Prashant J. Shenoy

Agile dynamic provisioning of multi-tier Internet applications

Dynamic capacity provisioning is a useful technique for handling the multi-time-scale variations seen in Internet workloads. In this article, we propose a novel dynamic provisioning technique for multi-tier Internet applications that employs (1) a flexible queuing model to determine how much of the resources to allocate to each tier of the application, and (2) a combination of predictive and reactive methods that determine when to provision these resources, both at large and small time scales. We propose a novel data center architecture based on virtual machine monitors to reduce provisioning overheads. Our experiments on a forty-machine Xen/Linux-based hosting platform demonstrate the responsiveness of our technique in handling dynamic workloads. In one scenario where a flash crowd caused the workload of a three-tier application to double, our technique was able to double the application capacity within five minutes, thus maintaining response-time targets. Our technique also reduced the overhead of switching servers across applications from several minutes to less than a second, while meeting the performance targets of residual sessions.

An analytical model for multi-tier internet services and its applications

Since many Internet applications employ a multi-tier architecture, in this paper, we focus on the problem of analytically modeling the behavior of such applications. We present a model based on a network of queues, where the queues represent different tiers of the application. Our model is sufficiently general to capture (i) the behavior of tiers with significantly different performance characteristics and (ii) application idiosyncrasies such as session-based workloads, concurrency limits, and caching at intermediate tiers. We validate our model using real multi-tier applications running on a Linux server cluster. Our experiments indicate that our model faithfully captures the performance of these applications for a number of workloads and configurations. For a variety of scenarios, including those with caching at one of the application tiers, the average response times predicted by our model were within the 95% confidence intervals of the observed average response times. Our experiments also demonstrate the utility of the model for dynamic capacity provisioning, performance prediction, bottleneck identification, and session policing. In one scenario, where the request arrival rate increased from less than 1500 to nearly 4200 requests/min, a dynamic provisioning technique employing our model was able to maintain response time targets by increasing the capacity of two of the application tiers by factors of 2 and 3.5, respectively.

Resource overbooking and application profiling in shared hosting platforms

In this paper, we present techniques for provisioning CPU and network resources in shared hosting platforms running potentially antagonistic third-party applications. The primary contribution of our work is to demonstrate the feasibility and benefits of overbooking resources in shared platforms, to maximize the platform yield: the revenue generated by the available resources. We do this by first deriving an accurate estimate of application resource needs by profiling applications on dedicated nodes, and then using these profiles to guide the placement of application components onto shared nodes. By overbooking cluster resources in a controlled fashion, our platform can provide performance guarantees to applications even when overbooked, and combine these techniques with commonly used QoS resource allocation mechanisms to provide application isolation and performance guarantees at run-time. When compared to provisioning based on the worst-case, the efficiency (and consequently revenue) benefits from controlled overbooking of resources can be dramatic. Specifically, experiments on our Linux cluster implementation indicate that overbooking resources by as little as 1% can increase the utilization of the cluster by a factor of two, and a 5% overbooking yields a 300--500% improvement, while still providing useful resource guarantees to applications.

SensEye : a multi-tier camera sensor network

This paper argues that a camera sensor network containing heterogeneous elements provides numerous benefits over traditional homogeneous sensor networks. We present the design and implementation of senseye---a multi-tier network of heterogeneous wireless nodes and cameras. To demonstrate its benefits, we implement a surveillance application using senseye comprising three tasks: object detection, recognition and tracking. We propose novel mechanisms for low-power low-latency detection, low-latency wakeups, efficient recognition and tracking. Our techniques show that a multi-tier sensor network can reconcile the traditionally conflicting systems goals of latency and energy-efficiency. An experimental evaluation of our prototype shows that, when compared to a single-tier prototype, our multi-tier senseye can achieve an order of magnitude reduction in energy usage while providing comparable surveillance accuracy.

Private memoirs of a smart meter

Household smart meters that measure power consumption in real-time at fine granularities are the foundation of a future smart electricity grid. However, the widespread deployment of smart meters has serious privacy implications since they inadvertently leak detailed information about household activities. In this paper, we show that even without a priori knowledge of household activities or prior training, it is possible to extract complex usage patterns from smart meter data using off-the-shelf statistical methods. Our analysis uses two months of data from three homes, which we instrumented to log aggregate household power consumption every second. With the data from our small-scale deployment, we demonstrate the potential for power consumption patterns to reveal a range of information, such as how many people are in the home, sleeping routines, eating routines, etc. We then sketch out the design of a privacy-enhancing smart meter architecture that allows an electric utility to achieve its net metering goals without compromising the privacy of its customers.

