Akshat Aranya

Simba: tunable end-to-end data consistency for mobile apps

Developers of cloud-connected mobile apps need to ensure the consistency of application and user data across multiple devices. Mobile apps demand different choices of distributed data consistency under a variety of usage scenarios. The apps also need to gracefully handle intermittent connectivity and disconnections, limited bandwidth, and client and server failures. The data model of the apps can also be complex, spanning inter-dependent structured and unstructured data, and needs to be atomically stored and updated locally, on the cloud, and on other mobile devices. In this paper we study several popular apps and find that many exhibit undesirable behavior under concurrent use due to inadequate treatment of data consistency. Motivated by the shortcomings, we propose a novel data abstraction, called a sTable, that unifies a tabular and object data model, and allows apps to choose from a set of distributed consistency schemes; mobile apps written to this abstraction can effortlessly sync data with the cloud and other mobile devices while benefiting from end-to-end data consistency. We build Simba, a data-sync service, to demonstrate the utility and practicality of our proposed abstraction, and evaluate it both by writing new apps and porting existing inconsistent apps to make them consistent. Experimental results show that Simba performs well with respect to sync latency, bandwidth consumption, server throughput, and scales for both the number of users and the amount of data.

TBF: A memory-efficient replacement policy for flash-based caches

The performance and capacity characteristics of flash storage make it attractive to use as a cache. Recency-based cache replacement policies rely on an in-memory full index, typically a B-tree or a hash table, that maps each object to its recency information. Even though the recency information itself may take very little space, the full index for a cache holding N keys requires at least log N bits per key. This metadata overhead is undesirably high when used for very large flash-based caches, such as key-value stores with billions of objects. To solve this problem, we propose a new RAM-frugal cache replacement policy that approximates the least-recently-used (LRU) policy. It uses two in-memory Bloom sub-filters (TBF) for maintaining the recency information and leverages an on-flash key-value store to cache objects. TBF requires only one byte of RAM per cached object, making it suitable for implementing very large flash-based caches. We evaluate TBF through simulation on traces from several block stores and key-value stores, as well as evaluate it using the Yahoo! Cloud Serving Benchmark in a real system implementation. Evaluation results show that TBF achieves cache hit rate and operations per second comparable to those of LRU in spite of its much smaller memory requirements.

Building a Delay-Tolerant Cloud for Mobile Data

Mobile data usage is on a tremendous rise, due not only to increasing number of users but also to an increase in the number of applications that transfer data over the network. Moreover, applications for sharing, sensing, and collaboration have become more popular, causing significant amounts of data to be generated on devices. Managing this data -syncing it to the cloud, or with other users or devices- is a crucial and often challenging part of writing mobile apps and services. In spite of plenty of good advice and best practices from OS vendors and network operators, storing and transferring mobile data is fraught with issues. On the one hand, an app developer needs to worry about the semantics of data storage and synchronization, while on the other, about the end-user experience, which maybe impacted by poor and intermittent network connectivity. To address the needs of the app developers and the end-users, we have built Izzy: a platform to rapidly develop and deploy data-centric mobile apps. Izzy provides well-defined and easy to use semantics for accessing local storage and for synchronizing data with a remote, scalable, global store. Izzy also provides global store access to the cloud-resident part of the applications (if any) through a similar server API. Last but not least, Izzy is designed to be frugal: it conserves mobile device resources by applying delay-tolerance and data reduction techniques (message coalescing and compression) across applications on a mobile device. In this paper we present the design of Izzy and our early experiences with using it.