• International Journal of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.79-84
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• 2023

Due to the rapid performance improvement of smartphones, multitasking on mobile platforms has become an essential feature. Unlike traditional desktop or server environments, mobile applications are mostly interactive jobs where response time is important, and some applications are classified as real-time jobs with deadlines. When interactive and real-time jobs run concurrently, memory allocation between multitasking applications is a challenging issue as they have different time requirements. In this paper, we study how to allocate memory space when real-time and interactive jobs are simultaneously executed in a smartphone to meet the multitasking requirements between heterogeneous jobs. Specifically, we analyze the memory size required to satisfy the constraints of real-time jobs and present a new model for allocating memory space between heterogeneous multitasking jobs. Trace-driven simulations show that the proposed model provides reasonable performance for interactive jobs while guaranteeing the requirement of real-time jobs.

• International journal of advanced smart convergence
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• v.8 no.1
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• pp.9-17
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• 2019

In this article, we present a performance enhancement scheme for mobile applications by adopting persistent memory. The proposed scheme supports the deadline guarantee of real-time applications like a video player, and also provides reasonable performances for non-real-time applications. To do so, we analyze the program execution path of mobile software platforms and find two sources of unpredictable time delays that make the deadline-guarantee of real-time applications difficult. The first is the irregular activation of garbage collection in flash storage and the second is the blocking and time-slice based scheduling used in mobile platforms. We resolve these two issues by adopting high performance persistent memory as the storage of real-time applications. By maintaining real-time applications and their data in persistent memory, I/O latency can become predictable because persistent memory does not need garbage collection. Also, we present a new scheduler that exclusively allocates a processor core to a real-time application. Although processor cycles can be wasted while a real-time application performs I/O, we depict that the processor utilization is not degraded significantly due to the acceleration of I/O by adopting persistent memory. Simulation experiments show that the proposed scheme improves the deadline misses of real-time applications by 90% in comparison with the legacy I/O scheme used in mobile systems.

• Journal of Computing Science and Engineering
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• v.9 no.2
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• pp.51-72
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• 2015

Time predictability is crucial in hard real-time and safety-critical systems. Cache memories, while useful for improving the average-case memory performance, are not time predictable, especially when they are shared in multicore processors. To achieve time predictability while minimizing the impact on performance, this paper explores several time-predictable scratch-pad memory (SPM) based architectures for multicore processors. To support these architectures, we propose the dynamic memory objects allocation based partition, the static allocation based partition, and the static allocation based priority L2 SPM strategy to retain the characteristic of time predictability while attempting to maximize the performance and energy efficiency. The SPM based multicore architectural design and the related allocation methods thus form a comprehensive solution to hard real-time multicore based computing. Our experimental results indicate the strengths and weaknesses of each proposed architecture and the allocation method, which offers interesting on-chip memory design options to enable multicore platforms for hard real-time systems.

• ETRI Journal
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• v.33 no.2
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• pp.230-239
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• 2011

Dynamic memory allocators for real-time embedded systems need to fulfill three fundamental requirements: bounded worst-case execution time, fast average execution time, and minimal fragmentation. Since embedded systems generally run continuously during their whole lifetime, fragmentation is one of the most important factors in designing the memory allocator. This paper focuses on minimizing fragmentation while other requirements are still satisfied. To minimize fragmentation, a part of a memory region is segregated by the proposed budgeting method that exploits the memory profile of the given application. The budgeting method can be applied for any existing memory allocators. Experimental results show that the memory efficiency of allocators can be improved by up to 18.85% by using the budgeting method. Its worst-case execution time is analyzed to be bounded.

• Journal of Information Processing Systems
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• v.11 no.1
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• pp.39-56
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• 2015

Continuous multi-interval prediction (CMIP) is used to continuously predict the trend of a data stream based on various intervals simultaneously. The continuous integrated hierarchical temporal memory (CIHTM) network performs well in CMIP. However, it is not suitable for CMIP in real-time mode, especially when the number of prediction intervals is increased. In this paper, we propose a real-time integrated hierarchical temporal memory (RIHTM) network by introducing a new type of node, which is called a Zeta1FirstSpecializedQueueNode (ZFSQNode), for the real-time continuous multi-interval prediction (RCMIP) of data streams. The ZFSQNode is constructed by using a specialized circular queue (sQUEUE) together with the modules of original hierarchical temporal memory (HTM) nodes. By using a simple structure and the easy operation characteristics of the sQUEUE, entire prediction operations are integrated in the ZFSQNode. In particular, we employed only one ZFSQNode in each level of the RIHTM network during the prediction stage to generate different intervals of prediction results. The RIHTM network efficiently reduces the response time. Our performance evaluation showed that the RIHTM was satisfied to continuously predict the trend of data streams with multi-intervals in the real-time mode.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.232-241
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• 2018

