Hyeonsang Eom

Ozone (O3): An Out-of-Order Flash Memory Controller Architecture

Ozone (O3) is a flash memory controller that increases the performance of a flash storage system by executing multiple flash operations out of order. In the O3 flash controller, data dependencies are the only ordering constraints on the execution of multiple flash operations. This allows O3 to exploit the multichip parallelism inherent in flash memory much more effectively than interleaving. The O3 controller also provides a prioritized handling of flash operations, equipping flash management software, such as the FTL (flash translation layer), with control knobs for managing flash operations of different time criticalities. Running a range of workloads on an FPGA implementation showed that the O3 flash controller achieves 3 to 100 percent more throughput than interleaving, with 46 to 88 percent lower response times.

NCQ vs. I/O scheduler: Preventing unexpected misbehaviors

Native Command Queueing (NCQ) is an optimization technology to maximize throughput by reordering requests inside a disk drive. It has been so successful that NCQ has become the standard in SATA 2 protocol specification, and the great majority of disk vendors have adopted it for their recent disks. However, there is a possibility that the technology may lead to an information gap between the OS and a disk drive. A NCQ-enabled disk tries to optimize throughput without realizing the intention of an OS, whereas the OS does its best under the assumption that the disk will do as it is told without specific knowledge regarding the details of the disk mechanism. Let us call this expectation discord, which may cause serious problems such as request starvations or performance anomaly. In this article, we (1) confirm that expectation discord actually occurs in real systems; (2) propose software-level approaches to solve them; and (3) evaluate our mechanism. Experimental results show that our solution is simple, cheap (no special hardware required), portable, and effective.

Energy-centric DVFS controlling method for multi-core platforms

Dynamic voltage and frequency scaling (DVFS) is a well-known and effective technique for reducing energy consumption in modern processors. However, accurately predicting the effect of frequency scaling on system performance is a challenging problem in real environments. In this paper, we propose a realistic DVFS performance prediction method, and a practical DVFS control policy (eDVFS) that aims to minimize total energy consumption in multi-core platforms. We also present power consumption estimation models for CPU and DRAM by exploiting a hardware energy monitoring unit. We implemented eDVFS in Linux, and our evaluation results show that eDVFS can save a substantial amount of energy compared with Linux “on-demand” CPU governor in diverse environments.

Virtual machine consolidation based on interference modeling

Server consolidation is very attractive for cloud computing platforms to improve energy efficiency and resource utilization. Advances in multi-core processors and virtualization technologies have enabled many workloads to be consolidated in a physical server. However, current virtualization technologies do not ensure performance isolation among guest virtual machines, which results in degraded performance due to contention in shared resources along with violation of service level agreement (SLA) of the cloud service. In that sense, minimizing performance interference among co-located virtual machines is the key factor of successful server consolidation policy in the cloud computing platforms. In this work, we propose a performance model that considers interferences in the shared last-level cache and memory bus. Our performance interference model can estimate how much an application will hurt others and how much an application will suffer from others. We also present a virtual machine consolidation method called swim which is based on our interference model. Experimental results show that the average performance degradation ratio by swim is comparable to the optimal allocation.

Motion Object and Regional Detection Method Using Block-Based Background Difference Video Frames

Smart CCTV (Closed-Circuit Television) technology has increasingly been developed in the last few years to judge the situation and notify the administrator or take immediate action for security and surveillance reasons. Currently the methods to detect object motion typically include the Frame Difference Method (FDM) which can detect moving objects and the Background Subtraction Method (BSM) which is able to detect motionless objects. Those results can be obtained only if there were some background images ready in advance. The Adaptive Background Subtraction Method (ABSM) also could not recognize an object very well if there are rapid scene changes or an object does not move relatively for a long time. To resolve such a problem, in this research, a filmed image has been divided into the regular sized blocks and then, only the necessary parts of the previous frame image are updated in real-time and a background image was generated so that it is insensible to surrounding environment changes such as object motion, noise or light variations. We proposed a novel moving object detection method which showed high performance with regard to the MSE (Mean Squared Error) and the accuracy of detecting the moving object contours compared to other existing methods. We also evaluated quantitatively the detectability for a moving object region by quickly creating a background image even if it is difficult to shoot a background image or we do not have the baseline image prepared in advance. The proposed method could be used for cases that any background image does not exist or hard to be generated. It is also good for observation of many places at the same time with only a single CCTV system since it is especially robust to abrupt scene changes.

