Nathan Pemberton

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2022-86

May 13, 2022

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-86.pdf

Datacenters have grown beyond a simple collection of independent computers. They are now a complex and interconnected ecosystem of heterogeneous hardware and software services: a warehouse-scale computer. These computers are wildly expensive to provision and operate, yet we struggle to effectively utilize them. It is not uncommon to have half of allocated resources unused, while other resources cannot be allocated at all. Resource needs vary widely, both between jobs, and even over time within a single job. When we aggregate resources into fixed “slots” (i.e., servers), we take away the flexibility needed to accommodate these varying needs. I propose a different approach: resource disaggregation. Rather than requiring the system to fit jobs into fixed-sized servers, we make any resource in the system available to any job (physical disaggregation). Rather than requiring jobs to allocate all their resources up-front, we allow them to allocate resources only when they actually need them (logical disaggregation). I argue that unlocking the full potential of physical disaggregation requires moving logical interfaces to a fundamentally disaggregated paradigm. Likewise, logically disaggregated systems can provide some benefit on today’s hardware, but only reach their full potential when co-designed with physically disaggregated hardware. In this dissertation, I present tools and methodologies I developed to support that hardware/software co-design. I then describe how I used these tools to implement a simple hardware accelerator that works with the operating system to improve the performance of physically disaggregated memory. I evaluate it with end-to-end benchmarks in RTL simulation and find that it reduces the latency of remote memory access by 2.2x and improves end-to-end performance by 20% over a software-only approach. Next, I show how I extended the logically disaggregated serverless programming model to heterogeneous compute resources. My prototype achieves 50x better performance with fewer resources than today’s aggregated approaches. Together, these techniques form a vision of a serverless datacenter that unlocks the promise of pay-per-use and rapid innovation that warehouse-scale computers should provide.

Advisors: Randy H. Katz and Joseph Gonzalez

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BibTeX citation:

@phdthesis{Pemberton:EECS-2022-86,
    Author= {Pemberton, Nathan},
    Title= {The Serverless Datacenter: Hardware and Software Techniques for Resource Disaggregation},
    School= {EECS Department, University of California, Berkeley},
    Year= {2022},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-86.html},
    Number= {UCB/EECS-2022-86},
    Abstract= {Datacenters have grown beyond a simple collection of independent computers. They are now a complex and interconnected ecosystem of heterogeneous hardware and software services: a warehouse-scale computer. These computers are wildly expensive to provision and operate, yet we struggle to effectively utilize them. It is not uncommon to have half of allocated resources unused, while other resources cannot be allocated at all. Resource needs vary widely, both between jobs, and even over time within a single job. When we aggregate resources into fixed “slots” (i.e., servers), we take away the flexibility needed to accommodate these varying needs. I propose a different approach: resource disaggregation. Rather than requiring the system to fit jobs into fixed-sized servers, we make any resource in the system available to any job (physical disaggregation). Rather than requiring jobs to allocate all their resources up-front, we allow them to allocate resources only when they actually need them (logical disaggregation). I argue that unlocking the full potential of physical disaggregation requires moving logical interfaces to a fundamentally disaggregated paradigm. Likewise, logically disaggregated systems can provide some benefit on today’s hardware, but only reach their full potential when co-designed with physically disaggregated hardware. In this dissertation, I present tools and methodologies I developed to support that hardware/software co-design. I then describe how I used these tools to implement a simple hardware accelerator that works with the operating system to improve the performance of physically disaggregated memory. I evaluate it with end-to-end benchmarks in RTL simulation and find that it reduces the latency of remote memory access by 2.2x and improves end-to-end performance by 20% over a software-only approach. Next, I show how I extended the logically disaggregated serverless programming model to heterogeneous compute resources. My prototype achieves 50x better performance with fewer resources than today’s aggregated approaches. Together, these techniques form a vision of a serverless datacenter that unlocks the promise of pay-per-use and rapid innovation that warehouse-scale computers should provide.},
}

EndNote citation:

%0 Thesis
%A Pemberton, Nathan 
%T The Serverless Datacenter: Hardware and Software Techniques for Resource Disaggregation
%I EECS Department, University of California, Berkeley
%D 2022
%8 May 13
%@ UCB/EECS-2022-86
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-86.html
%F Pemberton:EECS-2022-86