Operational Transformation (OT) algorithms for real-time collaborative editing systems are becoming increasingly important due to the increased demand for collaborative data processing. The operational transformation algorithm is a technique for real-time concurrency control and consistency maintenance with non-locking technique, and many studies have been conducted to overcome three issues of convergence, causality-prevention, and intention-prevention. However, previous work has the disadvantage of wasting memory by storing all operations that occurred during an edit operation in the history buffer to solve this problem. Therefore, we propose a memory-saving real-time collaborative editing system that maintains a constant memory space and concurrency control through a method of applying the valid-time to each user-generated operation in order to reduce memory waste. This system prevents long-term memory occupation of client-generated operations, thus it reduces the space and time complexity even with low-rate of collaboration work, so that the performance degradation avoids.

• Journal of the Korea Society for Simulation
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• v.11 no.3
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• pp.23-34
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• 2002

Dynamic storage allocation (DSA) is a field fairly well studied for a long time as a basic problem of system software area. Due to memory fragmentation problem of DSA and its unpredictable worst case execution time, real-time system designers have believed that DSA may not be promising for real-time application service. Recently, the need for an efficient DSA algorithm is widely discussed and the algorithm is considered to be very important in the real-time system. This paper proposes an efficient DSA algorithm called QSB (quick semi-buddy) which is designed to be suitable for real-time environment. QSB scheme effectively maintains free lists based on quick-fit approach to quickly accommodate small and frequent memory requests, and the other free lists devised with adaptation upon a typical binary buddy mechanism for bigger requests in harmony for the .improved performance. Comprehensive simulation results show that the proposed scheme outperforms QHF which is known to be effective in terms of memory fragmentation up to about 16%. Furthermore, the memory allocation failure ratio is significantly decreased and the worst case execution time is predictable.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.672-674
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• 1998

Java virtual machine has the garbage collector that automate memory management. Mark-compact algorithm is one of the garbage collection algorithm that operating in 2 phases, marking and sweeping. One is Marking is marking live objects reachable from root object set. Sweeping is sweeping unmarked object from memory(return to free memory pool). This algorithm is easy to implement but cause a memory fragmentation. So compacting memory, before memory defragmentation become serious. When compacting memory, all other processes are suspended. It is critical for embedded system that must guarantee real-time processing. This paper introduce enhanced mark-compact garbage collection algorithm. Grouping the objects by their size that minimize memory fragmentation. Then apply smart algorithm to the grouped objects when allocating objects and compacting memory.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.71-76
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• 2021

Recently, as the size of IoT data grows, the memory power consumption of real-time systems increases rapidly. This is because real-time systems always place entire tasks in memory, which increases the demand of DRAM significantly. In this paper, we adopt emerging fast storage media and move a certain portion of real-time tasks from DRAM to storage. The part of tasks in storage are, then, loaded into memory when they are actually used. We incorporate our memory/storage power-saving into the dynamic voltage/frequency scaling of processors, thereby optimizing power consumptions in CPU and memory simultaneously. Specifically, the proposed technique aims at minimizing the CPU idle time and the DRAM memory size by determining appropriate voltage modes of CPU and the swap ratio of memory, without violating the deadlines of all tasks. Through simulation experiments, we show that the proposed technique significantly reduces the power consumption of real-time systems.

• The KIPS Transactions:PartD
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• v.10D no.2
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• pp.187-194
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• 2003

Main Memory storage system can increase the performance of the system by assigning enough slack time to real-time transactions. Due to its high response time of main memory devices, main memory resident data management systems have been used for location management of personal mobile clients to cope with urgent location related operations. In this paper we have developed a multi-threaded main memory storage system as a core component of real-time retrieval system to handle a huge amount of readers and writers of main memory resident data. The storage system is implemented as an embedded component which is working with the help of a disk resident database system. It uses multi-threaded executions and utilizes latches for its concurrency control rather than complex locking method. It only saves most recent data on main memory and data synchronization is done only when disk resident database asks for update transactions. The system controls the number of read threads and update threads to guarantee the minimum requirements of real-time retrievals.