EDBT 2026 Demo / reviewers in the wild / expert
A. K. M. Fazla Mehrab
dblp:243/3641
· DBLP profile ↗
3ranked-venue papers
1as first author
2since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 3 · 1 first-author · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Cloud and datacenter computing · 61% Embedded and real-time systems · 30% High-performance computing · 9% | |
| Software engineering, system software, and programming languages
1 paper |
Operating systems · 100% | |
| Network and information security
1 paper |
Systems and software security · 100% |
Topics — the 6 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Operating systems › operating system design
unikernel |
0.6 | 1 | 2022 | Kite: lightweight critical service domains · EuroSys 2022 |
Operating systems
virtualization |
0.6 | 1 | 2022 | Kite: lightweight critical service domains · EuroSys 2022 |
Embedded and real-time systems
energy-efficient embedded systems |
0.4 | 1 | 2019 | HEXO: Offloading HPC Compute-Intensive Workloads on Low-Cost, Low-Power Embedded Systems · HPDC 2019 |
Cloud and datacenter computing
resource management |
0.4 | 1 | 2019 | HEXO: Offloading HPC Compute-Intensive Workloads on Low-Cost, Low-Power Embedded Systems · HPDC 2019 |
Cloud and datacenter computing › virtualization › virtual machine management
server consolidation |
0.4 | 1 | 2019 | HEXO: Offloading HPC Compute-Intensive Workloads on Low-Cost, Low-Power Embedded Systems · HPDC 2019 |
Systems and software security
isolation |
0.2 | 1 | 2022 | Kite: lightweight critical service domains · EuroSys 2022 |
Methods — techniques the papers use, named apart from their topics
device driver borrowing · 1.1unikernel · 0.4offloading · 0.4heterogeneous-ISA migration · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | HEXO: Offloading Long-Running Compute- and Memory-Intensive Workloads on Low-Cost, Low-Power Embedded SystemsabstractOS-capable embedded systems exhibiting a very low power consumption are available at an extremely low price point. It makes them highly compelling in a datacenter context. We show that sharing long-running, compute-intensive datacenter workloads between a server machine and one or a few connected embedded boards of negligible cost and power consumption can yield significant performance and energy benefits. Our approach, named Heterogeneous EXecution Offloading (HEXO), selectively offloads Virtual Machines (VMs) from server-class machines to embedded boards. Our design tackles several challenges. We address the Instruction Set Architecture (ISA) difference between typical servers (x86) and embedded systems (ARM) through hypervisor and guest OS-level support for heterogeneous-ISA runtime VM migration. We cope with the low amount of resources in embedded systems by using lightweight VMs – unikernels – and by using the server's free RAM as remote memory for embedded boards through a transparent lightweight memory disaggregation mechanism for heterogeneous server-embedded clusters, called Netswap. VMs are offloaded based on an estimation of the slowdown expected from running on a given board. We build a prototype of HEXO and demonstrate significant increases in throughput (up to 67%) and energy efficiency (up to 56%) using benchmarks representative of compute-intensive long-running workloads. Pierre Olivier, A. K. M. Fazla Mehrab, Sandeep Errabelly, Stefan Lankes, Mohamed Lamine Karaoui, Robert Lyerly, Sang-Hoon Kim, Antonio Barbalace, Binoy Ravindran |
IEEE Trans. Cloud Comput. | 2 |
| 2022 | Kite: lightweight critical service domainsabstractConverged multi-level secure (MLS) systems, such as Qubes OS or SecureView, heavily rely on virtualization and service virtual machines (VMs). Traditionally, driver domains - isolated VMs that run device drivers - and daemon VMs use full-blown general-purpose OSs. It seems that specialized lightweight OSs, known as unikernels, would be a better fit for those. Surprisingly, to this day, driver domains can only be built from Linux. We discuss how unikernels can be beneficial in this context - they improve security and isolation, reduce memory overheads, and simplify software configuration and deployment. We specifically propose to use unikernels that borrow device drivers from existing general-purpose OSs. A. K. M. Fazla Mehrab, Ruslan Nikolaev 0001, Binoy Ravindran |
EuroSys | 1 |
| 2019 | HEXO: Offloading HPC Compute-Intensive Workloads on Low-Cost, Low-Power Embedded SystemsabstractOS-capable embedded systems exhibiting a very low power consumption are available at an extremely low price point. It makes them highly compelling in a datacenter context. In this paper we show that sharing long-running, compute-intensive datacenter HPC workloads between a server machine and one or a few connected embedded boards of negligible cost and power consumption can bring significant benefits in terms of consolidation. Our approach, named Heterogeneous EXecution Offloading (HEXO), selectively offloads Virtual Machines (VMs) from server class machines to embedded boards. Our design tackles several challenges. We address the Instruction Set Architecture (ISA) difference between typical servers (x86) and embedded systems (ARM) through hypervisor and guest OS-level support for heterogeneous-ISA runtime VM migration. We cope with the low amount of resources in embedded systems by using lightweight VMs: unikernels. VMs are offloaded based on an estimation of the slowdown expected from running on a given board. We build a prototype of HEXO and demonstrate significant increase in throughput (up to 67%) and energy efficiency (up to 56%) over a set of macro-benchmarks running datacenter compute-intensive jobs. Pierre Olivier, A. K. M. Fazla Mehrab, Stefan Lankes, Mohamed Lamine Karaoui, Robert Lyerly, Binoy Ravindran |
HPDC | 2 |