Since our pioneering work on Infiniswap that attempted to make memory disaggregation practical, there has been quite a few proposals to use different application-level interfaces to remote memory over RDMA. A common issue faced by all these approaches is the high overhead of existing kernel data paths whether they use the swapping … Continue Reading ››
Tag Archives: Disaggregation
Received VMware Early Career Faculty Award!
A few weeks ago, I received a cold email from VMware Research's Irina Calciu with this great news! The award is to support our ongoing research on memory disaggregation. VMware is doing some cool work in this space as well, and I look forward to collaborating with them in near future.
Received NSF CAREER Award. Thanks NSF!
The overarching goal of the proposal is to take a holistic view to make memory disaggregation practical by addressing challenges in the end host, inside the network, and throughout the entire cluster.
Thanks NSF for supporting the proposal and Deep Medhi for championing it. I'd like to also thank … Continue Reading ››
DSLR Accepted to Appear at SIGMOD’2018
High-throughput, low-latency lock managers are useful for building a variety of distributed applications. A key tradeoff in this context can be expressed in terms of the amount of knowledge available to the lock manager. On the one hand, a decentralized lock manager can increase throughput by parallelization, but it can starve certain categories of applications. … Continue Reading ››
Infiniswap in USENIX ;login: and Elsewhere
Since our first open-source release of Infiniswap over the summer, we have seen growing interest with many follow-ups within our group and outside.
Here is a quick summary of selected writeups on Infiniswap:
- USENIX ;login: Decentralized Memory Disaggregation Over Low-Latency Networks
- The Morning Paper: Efficient memory disaggregation with Infiniswap
- University of Michigan News … Continue Reading ››
Received Two Alibaba Innovation Research Grants
More resources for following up on our recent memory disaggregation and erasure coding works! One of the awards is a collaboration with Harsha Madhyastha. Looking forward to working with Alibaba.
In 2017, many proposals are received by AIR (Alibaba Innovation Research), which are from 99 universities and institutes (domestic 54; overseas 45) in 13 countries … Continue Reading ››
Infiniswap Released on GitHub
Today we are glad to announce the first open-source release of Infiniswap, the first practical, large-scale memory disaggregation system for cloud and HPC clusters.
Infiniswap is an efficient memory disaggregation system designed specifically for clusters with fast RDMA networks. It opportunistically harvests and transparently exposes unused cluster memory to unmodified applications by dividing the … Continue Reading ››
FaiRDMA Accepted to Appear at KBNets’2017
As cloud providers deploy RDMA in their datacenters and developers rewrite/update their applications to use RDMA primitives, a key question remains open: what will happen when multiple RDMA-enabled applications must share the network? Surprisingly, this simple question does not yet have a conclusive answer. This is because existing work focus primarily on improving individual application's … Continue Reading ››
Infiniswap Accepted to Appear at NSDI’2017
Update: Camera-ready version is available here. Infiniswap code is now on GitHub!
As networks become faster, the difference between remote and local resources is blurring everyday. How can we take advantage of these blurred lines? This is the key observation behind resource disaggregation and, to some extent, rack-scale computing. In this paper, we take our … Continue Reading ››
TWO NSF Proposals as the Lead PI Awarded. Thanks NSF!
The first one is on rack-scale computing using RDMA-enabled networks with Barzan Mozafari at the University of Michigan, and the second is on theoretical and systems implications of long-term fairness in cluster computing with Zhenhua Liu (Stony Brook University).
Thanks NSF!
Combined with the recent awards on geo-distributed analytics from NSF and Continue Reading ››