {Infrastructure-as-a-service (IaaS) Clouds concurrently accommodate diverse sets of user requests, requiring an efficient strategy for storing and retrieving virtual machine images (VMIs) at a large scale. The VMI storage management requires dealing with multiple VMIs, typically in the magnitude of gigabytes, which entails VMI sprawl issues hindering the elastic resource management and provisioning. Unfortunately, existing techniques to facilitate VMI management overlook VMI semantics (i.e at the level of base image and software packages), with either restricted possibility to identify and extract reusable functionalities or with higher VMI publishing and retrieval overheads. In this paper, we propose Expelliarmus, a novel VMI management system that helps to minimize VMI storage, publishing and retrieval overheads. To achieve this goal, Expelliarmus incorporates three complementary features. First, it models VMIs as semantic graphs to facilitate their similarity computation. Second, it provides a semantically-aware VMI decomposition and base image selection to extract and store non-redundant base image and software packages. Third, it assembles VMIs based on the required software packages upon user request. We evaluate Expelliarmus through a representative set of synthetic Cloud VMIs on a real test-bed. Experimental results show that our semantic-centric approach is able to optimize the repository size by 2.3 - 22  times compared to state-of-the-art systems (e.g. IBM’s Mirage and Hemera) with significant VMI publishing and slight retrieval performance improvement.},
  doi = {10.1016/j.jpdc.2020.08.001},
  keywords = {Theoretical Computer Science, Computer Networks and Communications, Hardware and Architecture, Software, Artificial Intelligence},
  publisher = {Elsevier BV},
  url = {https://www.sciencedirect.com/science/article/pii/S0743731520303415}
}