scholarly journals Analisa Performa RStudio Server Berbasis Cloud Menggunakan Elastic Stack sebagai Sistem Manajemen Metrik

2021 ◽  
Vol 7 (3) ◽  
pp. 450
Author(s):  
Krisna Aditama Ashari ◽  
Is Mardianto ◽  
Dedy Sugiarto
Keyword(s):  
Memory Usage ◽  
Cpu Usage ◽  
Run Time

Reliabilitas atau keandalan merupakan salah satu sifat penting pada sebuah server dalam melayani pengguna. Salah satu cara mengukurnya ialah dengan melakukan uji perfoma. Penelitian ini bertujuan untuk mengetahui kemampuan RStudio Server pada infrastruktur cloud saat digunakan oleh multiuser dengan Elastic Stack sebagai sistem yang menangani pengumpulan, penyimpanan dan visualisasi data metriknya. Tahapan dimulai dengan mengumpulkan data berupa metrik sistem oleh Metricbeat, lalu diproses Logstash dan disimpan menjadi index dalam Elasticsearch, visualisasi data ditampilkan oleh Kibana. Pengujian kinerja server dilakukan dengan menjalankan script R berdurasi 2 menit dan 7 menit secara simultan. Hasil pengujian berupa catatan CPU Usage, Memory Usage dan durasi penyelesaian script selanjutnya di plotting pada R. Hasil analisa dari plotting data menunjukkan jumlah user yang dapat menggunakan Rstudio Server dengan spesifikasi 2 CPU dan RAM 4GB secara optimal ialah maksimal 2 user pada script dengan run time 2 menit dan 7 menit, lebih dari jumlah user itu akan mempengaruhi waktu proses penyelesaian script menjadi tingkat performa sedang hingga berat.

scholarly journals Animations in Cross-Platform Mobile Applications: An Evaluation of Tools, Metrics and Performance

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2081 ◽  
Author(s):  
Andreas Biørn-Hansen ◽  
Tor-Morten Grønli ◽  
Gheorghita Ghinea
Keyword(s):  
User Interfaces ◽  
Mobile Apps ◽  
Memory Usage ◽  
Performance Profiling ◽  
App Development ◽  
Cpu Usage ◽  
Cross Platform ◽  
Platform Development ◽  
And Performance ◽  
The Impact

Along with the proliferation of high-end and performant mobile devices, we find that the inclusion of visually animated user interfaces are commonplace, but that research on their performance is scarce. Thus, for this study, eight mobile apps have been developed for scrutiny and assessment to report on the device hardware impact and penalties caused by transitions and animations, with an emphasis on apps generated using cross-platform development frameworks. The tasks we employ for animation performance measuring, are those of (i) a complex animation consisting of multiple elements, (ii) the opening sequence of a side menu navigation pattern, and (iii) a transition animation during in-app page navigation. We employ multiple performance profiling tools, and scrutinize metrics including frames per second (FPS), CPU usage, device memory usage and GPU memory usage, all to uncover the impact caused by executing transitions and animations. We uncover important differences in device hardware utilization during animations across the different cross-platform technologies employed. Additionally, Android and iOS are found to differ greatly in terms of memory consumption, CPU usage and rendered FPS, a discrepancy that is true for both the native and cross-platform apps. The findings we report are indeed factors contributing to the complexity of app development.

scholarly journals Asymptotically optimal minimizers schemes

2018 ◽  
Author(s):  
Guillaume Marçais ◽  
Dan DeBlasio ◽  
Carl Kingsford
Keyword(s):  
Asymptotic Behavior ◽  
Lower Bound ◽  
Efficient Algorithm ◽  
Memory Usage ◽  
Theoretical Understanding ◽  
Asymptotically Optimal ◽  
Bioinformatics Tools ◽  
Bioinformatics Software ◽  
Run Time ◽  
Local Schemes

