scholarly journals Peningkatan Kinerja Jaringan Dengan Menggunakan Multi-Rule Algorithm

2021 ◽  
Vol 8 (1) ◽  
pp. 69
Author(s):  
Tanwir Tanwir ◽  
Parma Hadi Rantelinggi ◽  
Sri Widiastuti
Keyword(s):  
Response Time ◽  
Cache Replacement ◽  
Average Response ◽  
Average Response Time ◽  
Hit Rate ◽  
Cache Size ◽  
Long Time ◽  
Replacement Algorithms

<p>Algoritma pergantian adalah suatu mekanisme pergantian objek dalam cache yang lama dengan objek baru, dengan mekanisme  melakukan penghapusan objek sehingga mengurangi penggunaan bandwidth dan server load. Penghapusan dilakukan apabila cache penuh sehingga penyimpanan entri baru diperlukan. Secara umum algoritma FIFO, LRU dan LFU sering digunakan dalam pergantian objek, akan tetapi diperoleh suatu objek yang sering digunakan namun terhapus dalam pergantian cache sedangkan objek tersebut masih digunakan, akibatnya pada waktu klien melakukan permintaan dibutuhkan waktu yang lama dalam browsing objek. Untuk mengatasi masalah tersebut dilakukan kombinasi algoritma pergantian cache Multi-Rule Algorithm, dalam bentuk algoritma kombinasi ganda FIFO-LRU dan triple FIFO-LRU-LFU. Algoritma Mural (Multi-Rule Algorithm) menghasilkan respon pada cache size 200 MB dengan waktu tanggapan rata-rata berturut-turut 56,33 dan 42 ms, sedangkan pada algoritma tunggal memerlukan waktu tanggapan rata-rata 77 ms. Sehingga Multi-Rule Algorithm dapat meningkatkan kinerja terhadap waktu penundaan, throughput, dan hit rate. Dengan demikian, algoritma pergantian cache Mural, sangat direkomendasikan untuk meningkatkan akses klien.</p><p> </p><p class="Judul2"><em>Abstract</em></p><p class="Abstract">Substitution algorithm is a mechanism to replace objects in the old cache with new objects, with a mechanism to delete objects so that it reduces bandwidth usage and server load. Deletion is done when the cache is full so saving new entries is needed. In general, FIFO, LRU and LFU algorithms are often used in object changes, but an object that is often used but is deleted in the cache changes while the object is still being used, consequently when the client makes a request it takes a long time to browse the object. To overcome this problem a combination of Multi-Rule Algorithm cache replacement algorithms is performed, in the form of a double combination algorithm FIFO-LRU and triple FIFO-LRU-LFU. The Mural algorithm (Multi-Rule Algorithm) produces a response on a cache size of 200 MB with an average response time of 56.33 and 42 ms respectively, whereas a single algorithm requires an average response time of 77 ms. So the Multi-Rule Algorithm can improve the performance of the delay, throughput, and hit rate. Thus, the Mural cache change algorithm, is highly recommended to improve client access.</p><p><br /><em></em></p>

scholarly journals ANALISA PELAYANAN KEFARMASIAN PADA PASIEN RAWAT JALAN DI DEPO FARMASI RSUD IBNU SINA GRESIK BERDASARKAN WAKTU TUNGGU PELAYANAN RESEP

2019 ◽  
Vol 1 (01) ◽  
pp. 20
Author(s):  
Nur Anisaus Sholikhah ◽  
Pemta Tiadeka ◽  
Siti Nur Aisiyah
Keyword(s):  
Response Time ◽  
Pharmacy Service ◽  
The Other ◽  
Average Response ◽  
Average Response Time ◽  
Medicine Prescription ◽  
Long Time ◽  
Time Average ◽  
To Come ◽  
Prescription Services

