Raju Rangaswami

I/O Deduplication: Utilizing content similarity to improve I/O performance

Duplication of data in storage systems is becoming increasingly common. We introduce I/O Deduplication, a storage optimization that utilizes content similarity for improving I/O performance by eliminating I/O operations and reducing the mechanical delays during I/O operations. I/O Deduplication consists of three main techniques: content-based caching, dynamic replica retrieval, and selective duplication. Each of these techniques is motivated by our observations with I/O workload traces obtained from actively-used production storage systems, all of which revealed surprisingly high levels of content similarity for both stored and accessed data. Evaluation of a prototype implementation using these workloads showed an overall improvement in disk I/O performance of 28 to 47% across these workloads. Further breakdown also showed that each of the three techniques contributed significantly to the overall performance improvement.

Application performance modeling in a virtualized environment

Performance models provide the ability to predict application performance for a given set of hardware resources and are used for capacity planning and resource management. Traditional performance models assume the availability of dedicated hardware for the application. With growing application deployment on virtualized hardware, hardware resources are increasingly shared across multiple virtual machines. In this paper, we build performance models for applications in virtualized environments. We identify a key set of virtualization architecture independent parameters that influence application performance for a diverse and representative set of applications. We explore several conventional modeling techniques and evaluate their effectiveness in modeling application performance in a virtualized environment. We propose an iterative model training technique based on artificial neural networks which is found to be accurate across a range of applications. The proposed approach is implemented as a prototype in Xen-based virtual machine environments and evaluated for accuracy, sensitivity to the training process, and overhead. Median modeling error in the range 1.16-6.65% across a diverse application set and low modeling overhead suggest the suitability of our approach in production virtualized environments.

EXCES: External caching in energy saving storage systems

Power consumption within the disk-based storage subsystem forms a substantial portion of the overall energy footprint in commodity systems. Researchers have proposed external caching on a persistent, low-power storage device, which we term external caching device (ECD), to minimize disk activity and conserve energy. While recent simulation-based studies have argued in favor of this approach, the lack of an actual system implementation has precluded answering several key questions about external caching systems. We present the design and implementation of EXCES, an external caching system that employs prefetching, caching, and buffering of disk data for reducing disk activity. EXCES addresses important questions related to external caching, including the estimation of future data popularity, I/O indirection, continuous reconfiguration of the ECD contents, and data consistency. We evaluated EXCES with both micro- and macro-benchmarks that address idle, I/O intensive, and real-world workloads. Overall system energy savings was found to lie in the modest 2-14% range, depending on the workload, in somewhat of a contrast to the higher values predicted by earlier studies. Furthermore, while the CPU and memory overheads of EXCES were well within acceptable limits, we found that flash-based external caching can substantially degrade I/O performance. We believe that external caching systems hold promise. Further improvements in ECD technology, both in terms of their power consumption and performance characteristics can help realize the full potential of such systems.

CVM - A communication virtual machine

The convergence of data, voice, and multimedia communication over digital networks, coupled with continuous improvement in network capacity and reliability has resulted in a proliferation of communication technologies. Unfortunately, despite these new developments, there is no easy way to build new application-specific communication services. The stovepipe approach used today for building new communication services results in rigid technology, limited utility, lengthy and costly development cycle, and difficulty in integration. In this paper, we introduce communication virtual machine (CVM) that supports rapid conception, specification, and automatic realization of new application-specific communication services through a user-centric, model-driven approach. We present the concept, architecture, modeling language, prototypical design, and implementation of CVM in the context of a healthcare application.

A Communication Virtual Machine

The convergence of data, voice and multimedia communication over digital networks, coupled with continuous improvement in network capacity and reliability has significantly enriched the ways we communicate. However, the stovepipe approach used to develop todays communication applications and tools results in rigid technology, limited utility, lengthy and costly development cycle, difficulty in integration, and hinders innovation. In this paper, we present a fundamentally different approach, which we call communication virtual machine (CVM) to address these problems. CVM provides a user-centric, model-driven approach for conceiving, synthesizing and delivering communication solutions across application domains. We argue that CVM represents a far more effective paradigm for engineering communication solutions. The concept, architecture, modeling language, prototypical design and implementation of CVM are discussed

