This poster presents a technique to improve uploading datasets to Object Storage. By using client-side and server-side cooperative processing of merging and splitting, Targoat, our implementation of the technique, improves data movement time of datasets consist of many small objects. Our technique is simple, effective, and can be easily implemented in any Object Storage.

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.

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.

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

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 PigOut's 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 PigOut's automated approach.

This paper focuses on large graph processing based on the remote memory system. Using our remote memory system enables applications to deal with large data sets, especially graph data, which do not fit into the machines main memory. Although recent dramatic increases in DRAM capacity now allow us to build inexpensive computers with very large amounts of main memory, the rise in brand-new Internet services has resulted in rapid increases in data size. This is especially true for on-line social network services that generate various data sets that can be represented as graphs. On the other hand, high-speed networking technologies such as Infini Band, Myrinet and 10G Ethernet now enable us to transfer data with low latency and high throughput. The advanced networking technologies reduce the latency/bandwidth gap between main memory and remote memory. Thus, remote memory based processing could now be helpful in accelerating large-scale graph process when main memory space is insufficient to store application data. In this paper, we present our design and implementation of remote memory system that efficiently processes large graph data. We also evaluate a breadth-first search of various types of graphs using our system and show that our approach is good for large graph data processing.

MapReduce is Google's programming model for easy development of scalable parallel applications which process huge quantity of data on many clusters. Due to its conveniency and efficiency, MapReduce is used in various applications (e.g., Web search services and online analytical processing). However, there are only few good benchmarks to evaluate MapReduce implementations by realistic testsets. In this paper, we present MRBench that is a benchmark for evaluating MapReduce systems. MRBench focuses on processing business oriented queries and concurrent data modifications. To this end, we build MRBench to deal with large volumes of relational data and execute highly complex queries. By MRBench, users can evaluate the performance of MapReduce systems while varying environmental parameters such as data size and the number of (map/reduce) tasks. Our extensive experimental results show that MRBench is a useful tool to benchmark the capability of answering critical business questions.