activity
20202026
most citedCarbon Explorer: A Holistic Approach for Designing Carbon Aware Datacenters

168 citations · 202 across the 11 of their papers we have counts for

collaborators

12 papers

eess.SY2026

Multi-Scale Datacenter Power Modulation

Akshay Sreekumar, Nicolas Christianson, Fiodar Kazhamiaka +1

Cloud datacenters must increasingly modulate power in response to time-varying grid and infrastructure constraints. We study this problem as finite-horizon control of a networked h…

cs.DC2026

Slasher: Power Flexibility for Cloud Datacenters

Liuzixuan Lin, Fiodar Kazhamiaka, Alok Gautam Kumbhare +10

Datacenters consume many megawatts of power, and regularly encounter scenarios that require modulating their power draw. These scenarios include datacenter infrastructure failures,…

cs.AI2026

Natural Language Query to Configuration for Retrieval Agents

Melissa Z. Pan, Negar Arabzadeh, Mathew Jacob +3

Modern retrieval agents expose many configuration choices -- LLM, retriever, number of documents, number of hops, and synthesis strategy -- each shaping both answer quality and ser…

cs.DC2026

Designing Datacenter Power Delivery Hierarchies for the AI Era

Grant Wilkins, Fiodar Kazhamiaka, Alok Gautam Kumbhare +2

Demand for AI accelerators is rapidly increasing rack power density, with projections approaching 1MW per deployment by 2027. This poses a major challenge for datacenter power deli…

cs.DC2026

From Servers to Sites: Compositional Power Trace Generation of LLM Inference for Infrastructure Planning

Grant Wilkins, Fiodar Kazhamiaka, Ram Rajagopal

Datacenter operators and electrical utilities rely on power traces at different spatiotemporal scales. Operators use fine-grained traces for provisioning, facility management, and…

cs.LG2025★ 5 cited

Energy Use of AI Inference, Efficiency Pathways, and Test-Time Scaling

Felipe Oviedo, Fiodar Kazhamiaka, Esha Choukse +5

As AI inference scales to billions of queries, estimates of per-query energy use are increasingly important for capacity planning, efficiency interventions, and policy. Yet many pu…

cs.AR2025★ 2 cited

Octopus: Enhancing CXL Memory Pods via Sparse Topology

Yuhong Zhong, Fiodar Kazhamiaka, Pantea Zardoshti +4

The Compute Express Link (CXL) interconnect enables compute "pods" that pool memory across servers to reduce cost and improve efficiency. These pods also facilitate pairwise commun…

cs.DC2022★ 168 cited

Carbon Explorer: A Holistic Approach for Designing Carbon Aware Datacenters

Bilge Acun, Benjamin Lee, Fiodar Kazhamiaka +5

Technology companies have been leading the way to a renewable energy transformation, by investing in renewable energy sources to reduce the carbon footprint of their datacenters. I…

cs.DC2021

Solving Large-Scale Granular Resource Allocation Problems Efficiently with POP

Deepak Narayanan, Fiodar Kazhamiaka, Firas Abuzaid +5

Resource allocation problems in many computer systems can be formulated as mathematical optimization problems. However, finding exact solutions to these problems using off-the-shel…

math.OC2021

Allocation of Fungible Resources via a Fast, Scalable Price Discovery Method

Akshay Agrawal, Stephen Boyd, Deepak Narayanan +2

We consider the problem of assigning or allocating resources to a set of jobs. We consider the case when the resources are fungible, that is, the job can be done with any mix of th…

cs.DC2021★ 1 cited

Don't Give Up on Large Optimization Problems; POP Them!

Deepak Narayanan, Fiodar Kazhamiaka, Firas Abuzaid +2

Resource allocation problems in many computer systems can be formulated as mathematical optimization problems. However, finding exact solutions to these problems using off-the-shel…

cs.DC2020★ 26 cited

Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads

Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka +2

Specialized accelerators such as GPUs, TPUs, FPGAs, and custom ASICs have been increasingly deployed to train deep learning models. These accelerators exhibit heterogeneous perform…