Kwanghyun La

ABC: dynamic configuration management for MicroBrick-based cloud computing systems

In designing a high-performance cloud computing platform, it is important to support diverse system resource requirements of various cloud computing services/applications in a scalable fashion. In this poster, we propose an intelligent middleware for our prototype cloud computing system which automatically changes configurations of modules for high performance under varying cloud service/application workload. Our initial evaluation results show that efficient resource management during run time is a key enabling technique for developing high-performance cloud computing systems.

Tool support for new test criteria on embedded systems: Justitia

In this paper, we introduce a testing tool to support new test criteria in interface testing technique. Justitia is an automated testing tool for embedded software that assists an integration testing on software phase during embedded software testing cycle. Justitia has been successfully applied to device driver testing cooperated with Samsung Co.

Cgroup++: Enhancing I/O Resource Management of Linux Cgroup on NUMA Systems with NVMe SSDs

For container-based virtualization such as Linux container (LXC), efficient and proportional resource sharing is an important design requirement. However, existing container resource management techniques do not adequately meet this requirement on modern server machines, especially NUMA machines with NVMe SSDs. In this paper, we propose an efficient proportional-share Linux Cgroup, called Cgroup++, for container-based virtualization. Unlike Cgroup, Cgroup++ takes into account of the storage asymmetry of modern NUMA machines in managing storage I/O requests. By exploiting the storage asymmetry in scheduling CPU cores for a given Cgroup instance, Cgroup++ improves the I/O performance of the Cgroup instance. Cgroup++ also supports proportional I/O sharing among multiple Cgroup instances using a weight-based throttling scheme in the I/O throttling layer for the NVMe SSDs.

An evaluation of alternative shared-nothing architecture for analytical processing systems

Data analysis, mining, and machine learning on large-scale data sets have gained much attention in the academia and industry. Tremendous computational and storage capacities are required in order to handle such large data sets. In these days, the conventional wisdom is to build a large cluster which consists of a number of commodity x86 machines, each of which is equipped with two or four physical CPUs and several HDD or SSD drives, connected via high-speed network. In this paper, we have asked is there any alternative approach? We introduce MicroBrick cluster, a prototype cluster machine architecture by Samsung Electronics. We investigate the possibility of MicroBricks cluster architecture as an alternative cluster infrastructure for shared-nothing analytical processing systems. A MicroBricks cluster consists of multiple MicroBricks chassis. Unlike commodity x86 clusters where each machine has its own CPUs, memory, and disk drives, a single MicroBricks chassis consists of multiple highly dense and pluggable computing and storage modules, and the modules are connected through high-speed inter connection on a single board. As a result, MicroBricks clusters occupy much smaller space and have high bandwidth connectivity required for shared-nothing distributed processing. In addition, a MicroBricks cluster is likely to consume lower power. These characteristics are very suitable for large clusters built in data centers. In order to prove this possibility, we carried out the comparison experiments of both MicroBricks cluster as well as commodity cluster. We carried out TPC-H benchmark by means of an open source distributed SQL engine in Hadoop in both architectures. In order to deeply analyze both architecture, we collected the profiling information during the TPC-H benchmark, and we conducted micro benchmark with the profile results. The experimental results are promising for the MicroBricks computing, and the results show that the query response times of MicroBricks computing architecture outperforms those of commodity cluster without hurting the innate advantages of the MicroBricks cluster architecture.

Improving I/O performance of NVMe SSD on virtual machines

The ever increasing demand of effective resource utilization in data centers has resulted in the dramatic development of various virtualization environments. Furthermore, the requirements on rapid processing of large data has not only caused to the replacement of spinning disks with flash-based SSD but also has led to the implementation of efficient software I/O stacks for SSDs. The software I/O stacks in most hypervisors have been developed for SATA interface-based storages. Therefore, high throughput and various functionalities provided by the NVMebased SSDs cannot be fully utilized. Also, it was found that the inefficiency of the existing storage I/O is due to the nonoptimized I/O stack of virtual machines in a hypervisor. In this paper, we have proposed a new I/O architecture that optimizes the I/O path by eliminating the overhead of user-level threads, bypassing unnecessary I/O routines and enhancing the interrupt delivery delay. The purpose of the proposed architecture is to enhance the throughput and scalability by mitigating the overhead of the existing software stack to take full advantage of NVMe SSDs. Experimental results with a real system show that the proposed approach improves the I/O performance by up to 47% compared to the existing approach.