scholarly journals Pengujian Optimization dan Non-Optimization Query Metode Topsis untuk Menentukan Tingkat Kerusakan Sektor Bencana Alam

2022 ◽  
Vol 6 (1) ◽  
pp. 89-99
Author(s):  
Annisa Heparyanti Safitri ◽  
Agung Teguh Wibowo Almais ◽  
A'la Syauqi ◽  
Roro Inda Melani
Keyword(s):  
Response Time ◽  
Query Optimization ◽  
Average Response ◽  
Volume Data ◽  
Average Response Time

Volume data yang sangat besar dari tim surveyor Perencanaan dan Pengendalian Penanganan Bencana(P3B) menciptakan masalah yang luas dan beragam sehingga dapat menghabiskan sumber daya sistem dan waktu pemrosesan yang terbilang lama. Oleh karena itu penelitian ini mengusulkan solusi dengan melakukan Optimasi query pada metode TOPSIS yang diimplementasikan pada sistem pendukung kepeutusan untuk menentukan tingkat kerusakan pasca bencana. Berdasarkan 3 kali uji coba dengan jumlah data yang berbeda-beda yaitu ujicoba ke-1 menggunakan 114 data, ujicoba ke-2 sebanyak 228 data dan ujicoba ke-3 menggunakan 334 data. Selain itu, setiap ujicoba dilakukan lagi pengukuran re-spons time sebanyak 3 kali maka didapatkan hasil rata-rata (average) response time dari masing-masing langkah metode TOPSIS. Didapati bahwa hasil dari tahapan perangkingan menggunakan query optimiza-tion lebih cepat 0.00076 dibandingakan dengan qury non-optimization. Sehingga dapat di simpulkan bahwa response time yang didapat query optimization pada setiap langkah metode TOPSIS pada sistem pendukung keputusan kerusakan sektor pasca bencana alam lebih kecil dibandingkan dengan response time pada query non-optimization.

scholarly journals Crucial File Selection Strategy (CFSS) for Enhanced Download Response Time in Cloud Replication Environments

2021 ◽  
Vol 18 (4(Suppl.)) ◽  
pp. 1356
Author(s):  
M.A. Fazlina ◽  
Rohaya Latip ◽  
Azizol Abdullah ◽  
Hamidah Ibrahim ◽  
Mohamed A. Alrshah
Keyword(s):  
Response Time ◽  
Poor Response ◽  
High Volume ◽  
Selection Strategy ◽  
Individual Data ◽  
Average Response ◽  
Volume Data ◽  
Average Response Time ◽  
High Demand ◽  
Replication Strategy

Cloud Computing is a mass platform to serve high volume data from multi-devices and numerous technologies. Cloud tenants have a high demand to access their data faster without any disruptions. Therefore, cloud providers are struggling to ensure every individual data is secured and always accessible. Hence, an appropriate replication strategy capable of selecting essential data is required in cloud replication environments as the solution. This paper proposed a Crucial File Selection Strategy (CFSS) to address poor response time in a cloud replication environment. A cloud simulator called CloudSim is used to conduct the necessary experiments, and results are presented to evidence the enhancement on replication performance. The obtained analytical graphs are discussed thoroughly, and apparently, the proposed CFSS algorithm outperformed another existing algorithm with a 10.47% improvement in average response time for multiple jobs per round.

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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