Euiseong Seo

Energy Efficient Scheduling of Real-Time Tasks on Multicore Processors

Multicore processors deliver a higher throughput at lower power consumption than unicore processors. In the near future, they will thus be widely used in mobile real-time systems. There have been many research on energy-efficient scheduling of real-time tasks using DVS. These approaches must be modified for multicore processors, however, since normally all the cores in a chip must run at the same performance level. Thus, blindly adopting existing DVS algorithms that do not consider the restriction will result in a waste of energy. This article suggests Dynamic Repartitioning algorithm based on existing partitioning approaches of multiprocessor systems. The algorithm dynamically balances the task loads of multiple cores to optimize power consumption during execution. We also suggest Dynamic Core Scaling algorithm, which adjusts the number of active cores to reduce leakage power consumption under low load conditions. Simulation results show that Dynamic Repartitioning can produce energy savings of about 8 percent even with the best energy-efficient partitioning algorithm. The results also show that Dynamic Core Scaling can reduce energy consumption by about 26 percent under low load conditions.

Energy-credit scheduler: An energy-aware virtual machine scheduler for cloud systems

Virtualization facilitates the provision of flexible resources and improves energy efficiency through the consolidation of virtualized servers into a smaller number of physical servers. As an increasingly essential component of the emerging cloud computing model, virtualized environments bill their users based on processor time or the number of virtual machine instances. However, accounting based only on the depreciation of server hardware is not sufficient because the cooling and energy costs for data centers will exceed the purchase costs for hardware. This paper suggests a model for estimating the energy consumption of each virtual machine without dedicated measurement hardware. Our model estimates the energy consumption of a virtual machine based on in-processor events generated by the virtual machine. Based on this estimation model, we also propose a virtual machine scheduling algorithm that can provide computing resources according to the energy budget of each virtual machine. The suggested schemes are implemented in the Xen virtualization system, and an evaluation shows that the suggested schemes estimate and provide energy consumption with errors of less than 5% of the total energy consumption. ? We conceptualize an energy-cost-aware billing policy for cloud systems. ? We propose a per-VM energy consumption estimation model. ? We identify the relationships between in-processor activities and processor energy consumption. ? We design and evaluate an energy-budget-aware virtual machine scheduler.

Exploiting Internal Parallelism of Flash-based SSDs

For the last few years, the major driving force behind the rapid performance improvement of SSDs has been the increment of parallel bus channels between a flash controller and flash memory packages inside the solid-state drives (SSDs). However, there are other internal parallelisms inside SSDs yet to be explored. In order to improve performance further by utilizing the parallelism, this paper suggests request rescheduling and dynamic write request mapping. Simulation results with real workloads have shown that the suggested schemes improve the performance of the SSDs by up to 15% without any additional hardware support.

Guest-Aware Priority-Based Virtual Machine Scheduling for Highly Consolidated Server

The use of virtualization is rapidly expanding from server consolidation to various computing systems including PC, multimedia set-top box and gaming console. However, different from the server environment, timeliness response for the external input is an essential property for the user-interactive applications. To provide timeliness scheduling of virtual machine this paper presents a priority-based scheduling scheme for virtual machine monitors. The suggested scheduling scheme selects the next task to be scheduled based on the task priorities and the I/O usage stats of the virtual machines. The suggested algorithm was implemented and evaluated on Xen virtual machine monitor. The results showed that the average response time to I/O events is improved by 5~22% for highly consolidated environment.

PABC: Power-Aware Buffer Cache Management for Low Power Consumption

Power consumed by memory systems becomes a serious issue as the size of the memory installed increases. With various low power modes that can be applied to each memory unit, the operating system can reduce the number of active memory units by collocating active pages onto a few memory units. This paper presents a memory management scheme based on this observation, which differs from other approaches in that all of the memory space is considered, while previous methods deal only with pages mapped to user address spaces. The buffer cache usually takes more than half of the total memory and the pages access patterns are different from those in user address spaces. Based on an analysis of buffer cache behavior and its interaction with the user space, our scheme achieves up to 63 percent more power reduction. Migrating a page to a different memory unit increases memory latencies, but it is shown to reduce the power consumed by an additional 4.4 percent

