The high degree of storage consolidation in modern virtualized datacenters requires flexible and efficient ways to allocate IO resources among virtual machines (VMs). Existing IO resource management techniques have two main deficiencies: (1) they are restricted in their ability to allocate resources across multiple hosts sharing a storage device, and (2) they do not permit the administrator to set allocations for a group of VMs that are providing a single service or belong to the same application.

In this paper we present the design and implementation of a novel software system called Storage Resource Pools (SRP). SRP supports the logical grouping of related VMs into hierarchical pools. SRP allows reservations, limits and proportional shares, at both the VM and pool levels. Spare resources are allocated to VMs in the same pool in preference to other VMs. The VMs may be distributed across multiple physical hosts without consideration of their logical groupings. We have implemented a prototype of storage resource pools in the VMware ESX hypervisor. Our results demonstrate that SRP provides hierarchical performance isolation and sharing among groups of VMs running across multiple hosts, while maintaining high utilization of the storage device.

Distributed systems designs often employ replication to solve two different kinds of availability problems. First, to prevent the loss of data through the permanent destruction or disconnection of a distributed node, and second, to allow prompt retrieval of data when some distributed nodes respond slowly. For simplicity, many systems further handle crash-restart failures and timeouts by treating them as a permanent disconnection followed by the birth of a new node, relying on peer replication rather than persistent storage to preserve data. We posit that for applications deployed in modern managed infrastructures, delays are typically transient and failed processes and machines are likely to be restarted promptly, so it is often desirable to resume crashed processes from persistent checkpoints. In this paper we present MaceKen, a synthesis of complementary techniques including Ken, a lightweight and decentralized rollback-recovery protocol that transparently masks crash-restart failures by careful handling of messages and state checkpoints; and Mace, a programming toolkit supporting development of distributed applications and application-specific availability via replication. MaceKen requires near-zero additional developer effort—systems implemented in Mace can immediately benefit from the Ken protocol by virtue of following the Mace execution model. Moreover, this model allows multiple, independently developed application components to be seamlessly composed, preserving strong global reliability guarantees. Our implementation is available as open source software.

Grace is a graph-aware, in-memory, transactional graph management system, specifically built for real-time queries and fast iterative computations. It is designed to run on large multi-cores, taking advantage of the inherent parallelism to improve its performance. Grace contains a number of graph-specific and multi-core-specific optimizations including graph partitioning, careful in-memory vertex ordering, updates batching, and load-balancing. It supports queries, searches, iterative computations, and transactional updates. Grace scales to large graphs (e.g., a Hotmail graph with 320 million vertices) and performs up to two orders of magnitude faster than commercial key-value stores and graph databases.

The scalability of multithreaded applications on current multicore systems is hampered by the performance of lock algorithms, due to the costs of access contention and cache misses. In this paper, we propose a new lock algorithm, Remote Core Locking (RCL), that aims to improve the performance of critical sections in legacy applications on multicore architectures. The idea of RCL is to replace lock acquisitions by optimized remote procedure calls to a dedicated server core. RCL limits the performance collapse observed with other lock algorithms when many threads try to acquire a lock concurrently and removes the need to transfer lock-protected shared data to the core acquiring the lock because such data can typically remain in the server core’s cache.

We have developed a profiler that identifies the locks that are the bottlenecks in multithreaded applications and that can thus benefit from RCL, and a reengineering tool that transforms POSIX locks into RCL locks. We have evaluated our approach on 18 applications: Memcached, Berkeley DB, the 9 applications of the SPLASH-2 benchmark suite and the 7 applications of the Phoenix2 benchmark suite. 10 of these applications, including Memcached and Berkeley DB, are unable to scale because of locks, and benefit from RCL. Using RCL locks, we get performance improvements of up to 2.6 times with respect to POSIX locks on Memcached, and up to 14 times with respect to Berkeley DB.

