Diagnosing performance issues is often difficult, especially when they occur only during some program executions. Profilers can help with performance debugging, but are ineffective when the most costly functions are not the root causes of performance issues. To address this problem, we introduce a new profiling methodology, value-assisted cost profiling, and a tool vProf. Our insight is that capturing the values of variables can greatly help diagnose performance issues. vProf continuously records values while profiling normal and buggy program executions. It identifies anomalies in the values and the functions where they occur to pinpoint the real root causes of performance issues. Using a set of 15 real-world performance bugs in four widely used applications, we show that vProf is effective at diagnosing all of the issues while other state-of-the-art tools diagnose only a few of them. We further use vProf to diagnose longstanding performance issues in these applications that have been unresolved for over four years.

Web applications are getting ubiquitous every day because they offer many useful services to consumers and businesses. Many of these web applications are quite storage-intensive. Cloud computing offers attractive and economical choices for meeting their storage needs. Unfortunately, it remains challenging for developers to best leverage them to minimize cost. This paper presents Grandet, an extensible storage system that significantly reduces storage cost for web applications deployed in the cloud. Grandet provides both a key-value interface and a file system interface, supporting a broad spectrum of web applications. Under the hood, it supports multiple heterogeneous stores and unifies them by placing each data object at the store deemed most economical. We implemented Grandet on Amazon Web Services and evaluated Grandet on a diverse set of four popular open-source web applications. Our results show that Grandet reduces their cost by an average of 42.4%, and it is fast, scalable, and easy to use. The source code of Grandet is at http://columbia.github.io/grandet.

With the evolvement of hardware, 64-bit Central Processing Units (CPUs) and 64-bit Operating Systems (OSs) have dominated the market. This article investigates the performance of virtual memory management of Virtual Machines (VMs) with a large virtual address space in 64-bit OSs, which imposes different pressure on memory virtualization than 32-bit systems. Each of the two conventional memory virtualization approaches, Shadowing Paging (SP) and Hardware-Assisted Paging (HAP), causes different overhead for different applications. Our experiments show that 64-bit applications prefer to run in a VM using SP, while 32-bit applications do not have a uniform preference between SP and HAP. In this article, we trace this inconsistency between 32-bit applications and 64-bit applications to its root cause through a systematic empirical study in Linux systems and discover that the major overhead of SP results from memory management in the 32-bit GNU C library ( glibc ). We propose enhancements to the existing memory management algorithms, which substantially reduce the overhead of SP. Based on the evaluations using SPEC CPU2006, Parsec 2.1, and cloud benchmarks, our results show that SP, with the improved memory allocators, can compete with HAP in almost all cases, in both 64-bit and 32-bit systems. We conclude that without a significant breakthrough in HAP, researchers should pay more attention to SP, which is more flexible and cost effective.