• KIPS Transactions on Computer and Communication Systems
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• v.3 no.10
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• pp.311-322
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• 2014

IoT(Internet of Things) is a concept of connected internet pursuing direct access to devices or sensors in fused environment of personal, industrial and public area. In IoT environment, it is possible to access realtime data, and the data format and topology of devices are diverse. Also, there are bidirectional communications between users and devices to control actuators in IoT. In this point, IoT is different from the conventional internet in which data are produced by human desktops and gathered in server systems by way of one-sided simple internet communications. For the cloud or portal service of IoT, there should be a file management framework supporting systematic naming service and unified data access interface encompassing the variety of IoT things. This paper implements a DB-based virtual file system maintaining attributes of IoT things in a UNIX-styled file system view. Users who logged in the virtual shell are able to explore IoT things by navigating the virtual file system, and able to access IoT things directly via UNIX-styled file I O APIs. The implemented virtual file system is lightweight and flexible because it maintains only directory structure and descriptors for the distributed IoT things. The result of a test for the virtual shell primitives such as mkdir() or chdir() shows the smooth functionality of the virtual file system, Also, the exploring performance of the file system is better than that of Window file system in case of adopting a simple directory cache mechanism.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.6
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• pp.127-138
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• 2019

The data response speed is a critical issue of cloud services because it directly related to the user experience. As such, the in-memory database is widely adopted in many cloud-based applications for achieving fast data response. However, the current implementation of the in-memory database is mostly based on the linked list-based hash table which cannot guarantee the constant data response time. Thus, cuckoo hashing was introduced as an alternative solution, however, there is a disadvantage that only half of the allocated memory can be used for storing data. Subsequently, bucketized cuckoo hashing (BCH) improved the performance of cuckoo hashing in terms of memory efficiency but still cannot overcome the limitation that the insert overhead. In this paper, we propose a data management solution called Wall Cuckoo which aims to improve not only the insert performance but also lookup performance of BCH. The key idea of Wall Cuckoo is that separates the data among a bucket according to the different hash function be used. By doing so, the searching range among the bucket is narrowed down, thereby the amount of slot accesses required for the data lookup can be reduced. At the same time, the insert performance will be improved because the insert is following up the operation of the lookup. According to analysis, the expected value of slot access required for our Wall Cuckoo is less than that of BCH. We conducted experiments to show that Wall Cuckoo outperforms the BCH and Sorting Cuckoo in terms of the amount of slot access in lookup and insert operations and in different load factor (i.e., 10%-95%).

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.8
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• pp.367-376
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• 2007

In this paper, we introduce an application-specific and adaptive power management technique for portable systems that support dynamic voltage scaling (DVS). We exploit both the idle time of multitasking systems running soft real-time tasks as well as memory- or CPU-bound code regions. Detailed power and execution time profiles guide an adaptive power manager (APM) that is linked to the operating system. A post-pass optimizer marks candidate regions for DVS by inserting calls to the APM. At runtime, the APM monitors the CPU's performance counters to dynamically determine the affinity of the each marked region. for each region, the APM computes the optimal voltage and frequency setting in terms of energy consumption and switches the CPU to that setting during the execution of the region. Idle time is exploited by monitoring system idle time and switching to the energy-wise most economical setting without prolonging execution. We show that our method is most effective for periodic workloads such as video or audio decoding. We have implemented our method in a multitasking operating system (Microsoft Windows CE) running on an Intel XScale-processor. We achieved up to 9% of total system power savings over the standard power management policy that puts the CPU in a low Power mode during idle periods.