Optimizing Paxos with batching and pipelining

Paxos is probably the most popular state machine replication protocol. Two optimizations that can greatly improve its performance are batching and pipelining. However, tuning these two optimizations to achieve high-throughput can be challenging, as their effectiveness depends on many parameters like the network latency and bandwidth, the speed of the nodes, and the properties of the application. We address this question by presenting an analytical model of the performance of Paxos, and showing how it can be used to determine configuration values for the batching and pipelining optimizations that result in high-throughput We then validate the model, by presenting the results of experiments where we investigate the interaction of these two optimizations both in LAN and WAN environments, and comparing these results with the prediction from the model. The results confirm the accuracy of the model, with the predicted values being usually very close to the ones that provide the highest performance in the experiments. Furthermore, both the model and the experiments give useful insights into the relative effectiveness of batching and pipelining. They show that although batching by itself provides the largest gains in all scenarios, in most cases combining it with pipelining provides a very significant additional increase in throughput, with this being true not only on high-latency WAN but also in many LAN scenarios. (C) 2012 Elsevier B.V. All rights reserved

Replicating multithreaded services

textFor the last 40 years, the systems community has invested a lot of effort in designing techniques for building fault tolerant distributed systems and services. This effort has produced a massive list of results: the literature describes how to design replication protocols that tolerate a wide range of failures (from simple crashes to malicious "Byzantine" failures) in a wide range of settings (e.g. synchronous or asynchronous communication, with or without stable storage), optimizing various metrics (e.g. number of messages, latency, throughput). These techniques have their roots in ideas, such as the abstraction of State Machine Replication and the Paxos protocol, that were conceived when computing was very different than it is today: computers had a single core; all processing was done using a single thread of control, handling requests sequentially; and a collection of 20 nodes was considered a large distributed system. In the last decade, however, computing has gone through some major paradigm shifts, with the advent of multicore architectures and large cloud infrastructures. This dissertation explains how these profound changes impact the practical usefulness of traditional fault tolerant techniques and proposes new ways to architect these solutions to fit the new paradigms.Computer Science

