Performance modeling of cloud applications
(August 2014 - present)
Many online services are now hosted on the cloud. However, cloud adoption is still very limited
when it comes to performance sensitive applications. One of the primary challenges for performance
management of cloud applications is a lack of understanding of how cloud resource allocation
relates to application performance. This is because user deployments are opaque:
cloud service providers cannot control or access a user's workload or application. To make matters worse, the effective capacity of a user's cloud instance can change dynamically due to interference from
other users. As a result, user deployments are plagued with performance issues.
The goal of this project is to develop novel performance models to help users understand the dynamic resource requirements of their applications without extensive benchmarking or instrumentation.
- Modeling and Analysis of Performance under Interference in the Cloud.
Scott Votke, Seyyed Ahmad Javadi, Anshul Gandhi
MASCOTS 2017 [pdf]
- Dynamic Interference-Aware Load Balancing.
Seyyed Ahmad Javadi, Himanshu Rajput, Anshul Gandhi
Poster @ SOCC 2016
- UIE: User-centric Interference Estimation for Cloud Applications
Seyyed Ahmad Javadi, Sagar Mehra, Bharath Kumar Reddy Vangoor, Anshul Gandhi
IC2E 2016 [pdf]
- Autoscaling for Hadoop Clusters
Anshul Gandhi, Parijat Dube, Andrzej Kochut, Li Zhang, Sidhartha Thota
IC2E 2016 [pdf] (also, Poster @ SOCC 2015 and Poster @ ICAC 2015 [pdf])
- HALO: Heterogeneity-Aware Load Balancing
Anshul Gandhi, Xi Zhang, Naman Mittal
MASCOTS 2015 [pdf]
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