BigData’24

source/_data/SymbioticLab.bib

@@ -1921,3 +1921,18 @@ @InProceedings{openinfra:hotinfra24
     Critical infrastructures like datacenters, power grids, and water systems are interdependent, forming complex "infrastructure nexuses" that require co-optimization for efficiency, resilience, and sustainability. We present OpenInfra, a co-simulation framework designed to model these interdependencies by integrating domain-specific simulators for datacenters, power grids, and cooling systems but focusing on stitching them together for end-to-end experimentation. OpenInfra enables seamless integration of diverse simulators and flexible configuration of infrastructure interactions. Our evaluation demonstrates its ability to simulate large-scale infrastructure dynamics, including 7,392 servers over 100+ hours.
   }
 }
+
+@InProceedings{infa-finops:bigdata24,
+  author    = {Atam Prakash Agrawal and Anant Mittal and Shivangi Srivastava and Michael Brevard and Valentin Moskovich and Mosharaf Chowdhury},
+  title     = {{INFA-FinOps} for Cloud Data Integration},
+  booktitle = {BigData},
+  year          = {2024},
+  month         = {December},
+  publist_confkey = {BigData'24},
+  publist_link = {paper || infa-finops-bigdata24.pdf},
+  publist_abstract = {
+Over the past decade, businesses have migrated to the cloud for its simplicity, elasticity, and resilience. Cloud ecosystems offer a variety of computing and storage options, enabling customers to choose configurations that maximize productivity. However, determining the right configuration to minimize cost while maximizing performance is challenging, as workloads vary and cloud offerings constantly evolve. Many businesses are overwhelmed with choice overload and often end up making suboptimal choices that lead to inflated cloud spending and/or poor performance.
+
+In this paper, we describe INFA-FinOps, an automated system that helps Informatica customers strike a balance between cost efficiency and meeting SLAs for Informatica Advanced Data Integration (aka CDI-E) workloads. We first describe common workload patterns observed in CDI-E customers and show how INFA-FinOps selects optimal cloud resources and configurations for each workload, adjusting them as workloads and cloud ecosystems change. It also makes recommendations for actions that require user review or input. Finally, we present performance benchmarks on various enterprise use cases and conclude with lessons learned and potential future enhancements.
+}
+}

source/publications/index.md

@@ -255,6 +255,14 @@ venues:
       date: 2022-09-05
       url: https://vldb.org/2022/
       acceptance: 36.67%
+  BigData:
+    category: Conferences
+    occurrences:
+    - key: BigData'24
+      name: 2024 IEEE International Conference on Big Data
+      date: 2024-12-15
+      url: http://bigdataieee.org/BigData2024/
+      acceptance: 35.19%
   APNet:
     category: Workshops
     occurrences: