I/O Schedulers for Proportionality and Stability on Flash-Based SSDs in Multi-Tenant Environments

The use of flash based Solid State Drives (SSDs) has expanded rapidly into the cloud computing environment. In cloud computing, ensuring the service level objective (SLO) of each server is the major criterion in designing a system. In particular, eliminating performance interference among virtual machines (VMs) on shared storage is a key challenge. However, studies on SSD performance to guarantee SLO in such environments are limited. In this paper, we present analysis of I/O behavior for a shared SSD as storage in terms of proportionality and stability. We show that performance SLOs of SSD based storage systems being shared by VMs or tasks are not satisfactory. We present and analyze the reasons behind the unexpected behavior through examining the components of SSDs such as channels, DRAM buffer, and Native Command Queuing (NCQ). We introduce two novel SSD-aware host level I/O schedulers on Linux, called A & x002B;CFQ and H & x002B;BFQ, based on our analysis and findings. Through experiments on Linux, we analyze I/O proportionality and stability in multi-tenant environments. In addition, through experiments using real workloads, we analyze the performance interference between workloads on a shared SSD. We then show that the proposed I/O schedulers almost eliminate the interference effect seen in CFQ and BFQ, while still providing I/O proportionality and stability for various I/O weighted scenarios

Improving I/O performance through an in-kernel disk simulator

This paper presents two mechanisms that can significantly improve the I/O performance of both hard and solid-state drives for read operations: KDSim and REDCAP. KDSim is an in-kernel disk simulator that provides a framework for simultaneously simulating the performance obtained by different I/O system mechanisms and algorithms, and for dynamically turning them on and off, or selecting between different options or policies, to improve the overall system performance. REDCAP is a RAM-based disk cache that effectively enlarges the built-in cache present in disk drives. By using KDSim, this cache is dynamically activated/deactivated according to the throughput achieved. Results show that, by using KDSim and REDCAP together, a system can improve its I/O performance up to 88% for workloads with some spatial locality on both hard and solid-state drives, while it achieves the same performance as a ‘regular system’ for workloads with random or sequential access patterns.Peer ReviewedPostprint (author's final draft

ON OPTIMIZATIONS OF VIRTUAL MACHINE LIVE STORAGE MIGRATION FOR THE CLOUD

Virtual Machine (VM) live storage migration is widely performed in the data cen- ters of the Cloud, for the purposes of load balance, reliability, availability, hardware maintenance and system upgrade. It entails moving all the state information of the VM being migrated, including memory state, network state and storage state, from one physical server to another within the same data center or across different data centers. To minimize its performance impact, this migration process is required to be transparent to applications running within the migrating VM, meaning that ap- plications will keep running inside the VM as if there were no migration operations at all. In this dissertation, a thorough literature review is conducted to provide a big picture of the VM live storage migration process, its problems and existing solutions. After an in-depth examination, we observe that a severe IO interference between the VM IO threads and migration IO threads exists and causes both types of the IO threads to suffer from performance degradation. This interference stems from the fact that both types of IO threads share the same critical IO path by reading from and writing to the same shared storage system. Owing to IO resource contention and requests interference between the two different types of IO requests, not only will the IO request queue lengthens in the storage system, but the time-consuming disk seek operations will also become more frequent. Based on this fundamental observation, this dissertation research presents three related but orthogonal solutions that tackle the IO interference problem in order to improve the VM live storage migration performance. First, we introduce the Workload-Aware IO Outsourcing scheme, called WAIO, to improve the VM live storage migration efficiency. Second, we address this problem by proposing a novel scheme, called SnapMig, to improve the VM live storage migration efficiency and eliminate its performance impact on user applications at the source server by effectively leveraging the existing VM snapshots in the backup servers. Third, we propose the IOFollow scheme to improve both the VM performance and migration performance simultaneously. Finally, we outline the direction for the future research work. Advisor: Hong Jian

Enhancing Mobile Device System using Information from Users and Upper Layers

Despite the rapid hardware upgrades, a common complaint among smartphone owners is the poor battery life. to many users, being required to charge the smartphone after a single day of moderate usage is unacceptable. Moreover, current smartphones suffer various unpredictable delays during operation, e.g., when launching an app, leading to poor user experience. In this dissertation, we provide solutions that enhance systems on portable devices using information obtained from their users and upper layers on the I/O path. First, we provide an experimental study on how storage I/O path upper layers affect power levels in smartphones, and introduce energy-efficient approaches to reduce energy consumption facilitating various usage patterns. at each layer, we investigate the amount of energy that can be saved, and use that to design and implement a prototype with optimal energy savings named SmartStorage. We evaluate our prototype by using the 20 most popular android applications, and our energy-efficient approaches achieve from 23% to 52% of energy savings compared to using the current techniques. Next, we conduct the first large-scale user study on the I/O delay of android using the data collected from our android app running on 2611 devices within nine months. Among other factors, we observe that reads experience up to 626% slowdown when blocked by concurrent writes for certain workloads. We use this obtained knowledge to design a system called SmartIO that reduces application delays by prioritizing reads over writes. SmartIO is evaluated extensively on several groups of popular applications. The results show that our system reduces launch delays by up to 37.8%, and run-time delays by up to 29.6%. Finally, we study the impact of memory on smartphone user-perceived performance. Our heap usage investigation of 20 popular applications indicates that rich multimedia applications have high heap usage and go above allowed boundaries, up to 5.63 times more heap than guaranteed by the system, and may cause crashes and erroneous behaviors. Moreover, limited heap may not only cause an app to crash, but may even prevent an app from launching. Therefore, we present iRAM, a system that maintains optimal heap size limits to avoid crashes, efficiently maximizes free memory levels, and cleans low-priority processes to reduce application delays. The evaluation indicates that iRAM reduces application crashes by up to 14 percent