SSD-HDD-hybrid virtual disk in consolidated environments

With the prevalence of multi-core processors and cloud computing, the server consolidation using virtualization has increasingly expanded its territory, and the degree of consolidation has also become higher. As a large number of virtual machines individually require their own disks, the storage capacity of a data center could be exceeded. To address this problem, copy-on-write storage systems allow virtual machines to initially share a template disk image. This paper proposes a hybrid copy-on-write storage system that combines solid-state disks and hard disk drives for consolidated environments. In order to take advantage of both devices, the proposed scheme places a read-only template disk image on a solid-state disk, while write operations are isolated to the hard disk drive. In this hybrid architecture, the disk I/O performance benefits from the fast read access of the solid-state disk, especially for random reads, precluding write operations from the degrading flash memory performance. We show that the hybrid virtual disk, in terms of performance and cost, is more effective than the pure copy-on-write disks for a highly consolidated system.

Energy-Based Accounting and Scheduling of Virtual Machines in a Cloud System

Currently, cloud computing systems using virtual machines bill users for the amount of their allocated processor time, or the number of their virtual machine instances. However, accounting without cooling and energy cost is not sufficient because the cooling and energy cost is expected to exceed the cost for purchasing the servers eventually. This paper suggests a model to estimate the energy consumption of each virtual machine. Our model estimates the energy consumption of a virtual machine based on the in-processor events generated by the virtual machine. Based on the suggested estimation model, this paper also proposes a virtual machine scheduling algorithm that conforms to the energy budget of each virtual machine. Our evaluation shows the suggested schemes estimate and provide energy consumption with errors less than 5% of the total energy consumption.

A comprehensive study of energy efficiency and performance of flash-based SSD

Use of flash memory as a storage medium is becoming popular in diverse computing environments. However, because of differences in interface, flash memory requires a hard-disk-emulation layer, called FTL (flash translation layer). Although the FTL enables flash memory storages to replace conventional hard disks, it induces significant computational and space overhead. Despite the low power consumption of flash memory, this overhead leads to significant power consumption in an overall storage system. In this paper, we analyze the characteristics of flash-based storage devices from the viewpoint of power consumption and energy efficiency by using various methodologies. First, we utilize simulation to investigate the interior operation of flash-based storage of flash-based storages. Subsequently, we measure the performance and energy efficiency of commodity flash-based SSDs by using microbenchmarks to identify the block-device level characteristics and macrobenchmarks to reveal their filesystem level characteristics.

TSB: A DVS algorithm with quick response for general purpose operating systems

DVS is becoming an essential feature of state-of-the-art mobile processors. Interval-based DVS algorithms are widely employed in general purpose operating systems thanks to their simplicity and transparency. Such algorithms have a few problems, however, such as delayed response, prediction inaccuracies, and underestimation of the performance demand. In this paper we propose TSB (time slice based), a new DVS algorithm that takes advantage of the high transition speeds available in state-of-the-art processors. TSB adjusts processor performance at every context switch in order to match the performance demand of the next scheduled task. The performance demand of a task is predicted by analyzing its usage pattern in the previous time slice. TSB was evaluated and compared to other interval-based power management algorithms on the Linux kernel. The results show that TSB achieved similar or better energy efficiency compared to existing interval-based algorithms. In addition, TSB dramatically reduced the side effect of prolonging short-term execution times. For a task requiring 50ms to run without a DVS algorithm, TSB prolonged the execution time by only 6% compared to results of 136% for CPUSpeed and 20% for Ondemand.

Extensible Video Processing Framework in Apache Hadoop

Digital video is prominent big data spread all over the Internet. It is large not only in size but also in required processing power to extract useful information. Fast processing of excessive video reels is essential on criminal investigations, such as terrorism. This demo presents an extensible video processing framework in Apache Hadoop to parallelize video processing tasks in a cloud environment. Except for video transcending systems, there have been few systems that can perform various video processing in cloud computing environments. The framework employs FFmpeg for a video coder, and OpenCV for a image processing engine. To optimize the performance, it exploits MapReduce implementation details to minimize video image copy. Moreover, FFmpeg source code was modified and extended, to access and exchange essential data and information with Hadoop, effectively. A face tracking system was implemented on top of the framework for the demo, which traces the continuous face movements in a sequence of video frames. Since the system provides a web-based interface, people can try the system on site. In an 8-core environment with two quad-core systems, the system shows 75% of scalability.