Woomin Hwang

HyperDealer: Reference-Pattern-Aware Instant Memory Balancing for Consolidated Virtual Machines

Memory contention among consolidated virtual machines (VMs) creates the need for a memory balancing operation. In an attempt to provide a prompt memory balancing mechanism, we found problems with the retardation of memory transfer by the reclamation delay. The scheduling of the VMs generates the delay, and a conflicts of two reclamation policies between the guest OS and the hypervisor deteriorates it. As a remedy to these problems, we propose HyperDealer, which selects the victim page by applying reference patterns, reclaims the pages with hypervisor-level paging, and transfers those pages with ballooning of the guest OS. Our scheme eliminates the involvement of the victim VM in memory balancing and extends the dwell time of reclaimed pages in the reclaimed state. Consequently, HyperDealer significantly reduces the time taken to transfer memory with a low overhead and enhances the value of additional memory for the recipient VM. The experimental results of our scheme show that the application performance in the recipient VM is 11% more time-efficient and has a penalty which is 50% less than previous approaches.

MN-Mate: Resource Management of Manycores with DRAM and Nonvolatile Memories

The advent of many core era breaks the performance wall but it causes severe energy consumption. NVRAM as a main memory can be a good solution to reduce energy consumption due to large size of DRAM. In this paper, we propose MN-MATE, a novel architecture and management techniques for resource allocation of a number of cores and large size of DRAM and NVRAM. In MN-MATE, a hyper visor partitions and allocates cores and memory for guest OSes dynamically. It is clear that optimized matching of heterogeneous cores, DRAM, and NVRAM enhances system performance. Selective locating of data in a main memory composed of DRAM and NVRAM significantly reduces energy consumptions. Preliminary results show that integration of dynamic resource partitioning and selective memory allocation scheme with MN-MATE reduces energy usage significantly and suppresses performance loss from NVRAM’s characteristics.

MN-MATE: Elastic Resource Management of Manycores and a Hybrid Memory Hierarchy for a Cloud Node

Recent advent of manycore system increases needs for larger but faster memory hierarchy. Emerging next generation memories such as on-chip DRAM and nonvolatile memory (NVRAM) are promising candidates for replacement of DRAM-only main memory. Combined with the manycore trends, it gives an opportunity to rethink conventional resource management system with a memory hierarchy for a single cloud node. In an attempt to mitigate the energy and memory problems, we propose MN-MATE, an elastic resource management architecture for a single cloud node with manycores, on-chip DRAM, and large size of off-chip DRAM and NVRAM. In MN-MATE, the hypervisor places consolidated VMs and balances memory among them. Based on the monitored information about the allocated memory, a guest OS co-schedules tasks accessing different types of memory with complementary access intensity. Polymorphic management of DRAM hierarchy accelerates average memory access speed inside each guest OS. A guest OS reduces energy consumption with small performance loss based on the NVRAM-aware data placement policy and the hybrid page cache. A new lightweight kernel is developed to reduce the overhead from the guest OS for scientific applications. Experiment results show that our techniques in MN-MATE platform improve system performance and reduce energy consumption.

Efficient memory management of a hierarchical and a hybrid main memory for MN-MATE platform

The advent of manycore in computing architecture causes severe energy consumption and memory wall problem. Thus, emerging technologies such as on-chip memory and nonvolatile memory (NVRAM) have led to a paradigm shift in computing architecture era. For instance, nonvolatile memories like PRAM can be viable DRAM replacements, achieving competitive speeds at lower power consumption. On-chip memory such as 3D-stacked memory can solve the limitation of memory bandwidth. The confluence of these trends offers a new opportunity to rethink traditional computing system and memory hierarchies. In an attempt to mitigate the energy and memory wall, we propose a new architecture with a hierarchical and a hybrid main memory for manycore system, termed MN-MATE. The hierarchical memory consists of on-chip memory, which is called M1 memory, and a conventional DRAM memory is replaced by a hybrid memory of DRAM and PRAM, called M2 memory. On the top of the system, we designed and evaluated efficient management techniques to achieve the high performance and the low energy usage, including hierarchical memory management, power-aware hybrid memory management, and file caching on a hybrid memory. Preliminary results show that these techniques can improve performance and reduce energy usage. As a case study, we introduce the MaaS (Matching-as-a-Service) application which requires the large amount of memory and high computing power.

