Rajesh Daruvuri
Rajesh Daruvuri is a seasoned Cloud Solution Consultant at Google, specializing in enterprise data solutions, AI, and business intelligence. With 15 years of experience, Rajesh brings a wealth of knowledge in advanced cloud computing, data management, and analytics. He is driven by a passion for creating innovative AI products that deliver exceptional user experiences and solve real-world problems.
Rajesh's expertise spans a wide range of technologies, including Google Cloud AI, Looker, BigQuery, Databricks, Azure Synapse, Snowflake, and Microsoft BI tools. He is adept at working with ETL processes, data warehousing, and analytics platforms like Power BI and Tableau. His recent focus has been on leveraging the power of Looker to help organizations derive actionable insights from their data. He has a proven track record of enhancing Looker's performance, training Looker admin teams, and ensuring seamless integration with other data platforms like BigQuery, Databricks, BigLake, and GenAI.
Rajesh is a results-oriented professional with a strong focus on building win-win partnerships and achieving business outcomes. He is eager to contribute his expertise to the academic community and foster the next generation of data and AI professionals.
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Cloud computing architectures are more scalable and economical which is the main reason that has contributed to its popularity. However, they bring their own set of challenges when it comes to workload scheduling and resource utilization because virtual machines (VM) and applications have to share different types of resources like servers, storage, etc. Historically, other strategies for workload balancing and resource management include manual configuration or simplistic heuristics that do not provide effective optimizations of resource usage and performance. In this technical brief, we propose an approach built on the use of unsupervised learning techniques to detect usage patterns perceptively and improve resource utilization, which corresponds to both optimal performance and automatically balanced workload among VMs. We are making use of clustering algorithms to cluster similar workloads and then resource allocation for each group based on demand. The point of this step is to use the resources more effectively so we do not run into resource exhaustion. We also integrate anomaly detection methods within our system for identifying and handling abnormal behavior by both monitoring and placing resources. We experiment with region traces from production workloads to demonstrate the benefits of our approach, showing marked improvements in workload balancing and resource utilization over current practices.
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