Usually, response time is a waiting time that counts from patient giving prescription until they receive the medicine. Response time colud influence patients satisfied if took a long time to get the medicine. Besides that, those phenomena could decrease patient satisfaction dan they will think twice to come back to the hospital. Ibnu Sina Hospital tries to increase the pharmacy service with the observation of response time in Depo Pharmacy Pavilion. The purpose of that observation is to optimized pharmacy service by medicine prescription to dispended medicine prescription. This observation is done by retropective methode from January to March 2018. The result of this observation showed that respon time average for medicine prescription is 26 minutes and the average response time for dispende medicine is 36 minutes. This result is suitable with Phramcacautical Service Standart from Kepmenkes No.129/Menkes/SK/II/2008 about prescription services that said respon time for less medicine prescription is than 30 minutes meanwhile for the other one is less than 60 minutes.

scholarly journals Application of A* (A Star) Algorithm on Automation of Trash Can

2019 ◽  
Vol 8 (2S9) ◽  
pp. 261-263
Keyword(s):  
Response Time ◽  
Global Positioning System ◽  
Industrial Revolution ◽  
Human Life ◽  
Positioning System ◽  
Average Response ◽  
Average Response Time ◽  
Stopping Distance ◽  
Coordinate Position ◽  
Global Positioning

The industrial revolution 4.0 demands the convenience of a human life facility. Not to forget also in the cleaning service. When we will dispose of trash, we do not need to look for the trash can, it is precisely the trash can that will approach us. This smartphone-based application uses the A * (A star) algorithm as the basis for its work, while for communication between smartphones with the trash can system using blue tooth. The smartphone sends its coordinate position through the Global Positioning System facility, then the trash can system will search for the sender's location. The experimental results show that the average stopping distance indoors without barrier is 7.03 meters with an average time response of 25.3 seconds, the average stopping distance in the room with a barrier of 7.2 meters with the average response time 3.6 seconds average, stopping distance outdoor without a barrier of 5.7 meters with an average response time of 258.3 seconds, and the average outdoor stopping distance with a barrier of 2.73 meters with a response an average time of 141.3 seconds.

scholarly journals Identification of the flow experience in gamified educational environment

2021 ◽  
pp. 167-176
Author(s):  
Veljko Aleksić ◽  
Olga Ristić
Keyword(s):  
Response Time ◽  
User Experience ◽  
Educational Environment ◽  
Assessment Tools ◽  
Flow Experience ◽  
Sense Of Control ◽  
Average Response ◽  
Average Response Time ◽  
Educational Environments ◽  
Experience Of Flow

Determining and understanding the user experience in gamified educational environments is a contemporary challenge, especially when analyzing the flow experience (balance of challenge and skills, conscious actions, clear goals, clear feedback, sense of control, etc.). The reason for this lies in the assessment tools that most often created and implemented to separate the user from the experience of flow and/or cannot be applied en masse.The paper presents the results of a study in which flow experience was modeled based on data logs (e.g. number of mouse actions or average response time) in gamified educational environment on a sample of 31HE students. The results indicate the existence of correlations between data logs and flow experience dimensions.

scholarly journals Improving the Average Response Time in Collective I/O

2011 ◽  
pp. 71-80 ◽  
Author(s):  
Chen Jin ◽  
Saba Sehrish ◽  
Wei-keng Liao ◽  
Alok Choudhary ◽  
Karen Schuchardt
Keyword(s):  
Response Time ◽  
Average Response ◽  
Average Response Time

scholarly journals Average Response Time (ART):Real-Time Traffic Management in VFC Enabled Smart Cities

2020 ◽  
Vol 981 ◽  
pp. 022054
Author(s):  
Shabana ◽  
Sallauddin Mohmmad ◽  
Mohammed Ali Shaik ◽  
K Mahender ◽  
Ranganath Kanakam ◽  
...  
Keyword(s):  
Response Time ◽  
Real Time ◽  
Traffic Management ◽  
Smart Cities ◽  
Average Response ◽  
Average Response Time ◽  
Real Time Traffic

scholarly journals IMPLEMENTING LOAD BALANCING BY MODIFIED WEIGHTED ROUND ROBIN-RETRIEVE PACKET ALGORITHM ON THE SOFTWARE-DEFINED NETWORKING

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
ZAINAL ABIDIN ◽  
Tutuk Indriyani ◽  
Danang Haryo Sulaksono
Keyword(s):  
Load Balancing ◽  
Response Time ◽  
Software Defined Networking ◽  
Round Robin ◽  
Traffic Load ◽  
Single Server ◽  
Average Response ◽  
Average Response Time ◽  
Time Limits