Resource Management of Manycores with a Hierarchical and a Hybrid Main Memory for MN-MATE Cloud Node

The advent of manycore in computing architecture causes severe energy consumption and memory wall problem. Emerging technologies such as on-chip DRAM and nonvolatile memory (NVRAM) receive attention as promising solutions for them. Nonvolatile memory is a viable DRAM replacement, achieving competitive performance at lower power consumption. On-chip DRAM extends the memory bandwidth. The confluence of these trends offers a new opportunity to rethink traditional computing system and memory hierarchies. In an attempt to mitigate the energy and memory wall, we propose MN-MATE, a novel architecture and management techniques for resource allocation of a number of cores, onchip DRAM, and large size of off-chip DRAM and NVRAM. In MN-MATE, each guest OS utilizes cores and various memories allocated by the hypervisor. Based on the knowledge about the allocated resources, a guest OS co-schedules tasks accessing different types of memory with complementary access intensity. Memory management system of the OS utilizes on-chip DRAM as a part of main memory having low latency. It also selects proper location of data from the three types of memory based on the datas access characteristics. Preliminary experimental results show that these techniques with the new architecture improve system performance and reduce energy consumption.

FalconEye: Data Center Status Extraction via Vision Transformation Techniques

In this study, a vision monitoring system that is applicable to the maintenance of data centers was developed and applied to data center status extraction. The vision monitoring system, which is intended to complement system monitoring tools, such as IPMI and Nagios, has the additional benefit of enabling continuous monitoring of the external status of data centers. This system, which is based on vision transformation techniques, involves three main steps: camera calibration, where the characterized physical point is determined in the data center by the setting of the system parameters; the development of vision transformation subroutines, which are aimed at transforming relatively large images into vertically expanded image streams; and the development of image analysis subroutines for the purpose of investigating images that are transformed for the data center status extraction. This Falcon Eye system of this work is implemented with the aid of a cloud computing platform, called iCube Cloud.

MN-GEMS: A Timing-Aware Simulator for a Cloud Node with Manycore, DRAM, and Non-volatile Memories

In this paper, we describe a part of our on-going research project aimed at the management of many core and Hybrid Main Memory with DRAM and Non-Volatile RAMs (NVRAMs).By the needs of simulation and through investigation of the requirements for the target management system, we found that the simulation platform requires support for many core, a timing-aware simulation of hybrid memory with DRAM and NVRAM, and a Performance Monitoring Unit (PMU).Therefore, we built MN-GEMS, a full-system simulator for the consolidated VMs of a cloud node satisfying all these requirements.

Malfinder: Accelerated malware classification system through filtering on manycore system

Control flow matching methods have been utilized to detect malware variants. However, as the number of malware variants has soared, it has become harder and harder to detect all malware variants while maintaining high accuracy. Even though many researchers have proposed control flow matching methods, there is still a trade-off between accuracy and performance. To solve this trade-off, we designed Malfinder, a method based on approximate matching, which is accurate but slow. To overcome its low performance, we resolve its performance bottleneck and non-parallelism on three fronts: I-Filter for identical string matching, table division to exclude unnecessary comparisons with some malware and dynamic resource allocation for efficient parallelism. Our performance evaluation shows that the total performance improvement is 280.9 times.

Reference Pattern-Aware Instant Memory Balancing for Consolidated Virtual Machineson Manycores

Memory contention among consolidated VMs on the same hardware has created the need for repetitive memory balancing operations. In an attempt to provide a prompt memory balancing mechanism, we found problems with the retardation of memory reallocation by the reclamation delay. The scheduling of the VMs and their VCPUs generates the delay, the dirtiness of the candidate pages for balancing makes the delay fluctuated, and a conflict of two reclamation policies between the guest OS and the hypervisor deteriorates the application performance. As a remedy to these problems, we propose HyperDealer2 (HD2), which selects the victim pages based on the reference patterns of clean pages, reclaims them with hypervisor-level paging, and reallocates those pages with explicit ballooning of the recipient guest OS. HD2 eliminates the involvement of victim VMs in memory reclamation and extends the dwell time of reclaimed pages in the reclaimed state. Consequently, HD2 significantly reduces the time taken to reallocate memory with a low overhead and enhances the value of additional memory for the recipient VMs. The experimental results of HD2 show that the execution time of memory-intensive applications in the recipient VM is reduced by up to 50 percent in spite of less than 2 percent performance penalty.

I-Filter: Identical Structured Control Flow String filter for accelerated malware variant classification

As the number of malware variants has grown rapidly, classification speed has become crucial in security issues. While several techniques for malware variant classification have been proposed, they involve a speed-accuracy trade-off. In an attempt to achieve a speedy and accurate malware variant classification, we thoroughly analyze previously proposed methods and identify a critical performance bottleneck in string-to-string matching. This paper presents and evaluates a technique called I-Filter that enhances the performance of the previous approach, approximate matching. I-Filter has the following novel mechanism, the hash-based equivalent procedure matching technique. Our performance evaluation confirms that a performance improvement of on average 1,043 times through I-Filtering.