About the efficiency of partial replication to implement Distributed Shared Memory

Distributed Shared Memory abstraction (DSM) is traditionally realized through a distributed memory consistency system(MCS) on top of a message passing system. In this paper we analyze the impossibility of efficient partial replication implementation of causally consistent DSM. Efficiency is discussed in terms of control information that processes have to propagate to maintain consistency. We introduce the notions of share graph and hoop to model variable distribution and the concept of dependency chain to characterize processes that have to manage information about a variable even though they do not read or write that variable. Then, we weaken causal consistency to try to define new consistency criteria weaker enough to allow efficient partial replication implementations and strong enough to solve interesting problems. Finally, we prove that PRAM is such a criterion, and illustrate its power with the Bellman-Ford shortest path algorithm. / Les mémoires partagées réparties constituent une abstraction qui est traditionellement concrétisée par un système réparti de mémoire cohérente, au-dessus d'un système de communication par messages. Dans ce rapport, on analyse l'impossibilité d'avoir une implémentation efficace de mémoire partagée répartie à cohérence causale, basée sur la duplication partielle des variables. L'efficacité est envisagée en terme d'information contrôle qui doit être propagée pour assurer la cohérence. On introduit les notions de graphe de partage et d'arceau, qui modélisent la répartition des variables et la notion de chaîne de dépendance pour caractériser les processus qui doivent gérer des informations relatives à une variable dont ils ne possèdent pas de copie locale. Ensuite, on affaiblit le critère de cohérence causale, dans le but de déterminer un nouveau critère de cohérence qui soit suffisament faible pour permettre un implémentation efficace basée sur la duplication partielle, mais suffisament forte pour pouvoir résoudre des problèmes intéressants. Finalement, on prouve que le critère appelé PRAM satisfait ces exigences, et illustrons sa pertinence en montrant une implémentation de l'algorithme de plus court chemin de Bellman-Ford

Parallel Deferred Update Replication

Deferred update replication (DUR) is an established approach to implementing highly efficient and available storage. While the throughput of read-only transactions scales linearly with the number of deployed replicas in DUR, the throughput of update transactions experiences limited improvements as replicas are added. This paper presents Parallel Deferred Update Replication (P-DUR), a variation of classical DUR that scales both read-only and update transactions with the number of cores available in a replica. In addition to introducing the new approach, we describe its full implementation and compare its performance to classical DUR and to Berkeley DB, a well-known standalone database

TOFEC: Achieving Optimal Throughput-Delay Trade-off of Cloud Storage Using Erasure Codes

Our paper presents solutions using erasure coding, parallel connections to storage cloud and limited chunking (i.e., dividing the object into a few smaller segments) together to significantly improve the delay performance of uploading and downloading data in and out of cloud storage. TOFEC is a strategy that helps front-end proxy adapt to level of workload by treating scalable cloud storage (e.g. Amazon S3) as a shared resource requiring admission control. Under light workloads, TOFEC creates more smaller chunks and uses more parallel connections per file, minimizing service delay. Under heavy workloads, TOFEC automatically reduces the level of chunking (fewer chunks with increased size) and uses fewer parallel connections to reduce overhead, resulting in higher throughput and preventing queueing delay. Our trace-driven simulation results show that TOFEC's adaptation mechanism converges to an appropriate code that provides the optimal delay-throughput trade-off without reducing system capacity. Compared to a non-adaptive strategy optimized for throughput, TOFEC delivers 2.5x lower latency under light workloads; compared to a non-adaptive strategy optimized for latency, TOFEC can scale to support over 3x as many requests