Client’s request for traffic problems is so huge that causes the single server difficult in handling the traffic load. Therefore, the system of load balancing is required as it is a technique to equally distribute the traffic load on the two or more connection lines so that the traffic can run optimally. Thus, the load balancing is crucial to implement by using Modified Weighted Round Robin-Retrieve Packet on the Software-Defined Networking. Based on the parameter of average response-time in time limits 0.1, 0.2, and 0.3 seconds, the scores were 0.016-0.04, 0.02-0.04, and 0.014-0.032 seconds consecutively. Based on the parameter of data transaction per second in time limits 0.1; 0.2, and 0.3 seconds, the scores respectively were 49.614-111.306, 41.678-107.032, and 37.806-102.84 data transaction/second. 

scholarly journals Green Metascheduler Architecture to Provide QoS in Cloud Computing

2015 ◽  
Author(s):  
Osvaldo Adilson De Carvalho Junior ◽  
Sarita Mazzini Bruschi ◽  
Regina Helena Carlucci Santana ◽  
Marcos José Santana
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Energy Consumption ◽  
Response Time ◽  
Large Scale ◽  
Quality Of Services ◽  
Average Response ◽  
Average Response Time ◽  
Private Cloud

The aim of this paper is to propose and evaluate GreenMACC (Green Metascheduler Architecture to Provide QoS in Cloud Computing), an extension of the MACC architecture (Metascheduler Architecture to provide QoS in Cloud Computing) which uses greenIT techniques to provide Quality of Service. The paper provides an evaluation of the performance of the policies in the four stages of scheduling focused on energy consumption and average response time. The results presented confirm the consistency of the proposal as it controls energy consumption and the quality of services requested by different users of a large-scale private cloud.

scholarly journals Multi-criteria site selection for fire services: the interaction with analytic hierarchy process and geographic information systems

2010 ◽  
Vol 10 (10) ◽  
pp. 2127-2134 ◽  
Author(s):  
T. Erden ◽  
M. Z. Coşkun
Keyword(s):  
Response Time ◽  
Site Selection ◽  
Decision Makers ◽  
Average Response ◽  
Average Response Time ◽  
Hazardous Material ◽  
Fire Brigade ◽  
Fire Station ◽  
Study Results ◽  
Fire Stations

Abstract. This study combines AHP and GIS to provide decision makers with a model to ensure optimal site location(s) for fire stations selected. The roles of AHP and GIS in determining optimal locations are explained, criteria for site selection are outlined, and case study results for finding the optimal fire station locations in Istanbul, Turkey are included. The city of Istanbul has about 13 million residents and is the largest and most populated city in Turkey. The rapid and constant growth of Istanbul has resulted in the increased number of fire related cases. Fire incidents tend to increase year by year in parallel with city expansion, population and hazardous material facilities. Istanbul has seen a rise in reported fire incidents from 12 769 in 1994 to 30 089 in 2009 according to the interim report of Istanbul Metropolitan Municipality Department of Fire Brigade. The average response time was approximately 7 min 3 s in 2009. The goal of this study is to propose optimal sites for new fire station creation to allow the Fire Brigade in Istanbul to reduce the average response time to 5 min or less. After determining the necessity of suggesting additional fire stations, the following steps are taken into account: six criteria are considered in this analysis. They are: High Population Density (HPD); Proximity to Main Roads (PMR); Distance from Existing Fire Stations (DEF); Distance from Hazardous Material Facilities (DHM); Wooden Building Density (WBD); and Distance from the Areas Subjected to Earthquake Risk (DER). DHM criterion, with the weight of 40%, is the most important criterion in this analysis. The remaining criteria have a weight range from 9% to 16%. Moreover, the following steps are performed: representation of criterion map layers in GIS environment; classification of raster datasets; calculating the result raster map (suitability map for potential fire stations); and offering a model that supports decision makers in selecting fire station sites. The existing 35 fire stations are used and 17 fire stations are newly suggested in the study area.

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