Incremental Consistency Guarantees for Replicated Objects

Programming with replicated objects is difficult. Developers must face the fundamental trade-off between consistency and performance head on, while struggling with the complexity of distributed storage stacks. We introduce Correctables, a novel abstraction that hides most of this complexity, allowing developers to focus on the task of balancing consistency and performance. To aid developers with this task, Correctables provide incremental consistency guarantees, which capture successive refinements on the result of an ongoing operation on a replicated object. In short, applications receive both a preliminary---fast, possibly inconsistent---result, as well as a final---consistent---result that arrives later. We show how to leverage incremental consistency guarantees by speculating on preliminary values, trading throughput and bandwidth for improved latency. We experiment with two popular storage systems (Cassandra and ZooKeeper) and three applications: a Twissandra-based microblogging service, an ad serving system, and a ticket selling system. Our evaluation on the Amazon EC2 platform with YCSB workloads A, B, and C shows that we can reduce the latency of strongly consistent operations by up to 40% (from 100ms to 60ms) at little cost (10% bandwidth increase, 6% throughput drop) in the ad system. Even if the preliminary result is frequently inconsistent (25% of accesses), incremental consistency incurs a bandwidth overhead of only 27%.Comment: 16 total pages, 12 figures. OSDI'16 (to appear

Managing Data Replication and Distribution in the Fog with FReD

The heterogeneous, geographically distributed infrastructure of fog computing poses challenges in data replication, data distribution, and data mobility for fog applications. Fog computing is still missing the necessary abstractions to manage application data, and fog application developers need to re-implement data management for every new piece of software. Proposed solutions are limited to certain application domains, such as the IoT, are not flexible in regard to network topology, or do not provide the means for applications to control the movement of their data. In this paper, we present FReD, a data replication middleware for the fog. FReD serves as a building block for configurable fog data distribution and enables low-latency, high-bandwidth, and privacy-sensitive applications. FReD is a common data access interface across heterogeneous infrastructure and network topologies, provides transparent and controllable data distribution, and can be integrated with applications from different domains. To evaluate our approach, we present a prototype implementation of FReD and show the benefits of developing with FReD using three case studies of fog computing applications

Towards quality-of-service driven consistency for Big Data management

International audienceWith the advent of Cloud Computing, Big Data management has become a fundamental challenge during the deployment and operation of distributed highly available and fault-tolerant storage systems such as the HBase extensible record-store. These systems can provide support for geo-replication, which comes with the issue of data consistency among distributed sites. In order to offer a best-in-class service to applications, one wants to maximise performance while minimising latency. In terms of data replication, that means incurring in as low latency as possible when moving data between distant data centres. Traditional consistency models introduce a significant problem for systems architects, which is specially important to note in cases where large amounts of data need to be replicated across wide-area networks. In such scenarios it might be suitable to use eventual consistency, and even though not always convenient, latency can be partly reduced and traded for consistency guarantees so that data-transfers do not impact performance. In contrast, this work proposes a broader range of data semantics for consistency while prioritising data at the cost of putting a minimum latency overhead on the rest of non-critical updates. Finally, we show how these semantics can help in finding an optimal data replication strategy for achieving just the required level of data consistency under low latency and a more efficient network bandwidth utilisation