Non-volatile memory express (NVMe) based SSDs and the NUMA platform are widely adopted in servers to achieve faster storage speed and more powerful processing capability. As of now, very little research has been conducted to investigate the performance and energy efficiency of the state-of-the-art NUMA architecture integrated with NVMe SSDs, an emerging technology used to host parallel I/O threads. As this technology continues to be widely developed and adopted, we need to understand the runtime behaviors of such systems in order to design software runtime systems that deliver optimal performance while consuming only the necessary amount of energy. This paper characterizes the runtime behaviors of a Linux-based NUMA system employing multiple NVMe SSDs. Our comprehensive performance and energy-efficiency study using massive numbers of parallel I/O threads shows that the penalty due to CPU contention is much smaller than that due to remote access of NVMe SSDs. Based on this insight, we develop a dynamic "lesser evil" algorithm called ESN, to minimize the impact of these two types of penalties. ESN is an energy-efficient profiling-based I/O thread scheduler for managing I/O threads accessing NVMe SSDs on NUMA systems. Our empirical evaluation shows that ESN can achieve optimal I/O throughput and latency while consuming up to 50% less energy and using fewer CPUs.

Recent studies have found that parallel garbage collection performs worse with more CPUs and more collector threads. As part of this work, we further investigate this enomenon and find that poor scalability is worst in highly scalable Java applications. Our investigation to find the causes clearly reveals that efficient multi-threading in an application can prolong the average object lifespan, which results in less effective garbage collection. We also find that prolonging lifespan is the direct result of Linux's Completely Fair Scheduler due to its round-robin like behavior that can increase the heap contention between the application threads. Instead, if we use pseudo first-in-first-out to schedule application threads in large multicore systems, the garbage collection scalability is significantly improved while the time spent in garbage collection is reduced by as much as 21%. The average execution time of the 24 Java applications used in our study is also reduced by 11%. Based on this observation, we propose two approaches to optimally select scheduling policies based on application scalability profile. Our first approach uses the profile information from one execution to tune the subsequent executions. Our second approach dynamically collects profile information and performs policy selection during execution.

Virtual Machine (VM) live storage migration is widely performed in the current cloud data centers, for the purposes of load balance, hardware maintenance and system upgrade. Nevertheless, conventional migration approaches, such as Dirty Block Tracking (DBT), do not address the problem of IO interference between VM IO requests and Migration IO requests during the migration period, which degrades both the VM IO performance and the migration performance. In this paper, we propose a Workload-Aware IO Outsourcing scheme, short for WAIO, to improve the VM live storage migration efficiency. WAIO effectively outsources the VM's working set to a surrogate device during the migration and creates separate IO path for servicing the VM IO requests. By outsourcing VM IO requests from the original storage to the surrogate device, the VM live storage migration process can be performed on the original storage, no longer interfered, while the outsourced VM IO requests are serviced separately and thus much more quickly. Our lightweight prototype implementation of WAIO and extensive trace-driven experiments demonstrate that, compared with the existing migration approach DBT, WAIO significantly improves the VM's IO performance during the migration process. Moreover, WAIO allows the hypervisor to migrate a VM at a higher migration speed, without sacrificing the VM's IO performance.

Modern Java applications employ multithreading to improve performance by harnessing execution parallelism available in today’s multicore processors. However, as the numbers of threads and processing cores are scaled up, many applications do not achieve the desired level of performance improvement. In this paper, we explore two factors, lock contention and garbage collection performance that can affect scalability of Java applications. Our initial result reveals two new observations. First, applications that are highly scalable may experience more instances of lock contention than those experienced by applications that are less scalable. Second, efficient multithreading can make garbage collection less effective, and therefore, negatively impacting garbage collection performance.

Predicting soon-to-fail (STF) disks is fundamental to keeping disk data safe and enforcing quality of service. Most current proactive prediction approaches achieve high prediction rate at the cost of high false alarm rate, labeling healthy disks as STF, because of the imbalanced fraction of failed disks in the training dataset and the characteristics of the machine learning (ML) techniques used. Given the known fact that healthy disks far outnumber STF disks, high false alarm rate means that more healthy disks than the actual STF disks maybe labeled as STF and results in undue waste of resources such as network bandwidth and new disks. The cumulative number of false alarms can be even larger considering that the prediction is taken periodically. This paper presents a priority based proactive prediction algorithm for STF disks (or P3), which leverages a combination of ML models. The predictor takes the attributes of the self monitoring facility (SMART) of all disks as input and outputs predicted STF disks. Compared to existing approaches, P3 can achieve lower false alarm rate, which is important to efficiently schedule resources for disk data and service migration. The tradeoff is slight decrease in prediction rate, which is negligible because the prediction is made periodically and reactive prediction can be employed as backup. In an evaluation on a population of 7,018 disks with Weka, the predictor can predict 112 out of the 130 failed disks with 36 false alarms. Compared with the state-of-art ML models that predict 122 failed disks with 34 false alarms, P3 is able to predict 113 failed disks with 7 false alarms.