AbstractMotivationThe minimizers technique is a method to sample k-mers that is used in many bioinformatics software to reduce computation, memory usage and run time. The number of applications using minimizers keeps on growing steadily. Despite its many uses, the theoretical understanding of minimizers is still very limited. In many applications, selecting as few k-mers as possible (i.e. having a low density) is beneficial. The density is highly dependent on the choice of the order on the k-mers. Different applications use different orders, but none of these orders are optimal. A better understanding of minimizers schemes, and the related local and forward schemes, will allow designing schemes with lower density, and thereby making existing and future bioinformatics tools even more efficient.ResultsFrom the analysis of the asymptotic behavior of minimizers, forward and local schemes, we show that the previously believed lower bound on minimizers schemes does not hold, and that schemes with density lower than thought possible actually exist. The proof is constructive and leads to an efficient algorithm to compare k-mers. These orders are the first known orders that are asymptotically optimal. Additionally, we give improved bounds on the density achievable by the 3 type of [email protected]@cs.cmu.edu

scholarly journals OwnKit: Ownership Inference for Java

2021 ◽  
Author(s):  
◽  
Constantine Dymnikov
Keyword(s):  
Memory Usage ◽  
Ownership Type ◽  
Single Class ◽  
Strong Notion ◽  
Heavy Burden ◽  
Run Time ◽  
Type Information ◽  
Ownership Patterns

<p>Object ownership allows us to statically control run-time aliasing in order to provide a strong notion of object encapsulation. Unfortunately in order to use ownership, code must first be annotated with extra type information. This imposes a heavy burden on the programmer, and has contributed to the slow adoption of ownership. Ownership inference is the process of reconstructing ownership type information based on the existing ownership patterns in code. This thesis presents OwnKit—an automatic ownership inference tool for Java. OwnKit conducts inference in a modular way: by only considering a single class at the time. The modularity makes our algorithm highly scalable in both time and memory usage.</p>

scholarly journals An Enhanced Block Pre-processing of PRESENT Algorithm for Fingerprint Template Encryption in the Internet of Things Environment

2021 ◽  
Vol 13 (3) ◽  
Author(s):  
Norliza Katuk ◽  
Ikenna Rene Chiadighikaobi
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Memory Usage ◽  
Original Algorithm ◽  
Internet Of Things Environment ◽  
Cpu Usage ◽  
Encryption Algorithms ◽  
The Internet Of Things ◽  
Lightweight Encryption ◽  
Present Algorithm

Many previous studies had proven that The PRESENT algorithm is ultra-lightweight encryption. Therefore, it is suitable for use in an IoT environment. However, the main problem with block encryption algorithms like PRESENT is that it causes attackers to break the encryption key. In the context of a fingerprint template, it contains a header and many zero blocks that lead to a pattern and make it easier for attackers to obtain an encryption key. Thus, this research proposed header and zero blocks bypass method during the block pre-processing to overcome this problem. First, the original PRESENT algorithm was enhanced by incorporating the block pre-processing phase. Then, the algorithm’s performance was tested using three measures: time, memory usage, and CPU usage for encrypting and decrypting fingerprint templates. This study demonstrated that the proposed method encrypted and decrypted the fingerprint templates faster with the same CPU usage of the original algorithm but consumed higher memory. Thus, it has the potential to be used in IoT environments for security.

scholarly journals OwnKit: Ownership Inference for Java

2021 ◽  
Author(s):  
◽  
Constantine Dymnikov
Keyword(s):  
Memory Usage ◽  
Ownership Type ◽  
Single Class ◽  
Strong Notion ◽  
Heavy Burden ◽  
Run Time ◽  
Type Information ◽  
Ownership Patterns

<p>Object ownership allows us to statically control run-time aliasing in order to provide a strong notion of object encapsulation. Unfortunately in order to use ownership, code must first be annotated with extra type information. This imposes a heavy burden on the programmer, and has contributed to the slow adoption of ownership. Ownership inference is the process of reconstructing ownership type information based on the existing ownership patterns in code. This thesis presents OwnKit—an automatic ownership inference tool for Java. OwnKit conducts inference in a modular way: by only considering a single class at the time. The modularity makes our algorithm highly scalable in both time and memory usage.</p>

scholarly journals LOAD BALANCING UNTUK PERBANDINGAN KINERJA VIRTUALISASI SERVER VMWARE DAN XEN SERVER

2018 ◽  
Vol 5 (1) ◽  
pp. 32
Author(s):  
I Gede Primanata ◽  
Nyoman Putra Sastra ◽  
Dewa Made Wiharta
Keyword(s):  
Load Balancing ◽  
Data Access ◽  
Memory Usage ◽  
Shared Resource ◽  
Server Virtualization ◽  
Resource Needs ◽  
Cpu Usage ◽  
Virtualization Technology ◽  
Efficient Memory ◽  
Better Than

