MeNa: A memory navigator for modern hardware in a scale-out environment

Abstract

Scale-out infrastructure such as Cloud is built upon a large network of multi-core processors. Performance, power consumption, and capital cost of such infrastructure depend on the overall system configuration including number of processing cores, core frequency, memory hierarchy and capacity, number of memory channels, and memory data rate. Among these parameters, memory subsystem is known to be one of the performance bottlenecks, contributing significantly to the overall capital and operational cost of the server. Also, given the rise of Big Data and analytics applications, this could potentially pose an even bigger challenge to the performance of cloud applications and cost of cloud infrastructure. Hence it is important to understand the role of memory subsystem in cloud infrastructure and in particular for this emerging class of applications. Despite the increasing interest in recent years, little work has been done in understanding memory requirements trends and developing accurate and effective models to predict performance and cost of memory subsystem. Currently there is no well-defined methodology for selecting a memory configuration that reduces execution time and power consumption by considering the capital and operational cost of cloud. In this paper, through a comprehensive real-system empirical analysis of performance, we address these challenges by first characterizing diverse types of scale-out applications across a wide range of memory configuration parameters. The characterization helps to accurately capture applications' behavior and derive a model to predict their performance. Based on the developed predictive model, we propose MeNa, which is a methodology to maximize the performance/cost ratio of scale-out applications running in cloud environment. MeNa navigates memory and processor parameters to find the system configuration for a given application and a given budget, to maximum performance. Compared to brute force method, MeNa achieves more than 90% accuracy for identifying the right configuration parameters to maximize performance/cost ratio. Moreover, we show how MeNa can be effectively leveraged for server designers to find architectural insights or subscribers to allocate just enough budget to maximize performance of their applications in cloud