Improving Hadoop performance in intercloud environments

Intercloud is a federated environment of private clusters and public clouds. The performance of Hadoop could be degraded significantly in intercloud environments. Because previous solutions for intercloud environments rely on speculative execution, they require additional cost in the cloud. In this paper, we propose a new task scheduler that improves performance without the help of speculative execution in intercloud environments.

Scatter-Gather-Merge: An efficient star-join query processing algorithm for data-parallel frameworks

A data-parallel framework is very attractive for large-scale data processing since it enables such an application to easily process a huge amount of data on commodity machines. MapReduce, a popular data-parallel framework, is used in various fields such as web search, data mining and data warehouses; it is proven to be very practical for such a data-parallel application. A star-join query is a popular query in data warehouses that are a current target domain of data-parallel frameworks. This article proposes a new algorithm that efficiently processes star-join queries in data-parallel frameworks such as MapReduce and Dryad. Our star-join algorithm for general data-parallel frameworks is called Scatter-Gather-Merge, and it processes star-join queries in a constant number of computation steps, although the number of participating dimension tables increases. By adopting bloom filters, Scatter-Gather-Merge reduces a non-trivial amount of IO. We also show that Scatter-Gather-Merge can be easily applied to MapReduce. Our experimental results in both cluster and cloud environments show that Scatter-Gather-Merge outperforms existing approaches.

Optimizing the Block I/O Subsystem for Fast Storage Devices

Fast storage devices are an emerging solution to satisfy data-intensive applications. They provide high transaction rates for DBMS, low response times for Web servers, instant on-demand paging for applications with large memory footprints, and many similar advantages for performance-hungry applications. In spite of the benefits promised by fast hardware, modern operating systems are not yet structured to take advantage of the hardware’s full potential. The software overhead caused by an OS, negligible in the past, adversely impacts application performance, lessening the advantage of using such hardware. Our analysis demonstrates that the overheads from the traditional storage-stack design are significant and cannot easily be overcome without modifying the hardware interface and adding new capabilities to the operating system. In this article, we propose six optimizations that enable an OS to fully exploit the performance characteristics of fast storage devices. With the support of new hardware interfaces, our optimizations minimize per-request latency by streamlining the I/O path and amortize per-request latency by maximizing parallelism inside the device. We demonstrate the impact on application performance through well-known storage benchmarks run against a Linux kernel with a customized SSD. We find that eliminating context switches in the I/O path decreases the software overhead of an I/O request from 20 microseconds to 5 microseconds and a new request merge scheme called Temporal Merge enables the OS to achieve 87% to 100% of peak device performance, regardless of request access patterns or types. Although the performance improvement by these optimizations on a standard SATA-based SSD is marginal (because of its limited interface and relatively high response times), our sensitivity analysis suggests that future SSDs with lower response times will benefit from these changes. The effectiveness of our optimizations encourages discussion between the OS community and storage vendors about future device interfaces for fast storage devices.

Energy-Centric DVFS Controling Method for Multi-core Platforms

Saving data center energy consumption is a hot issue for the environment and the economy. CPU is the biggest energy consuming component in a server, and it has various energy saving technologies, such as C-state and P-state. Advances in multi-core processors and virtualization technologies have enabled many workloads to be consolidated in a physical server, and energy efficiency can be degraded due to the contention in shared resources. We observed that energy efficiency can be improved further by adjusting CPU frequency according to the degree of contention in shared resources, and the most energy-efficient CPU frequency is quite different for each situation. In this paper, we propose an energy-centric DVFS controlling method (eDVFS), which aims to minimize total energy consumption. Our experimental results show that eDVFS method is more energy-efficient and faster than current “on-demand” CPU governor.

Virtual machine scheduling for multicores considering effects of shared on-chip last level cache interference

As the cloud markets grow, the cloud providers are faced with new challenges such as reduction of power consumption and guaranteeing service level agreements (SLAs). One reason for these problems is the use of server consolidation policy based on virtualization technologies for maximizing the efficiency of resource usage. Because current virtualization technologies do not ensure performance isolation among active virtual machines (VMs), it is required to consider resource usage pattern of VMs to improve total throughput and quality of service. In this paper, we propose a virtual machine scheduler for multicore processors, which exploits the last-level cache (LLC) reference ratio. Specifically, we focus on the performance impact of contention in a shared LLC. We have found that the ratio of the number of LLC references to that of instructions (LLC reference ratio) is highly associated with the amount of cache demand, and a Performance-Maximizing VM (PMV) scheduling algorithm can be devised by using the ratio. We show that our PMV scheduler is effective by evaluation for various workloads.