[School of Engineering PhD Scholarships] Resource-Efficient Execution of Data Centre-Grade Workloads with Lightweight Operating Systems on Emerging Capability Hardware

About the project

About the Project Modern data-centre workloads (e.g., agentic AI) demand strong security guarantees and high performance, which come today at a high resource cost: data-centre hardware and software infrastructures exhibit significant memory footprints and use large amounts of CPU cycles and energy. This trade-off is untenable: the environmental impact of computing is rising, energy and hardware costs are climbing, and the industry urgently needs computer systems that are more frugal without sacrificing security and performance. The goal of this PhD is to design and implement a lightweight operating system (OS) capable of running modern data-centre workloads on emerging minimal hardware. The solution should 1) cut resource consumption relative to traditional approaches; 2) improve security by strengthening isolation guarantees; 3) match or exceed the performance of conventional solutions; and 4) remain transparently compatible with existing workloads. Minimal, MMU-less (without a Memory Management Unit) hardware is attractive for resource efficiency and performance. Although historically confined to small embedded devices, such hardware has recently gained capability-based architectural extensions that remove the security barriers previously excluding it from running data-centre workloads. However, combining capabilities with the absence of an MMU introduces significant compatibility challenges: the OS's isolation model and memory management must be reworked to recover key functionalities normally provided by MMU hardware. The project should extend NoMMU Linux by bringing back these crucial functionalities in the form of three contributions: 1. Process duplication. Building on our prior work enabling fast, lightweight duplication within a single virtual address space, we will construct a NoMMU Linux-based OS that leverages the target hardware's capability features to restore transparent copy-on-write process duplication within a single physical address space. 2. Memory allocation and compaction. Without an MMU, fragmentation is expected to be a major obstacle to efficient memory use. We will design allocation and compaction algorithms exploiting capability hardware's pointer-tracking to safely relocate data within a physical address space and optimise memory consumption. 3. Fine-grained swapping. We will develop a capability-based swapping mechanism operating at byte-level granularity, maximising memory and CPU-cycle savings relative to traditional page-granular approaches. Each contribution should be evaluated against a conventional MMU-based Linux baseline using real data-centre and agentic AI benchmarks, assessing performance, resource savings, security (isolation and attack surface), and compatibility with unmodified applications. This is systems research with a direct path to industry impact: the project is carried out in collaboration with Unikraft Incorporated, a cloud industry leader in resource-efficient data-centre infrastructure, and prototype MMU-less capability hardware is already available, so work can start immediately. We are looking for a highly motivated candidate with: - A strong background in operating systems, computer architecture, or systems programming (C and/or Rust). - Experience with, or strong interest in, the Linux kernel, low-level memory management, or hardware/software co-design. - Curiosity about emerging hardware security features (e.g., capability architectures such as CHERI) and a willingness to work close to the hardware. - Good analytical and experimental skills, and the ability to communicate research clearly. Prior experience with kernel development, embedded systems, or hardware security is a strong plus, but not required. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships . If you don’t already have an applicant account, please follow the instructions here. . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility : The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in a discipline directly relevant to the PhD Computer Science or Computer Engineering (or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in a discipline directly relevant to the PhD Computer Science or Computer Engineering (or international equivalent). This project will remain open until filled. If your application is submitted by 1 st November 2026, you can expect a decision by 18 th December 2026. If your application is submitted by 15 th January 2027, you can expect a decision by 30 th March 2027. Self or externally funded students can also be considered for this project. FSESoE

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