The Center for Systems Innovation at Scale (i-Scale) is thrilled to announce the projects selected for the center’s inaugural year of research funding. Five projects proposed by researchers from Boston University (BU) and Northeastern University (NEU) were selected to receive a $75,000 award—totaling over $375,000 in funds—to advance systems research in areas such as security, observability, and storage.
i-Scale began with a request for proposals from affiliated researchers across BU and NEU. Proposals were reviewed by a committee of systems research experts from academia and industry, who selected ten proposals to be presented to i-Scale’s industry advisory board (IAB)—comprised of representatives from each of its industry partner organizations: Cisco, Everpure, G-Research, Lenovo, Red Hat, and SHI. The selected proposals were reviewed and recommended for funding by the IAB at the 2026 i-Scale Kickoff Meeting, hosted at Boston University’s Hariri Institute for Computing with support from the MOC Alliance.
The awarded projects include:
Streaming GNN Inference for Real-Time Anomaly Detection on Microservice Graphs
PI: Vasia Kalavri (BU)
Graph Neural Networks (GNNs) have recently delivered breakthrough performance in several large-scale, real-world applications, such as recommendation, online advertising, and fraud detection. This project aims to investigate how graph-ML advances could be leveraged to enable security monitoring in cloud-native microservice environments.
Read the full project description on the i-Scale Project Page.
Shoring Up the Foundations: Systematically Strengthening UEFI Security
PIs: Manuel Egele (BU) & William Robertson (NEU)
A system’s Unified Extensible Firmware Interface (UEFI) code is executed immediately upon startup. Replacing much of the legacy BIOS functionality, UEFI’s execution model comprises a number of phases, including the Driver Execution Environment (DXE) and Run Time (RT) phases. Bugs and vulnerabilities in UEFI code give rise to powerful security exploits that have the capability to undermine the integrity of any security layers built on top, including secure boot (e.g., CVE-2021-3971 & CVE-2021-3972) and OS-provided security mechanisms (e.g., unprivileged user-space applications).
To shore up the security foundations that UEFI provides in modern systems, we propose the DARE (DXE Attack-surface Reduction and Emulation) project, which comprises the research and development thrusts necessary to improve the security of UEFI firmware along two complementary and synergistic axes – vulnerability prevention and vulnerability detection.
Read the full project description on the i-Scale Project Page.
Data Retention and Deletion Observability for Data Systems
PI: Manos Athanassoulis (BU)
Modern cloud storage is built around immutability: data is written once, compacted periodically, and never updated in place. At scale, this creates three compounding problems: wasted storage capacity, increased write amplification as invalid data is repeatedly recompacted, and degraded read performance as queries must filter growing volumes of stale entries. Most critically, it creates a privacy and compliance debt: deleted data is not persistently purged on any guaranteed timeline, directly conflicting with regulations such as GDPR, CCPA, and a growing body of US state privacy laws that mandate persistent deletion within fixed windows (typically 30–60 days).
This project proposes a QoS-driven deletion stack for cloud storage that treats deletion as a first-class system contract rather than a background side effect.
Read the full project description on the i-Scale Project Page.
Container Side Channels
PI: Ari Trachtenberg (BU)
This project investigates security vulnerabilities arising from shared resources in modern containerized computing environments.
This project systematically quantifies these risks and develops practical, low-overhead mitigations. Specifically, it will:
- Probe high-bandwidth, noise-resilient covert and side-channel attacks.
- Assess their effectiveness across workloads, container platforms, and storage systems.
- Gauge tradeoffs among performance, resource efficiency, and security.
- Engineer deployable mitigations that strengthen isolation while preserving performance.
Read the full project description on the i-Scale Project Page.
Device-Ordered Logs for Scalable High-Performance I/O
PI: Peter Desnoyers (NEU)
High-efficiency approaches to basic data storage abstractions (object, virtual disk, file) to deliver NVMe-level performance to user applications at acceptable capital cost.
Continuation of prior work [Socc25] using light-weight communication for consistency and coordination on top of direct access to single-device backend logs.
Read the full project description on the i-Scale Project Page.
