Jin Kyu Jeong

The use of virtualization is progressively accommodating diverse and unpredictable workloads as being adopted in virtual desktop and cloud computing environments. Since a virtual machine monitor lacks knowledge of each virtual machine, the unpredictableness of workloads makes resource allocation difficult. Particularly, virtual machine scheduling has a critical impact on I/O performance in cases where the virtual machine monitor is agnostic about the internal workloads of virtual machines. This

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 restricti

This paper introduces a tagless cache architecture for large in-package DRAM caches. The conventional die-stacked DRAM cache has both a TLB and a cache tag array, which are responsible for virtual-to-physical and physical-to-cache address translation, respectively. We propose to align the granularity of caching with OS page size and take a unified approach to address translation and cache tag management. To this end, we introduce cache-map TLB (cTLB), which stores virtual-to-cache, instead of vi

As processor architectures have been enhancing their computing capacity by increasing core counts, independent workloads can be consolidated on a single node for the sake of high resource efficiency in data centers. With the prevalence of virtualization technology, each individual workload can be hosted on a virtual machine for strong isolation between co-located workloads. Along with this trend, hosted applications have increasingly been multithreaded to take advantage of improved hardware para

Efficient cache tag management is a primary design objective for large, in-package DRAM caches. Recently, Tagless DRAM Caches (TDCs) have been proposed to completely eliminate tagging structures from both on-die SRAM and in-package DRAM, which are a major scalability bottleneck for future multi-gigabyte DRAM caches. However, TDC imposes a constraint on DRAM cache block size to be the same as OS page size (e.g., 4KB) as it takes a unified approach to address translation and cache tag management.

In data-intensive applications, such as databases and key-value stores, reducing the request handling latency is important for providing better data services. In such applications, I/O-intensive background tasks, such as checkpointing, are the major culprit in worsening the latency due to the contention in shared I/O stack and storage. To minimize the contention, properly prioritizing I/Os is crucial but the effectiveness of existing approaches is limited for two reasons. First, statically decid

Consolidation of workloads has emerged as a key mechanism to dampen the rapidly growing energy expenditure within enterprise-scale data centers. To gainfully utilize consolidation-based techniques, we must be able to characterize the power consumption of groups of colocated applications. Such characterization is crucial for effective prediction and enforcement of appropriate limits on power consumption-power budgets-within the data center. We identify two kinds of power budgets: 1) an average bu

Memory reservation in embedded systems is a prevalent approach to provide a physically contiguous memory region to its integrated devices, such as a camera device and a video decoder. Inefficiency of the memory reservation becomes a more significant problem in emerging embedded systems, such as smartphones and smart TVs. Many ways of using these systems increase the idle time of their integrated devices, and eventually decrease the utilization of their reserved memory. In this article, we propos

Most embedded systems require contiguous memory space to be reserved for each device, which may lead to memory under-utilization. Although several approaches have been proposed to address this issue, they have limitations of either inefficient memory usage or long latency for switching the reserved memory space between a device and general-purpose uses.

The resource management of an application is an essential task in smartphones. Optimizing the application launch process results in a faster and more efficient system, directly impacting the user experience. Predicting the next application that will be used can orient the smartphone to address the system resources to the correct application, making the system more intelligent and efficient. Neural networks have been presenting outstanding results in the state-of-the-art for mapping large sequenc