Specification of Replication Techniques, Semi-Passive Replication, and Lazy consensus*

This paper brings the following three main contributions: a hierarchy of specifications for replication techniques, semi-passive replication, and Lazy Consensus. Based on the definition of the Generic Replication problem, we difine two families of replication techniques: replication with parsimonious processing (e.g., passive replication), and replication with redundant processing (e.g., active replication). This helps relate replication techniques to each other. We define a novel replication technique with parsimonious processing, called semi-passive replication, for which we also give an algorithm. The most significant aspect of semi-passive replication is that it requires a weaker system model than existing techniques of the same family. We difine a variant of the Consensus problem, called Lazy Consensus, upon which our semi-passive replication algorithm is based. The main difference between Consensus and Lazy Consensus is a property of laziness which requires that initial values are computed only when they are actually needed

Agreement-related problems:from semi-passive replication to totally ordered broadcast

Agreement problems constitute a fundamental class of problems in the context of distributed systems. All agreement problems follow a common pattern: all processes must agree on some common decision, the nature of which depends on the specific problem. This dissertation mainly focuses on three important agreements problems: Replication, Total Order Broadcast, and Consensus. Replication is a common means to introduce redundancy in a system, in order to improve its availability. A replicated server is a server that is composed of multiple copies so that, if one copy fails, the other copies can still provide the service. Each copy of the server is called a replica. The replicas must all evolve in manner that is consistent with the other replicas. Hence, updating the replicated server requires that every replica agrees on the set of modifications to carry over. There are two principal replication schemes to ensure this consistency: active replication and passive replication. In Total Order Broadcast, processes broadcast messages to all processes. However, all messages must be delivered in the same order. Also, if one process delivers a message m, then all correct processes must eventually deliver m. The problem of Consensus gives an abstraction to most other agreement problems. All processes initiate a Consensus by proposing a value. Then, all processes must eventually decide the same value v that must be one of the proposed values. These agreement problems are closely related to each other. For instance, Chandra and Toueg [CT96] show that Total Order Broadcast and Consensus are equivalent problems. In addition, Lamport [Lam78] and Schneider [Sch90] show that active replication needs Total Order Broadcast. As a result, active replication is also closely related to the Consensus problem. The first contribution of this dissertation is the definition of the semi-passive replication technique. Semi-passive replication is a passive replication scheme based on a variant of Consensus (called Lazy Consensus and also defined here). From a conceptual point of view, the result is important as it helps to clarify the relation between passive replication and the Consensus problem. In practice, this makes it possible to design systems that react more quickly to failures. The problem of Total Order Broadcast is well-known in the field of distributed systems and algorithms. In fact, there have been already more than fifty algorithms published on the problem so far. Although quite similar, it is difficult to compare these algorithms as they often differ with respect to their actual properties, assumptions, and objectives. The second main contribution of this dissertation is to define five classes of total order broadcast algorithms, and to relate existing algorithms to those classes. The third contribution of this dissertation is to compare the expected performance of the various classes of total order broadcast algorithms. To achieve this goal, we define a set of metrics to predict the performance of distributed algorithms

Optimization Techniques for Replicating CORBA Objects

The CORBA Object Group Service (OGS) is a new CORBA service that provides support for fault-tolerance through the replication of CORBA objects. In this paper, we present several optimization techniques that are used to improve the performance of OGS. For each optimization, we analyse the impact on the throughput and the response time of OGS. The optimization techniques presented in this paper are quite generic and can be applied to many fault-tolerant distributed algorithms based on consensus