scholarly journals The Study on Real-time LDAP Interface in used Main Memory Resident Database System

2004 ◽  
Vol 11A (7) ◽  
pp. 475-482
Author(s):  
Jeong-Bae Lee ◽  
Sang-Gyun Cha ◽  
Hwan-Chul Kim ◽  
Byung-Kwan Park
Keyword(s):  
Real Time ◽  
Database System ◽  
Main Memory

A Novel Crash Recovery Scheme for Distributed Real-Time Databases

2009 ◽  
pp. 769-787
Author(s):  
Yingyuan Xiao
Keyword(s):  
Real Time ◽  
Agile Manufacturing ◽  
Database System ◽  
Main Memory ◽  
Information Support ◽  
Timing Constraints ◽  
Time Data ◽  
Main Memory Database ◽  
Distributed Database System ◽  
Hard Real Time

Recently, the demand for real-time data services has been increasing (Aslinger & Son, 2005). Many applications such as online stock trading, agile manufacturing, traffic control, target tracking, network management, and so forth, require the support of a distributed real-time database system (DRTDBS). Typically, these applications need predictable response time, and they often have to process various kinds of queries in a timely fashion. A DRTDBS is defined as a distributed database system within which transactions and data have timing characteristics or explicit timing constraints and system correctness that depend not only on the logic results but also on the time at which the logic results are produced. Similar to conventional real-time systems, transactions in DRTDBSs are usually associated with timing constraints. On the other hand, a DRTDBS must maintain databases for useful information, support the manipulation of the databases, and process transactions. Timing constraints of transactions in a DRTDBS are typically specified in the form of deadlines that require a transaction to be completed by a specified time. For soft realtime transactions, failure to meet a deadline can cause the results to lose their value, and for firm or hard real-time transactions, a result produced too late may be useless or harmful. DRTDBSs often process both temporal data that lose validity after their period of validity and persistent data that remain valid regardless of time. In order to meet the timing constraints of transactions and data, DRTDBSs usually adopt main memory database (MMDB) as their ground support. In an MMDB, “working copy” of a database is placed in the main memory, and a “secondary copy” of the database on disks serves as backup. Data I/O can be eliminated during a transaction execution by adopting an MMDB so that a substantial performance improvement can be achieved. We define a DRTDBS integrating MMDB as a distributed real-time main memory database system (DRTMMDBS).

Performance Evaluation of a Firm Real-Time DataBase System

1995 ◽  
Author(s):  
Stuart Shih ◽  
Young-Kuk Kim ◽  
Sang H. Son
Keyword(s):  
Performance Evaluation ◽  
Real Time ◽  
Database System ◽  
Real Time Database

Transaction Processing in the Rodain Real-Time Database System

1997 ◽  
pp. 355-377 ◽  
Author(s):  
Jukka Kiviniemi ◽  
Tiina Niklander ◽  
Pasi Porkka ◽  
Kimmo Raatikainen
Keyword(s):  
Real Time ◽  
Transaction Processing ◽  
Database System ◽  
Real Time Database

Optimizing security and quality of service in a Real-time database system using Multi-objective genetic algorithm

2016 ◽  
Vol 64 ◽  
pp. 11-23 ◽  
Author(s):  
Xuancai Zhao ◽  
Qiuzhen Lin ◽  
Jianyong Chen ◽  
Xiaomin Wang ◽  
Jianping Yu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Quality Of Service ◽  
Real Time ◽  
Database System ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Real Time Database

Memory-Aware Scheduling Parallel Real-Time Tasks for Multicore Systems

2021 ◽  
Vol 31 (04) ◽  
pp. 613-634
Author(s):  
Zhenyang Lei ◽  
Xiangdong Lei ◽  
Jun Long
Keyword(s):  
Real Time ◽  
Data Access ◽  
Main Memory ◽  
Second Phase ◽  
Shared Resources ◽  
Access Memory ◽  
Worst Case ◽  
Scheduling Policy ◽  
The Core ◽  
Level Scheduling

Shared resources on the multicore chip, such as main memory, are increasingly becoming a point of contention. Traditional real-time task scheduling policies focus on solely on the CPU, and do not take in account memory access and cache effects. In this paper, we propose parallel real-time tasks scheduling (PRTTS) policy on multicore platforms. Each set of tasks is represented as a directed acyclic graph (DAG). The priorities of tasks are assigned according to task periods Rate Monotonic (RM). Each task is composed of three phases. The first phase is read memory stage, the second phase is execution phase and the third phase is write memory phase. The tasks use locks and critical sections to protect data access. The global scheduler maintains the task pool in which tasks are ready to be executed which can run on any core. PRTTS scheduling policy consists of two levels: the first level scheduling schedules ready real-time tasks in the task pool to cores, and the second level scheduling schedules real-time tasks on cores. Tasks can preempt the core on running tasks of low priority. The priorities of tasks which want to access memory are dynamically increased above all tasks that do not access memory. When the data accessed by a task is in the cache, the priority of the task is raised to the highest priority, and the task is scheduled immediately to preempt the core on running the task not accessing memory. After accessing memory, the priority of these tasks is restored to the original priority and these tasks are pended, the preempted task continues to run on the core. This paper analyzes the schedulability of PRTTS scheduling policy. We derive an upper-bound on the worst-case response-time for parallel real-time tasks. A series of extensive simulation experiments have been performed to evaluate the performance of proposed PRTTS scheduling policy. The results of simulation experiment show that PRTTS scheduling policy offers better performance in terms of core utilization and schedulability rate of tasks.

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