Jacob R. Lorch

Farsite: federated, available, and reliable storage for an incompletely trusted environment

Farsite is a secure, scalable file system that logically functions as a centralized file server but is physically distributed among a set of untrusted computers. Farsite provides file availability and reliability through randomized replicated storage; it ensures the secrecy of file contents with cryptographic techniques; it maintains the integrity of file and directory data with a Byzantine-fault-tolerant protocol; it is designed to be scalable by using a distributed hint mechanism and delegation certificates for pathname translations; and it achieves good performance by locally caching file data, lazily propagating file updates, and varying the duration and granularity of content leases. We report on the design of Farsite and the lessons we have learned by implementing much of that design.

Improving dynamic voltage scaling algorithms with PACE

This paper addresses algorithms for dynamically varying (scaling) CPU speed and voltage in order to save energy. Such scaling is useful and effective when it is immaterial when a task completes, as long as it meets some deadline. We show how to modify any scaling algorithm to keep performance the same but minimize expected energy consumption. We refer to our approach as PACE (Processor Acceleration to Conserve Energy) since the resulting schedule increases speed as the task progresses. Since PACE depends on the probability distribution of the tasks work requirement, we present methods for estimating this distribution and evaluate these methods on a variety of real workloads. We also show how to approximate the optimal schedule with one that changes speed a limited number of times. Using PACE causes very little additional overhead, and yields substantial reductions in CPU energy consumption. Simulations using real workloads show it reduces the CPU energy consumption of previously published algorithms by up to 49.5%, with an average of 20.6%, without any effect on performance.

Software strategies for portable computer energy management

Limiting the energy consumption of computers, especially portables, is becoming increasingly important. Thus, new energy-saving computer components and architectures have been and continue to be developed. Many architectural features have both high-performance and low-power modes, with the mode selection under software control. The problem is to minimize energy consumption while not significantly impacting the effective performance. We group the software control issues as follows: transition, load-change, and adaptation. The transition problem is deciding when to switch to low-power, reduced-functionality modes. The load-change problem is determining how to modify the load on a component so that it can make further use of its low-power modes. The adaptation problem is determining how to create software that allows components to be used in novel, power-saving ways. We survey implemented and proposed solutions to software energy management issues created by existing and suggested hardware innovations.

Donnybrook: enabling large-scale, high-speed, peer-to-peer games

Without well-provisioned dedicated servers, modern fast-paced action games limit the number of players who can interact simultaneously to 16-32. This is because interacting players must frequently exchange state updates, and high player counts would exceed the bandwidth available to participating machines. In this paper, we describe Donnybrook, a system that enables epic-scale battles without dedicated server resources, even in a fast-paced game with tight latency bounds. It achieves this scalability through two novel components. First, it reduces bandwidth demand by estimating what players are paying attention to, thereby enabling it to reduce the frequency of sending less important state updates. Second, it overcomes resource and interest heterogeneity by disseminating updates via a multicast system designed for the special requirements of games: that they have multiple sources, are latency-sensitive, and have frequent group membership changes. We present user study results using a prototype implementation based on Quake III that show our approach provides a desirable user experience. We also present simulation results that demonstrate Donnybrooks efficacy in enabling battles of up to 900 players.

PACE: a new approach to dynamic voltage scaling

By dynamically varying CPU speed and voltage, it is possible to save significant amounts of energy while still meeting prespecified soft or hard deadlines for tasks; numerous algorithms have been published with this goal. We show that it is possible to modify any voltage scaling algorithm to minimize energy use without affecting perceived performance and present a formula to do so optimally. Because this formula specifies increased speed as the task progresses, we call this approach PACE (Processor Acceleration to Conserve Energy). This optimal formula depends on the probability distribution of the tasks work requirement and requires that the speed be varied continuously. We therefore present methods for estimating the task work distribution and evaluate how effective they are on a variety of real workloads. We also show how to approximate the optimal continuous schedule with one that changes speed a limited number of times. Using these methods, we find we can apply PACE practically and efficiently. Furthermore, PACE is extremely effective. Simulations using real workloads and the standard model for energy consumption as a function of voltage show that PACE can reduce the CPU energy consumption of existing algorithms by up to 49.5 percent, with an average of 20.6 percent, without any effect on perceived performance. The consequent PACE-modified algorithms reduce CPU energy consumption by an average of 65.4 percent relative to no dynamic voltage scaling, as opposed to only 54.3 percent without PACE.

