Real production applications ranging from enterprise applications to large e-commerce sites share a crucial but seldom-noted characteristic: The relative frequencies of transaction types in their workloads are nonstationary , i.e., the transaction mix changes over time. Accurately predicting application-level performance in business-critical production applications is an increasingly important problem. However, transaction mix nonstationarity casts doubt on the practical usefulness of prediction methods that ignore this phenomenon. This paper demonstrates that transaction mix nonstationarity enables a new approach to predicting application-level performance as a function of transaction mix. We exploit nonstationarity to circumvent the need for invasive instrumentation and controlled benchmarking during model calibration; our approach relies solely on lightweight passive measurements that are routinely collected in today's production environments. We evaluate predictive accuracy on two real business-critical production applications. The accuracy of our response time predictions ranges from 10% to 16% on these applications, and our models generalize well to workloads very different from those used for calibration. We apply our technique to the challenging problem of predicting the impact of application consolidation on transaction response times. We calibrate models of two testbed applications running on dedicated machines, then use the models to predict their performance when they run together on a shared machine and serve very different workloads. Our predictions are accurate to within 4% to 14%. Existing approaches to consolidation decision support predict post-consolidation resource utilizations . Our method allows application-level performance to guide consolidation decisions.

Deadlock in multithreaded programs is an increasingly important problem as ubiquitous multicore architectures force parallelization upon an ever wider range of software. This paper presents a theoretical foundation for dynamic deadlock avoidance in concurrent programs that employ conventional mutual exclusion and synchronization primitives (e.g., multithreaded C/Pthreads programs). Beginning with control flow graphs extracted from program source code, we construct a formal model of the program and then apply Discrete Control Theory to automatically synthesize deadlockavoidance control logic that is implemented by program instrumentation. At run time, the control logic avoids deadlocks by postponing lock acquisitions. Discrete Control Theory guarantees that the program instrumented with our synthesized control logic cannot deadlock. Our method furthermore guarantees that the control logic is maximally permissive: it postpones lock acquisitions only when necessary to prevent deadlocks, and therefore permits maximal runtime concurrency. Our prototype for C/Pthreads scales to real software including Apache, OpenLDAP, and two kinds of benchmarks, automatically avoiding both injected and naturally occurring deadlocks while imposing modest runtime overheads.

As information technology (IT) administration becomes increasingly complex, workflow technologies are gaining popularity for IT automation. Writing correct workflow programs is notoriously difficult. Although static analysis tools are available, fixing defects remains manual and error-prone. This paper applies discrete control theory to IT automation workflows. Discrete control detects flaws in workflows just as static analysis does, and more importantly it also allows safe execution of flawed workflows by dynamically avoiding run-time failures. Our approach can guarantee compliance with certain requirements and can partially decouple requirements from software, reducing the need to modify the latter if the former change. We have implemented a discrete control module for a real IT automation system. Experiments with workflows from a real production system and with randomly generated workflows show that our approach scales to workflows of practical size.