Partially Replicated Causally Consistent Shared Memory: Lower Bounds and An Algorithm

TL;DR

This paper investigates causal consistency in partially replicated distributed shared memory systems, establishing necessary conditions for causal tracking, providing an algorithm that meets these conditions, and deriving lower bounds on timestamp size.

Contribution

It introduces a necessary and sufficient condition for causal tracking in partial replication, along with an optimal algorithm and lower bounds on timestamp size.

Findings

The necessary condition for causal tracking is characterized by the share graph edges.

The proposed algorithm achieves causal consistency with minimal timestamp size.

Lower bounds on timestamp size match the algorithm in key cases.

Abstract

The focus of this paper is on causal consistency in a {\em partially replicated} distributed shared memory (DSM) system that provides the abstraction of shared read/write registers. Maintaining causal consistency in distributed shared memory systems has received significant attention in the past, mostly on {\em full replication} wherein each replica stores a copy of all the registers in the shared memory. To ensure causal consistency, all causally preceding updates must be performed before an update is performed at any given replica. Therefore, some mechanism for tracking causal dependencies is required, such as vector timestamps with the number of vector elements being equal to the number of replicas in the context of full replication. In this paper, we investigate causal consistency in {\em partially replicated systems}, wherein each replica may store only a subset of the shared registers. Building on the past work, this paper makes three key contributions: 1. We present a necessary condition on the metadata (which we refer as a {\em timestamp}) that must be maintained by each replica to be able to track causality accurately. The necessary condition identifies a set of directed edges in a {\em share graph} that a replica's timestamp must keep track of. 2. We present an algorithm for achieving causal consistency using a timestamp that matches the above necessary condition, thus showing that the condition is necessary and sufficient. 3. We define a measurement of timestamp space size and present a lower bound (in bits) on the size of the timestamps. The lower bound matches our algorithm in several special cases.