Can the fractal dimension of images be measured

Abstract Fractal dimension is a popular parameter for explaining certain phenomena and for describing natural textures. The problem of estimating the fractal dimension of a profile or an image is more difficult and devious than theory suggests. This paper studies the accuracy and robustness of two common estimators of fractal dimension (box counting and the variation method) using two types of data (Brownian and Takagi). Poor results are demonstrated from applying theory directly, called naive estimation. Data is then interpreted in the most optimistic way possible by matching the estimator to the known fractal dimension. Experiments quantify the effects of resolution, or fineness of sampling, and quantization, or rounding of sampled values. Increasing resolution enhances the estimators when true dimension, D, is large, but may, possibly due to quantization effect, degrade estimators when D is small. Quantization simply causes shifts in estimates. The results suggest that one should not place much reliance in the absolute value of a fractal estimate, but that the estimates do vary monotonically with D and might be useful descriptors in tasks such as image segmentation and description.

Memoir: Practical State Continuity for Protected Modules

To protect computation, a security architecture must safeguard not only the software that performs it but also the state on which the software operates. This requires more than just preserving state confidentiality and integrity, since, e.g., software may err if its state is rolled back to a correct but stale version. For this reason, we present Memoir, the first system that fully ensures the continuity of a protected software modules state. In other words, it ensures that a modules state remains persistently and completely inviolate. A key contribution of Memoir is a technique to ensure rollback resistance without making the system vulnerable to system crashes. It does this by using a deterministic module, storing a concise summary of the modules request history in protected NVRAM, and allowing only safe request replays after crashes. Since frequent NVRAM writes are impractical on modern hardware, we present a novel way to leverage limited trusted hardware to minimize such writes. To ensure the correctness of our design, we develop formal, machine-verified proofs of safety. To demonstrate Memoirs practicality, we have built it and conducted evaluations demonstrating that it achieves reasonable performance on real hardware. Furthermore, by building three useful Memoir-protected modules that rely critically on state continuity, we demonstrate Memoirs versatility.

The SMART way to migrate replicated stateful services

Many stateful services use the replicated state machine approach for high availability. In this approach, a service runs on multiple machines to survive machine failures. This paper describes SMART, a new technique for changing the set of machines where such a service runs, i.e., migrating the service. SMART improves upon existing techniques in three important ways. First, SMART allows migrations that replace non-failed machines. Thus, SMART enables load balancing and lets an automated system replace failed machines. Such autonomic migration is an important step toward full autonomic operation, in which administrators play a minor role and need not be available twenty-four hours a day, seven days a week. Second, SMART can pipeline concurrent requests, a useful performance optimization. Third, prior published migration techniques are described in insufficient detail to admit implementation, whereas our description of SMART is complete. In addition to describing SMART, we also demonstrate its practicality by implementing it, evaluating our implementations performance, and using it to build a consistent, replicated, migratable file system. Our experiments demonstrate the performance advantage of pipelining concurrent requests, and show that migration has only a minor and temporary effect on performance.

Scheduling techniques for reducing processor energy use in MacOS

The CPU is one of the major power consumers in a portable computer, and considerable power can be saved by turning off the CPU when it is not doing useful work. In Apples MacOS, however, idle time is often converted to busy waiting, and generally it is very hard to tell when no useful computation is occurring. In this paper, we suggest several heuristic techniques for identifying this condition, and for temporarily putting the CPU in a low‐power state. These techniques include turning off the processor when all processes are blocked, turning off the processor when processes appear to be busy waiting, and extending real time process sleep periods. We use trace‐driven simulation, using processor run interval traces, to evaluate the potential energy savings and performance impact. We find that these techniques save considerable amounts of processor energy (as much as 66%), while having very little performance impact (less than 2% increase in run time). Implementing the proposed strategies should increase battery lifetime by approximately 20% relative to Apples current CPU power management strategy, since the CPU and associated logic are responsible for about 32% of power use; similar techniques should be applicable to operating systems with similar behavior.

Reducing processor power consumption by improving processor time management in a single-user operating system

The CPU is one of the major power consumers in a portable computer, and considerable power can be saved by turning off the CPU when it is not doing useful work. In Apple’s MacOS, however, idle time is often converted to busy waiting, and generally it is very hard to tell when no useful computation is occurring. In this paper, we suggest several heuristic techniques for identifying this condition, and for temporarily putting the CPU in a low-power state. These techniques include turning off the processor when all processes are blocked, turning off the processor when processes appear to be busy waiting, and extending real time process sleep periods. We use trace-driven simulation, using processor run interval traces, to evaluate the potential energy savings and performance impact. We find that these techniques save considerable amounts of processor energy (as much as 66%), while having very little performance impact (less than 2% increase in run time). Implementing the proposed strategies should increase battery lifetime by approximately 20% relative to Apple’s current CPU power management strategy, since the CPU and associated logic are responsible for about 32% of power use; similar techniques should be applicable to operating systems with similar behavior.