• Modeling Paxos Performance in Wide Area – Part 3

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    Earlier I looked at modeling paxos performance in local networks, however nowadays people (companies) use paxos and its flavors in the wide area as well. Take Google Spanner and CockroachDB as an example. I was naturally curious to expand my performance model into wide area networks as well. Since our lab worked on WAN coordination…

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  • Modeling Paxos Performance – Part 2

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    In the previous posts I started to explore node-scalability of paxos-style protocols. In this post I will look at processing overheads that I estimate with the help of a queue or a processing pipeline. I show how these overheads cap the performance and affect the latency at different cluster loads. I look at the scalability for…

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  • Paxos Performance Modeling – Part 1.5

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    This post is a quick update/conclusion to the part 1. So, does the network variations make any impact at all? In the earlier simulation I showed some small performance degradation going from 3 to 5 nodes. The reality is that for paxos, network behavior makes very little difference on scalability, and in some cases no difference at…

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  • Do not Blame (only) Network for Your Paxos Scalability Issues. (PPM Part 1)

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    In the past few months our lab has been doing a lot of work with different flavors of paxos consensus algorithm. Paxos and its numerous flavors are widely used in today’s cloud infrastructure. Distributed systems rely on it for many different tasks to ensure safe operation. For instance, coordination services use some consensus protocol flavor…

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  • Trace Synchronization with HLC

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    Event logging or tracing is one of the most common techniques for collecting data about the software execution. For simple application running on the same machine, a trace of events timestamped with the machine’s hardware clock is typically sufficient. When the system grows and becomes distributed over multiple nodes, each node is going to produce…

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  • One Page Summary: Flease – Lease Coordination without a Lock Server

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    This paper talks about a decentralized lease management solution. In the past, many lock/lease services have been centralized, placing a single authority to manage all locks in the system. Google’s Chubby, Apache ZooKeeper, etcd, and others rely on a centralized approach and backed by some flavor of a consensus algorithm for fault-tolerance. According to Flease authors,…

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  • One Page Summary: “milliScope: a Fine-Grained Monitoring Framework for Performance Debugging of n-Tier Web Services”

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    Authors of the ICDCS2017 milliScope paper attack an interesting monitoring problem for distributed systems: detecting and determining a cause of short-lived events in the system. In particular, they address the issue of identifying very short bottlenecks (VSBs) in distributed web services. VSBs manifest themselves as performance degradation of a small number of requests, however they…

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  • Sonification of Distributed Systems with RQL

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    In the past, I have discussed sonification as a mean of representing monitoring data. Aside from some silly and toy examples, sonifications can be used for serious applications. In many monitoring cases, the presence of some phenomena is more important than the details about it. In such situations, simple sonification is a perfect way to…

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  • Retroscoping Zookeeper Staleness

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    ZooKeeper is a popular coordination service used as part of many large scale distributed systems. ZooKeeper provides a file-system inspired abstraction to the users on top of its replicated key-value store. Like other Paxos-inspired protocols, ZooKeeper is typically deployed on at least 3 nodes, and can tolerate F node failure for a cluster of size…

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  • Why Government IT is Expensive and Archaic

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    Disclaimer: I do not work for the government, and my rant below is based on my very limited exposure to how IT works at the US government setting. Why Government IT is Expensive and Archaic? I think, this can be a very long discussion, but I do have a quick answer:  standards imposed by government…

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Aleksey CharapkoI am an assistant professor of computer science at the University of New Hampshire. My research interests lie in distributed systems, distributed consensus, fault tolerance, reliability, and scalability.
X (twitter)@AlekseyCharapko
emailaleksey.charapko@unh.edu

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