High Level Synthesis (HLS) is a promising technology where algorithms described in high level languages are automatically transformed into a hardware design. Although many HLS tools exist, they are mainly targeting developers who want to use a high level programming language to design hardware modules. They are not designed to automatically compile a complete software system, such as a network packet processing application, into a hardware design.

In this paper, we describe a compiler toolchain that automatically transforms existing software in a limited domain to a functional hardware design. We have selected the Click Modular Router as the input system, and the Stanford NetFPGA as the target hardware platform. Our toolchain uses LLVM to transform Click C++ code into a form suitable for hardware implementation and then uses AHIR, a high level synthesis toolchain, to produce a VHDL netlist.

The resulting netlist has been verified with actual hardware on the NetFPGA platform. The resulting hardware can achieve 20-50 % of the performance compared to version handwritten in Verilog. We expect that improvements on the toolchain could provide better performance, but for the first prototype the results are good. We feel that one of the biggest contribution of this work is that it shows some new principles of high-level synthesis that could also be applied to different domains, source languages and targets.

Many applications (routers, traffic monitors, firewalls, etc.) need to send and receive packets at line rate even on very fast links. In this paper we present netmap, a novel framework that enables commodity operating systems to handle the millions of packets per seconds traversing 1..10 Gbit/s links, without requiring custom hardware or changes to applications.

In building netmap, we identified and successfully reduced or removed three main packet processing costs: per-packet dynamic memory allocations, removed by preallocating resources; system call overheads, amortized over large batches; and memory copies, eliminated by sharing buffers and metadata between kernel and userspace, while still protecting access to device registers and other kernel memory areas. Separately, some of these techniques have been used in the past. The novelty in our proposal is not only that we exceed the performance of most of previous work, but also that we provide an architecture that is tightly integrated with existing operating system primitives, not tied to specific hardware, and easy to use and maintain.

netmap has been implemented in FreeBSD and Linux for several 1 and 10 Gbit/s network adapters. In our prototype, a single core running at 900 MHz can send or receive 14.88 Mpps (the peak packet rate on 10 Gbit/s links). This is more than 20 times faster than conventional APIs. Large speedups (5x and more) are also achieved on user-space Click and other packet forwarding applications using a libpcap emulation library running on top of netmap.

BinArmor is a novel technique to protect existing C binaries from memory corruption attacks on both control data and non-control data. Without access to source code, non-control data attacks cannot be detected with current techniques. Our approach hardens binaries against both kinds of overflow, without requiring the programs’ source or symbol tables. We show that BinArmor is able to stop real attacks—including the recent noncontrol data attack on Exim. Moreover, we did not incur a single false positive in practice. On the downside, the current overhead of BinArmor is high—although no worse than competing technologies like taint analysis that do not catch attacks on non-control data. Specifically, we measured an overhead of 70% for gzip, 16%-180% for lighttpd, and 190% for the nbench suite.

Many Web applications (meaning sites that employ JavaScript) incorporate third-party code and, for reasons rooted in today’s Web ecosystem, are vulnerable to bugs or malice in that code. Our goal is to give Web developers a mechanism that (a) contains included code, limiting (or eliminating) its influence as appropriate; and (b) is deployable today, or very shortly. While the goal of containment is far from new, the requirement of deployability leads us to a new design point, one that applies the OS ideas of sandboxing and virtualization to the JavaScript context. Our approach, called TreeHouse, sandboxes JavaScript code by repurposing a feature of current browsers (namely Web Workers). TreeHouse virtualizes the browser’s API to the sandboxed code (allowing the code to run with few or no modifications) and gives the application author fine-grained control over that code. Our implementation and evaluation of TreeHouse show that its overhead is modest enough to handle performance-sensitive applications and that sandboxing existing code is not difficult.

