Sharath Chandrashekhara

Demo: BlueMountain: An Architecture to Customize Data Management on Mobile Systems

BlueMountain is a system that enables building pluggable data management solutions which can be linked with any Android app at runtime, without requiring any modifications to the Android platform. BlueMountain simplifies the app development, provides flexibility to end users, and works with existing apps.

PigOut: Making multiple Hadoop clusters work together

This paper presents PigOut, a system that enables federated data processing over multiple Hadoop clusters. Using PigOut, a user (such as a data analyst) can write a single script in a high-level language to efficiently use multiple Hadoop clusters. There is no need to manually write multiple scripts and coordinate the execution for different clusters. PigOut accomplishes this by automatically partitioning a single, user-supplied script into multiple scripts that run on different clusters. Additionally, PigOut generates workflow descriptions to coordinate execution across clusters. In doing so, PigOut leverages existing tools built around Hadoop, avoiding extra effort required from users or administrators. For example, PigOut uses Pig Latin, a popular query language for Hadoop MapReduce, in a (virtually) unmodified form. Through our evaluation with PigMix, the standard benchmark for Pig, we demonstrate that PigOuts automatically-generated scripts and workflow definitions have comparable performance to manual, hand-tuned ones. We also report our experience with manually writing multiple scripts for a set of federated clusters, and compare the process with PigOuts automated approach.

Cider: A Case for Block Level Variable Redundancy on a Distributed Flash Array

With the increase in data volumes, it is prudent to classify data depending on its criticality. One might prefer a cheap storage for a year old system logs but a highly fault tolerant storage for personal photos. The existing solutions include storing these two sets of data in two different systems or choosing a system with a fault tolerance level required by the most critical data. This means a higher storage footprint for the lesser critical data. In todays petabyte scale data centers, this will result in a significant increase in the overall storage footprint making the system unattractive. In addition, data storage solutions designed for traditional data centers have to be re-engineered to work at cloud scale. For instance, reliable storage using traditional RAID like systems are inflexible and do not provide a mechanism to easily change the level of redundancy once the system is set up. As a result, changes to the reliability requirements of data over time cannot be serviced efficiently. To address these problems, we propose Cider, which aims at providing an extremely flexible, reliable, and distributed block store using erasure codes. Cider takes a new approach to reduce the storage overhead by offering a variable degree of fault tolerance at a granularity of a single block. We achieve this by using a thin block translation layer and a block level metadata system. Cider can be readily used by many high performance and enterprise applications which require a block level interface. Through a case study of a novel flash based clustered storage system, we make a strong case for the adoption of Cider in future systems. We discuss various design trade-offs and implementation challenges associated with designing our system. Lastly, we discuss the preliminary implementation of our system and results from our initial experiments.

BlueMountain: An Architecture for Customized Data Management on Mobile Systems

In this paper, we design a pluggable data management solution for modern mobile platforms (e.g., Android). Our goal is to allow data management mechanisms and policies to be implemented independently of core app logic. Our design allows a user to install data management solutions as apps, install multiple such solutions on a single device, and choose a suitable solution each for one or more apps. It allows app developers to focus their effort on app logic and helps the developers of data management solutions to achieve wider deployability. It also gives increased control of data management to end users and allows them to use different solutions for different apps. We present a prototype implementation of our design called BlueMountain, and implement several data management solutions for file and database management to demonstrate the utility and ease of using our design. We perform detailed microbenchmarks as well as end-to-end measurements for files and databases to demonstrate the performance overhead incurred by our implementation.

System-E: Enhancing Privacy on Mobile Systems through Content-Based Classification and Storage

Mobile systems face privacy challenges including coarsegrained privacy control and the inability to distinguish private and public files. We propose System-E, a novel system which can enhance the user privacy on mobile systems (e.g., Android) by (1) enabling users to set finer grained permissions for apps accessing data, and (2) enabling automatic classification of data (e.g., photos) at the storage layer (e.g., by using deep learning) to prevent potentially sensitive data from being stored/accessed with open permissions.

Duvel: Enabling Context-driven, Multi-profile Apps on Android through Storage Sandboxing

We present a novel technique to achieve a dynamic, context-driven, multiple-profile manager for individual apps on stock Android. Our system allows users to use a single app with any number of accounts, allows incognito modes for every app, and allows a context-driven dynamic switching between the profiles (e.g., based on geolocation). Our technique achieves this by creating a sandboxed storage environment within each app through byte-code instrumentation. This allows for a clean separation of profile specific data and allows users to run personal and business accounts on the same phone, or sandbox an app in incognito mode without sharing any data between them. We present many more use cases where our solution can be used to improve user experience on mobile systems. In contrast to many of the existing solutions, our solution eliminates any modifications to the platform, does not require any special SDK to develop apps, and can use a context-driven policy to dynamically switch between profiles. We realize a storage sandbox environment called Duvel on Android, based on our previous work BlueMountain, and show how Duvel can enable using multiple accounts and incognito mode in popular apps.

Poster: Mobile Photo Data Management as a Platform Service

This poster presents Pixelsior, a new mobile platform service for photo data management in mobile apps.

Flexible Data Management on Mobile Systems

In this work, we propose a flexible data management solution for modern mobile platforms like Android. To increase flexibility, our system uses a pluggable solution which lets the data management mechanisms and policies to be developed independently of the core app logic. Our system simplifies app development and gives end-users a higher control over their personal data.