Observing how well data-intensive systems utilize hardware resources is a crucial preliminary step to improve system performance and reduce hardware waste. To do such observations, one has to collect a lot of monitoring data on hardware behavior through experiments. In our group, we have recently built a framework to aid the management of such monitoring data efficiently, called Resource-Aware …
Supervisors: Pınar Tözün, Ties Robroek
Semester: Fall 2023
Tags: benchmarking, data management, data visualization

State-of-the-art machine learning models are known to be compute- and power-hungry. On the other hand, modern servers come equipped with really powerful CPU-GPU co-processors. Not all machine learning models are able to use all the available hardware resources on such servers. Workload collocation is a mechanism to increase hardware utilization when a single workload is not able to utilize all the …
Supervisors: Pınar Tözün
Semester: Fall 2022
Tags: benchmarking, workload collocation, machine learning

Today, there are many compute- and memory-hungry data-intensive workloads from big data analytics applications to deep learning. These workloads increasingly run on shared hardware resources, which requires building hardware resource managers that can both serve the needs of workloads and utilize hardware well. Predicting the resource utilization of applications can aid such resource managers …
Supervisors: Pınar Tözün, Ehsan Yousefzadeh-Asl-Miandoab
Semester: Fall 2022
Tags: benchmarking, hardware resource consumption estimation, machine learning

NVMe SSDs are not a uniform class of devices. IO software stack is not uniform either. Understanding the performance characteristics of new-generation SSDs and the impact of the IO stack on their performance is crucial while determining how to design data-intensive systems. In this project, we would like to characterize the performance of a range of NVMe SSDs (e.g., Samsung Z-SSD, Intel Optane, …
Supervisors: Pınar Tözün
Semester: Fall 2021
Tags: SSD, benchmarking

Disaggregated storage has gained acceptance in data centers. With disaggregated storage, storage resources are decoupled from compute resources, and made available through fabric. We are particularly interested in storage resources composed of an ARM-based smartNIC, which acts as fabric target as well as storage controller for a collection of SSDs. The performance characteristics of the storage …
Supervisors: Philippe Bonnet
Semester: Fall 2021
Tags: benchmarking, ARM, SoC, fabric, SSD, computational storage

A data science infrastructure orchestrates the execution of widely used machine learning frameworks (e.g., TensorFlow , PyTorch) on a heterogeneous set of processing units (e.g., CPU, GPU, TPU, FPGA) while powering an increasingly diverse and complex range of applications (e.g., fraud detection, healthcare, virtual assistance, automatic driving). Understanding the resource consumption …
Supervisor: Pınar Tözün
Semester: Fall 2021
Tags: benchmarking, hardware resource consumption, deep learning frameworks

Consensus mechanisms for ensuring consistency are some of the most expensive operations in managing large amounts of data. Often, there is a trade off that involves reducing the coordination overhead at the price of accepting possible data loss or inconsistencies. As the demand for more efficient data centers increases, it is important to provide better ways of ensuring consistency without …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Benchmarking, Distributed Systems

Hash-based authentication is an effective way of protecting passwords in software systems. Hashing obscures the original passwords, such that it cannot be recovered in case of a database breach. However, as demonstrated by our paper titled Hash-Based Authentication Revisited in the Age of High-Performance Computers, the practical security depends on which hashing algorithm is used as well as the …
Supervisors: Pınar Tözün, Niclas Hedam
Tags: benchmarking, hashing, security, GPU, hacking, HPC

Blockchains are often used synonymously with crypto-currencies and unspent transaction output (UTXO) data models, but there are emerging blockchain platforms that offer a more general data model and smart contracts that can manipulate this data freely (e.g. Hyperledger Fabric [1]). As such, these platforms resemble in many ways distributed databases, storing a collection of records, organized as …
Supervisors: Zsolt István
Semester: Spring 2021
Tags: Blockchain, Data Management, Benchmarking

SSDs are not a uniform class of devices. SSD landscape is quite diverse now with many new-gen much faster / customizable devices being available. Understand their performance characteristics is crucial while determining what their impact on data systems software stack should be. In this project, we would like to characterize the performance of a broad range of such SSDs (e.g., ZNS, Samsung Z-SSD, …
Supervisors: Pınar Tözün, Philippe Bonnet
Semester: Fall 2020
Tags: SSD, benchmarking