State Machine Replication:from Analytical Evaluation to High-Performance Paxos

Since their invention more than half a century ago, computers have gone from being just an handful of expensive machines each filling an entire room, to being an integral part of almost every aspect of modern life. Nowadays computers are everywhere: in our planes, in our cars, on our desks, in our home appliances, and even in our pockets. This widespread adoption had a profound impact in our world and in our lives, so much that now we rely on them for many important aspects of everyday life, including work, communication, travel, entertainment, and even managing our money. Given our increased reliance on computers, their continuous and correct operation has become essential for modern society. However, individual computers can fail due to a variety of causes and, if nothing is done about it, these failures can easily lead to a disruption of the service provided by computer system. The field of fault tolerance studies this problem, more precisely, it studies how to enable a computer system to continue operation in spite of the failure of individual components. One of the most popular techniques of achieving fault tolerance is software replication, where a service is replicated on an ensemble of machines (replicas) such that if some of these machines fail, the others will continue providing the service. Software replication is widely used because of its generality (can be applied to most services) and its low cost (can use off-the-shelf hardware). This thesis studies a form of software replication, namely, state machine replication, where the service is modeled as a deterministic state machine whose state transitions consist of the execution of client requests. Although state machine replication was first proposed almost 30 years ago, the proliferation of online services during the last years has led to a renewed interest. Online services must be highly available and for that they frequently rely on state machine replication as part of their fault tolerance mechanisms. However, the unprecedented scale of these services, which frequently have hundreds of thousands or even millions of users, leads to a new set performance requirements on state machine replication. This thesis is organized in two parts. The goal of the first part is to study from a theoretical perspective the performance characteristics of the algorithms behind state machine replication and to propose improved variants of such algorithms. The second part looks at the problem from a practical perspective, proposing new techniques to achieve high-throughput and scalability. In the first part, we start with an analytical analysis of the performance of two consensus algorithms, one leader-free (an adaptation of the fast round of Fast Paxos) and another leader-based (an adaptation of classical Paxos). We express these algorithms in the Heard-Of round model and show that using this model it is fairly easy to determine analytically several interesting performance metrics. We then study the performance of round models in general. Round models are perceived as inefficient because in their typical implementation, the real-time duration of rounds is proportional to the (pessimistic) timeouts used on the underlying system. This contrasts with the failure detector or the partial synchronous system models, where algorithms usually progress at the speed of message reception. We show that there is no inherent gap in performance between the models, by proposing a round implementation that during stable periods advances at the speed of message reception. We conclude the first part by presenting a new leader election algorithm that chooses as leader a well-connected process, that is, a process whose time needed to perform a one-to-majority communication round is among the lowest in the system. This is useful mainly in systems where the latency between processes is not homogeneous, because the performance of leader-based algorithms is particularly sensitive to the performance and connectivity of the process acting as a leader. The second part of the thesis studies different approaches to achieve high-throughput with state machine replication. To support the experimental work done in this part, we have developed JPaxos, a fully-featured implementation of Paxos in Java. We start by looking at how to tune the batching and pipelining optimizations of Paxos; using an analytical model of the performance of Paxos we show how to derive good values for the bounds on the batch size and number of parallel instances. We then propose an architecture for implementing replicated state machines that is capable of leveraging multi-core CPUs to achieve very high-levels of performance. The final contribution of this thesis is based on the observation that most implementations of state machine replication have an unbalanced division of work among threads, with one replica, the leader, having a significantly higher workload than the other replicas. Naturally, the leader becomes the bottleneck of the system, while other replicas are only lightly loaded. We propose and evaluate S-Paxos, which evenly balances the workload among all replicas, and thus overcomes the leader bottleneck. The benefits are two-fold: S-Paxos achieves a higher throughput for a given number of replicas and its performance increases with the number of replicas (up to a reasonable number)

A Practical Analysis of the Gorums Framework: A Case Study on Replicated Services with Raft

Master's thesis in Computer scienceGorums is a novel RPC framework developed to make it easier to build fault tolerant distributed systems. We want to assess whether Gorums can simplify the implementation of a practical fault tolerant service that supports reconfiguration. The Raft consensus algorithm is implemented in Gorums with the ability to do single-server configuration changes. In addition, we perform a background study of two state of the art Raft implementations. The abstractions used in these implementations are then compared to the abstractions Gorums provides and how they are used in our Raft implementation. A service is created that can be used with any of the aforementioned Raft implementations for consistency and fault tolerance. This service is then used to evaluate the different implementations through experimentation. Our evaluation shows that the Raft implementation that uses Gorums perform better with regards to latency and overall throughput during normal operation. We do however discover this implementation to be sensitive to omission faults, which can further lead to availability issues if not handled properly. We solve this by developing extensions to Raft and Gorums. We show that these methods perform on a similar level when compared with the state of the art implementations. Results from our implementation efforts indicate that Raft's log replication process is problematic to implement with Gorums' abstractions. We discover that this is due to Raft adopting a monolithic design aimed to reduce the number of different RPC types, breaching the separation of concerns design principle

State Machine Replication Is More Expensive Than Consensus

Consensus and State Machine Replication (SMR) are generally considered to be equivalent problems. In certain system models, indeed, the two problems are computationally equivalent: any solution to the former problem leads to a solution to the latter, and vice versa. In this paper, we study the relation between consensus and SMR from a complexity perspective. We find that, surprisingly, completing an SMR command can be more expensive than solving a consensus instance. Specifically, given a synchronous system model where every instance of consensus always terminates in constant time, completing an SMR command does not necessarily terminate in constant time. This result naturally extends to partially synchronous models. Besides theoretical interest, our result also corresponds to practical phenomena we identify empirically. We experiment with two well-known SMR implementations (Multi-Paxos and Raft) and show that, indeed, SMR is more expensive than consensus in practice. One important implication of our result is that - even under synchrony conditions - no SMR algorithm can ensure bounded response times