Continuous resource monitoring for self-predicting DBMS

Administration tasks increasingly dominate the total cost of ownership of database management systems. A key task, and a very difficult one for an administrator, is to justify upgrades of CPU, memory and storage resources with quantitative predictions of the expected improvement in workload performance. Current database systems are not designed with such prediction in mind and hence offer only limited help to the administrator. This paper proposes changes to database system design that enable a Resource Advisor to answer “whatif” questions about resource upgrades. A prototype Resource Advisor built to work with a commercial DBMS shows the efficacy of our approach in predicting the effect of upgrading a key resource — buffer pool size — on OLTP workloads in a highly concurrent system.

Software Power Measurement

Abstract Effective system-level power management requires cheap, accurate and finegrained power measurement and accounting. Unfortunately current portable hardware does not provide this capability. We advocate software power measurement: estimation of power consumption by modelling it as a function of device state. The approach requires no additional hardware, and allows fine-grained, per-device and per-application power measurement. We describe a design and implementation of software power measurement, and a feasibility study showing significantly better accuracy than power profiling based on time averaging. We conclude with design recommendations for OS designers and portable hardware vendors to improve the ease and accuracy of power measurement

Predictive Resource Management for Wearable Computing

Achieving crisp interactive response in resource-intensive applications such as augmented reality, language translation, and speech recognition is a major challenge on resource-poor wearable hardware. In this paper we describe a solution based on multi-fidelity computation supported by predictive resource management. We show that such an approach can substantially reduce both the mean and the variance of response time. On a benchmark representative of augmented reality, we demonstrate a 60 % reduction in mean latency and a 30 % reduction in the coefficient of variation. We also show that a history-based approach to demand prediction is the key to this performance improvement.

Multi-Fidelity Algorithms for Interactive Mobile Applications

this paper, we show why interactive mobile applicationsrequire us to rethink this concept from first principles. Such applications are difficult to support because they place heavy resource demands on hardware that is typically optimized for weight, size and battery life rather than compute power. We show how the notion of an algorithm can be extended to help alleviate this problem, and examine the implications of this shift in viewpoint. The paper is organized in three parts: rationale, research agenda, and related wor

Multi-Fidelity Algorithms for Interactive Mobile Applications

... In this paper, we show why interactive mobile applications require us to rethink this concept from first principles. Such applications are difficult to support because they place heavy resource demands on hardware that is typically optimized for weight, size and battery life rather than compute power. We show how the notion of an algorithm can be extended to help alleviate this problem, and examine the implications of this shift in viewpoint. The paper is organized in three parts: rationale, research agenda, and related work