Software debugging is a time-consuming and challenging process. Supporting debugging has been a focus of the software engineering field since its inception with numerous empirical studies, theories, and tools to support developers in this task. Performance bugs and performance debugging is a sub-genre of debugging that has received less attention.

Performance bottlenecks related to a program's memory behavior are common, yet very hard to debug. Tools that attempt to aid software engineers in diagnosing these bugs are typically designed to handle specific use cases; they do not provide information to comprehensively explore memory problems and to find solutions. Detailed traces of memory accesses would enable developers to ask various questions about the program's memory behaviour, but these traces quickly become very large even for short executions. We present DINAMITE: a toolkit for Dynamic INstrumentation and Analysis for MassIve Trace Exploration. DINAMITE instruments every memory access with highly debug information and provides a suite of extensible analysis tools to aid programmers in pinpointing memory bottlenecks.

Effective resource management of virtualized environments is a challenging task. State-of-the-art management systems either rely on analytical models or evaluate resource allocations by running actual experiments. However, both approaches incur a significant overhead once the workload changes. The former needs to re-calibrate and re-validate models, whereas the latter has to run a new set of experiments to select a new resource allocation. During the adaptation period, the system may run with an inefficient configuration. In this paper, we propose DejaVu - a framework that (1) minimizes the resource management overhead by identifying a small set of workload classes for which it needs to evaluate resource allocation decisions, (2) quickly adapts to workload changes by classifying workloads using signatures and caching their preferred resource allocations at runtime, and (3) deals with interference by estimating an "interference index". We evaluate DejaVu by running representative network services on Amazon EC2. DejaVu achieves more than 10x speedup in adaptation time for each workload change relative to the state-of-the-art. By enabling quick adaptation, DejaVu saves up to 60% of the service provisioning cost. Finally, DejaVu is easily deployable as it does not require any extensive instrumentation or human intervention.