Earticle

The growth of mobile devices in Internet of Things (IoT) leads to a number of remote and controlling system related IoT applications. For instance, home automation controlling system uses client system such web apps on smartphone or web service to access the home server by sending control commands. The home server receives the command, then controls for instance the light system. The web service gateway responsible for handling clients’ requests attests an internet latency when an increasing number of end users requests submit toward it. Therefore, this web service gateway fails to detect several commands, slows down predefined actions which should be performed without human intervention. In this paper, we investigate the performance of a web server-side platgorm based event-driven, non-blocking approach called Node.js against traditional thread-based server side approach to handle a large number of client requests simultaneously for remote and controlling system in IoT remote monitoring applications. The Node.JS is 40% faster than the traditional web server side features thread-based approach. The use of Node.js server-side handles a large number of clients’ requests, then therefore, reduces delay in performing predefined actions automatically in IoT environment.

Debugging multithreaded or parallel programs is a hard problem due to unintended non-deterministic results of the program, called data races. Previous race detection techniques have limitations of coverage for synchronization constructs in detecting first races for parallel programs with random synchronizations. We present, in this paper, an efficient two-pass algorithm to detect the first races in a particular execution of parallel program, by eliminating independent accesses to make a performance gain and candidate accesses, and then analyzing the traced information with candidate accesses by replaying the program. Therefore, our technique makes the race detection more practical in debugging parallel programs which have random synchronizations.

Detecting data races in multi-threaded programs is a challenging problem in debugging, because the races could reveal nondeterministic program behavior in execution of such pro- grams. Nondeterministic runtime eects of a race are hard to identify and it is dicult to decide whether the reported data races can appear or not in the actual program executions. Previous techniques for detecting races cannot provide method to locate rst races in par- allel programs with random synchronization including lock/unlock mechnism. This paper presents an algorithm which extracts rst races by replaying the program and checking con- currency between sequenced traced data and candidate accesses, which are from a particular execution of parallel programs. We also present the correctness of our algorithm by showing that all the rst races are included in the traced accesses which are composed of key accesses.

Detecting data races is important in debugging shared memory parallel programs, because the races could exhibit unpredictable results in execution of programs. Unfortunately, previ- ous race detection techniques cannot guarantee that the detected accesses could be involved in the rst races to occur in parallel programs which contain random synchronizations. This paper presents a monitoring algorithm which collects ltered key accesses of local thread blocks that are involved in races comparing with the latest ltered accesses of the other concurrent thread blocks in a particular execution of parallel programs. We discard all the accesses other than key accesses in a block to perform the race condition determination, which makes our algorithm more ecient in time and space. We also demonstrate the cor- rectness of our algorithm by showing at least one of the collected candidate accesses would be included in the actual first races.

When concurrency is integrated to programming language, problem more than inheritance remains yet. Because more than inheritance happens by collision or cross fire between synchronization and inheritance of same time object, when synchronization code is not detached properly from method code about same time object, it makes expanded of code to make derivation class change synchronization code of super class and method code. In this paper, minimize right of inheritance method, When subclass is introduced to new synchronization limitation condition, wish to solve problem more than inheritance of three types that happen in same time programming embodying C++ class that do so that can avoid alteration of method reed.

동시성을 보장하는 프로그래밍은 개발자에게 있어서 필수적이다. 이를 사용하지 않는다면 하드웨어 자체의 기술 발전이 있지 않는 한 프로그램의 속도 향상을 기대하기 힘들다. 뛰어난 동시성 코드를 지원하는 프로그래밍 언어로 go, elixir, scala 등이 있다. 수많은 유용한 라이브러리를 지원하는 파이썬 역시 asyncio나 coroutine과 같은 동시성 프로그래밍을 지원하고 있다. 본 지에서는 동시성과 병렬성의 개념을 정의하며, 파이썬에서 동시성 프로그래밍을 작성할 시에 유의해야 할 점에 대해 설명한다. 웹 데이터를 수집하는 크롤러를 동시성 코드로 작성하여 순차, 멀티스레딩 코드로 작성된 프로그램과 성능을 비교한다.

Programming that ensures concurrency is essential for developers. If you do not use it, it is hard to expect the speed of the program to improve unless there is technical advancement of the hardware itself. Programming languages that support good concurrency code include go, elixir, and scala. Python, which supports a number of useful libraries, also supports concurrent programming like asyncio and coroutine. This paper defines the concepts of concurrency and parallelism, and explains what to note when writing concurrency programming in Python. The crawler that collects web data is written in concurrent code and compared with programs written in sequential, multithreaded code.

멀티코어 이전의 시대에는 하드웨어의 발전을 토대로 프로그램 성능 향상에 도움을 받을 수 있었다. 그러나 하나의 코어 대한 성능 향상이 한계에 봉착하며 여러 개의 코어를 사용하는 멀티코어라는 방법이 보편화되었다. 멀티코어를 사용하기 위해 커널 레벨의 스레드를 사용하는 기존의 방법을 발전시킨 현대적 동시성 프로그래밍이 대두되었다. 현대 동시성 프로그래밍이란 경량 스레드를 사용하여 멀티코어의 장점을 최적화한다. 또 공유 가능한 데이터가 변경할 수 있는지의 유무가 동시성 코드 작성 시 주요 고려사항이 된다. 본 논문은 동시성 코드 작성 시 주요 고려사항들을 설명하고, 이러한 사항들이 현대 동시성 기술을 지원하는 언어 중 하나인 '고'에서 어떤 식으로 지원되고, 나아가 어떻게 더 훌륭한 동시성 코드를 작성할 것인지 논의한다.

The period of the previous multi-core could be helped to improve program performance, based on the development of the hardware. However, one of the core performance enhancements for this encounter limitations and become the common way of multi-core with multiple cores. Modern programming concurrency that improves the conventional method for using threads of the kernel level in order to use the multi-core come to the fore. Using modern lightweight thread concurrency programming is to optimize the benefits of multi-core. Also sharing the absence of available data that can change is a major consideration when writing concurrent code. This paper describes the key considerations when creating a discussion concurrent code, and these issues are being supported in any way in the language of one 'go' of technologies that support the modern concurrency, and even how to write better code concurrency.

This paper introduces distributed processes- a new language concept for concurrent programming. It is proposed for real-time applications controlled by computer network with distributed storage. These processes communicate and syschronize by means of procedure calls and guarded regions. The paper gives several examples of distributed processes.