Merge pull request #289 from mosharaf/develop

source/_data/SymbioticLab.bib

@@ -1152,7 +1152,8 @@ @PhDThesis{fanlai:dissertation
 
   publist_confkey = {dissertation},
   publist_badge = {Dennis M. Ritchie Award Honorable Mention},
-  publist_badge = {David J. Kuck Dissertation Prize},
+  publist_badge = {Kuck Dissertation Prize},
+  publist_badge = {Towner Prize},
   publist_link = {paper || fanlai-dissertation.pdf},
   Abstract                 = {Skyrocketing data volumes, growing hardware capabilities, and the revolution in machine learning (ML) theory have collectively driven the latest leap forward in ML. Despite our hope to realize the next leap with new hardware and a broader range of data, ML development is reaching scaling limits in both realms. First, the exponential surge in ML workload volumes and their complexity far outstrip hardware improvements, leading to hardware resource demands surpassing the sustainable growth of capacity. Second, the mounting volumes of edge data, increasing awareness of user privacy, and tightening government regulations render conventional ML practices, which centralize all data into the cloud, increasingly unsustainable due to escalating costs and scrutiny.
 
@@ -1920,3 +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/funding/index.md

@@ -46,7 +46,6 @@ Any opinions, findings, and conclusions of our research projects are those of th
 
 ![NSF](images/nsf.png)
 ![VMware](images/vmware.png)
-![Salesforce](images/salesforce.png)
 ![Mozilla](images/mozilla.png)
 ![Ford](images/ford.png)
 ![Cisco](images/cisco.png)
@@ -57,6 +56,7 @@ Any opinions, findings, and conclusions of our research projects are those of th
 
 <div class='flex-row'>
 
+![Salesforce](images/salesforce.png)
 ![Meta](images/meta.png)
 ![Nvidia](images/nvidia.png)
 ![KLA](images/kla.png)

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: