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1. Tolerating Slowdowns in Replicated State Machines using Copilots

   Khiem Ngo, Siddhartha Sen (November 2020, 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’20))

   null (Ed.)

   Replicated state machines are linearizable, fault-tolerant groups of replicas that are coordinated using a consensus algorithm. Copilot replication is the first 1-slowdown-tolerant consensus protocol: it delivers normal latency despite the slowdown of any 1 replica. Copilot uses two distinguished replicas—the pilot and copilot—to proactively add redundancy to all stages of processing a client’s command. Copilot uses dependencies and deduplication to resolve potentially differing orderings proposed by the pilots. To avoid dependencies leading to either pilot being able to slow down the group, Copilot uses fast takeovers that allow a fast pilot to complete the ongoing work of a slow pilot. Copilot includes two optimizations—ping-pong batching and null dependency elimination—that improve its performance when there are 0 and 1 slow pilots respectively. Our evaluation of Copilot shows its performance is lower but competitive with Multi-Paxos and EPaxos when no replicas are slow. When a replica is slow, Copilot is the only protocol that avoids high latencies. 

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2. FT-VMP: Fault-Tolerant Virtual Machine Placement in Cloud Data Centers

   https://doi.org/10.1109/ICCCN49398.2020.9209676  

   Gonzalez, Christopher; Tang, Bin (August 2020, International Conference on Computer Communications and Networks (ICCCN 2020))

   Virtual machine (VM) replication is an effective technique in cloud data centers to achieve fault-tolerance, load-balance, and quick-responsiveness to user requests. In this paper we study a new fault-tolerant VM placement problem referred to as FT-VMP. Given that different VM has different fault-tolerance requirement (i.e., different VM requires different number of replica copies) and compatibility requirement (i.e., some VMs and their replicas cannot be placed into some physical machines (PMs) due to software or platform incompatibility), FT-VMP studies how to place VM replica copies inside cloud data centers in order to minimize the number of PMs storing VM replicas, under the constraints that i) for fault-tolerant purpose, replica copies of the same VM cannot be placed inside the same PM and ii) each PM has a limited amount of storage capacity. We first prove that FT-VMP is NP-hard. We then design an integer linear programming (ILP)-based algorithm to solve it optimally. As ILP takes time to compute thus is not suitable for large scale cloud data centers, we design a suite of efficient and scalable heuristic fault-tolerant VM placement algorithms. We show that a) ILP-based algorithm outperforms the state-of-the-art VM replica placement in a wide range of network dynamics and b) that all our fault-tolerant VM placement algorithms are able to turn off significant number of PMs to save energy in cloud data centers. In particular, we show that our algorithms can consolidate (i.e., turn off) around 100 PMs in a small data center of 256 PMs and 700 PMs in a large data center of 1028PMs. 

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3. Grove: a Separation-Logic Library for Verifying Distributed Systems

   https://doi.org/10.1145/3600006.3613172  

   Sharma, Upamanyu; Jung, Ralf; Tassarotti, Joseph; Kaashoek, Frans; Zeldovich, Nickolai (October 2023, ACM)

   Grove is a concurrent separation logic library for verifying distributed systems. Grove is the first to handle time-based leases, including their interaction with reconfiguration, crash recovery, thread-level concurrency, and unreliable networks. This paper uses Grove to verify several distributed system components written in Go, including GroveKV, a realistic distributed multi-threaded key-value store. GroveKV supports reconfiguration, primary/backup replication, and crash recovery, and uses leases to execute read-only requests on any replica. GroveKV achieves high performance (67-73% of Redis on a single core), scales with more cores and more backup replicas (achieving about 2× the throughput when going from 1 to 3 servers), and can safely execute reads while reconfiguring. 

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4. Rolis: a software approach to efficiently replicating multi-core transactions

   https://doi.org/10.1145/3492321.3519561  

   Shen, Weihai; Khanna, Ansh; Angel, Sebastian; Sen, Siddhartha; Mu, Shuai (March 2022, Proceedings of the European Conference on Computer Systems (EuroSys))

   This paper presents Rolis, a new speedy and fault-tolerant replicated multi-core transactional database system. Rolis's aim is to mask the high cost of replication by ensuring that cores are always doing useful work and not waiting for each other or for other replicas. Rolis achieves this by not mixing the multi-core concurrency control with multi-machine replication, as is traditionally done by systems that use Paxos to replicate the transaction commit protocol. Instead, Rolis takes an "execute-replicate-replay" approach. Rolis first speculatively executes the transaction on the leader machine, and then replicates the per-thread transaction log to the followers using a novel protocol that leverages independent Paxos instances to avoid coordination, while still allowing followers to safely replay. The execution, replication, and replay are carefully designed to be scalable and have nearly zero coordination overhead across cores. Our evaluation shows that Rolis can achieve 1.03M TPS (transactions per second) on the TPC-C workload, using a 3-replica setup where each server has 32 cores. This throughput result is orders of magnitude higher than traditional software approaches we tested (e.g., 2PL), and is comparable to state-of-the-art, fault-tolerant, in-memory storage systems built using kernel bypass and advanced networking hardware, even though Rolis runs on commodity machines. 

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5. Rashnu: Data-Dependent Order-Fairness

   https://doi.org/10.14778/3665844.3665861  

   Nagda, Heena; Singhal, Shubhendra Pal; Amiri, Mohammad Javad; Loo, Boon Thau (May 2024, Proceedings of the VLDB Endowment)

   Distributed data management systems use state Machine Replication (SMR) to provide fault tolerance. The SMR algorithm enables Byzantine Fault-Tolerant (BFT) protocols to guarantee safety and liveness despite the malicious failure of nodes. However, SMR does not prevent the adversarial manipulation of the order of transactions, where the order assigned by a malicious leader differs from the order in that transactions are received from clients. Whileorder-fairnesshas been recently studied in a few protocols, such protocols rely on synchronized clocks, suffer from liveness issues, or incur significant performance overhead. This paper presentsRashnu, a high-performance fair ordering protocol. Rashnu is motivated by the fact that fair ordering among two transactions is needed only when both transactions access a shared resource. Based on this observation, we define the notion ofdata-dependent order fairnesswhere replicas capture only the order of data-dependent transactions and the leader uses these orders to propose a dependency graph that represents fair ordering among transactions. Replicas then execute transactions using the dependency graph, resulting in the parallel execution of independent transactions. We implemented a prototype of Rashnu where our experimental evaluation reveals the low overhead of providing order-fairness in Rashnu. 

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