Mosh (mobile shell) is a remote terminal application that supports intermittent connectivity, allows roaming, and speculatively and safely echoes user keystrokes for better interactive response over high-latency paths. Mosh is built on the State Synchronization Protocol (SSP), a new UDP-based protocol that securely synchronizes client and server state, even across changes of the client’s IP address. Mosh uses SSP to synchronize a character-cell terminal emulator, maintaining terminal state at both client and server to predictively echo keystrokes. Our evaluation analyzed keystroke traces from six different users covering a period of 40 hours of real-world usage. Mosh was able to immediately display the effects of 70% of the user keystrokes. Over a commercial EV-DO (3G) network, median keystroke response latency with Mosh was less than 5 ms, compared with 503 ms for SSH. Mosh is free software, available from http://mosh.mit.edu. It was downloaded more than 15,000 times in the first week of its release.

YouTube traffic is bursty. These bursts trigger packet losses and stress router queues, causing TCP’s congestion-control algorithm to kick in. In this pa- per, we introduce Trickle, a server-side mechanism that uses TCP to rate limit YouTube video streaming. Trickle paces the video stream by placing an upper bound on TCP’s congestion window as a function of the streaming rate and the round-trip time. We evaluated Trickle on YouTube production data centers in Europe and India and analyzed its impact on losses, bandwidth, RTT, and video buffer under-run events. The results show that Trickle reduces the average TCP loss rate by up to 43% and the average RTT by up to 28% while maintaining the streaming rate requested by the application.

Applications do not typically view the kernel as a source of bad input. However, the kernel can behave in unusual (yet permissible) ways for which applications are badly unprepared. We present Murphy, a language-agnostic tool that helps developers discover and isolate run-time failures in their programs by simulating difficult-to-reproduce but completely-legitimate interactions between the application and the kernel. Murphy makes it easy to enable or disable sets of kernel interactions, called gremlins, so developers can focus on the failure scenarios that are im- portant to them. Gremlins are implemented using the ptrace interface, intercepting and potentially modifying an application’s system call invocation while requiring no invasive changes to the host machine.

We show how to use Murphy in a variety of modes to find different classes of errors, present examples of the kernel interactions that are tested, and explain how to apply delta debugging techniques to isolate the code causing the failure. While our primary goal was the development of a tool to assist in new software development, we successfully demonstrate that Murphy also has the capability to find bugs in hardened, widely-deployed software.

Metaverses are three-dimensional virtual worlds where anyone can add and script new objects. Metaverses today, such as Second Life, are dull, lifeless, and stagnant because users can see and interact with only a tiny region around them, rather than a large and immersive world. Current metaverses impose this distance restriction on visibility and interaction in order to scale to large worlds, as the restriction avoids appreciable shared state in underlying distributed systems.

We present the design and implementation of the Sirikata metaverse server. The Sirikata server scales to support large, complex worlds, even as it allows users to see and interact with the entire world. It achieves both goals simultaneously by leveraging properties of the real world and 3D environments in its core systems, such as a novel distributed data structure for virtual object queries based on visible size. We evaluate core services in isolation as well as part of the entire system, demonstrating that these novel designs do not sacrifice performance. Applications developed by Sirikata users support our claim that removing the distance restriction enables new, compelling applications that are infeasible in today’s metaverses.

Motivated by poor network connectivity from moving vehicles, we develop a new loss recovery method called opportunistic erasure coding (OEC). Unlike existing erasure coding methods, which are oblivious to the level of spare capacity along a path, OEC transmits coded packets only during instantaneous openings in a path’s spare capacity. This transmission strategy ensures that coded packets provide as much protection as the level of spare capacity allows, without delaying or stealing capacity from data packets. OEC uses a novel encoding that greedily maximizes the amount of new data recovered by the receiver with each coded packet. We design and implement a system called PluriBus that uses OEC in the vehicular context. We deploy it on two buses for two months and show that PluriBus reduces the mean flow completion time by a factor of 4 for a realistic workload. We also show that OEC outperforms existing loss recovery methods in a range of lossy environments.