Anatomy of a Real-Time Intrusion Prevention System

Host intrusion prevention systems for both servers and end-hosts must address the dual challenges of accuracy and performance. Researchers have mostly focused on addressing the former challenge, suggesting solutions based either on exploit- based penetration detection or anomaly-based misbehavior detection, but yet stopping short of comprehensive solutions that leverage merits of both approaches. The second challenge, however, is rarely addressed; doing so comprehensively is important since these systems can introduce substantial overhead and cause system slowdown, more so when the system load is high. We present Rootsense, a holistic and real-time intrusion prevention system that combines the merits of misbehavior- based and anomaly-based detection. Four principles govern the design and implementation of Rootsense. First, Rootsense audits events within different subsystems of the host operating system and correlates them to comprehensively capture the global system state. Second, Rootsense restricts the detection domain to root compromises only; doing so reduces run-time overhead and increases detection accuracy (root behavior is more easily modeled than user behavior). Third, Rootsense adopts a dual approach to intrusion detection - a root penetration detector detects activities that exploit system vulnerabilities to penetrate the security perimeter, and a root misbehavior detector tracks misbehavior by root processes. Fourth, Rootsense is designed to be configurable for overhead management allowing the system administrator to tune the overhead characteristics of the intrusion prevention system that affect foreground task performance. A Linux implementation of Rootsense is analyzed for both accuracy and performance, using several real-world exploits and a range of end-host and server benchmarks.

MEMS-based disk buffer for streaming media servers

The performance of streaming media servers has been limited due to the dual requirements of high throughput and low memory use. Although disk throughput has been enjoying a 40% annual increase, slower improvements in disk access times necessitate the use of large DRAM buffers to improve the overall streaming throughput. MEMS-based storage is an exciting new technology that promises to bridge the widening performance gap between DRAM and disk-drives in the memory hierarchy. We explore the impact of integrating these devices into the memory hierarchy on the class of streaming media applications. We evaluate the use of MEMS-based storage for buffering and caching streaming data. We also show how a bank of k MEMS devices can be managed in either configuration and that they can provide a k-fold improvement in both throughput and access latency. An extensive analytical study shows that using MEMS storage can reduce the buffering cost and improve the throughput of streaming servers significantly.

A User-Centric Network Communication Broker for Multimedia Collaborative Computing

The development of collaborative multimedia applications today follows a vertical development approach, which is a major inhibitor that drives up the cost of development and slows down the pace of innovation of new generations of collaborative applications. In this paper, we propose a network communication broker (NCB) that provides a unified higher-level abstraction that encapsulates the complexity of network-level communication control and media delivery for the class of multimedia collaborative applications. NCB expedites the development of next-generation applications with diverse communication logics. Furthermore, NCB-based applications can be easily ported to new network environments. In addition, the self-managing design of NCB supports dynamic adaptation in response to changes in network conditions and user requirements.

Systems support for preemptive disk scheduling

Allowing higher-priority requests to preempt ongoing disk IOs is of particular benefit to delay-sensitive and real-time systems. In this paper, we present semi-preemptible IO, which divides disk IO requests into small temporal units of disk commands to improve the preemptibility of disk access. We first lay out main design strategies to allow preemption of each component of a disk access-seek, rotation, and data transfer, namely, seek-splitting, JIT-seek, and chunking. We then present the preemption mechanisms for single and multidisk systems-JIT-preemption and JIT-migration. The evaluation of our prototype system showed that semi-preemptible IO substantially improved the preemptibility of disk access with little loss in disk throughput and that preemptive disk scheduling could improve the response time for high-priority interactive requests.

Platform-independent modeling and prediction of application resource usage characteristics

Application resource usage models can be used in the decision making process for ensuring quality-of-service as well as for capacity planning, apart from their general use in performance modeling, optimization, and systems management. Current solutions for modeling application resource usage tend to address parts of the problem by either focusing on a specific application, or a specific platform, or on a small subset of system resources. We propose a simple and flexible approach for modeling application resource usage in a platform-independent manner that enables the prediction of application resource usage on unseen platforms. The technique proposed is application agnostic, requiring no modification to the application (binary or source) and no knowledge of application-semantics. We implement a Linux-based prototype and evaluate it using four different workloads including real-world applications and benchmarks. Our experiments reveal prediction errors that are bound within 6-24% of the observed for these workloads when using the proposed approach.