Dynamic Provisioning of Multi-tier Internet Applications

Dynamic capacity provisioning is a useful technique for handling the multi-time-scale variations seen in Internet workloads. In this paper, we propose a novel dynamic provisioning technique for multitier Internet applications that employs (i) a flexible queuing model to determine how much resources to allocate to each tier of the application, and (ii) a combination of predictive and reactive methods that determine when to provision these resources, both at large and small time scales. Our experiments on a forty-machine Linux-based hosting platform demonstrate the responsiveness of our technique in handling dynamic workloads. In one scenario where a flash crowd caused the workload of a three-tier application to double, our technique was able to double the application capacity within five minutes, thus maintaining response time targets

Cello: a disk scheduling framework for next generation operating systems

In this paper, we present the Cello disk scheduling framework for meeting the diverse service requirements of applications. Cello employs a two-level disk scheduling architecture, consisting of a class-independent scheduler and a set of class-specific schedulers. The two levels of the framework allocate disk bandwidth at two time-scales: the class-independent scheduler governs the coarse-grain allocation of bandwidth to application classes, while the class-specific schedulers control the fine-grain interleaving of requests. The two levels of the architecture separate application-independent mechanisms from application-specific scheduling policies, and thereby facilitate the co-existence of multiple class-specific schedulers. We demonstrate that Cello is suitable for next generation operating systems since: (i) it aligns the service provided with the application requirements, (ii) it protects application classes from one another, (iii) it is work-conserving and can adapt to changes in work-load, (iv) it minimizes the seek time and rotational latency overhead incurred during access, and (v) it is computationally efficient.

Sandpiper: Black-box and gray-box resource management for virtual machines

Virtualization can provide significant benefits in data centers by enabling dynamic virtual machine resizing and migration to eliminate hotspots. We present Sandpiper, a system that automates the task of monitoring and detecting hotspots, determining a new mapping of physical to virtual resources, resizing virtual machines to their new allocations, and initiating any necessary migrations. Sandpiper implements a black-box approach that is fully OS- and application-agnostic and a gray-box approach that exploits OS- and application-level statistics. We implement our techniques in Xen and conduct a detailed evaluation using a mix of CPU, network and memory-intensive applications. Our results show that Sandpiper is able to resolve single server hotspots within 20s and scales well to larger, data center environments. We also show that the gray-box approach can help Sandpiper make more informed decisions, particularly in response to memory pressure.

Profiling and Modeling Resource Usage of Virtualized Applications

Next Generation Data Centers are transforming labor-inten- sive, hard-coded systems into shared, virtualized, automated, and fully managed adaptive infrastructures. Virtualization technologies promise great opportunities for reducing energy and hardware costs through server consolidation. However, to safely transition an application running natively on real hardware to a virtualized environment, one needs to estimate the additional resource requirements incurred by virtualization overheads. In this work, we design a general approach for estimating the resource requirements of applications when they are transferred to a virtual environment. Our approach has two key components: a set of microbenchmarks to profile the different types of virtualization overhead on a given platform, and a regression-based model that maps the native system usage profile into a virtualized one. This derived model can be used for estimating resource requirements of any application to be virtualized on a given platform. Our approach aims to eliminate error-prone manual processes and presents a fully automated solution. We illustrate the effectiveness of our methodology using Xen virtual machine monitor. Our evaluation shows that our automated model generation procedure effectively characterizes the different virtualization overheads of two diverse hardware platforms and that the models have median prediction error of less than 5% for both the RUBiS and TPC-W benchmarks.

Multimedia streaming via TCP: An analytic performance study

TCP is widely used in commercial multimedia streaming systems, with recent measurement studies indicating that a significant fraction of Internet streaming media is currently delivered over HTTP/TCP. These observations motivate us to develop analytic performance models to systematically investigate the performance of TCP for both live and stored-media streaming. We validate our models via ns simulations and experiments conducted over the Internet. Our models provide guidelines indicating the circumstances under which TCP streaming leads to satisfactory performance, showing, for example, that TCP generally provides good streaming performance when the achievable TCP throughput is roughly twice the media bitrate, with only a few seconds of startup delay.