Deduplication is a popular component of modern storage systems, with a wide variety of approaches. Unlike traditional storage systems, deduplication performance depends on data content as well as access patterns and meta-data characteristics. Most datasets that have been used to evaluate deduplication systems are either unrepresentative, or unavailable due to privacy issues, preventing easy comparison of competing algorithms. Understanding how both content and meta-data evolve is critical to the realistic evaluation of deduplication systems.

We developed a generic model of file system changes based on properties measured on terabytes of real, diverse storage systems. Our model plugs into a generic framework for emulating file system changes. Building on observations from specific environments, the model can generate an initial file system followed by ongoing modifications that emulate the distribution of duplicates and file sizes, realistic changes to existing files, and file system growth. In our experiments we were able to generate a 4TB dataset within 13 hours on a machine with a single disk drive. The relative error of emulated parameters depends on the model size but remains within 15% of real-world observations.

We present a large scale study of primary data deduplication and use the findings to drive the design of a new primary data deduplication system implemented in the Windows Server 2012 operating system. File data was analyzed from 15 globally distributed file servers hosting data for over 2000 users in a large multinational corporation.

The findings are used to arrive at a chunking and compression approach which maximizes deduplication savings while minimizing the generated metadata and producing a uniform chunk size distribution. Scaling of deduplication processing with data size is achieved using a RAM frugal chunk hash index and data partitioning – so that memory, CPU, and disk seek resources remain available to fulfill the primary workload of serving IO.

We present the architecture of a new primary data deduplication system and evaluate the deduplication performance and chunking aspects of the system.

This paper presents the design and implementation of a complete embedded Python run-time system for the ARM Cortex-M3 microcontroller. The Owl embedded Python run-time system introduces several key innovations, including a toolchain that is capable of producing relocatable memory images that can be utilized directly by the run-time system and a novel foreign function interface that enables the efficient integration of native C code with Python.

The Owl system demonstrates that it is practical to run high-level languages on embedded microcontrollers. Instrumentation within the system has led to an overall system design that enables Python code to be executed with low memory and speed overheads. Furthermore, this paper presents an evaluation of an autonomous RC car that uses a controller written entirely in Python. This demonstrates the ease with which complex embedded software systems can be built using the Owl infrastructure.

Persistence of in-memory data is necessary for many classes of application and systems software. We propose Software Persistent Memory (SoftPM), a new memory abstraction which allows malloc style allocations to be selectively made persistent with relative ease. Particularly, SoftPM’s persistent containers implement automatic, orthogonal persistence for all in-memory data reachable from a developer-defined root structure. Writing new applications or adapting existing applications to use SoftPM only requires identifying such root structures within the code. We evaluated the correctness, ease of use, and performance of SoftPM using a suite of microbenchmarks and real world applications including a distributed MPI application, SQLite (an in-memory database), and memcachedb (a distributed memory cache). In all cases, SoftPM was incorporated with minimal developer effort, was able to store and recover data successfully, and provide significant performance speedup (e.g., up to 10X for memcachedb and 83% for SQLite).

Recently, various key/value stores have been proposed targeting clusters built from low-power CPUs. The typical network configuration is that the nodes in those clusters are connected using 1 Gigabit Ethernet. During the last couple of years, 10 Gigabit Ethernet has become commodity and is increasingly used within the data centers providing cloud computing services. The boost in network link speed, however, poses a challenge to the cluster nodes because filling the network link can be a CPU-intensive task. In particular for CPUs running in low-power mode, it is therefore important to spend CPU cycles used for networking as efficiently as possible. In this paper, we propose a modified Memcached architecture to leverage the one-side semantics of RDMA. We show how the modified Memcached is more CPU efficient and can serve up to 20% more GET operations than the standard Memcached implementation on low-power CPUs. While RDMA is a networking technology typically associated with specialized hardware, our solution uses soft-RDMA which runs on standard Ethernet and does not require special hardware.

