Software deployments must be carefully provisioned to meet their performance requirements without wasting resources. Most resource provisioning solutions today employ predictions to estimate future demand and provision accordingly. However, naively employing predictions can negate its benefits. For example, provisioning resources based only on the mean of predictions can result in severe performance violations due to uncertainty in the predictor. On the other hand, while additionally provisioning for 2 standard deviations can eliminate performance violations, it can substantially increase resource wastage. The goal of this project is to develop and leverage error models to fully realize the potential of predictors.
The key intellectual contribution of this project is to bridge the gap between predictors and resource
provisioning solutions by investigating the prediction error model. This will be accomplished via three main thrusts: (i) constructing error models that capture the structure of prediction errors, including correlations and prediction quality over time; (ii) developing an algorithmic framework to incorporate the prediction error models and account for switching costs and penalty functions; and (iii) designing systems to exploit the new prediction error-aware algorithms, including multi-resource provisioning and resource placement solutions. The solutions will be experimentally evaluated using available application traces.
Collaborators:
Publications:
- FnSched: An Efficient Scheduler for Serverless Functions
Amoghavarsha Suresh, Anshul Gandhi
WoSC 2019 [pdf]
- User-Centric Interference-Aware Load Balancing for Cloud-Deployed Applications
Seyyed Ahmad Javadi, Anshul Gandhi
Transactions on Cloud Computing [link]
- Scavenger: A black-box batch workload resource manager for improving utilization in cloud environments
Seyyed Ahmad Javadi, Muhammad Wajahat, Amoghavarsha Suresh, Anshul Gandhi
SOCC 2019 [pdf]
- A Graybox Approach to Rehoming Service Chains.
Muhammad Wajahat, Bharath Balasubramanian, Anshul Gandhi, Gueyoung Jung, Shankaranarayanan Puzhavakath Narayanan
MASCOTS 2018 [pdf]
- ElMem: Towards an Elastic Memcached System.
Ubaid Ullah Hafeez, Muhammad Wajahat, Anshul Gandhi
ICDCS 2018 [pdf] (Best Student Paper Award)
- Using predictions in online optimization: looking forward with an eye on the past.
Niangjun Chen, Joshua Comden, Zhenhua Liu, Anshul Gandhi, Adam Wierman
SIGMETRICS 2016 [pdf]
Students:
Sponsors: