Today’s smartphone operating systems frequently fail to provide users with adequate control over and visibility into how third-party applications use their private data. the authors address these shortcomings with TaintDroid, an efficient, system-wide dynamic taint tracking and analysis system capable of simultaneously tracking multiple sources of sensitive data. Using TaintDroid to monitor the behavior of 30 popular third-party Android applications, we found 68 instances of potential misuse of users’ private information across 20 applications.

Computer networks lack a general control paradigm, as traditional networks do not provide any networkwide management abstractions.To address this, the authors present Onix, a platform on top of which a network control plane can be implemented as a distributed system. Control planes written within Onix operate on a global view of the network, and use basic state distribution primitives provided by the platform.

This paper describes how Spanner is structured, its feature set, the rationale underlying various design decisions, and a novel time API that exposes clock uncertainty. Spanner is Google’s scalable, multi-version, globallydistributed, and synchronously-replicated database.

Large-scale graph-structured computation is central to tasks ranging from targeted advertising to natural language processing and has led to the development of several graph-parallel abstractions. in this paper, the authors characterize the challenges of computation on natural graphs in the context of existing graphparallel abstractions. Leveraging the PowerGraph abstraction we introduce a new approach to distributed graph placement and representation that exploits the structure of power-law graphs.Finally, we describe three different implementation strategies for PowerGraph and discuss their relative merits with empirical evaluations on large-scale real-world problems demonstrating order of magnitude gains.

Current systems for graph computation require a distributed computing cluster to handle very large real-world problems. in this work, the authors present GraphChi, a disk-based system for computing efficiently on graphs with billions of edges. We further extend GraphChi to support graphs that evolve over time, and demonstrate that, on a single computer, GraphChi can process over one hundred thousand graph updates per second, while simultaneously performing computation.By repeating experiments reported for existing distributed systems, we show that, with only fraction of the resources, GraphChi can solve the same problems in very reasonable time.

the authors present Mantri, a system that monitors tasks and culls outliers using cause- and resource-aware techniques. We present Mantri, a system that monitors tasks and culls outliers using cause- and resource-aware techniques.Deployment in Bing’s production clusters and trace-driven simulations show that Mantri improves job completion times by 32%.

the authors introduce GraphX, an embedded graph processing framework built on top of Apache Spark, a widely used distributed dataflow system. We introduce GraphX, an embedded graph processing framework built on top of Apache Spark, a widely used distributed dataflow system.

A persistent problem in computer network research is validation. In this paper, the authors describe a way to build a testbed that is embedded in—and thus grows with—the network. When deciding how to evaluate a new feature or bug fix, a researcher or operator must trade-off realism (in terms of scale, actual user traffic, real equipment) and cost (larger scale costs more money, real user traffic likely requires downtime, and real equipment requires vendor adoption which can take years).The technique—embodied in our first prototype, FlowVisor—slices the network hardware by placing a layer between the control plane and the data plane.

the authors have built Percolator, a system for incrementally processing updates to a large data set, and deployed it to create the Google web search index. We have built Percolator, a system for incrementally processing updates to a large data set, and deployed it to create the Google web search index.By replacing a batch-based indexing system with an indexing system based on incremental processing using Percolator, we process the same number of documents per day, while reducing the average age of documents in Google search results by 50%.

the authors characterize the availability properties of cloud storage systems based on an extensive one year study of Google's main storage infrastructure and present statistical models that enable further insight into the impact of multiple design choices, such as data placement and replication strategies. With these models we compare data availability under a variety of system parameters given the real patterns of failures observed in our fleet.

This paper describes Haystack, an object storage system optimized for Facebook’s Photos application.

the authors propose a parameter server framework for distributed machine learning problems. We propose a parameter server framework for distributed machine learning problems.Both data and workloads are distributed over worker nodes, while the server nodes maintain globally shared parameters, represented as dense or sparse vectors and matrices.To demonstrate the scalability of the proposed framework, we show experimental results on petabytes of real data with billions of examples and parameters on problems ranging from Sparse Logistic Regression to Latent Dirichlet Allocation and Distributed Sketching.

This paper analyzes the scalability of seven system applications (Exim, memcached, Apache, PostgreSQL, gmake, Psearchy, and MapReduce) running on Linux on a 48- core computer.