Enterprises with existing IT infrastructure are beginning to employ a hybrid cloud model where the enterprise uses its own private resources for the majority of its computing, but then “bursts” into the cloud when local resources are insufficient. However, current approaches to cloud bursting cannot be effectively automated because they heavily rely on system administrator knowledge to make decisions. In this paper we describe Seagull, a system designed to facilitate cloud bursting by determining which applications can be transitioned into the cloud most economically, and automating the movement process at the proper time. We further optimize the deployment of applications into the cloud using an intelligent precopying mechanism that proactively replicates virtualized applications, lowering the bursting time from hours to minutes. Our evaluation illustrates how our prototype can reduce cloud costs by more than 45% when bursting to the cloud, and the incremental cost added by precopying applications is offset by a burst time reduction of nearly 95%.

On modern processors, hardware-assisted virtualization outperforms binary translation for most workloads. But hardware virtualization has a potential problem: virtualization exits are expensive. While hardware virtualization executes guest instructions at native speed, guest/VMM transitions can sap performance. Hardware designers attacked this problem both by reducing guest/VMM transition costs and by adding architectural extensions such as nested paging support to avoid exits.

This paper proposes complementary software techniques for reducing the exit frequency. In the simplest form, our VMM inspects guest code dynamically to detect back-to-back pairs of instructions that both exit. By handling a pair of instructions when the first one exits, we save 50% of the transition costs. Then, we generalize from pairs to clusters of instructions that may include loops and other control flow. We use a binary translator to generate, and cache, custom translations for handling exits. The analysis cost is paid once, when the translation is generated, but amortized over all future executions.

Our techniques have been fully implemented and validated in recent versions of VMware products. We show that clusters consistently reduce the number of exits for all examined workloads. When execution is dominated by exit costs, this translates into measurable runtime improvements. Most importantly, clusters enable substantial gains for nested virtual machines, delivering speedups as high as 1.52x. Intuitively, this result stems from the fact that transitions between the inner guest and VMM are extremely costly, as they are implemented in software by the outer VMM.

Graphics processing units (GPUs) have become a very powerful platform embracing a concept of heterogeneous many-core computing. However, application domains of GPUs are currently limited to specific systems, largely due to a lack of “first-class” GPU resource management for general-purpose multi-tasking systems.

We present Gdev, a new ecosystem of GPU resource management in the operating system (OS). It allows the user space as well as the OS itself to use GPUs as first-class computing resources. Specifically, Gdev’s virtual memory manager supports data swapping for excessive memory resource demands, and also provides a shared device memory functionality that allows GPU contexts to communicate with other contexts. Gdev further provides a GPU scheduling scheme to virtualize a physical GPU into multiple logical GPUs, enhancing isolation among working sets of multi-tasking systems.

Our evaluation conducted on Linux and the NVIDIA GPU shows that the basic performance of our prototype implementation is reliable even compared to proprietary software. Further detailed experiments demonstrate that Gdev achieves a 2x speedup for an encrypted file system using the GPU in the OS. Gdev can also improve the makespan of dataflow programs by up to 49% exploiting shared device memory, while an error in the utilization of virtualized GPUs can be limited within only 7%.

This paper describes Gnothi, a block replication system that separates data from metadata to provide efficient and available storage replication. Separating data from metadata allows Gnothi to execute disk accesses on subsets of replicas while using fully replicated metadata to ensure that requests are executed correctly and to speed up recovery of slow or failed replicas.

Performance evaluation shows that Gnothi can achieve 40-64% higher write throughput than previous work and significantly save storage space. Furthermore, while a failed replica recovers, Gnothi can provide about 100-200% higher throughput, while still retaining the same recovery time and while guaranteeing that recovery eventually completes.

We present Vivace, a key-value storage system for web applications that span many geographically-distributed sites. Vivace provides strong consistency and replicates data across sites for access locality and disaster tolerance. Vivace is designed to cope well with network congestion across sites, which occurs because the bandwidth across sites is smaller than within sites. To deal with congestion, Vivace relies on two novel algorithms that prioritize a small amount of critical data to avoid delays due to congestion. We evaluate Vivace to show its feasibility and effectiveness.