Reading Group
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Reading Group Paper List. Papers ##51-60.
With just four more papers to go in the DistSys Reading Group’s current batch, it is time to get the next set going. This round, we will have 10 papers that should last till the end of the spring semester. Our last batch was all about OSDI’20 papers, and this time around we will mix…
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Reading Group. Pegasus: Tolerating Skewed Workloads in Distributed Storage with In-Network Coherence Directories
Hard to imagine, but the reading group just completed the 45th session. We discussed “Pegasus: Tolerating Skewed Workloads in Distributed Storage with In-Network Coherence Directories,” again from OSDI’20. Pegasus is one of these systems that are very obvious in the hindsight. However, this “obviousness” is deceptive — Dan Ports, one of the authors behind the…
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Reading Group. Performance-Optimal Read-Only Transactions
Last meeting we looked at “Performance-Optimal Read-Only Transactions” from OSDI’20. This paper covers important topics of transactional reads in database/data-management systems. In particular, the paper discusses “one-shot” read-only transactions that complete in 1 network round-trip-time (RTT) without blocking and bloated and expensive messages. If this sounds too good to be true, it is. Before presenting…
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Reading Group. Microsecond Consensus for Microsecond Applications
Our 43rd reading group paper was about an extremely low-latency consensus using RDMA: “Microsecond Consensus for Microsecond Applications.” The motivation is pretty compelling — if you have a fast application, then you need fast replication to make your app reliable without holding it back. How fast are we talking here? Authors go for ~1 microsecond…
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Reading Group. Cobra: Making Transactional Key-Value Stores Verifiably Serializable.
This Wednesday, we were talking about serializability checking of production databases. In particular, we looked at the recent OSDI’20 paper: “Cobra: Making Transactional Key-Value Stores Verifiably Serializable.” The paper explores the problem of verifying serializability in a black-box production system from a client point of view. This makes sense as serializability is an operational, client-observable…
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Reading Group. A large scale analysis of hundreds of in-memory cache clusters at Twitter.
In the 41st distributed systems reading group meeting, we have looked at in-memory caches through the lens of yet another OSDI20 paper: “A large scale analysis of hundreds of in-memory cache clusters at Twitter.” This paper explores various cache usages at Twitter and distills the findings into a digestible set of figures. I found the…
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Reading Group. Virtual Consensus in Delos
We are continuing through the OSDI 2020 paper list in our reading group. This time we have discussed “Virtual Consensus in Delos,” a consensus paper (Delos is yet another greek island to continue the consensus naming tradition). Delos relies on the log abstraction to keep track of all commands/operations and their order. Traditionally, some consensus…
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Reading Group. hXDP: Efficient Software Packet Processing on FPGA NICs
Last reading group meeting we have discussed “hXDP: Efficient Software Packet Processing on FPGA NICs.” This paper talks about using FPGA NICs to offload some CPU cycles doing certain routine packet processing tasks. In particular, the paper implements XDP purely in FPGA and achieves a performance similar to that of a single x86 CPU core.…
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Reading Group. Toward a Generic Fault Tolerance Technique for Partial Network Partitioning
Short Summary We have resumed the distributed systems reading group after a short holiday break. Yesterday we discussed the “Toward a Generic Fault Tolerance Technique for Partial Network Partitioning” paper from OSDI 2020. The paper studies a particular type of network partitioning – partial network partitioning. Normally, we expect that every node can reach every…
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Reading Group. Near-Optimal Latency Versus Cost Tradeoffs in Geo-Distributed Storage
Short Summary Yesterday we discussed Pando, a geo-replication system achieving near-optimal latency-cost tradeoff in storage systems. Pando uses large Flexible Paxos deployments and erasure coding to do its magic. Pando relies on having many storage sites to locate sites closer to users. It then uses Flexible Paxos to optimize read and write quorums to have…
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