Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.
AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.
We present a new lazy replication technique, suitable for scaling the back-end database of a dynamic content site using a cluster of commodity computers. Our technique, called conflict-aware scheduling, provides both throughput scaling and 1-copy serializability. It has generally been believed that this combination is hard to achieve through replication because of the growth of the number of conflicts. We take advantage of the presence in a database cluster of a scheduler through which all incoming requests pass. We require that transactions specify the tables that they access at the beginning of the transaction. Using that information, a conflictaware scheduler relies on a sequence-numbering scheme to implement 1-copy serializability, and directs incoming queries in such a way that the number of conflicts is reduced. We evaluate conflict-aware scheduling using the TPCW e-commerce benchmark. For small clusters of up to eight database replicas, our evaluation is performed through measurements of a web site implementing the TPC-W specification. We use simulation to extend our measurement results to larger clusters, faster database engines, and lower conflict rates. Our results show that conflict-awareness brings considerable benefits compared to both eager and conflictoblivious lazy replication for a large range of cluster sizes, database speeds, and conflict rates. Conflict-aware scheduling provides near-linear throughput scaling up to a large number of database replicas for the browsing and shopping workloads of TPC-W. For the write-heavy ordering workload, throughput scales, but only to a smaller number of replicas.