Global Stabilization for Causally Consistent Partial Replication

Abstract

Causally consistent distributed storage systems have received significant attention recently due to the potential for providing high throughput and causality guarantees. Global stabilization is a technique established for achieving causal consistency in distributed multi-version key-value store systems, adopted by the previous work such as GentleRain [1] and Cure [2]. Intuitively, this approach serializes all updates by their physical time and computes the "Global Stable Time" which is a time point t such that versions with timestamp ≤ t can be returned to the client without violating causality. However, all previous designs with global stabilization assume full replication, where each data center stores a full copy of data, and each client is restricted to access servers within one data center. In this paper, we propose a theoretical framework to support general partial replication with causal consistency via global stabilization, where each server can store an arbitrary subset of the data, and each client is allowed to communicate with any subset of the servers and migrate among them without extra delays. We propose an algorithm that implements causal consistency for distributed multi-version key-value stores with general partially replication. We prove the optimality of the Global Stable Time computation in our algorithm regarding the remote update visibility latency, i.e. how fast update from a remote server is visible to the client, under general partial replication. We also provide trade-offs to further optimize the remote update visibility by introducing extra delays during client's migration. Simulation results on the performance of our algorithm compared to the previous work are also provided.

PaRiS: Causally Consistent Transactions with Non-blocking Reads and Partial Replication

“…A few systems support partial replication, i.e., Saturn [34], C 3 [35], Karma [36] and the one by Xiang and Vaidya [37]. These systems, however, implement only single-object read and write operations.…”

“…Txs Nonbl. reads Partial rep. Meta-data COPS [1] ROT O|deps| Eiger [2] ROT/WOT O|deps| ChainReaction [8] ROT M Orbe [7] ROT 1 ts Gentlerain [6] ROT 1 ts POCC [9] ROT M COPS-SNOW [14] ROT O|deps| OCCULT [5] Generic O(M) Cure [4] Generic M Wren [25] Generic 2 ts AV [15] Generic M Xiang, Vaidya [37] 1 ts Contrarian [10] ROT M C 3 [35] M Saturn [34] 1 ts Karma [36] ROT O|deps| CausalSpartan [11] M Bolt-on CC [33] M EunomiaKV [26] M PaRiS (this work) Generic 1 ts TABLE I: Taxonomy of the main CC systems. M is the number of DCs.…”

CausalEC: A Causally Consistent Data Storage Algorithm based on Cross-Object Erasure Coding

“…The strategy of every server having a replica of the every object in the data store is expensive in its storage space requirements, and therefore limits the scalability of the system. To address this, there is much recent interest -amongst both theory and systems researchers -in causally consistent data stores based on partial replication [40,47,48,23,10,33,4]. In partially replicated data stores, a given server node stores only a subset of the objects.…”

Adjoin: A causal consistency model based on the adjacency list in a distributed system

“…Geo‐replication reduces the latency by keeping a copy of the data closer to the client and enables availability by replicating data to the data centers (DCs) distributed across geographical locations. Generally, it can be divided into two types: full geo‐replication and partial geo‐replication 1 . Full geo‐replication stipulates that each DC stores the same data, ensuring that a node can still provide normal service in case of downtime or natural disasters.…”