This study aims to compare VMware server virtualization technology and Xen server that simulates hardware into the software. In order to share loads can be balanced on both virtual servers, this research used HAproxy as Load Balancing which is implemented on Apache Web Server so performance from CPU usage side, memory and access speed from VMware and Xen server virtualization can be compared. By comparing these two models, it is expected to be used as a reference for selecting virtualization models for shared resource needs on one or more physical servers. From the results of testing in this study, it was found that the performance of VMware is better than Xen when viewed from the CPU usage. CPU usage in VMware is lower than Xen with 1.57% when handling one website and 1.58% when handling two websites. In terms of memory usage Xen Server is more efficient in memory usage when compared to VMware, Xen is more efficient memory of 29.11% and 34.84% for handling one and two websites when compared to VMware. While in terms of data access speed on Xen faster than VMware, which is about 42,132 Kbps.

Penggunaan Virtual Machine untuk Mengoptimalkan Server pada Cloud Gaming dengan GamingAnywhere

2020 ◽  
Vol 4 (6) ◽  
pp. 1028-1035
Author(s):  
Tiko Hadi Prabowo ◽  
Sofia Naning Hertiana ◽  
Sussi Sussi
Keyword(s):  
Virtual Machine ◽  
Packet Loss ◽  
Virtual Machines ◽  
Network Technology ◽  
Memory Usage ◽  
Measurement Results ◽  
Cpu Usage ◽  
Minimum Bandwidth ◽  
Game Industry

The development of the game industry is increasingly advanced until the emergence of cloud gaming network technology. Cloud gaming allows low-spec clients to play high-spec games. An open-source cloud gaming platform is GamingAnywhere. In this study, we will implement a cloud gaming server using GamingAnywhere and combine it with a virtual machine. The virtual machines that will be used are VirtualBox and VMware. This research is aimed at providing information about resource usage on servers and clients as well as Quality of Service (QoS) and Frames Per Second (FPS) from GamingAnywhere running on virtual machines. From the results of server measurements it only takes 12-21% CPU usage, 5-7% GPU usage, and 75-77% memory usage for VirtualBox and 17-26% CPU usage, 26-35% GPU usage, and 64-65% memory usage for VMware. From the FPS measurement results obtained on the client, it has an average of more than 59 fps for the three test games when GamingAnywhere is running on VirtualBox, VMware, and without using a virtual machine. From the measurement results, to get optimal QoS in accessing games with GamingAnywhere, a minimum bandwidth of 5 Mbps is needed and the distance between the client and the router is a maximum of 7 meters. If the bandwidth is less than 5 Mbps, the system experiences a delay of ± 0.003 seconds and the packet loss is more than 10%.

Implementation of User Management in a Laboratory with a Linux Primary Domain Controller

2019 ◽  
Vol 3 (2) ◽  
pp. 73-79
Author(s):  
Dhiemas Aditya Oktara ◽  
Rahmat Suhatman ◽  
Ibnu Surya
Keyword(s):  
Computer Networks ◽  
Educational Institutions ◽  
Network Technology ◽  
Memory Usage ◽  
Test Results ◽  
User Management ◽  
Storage Media ◽  
Cpu Usage

The development of network technology now allows many applications that can be done with computer networks. This can be seen from the needs of educational institutions for computer networks.  One of them is the Polytechnic Caltex Riau (PCR). The use of computers in the laboratory has not yet applied the concept of user management, so that the data stored on the computer can be accessed by more than one user which causes data to be reduced further. One way to overcome this problem is to implement a Primary Domain Controller (PDC), a server that can store and manage computer activities. Requires the client to have an account to log in to the computer and have their own storage media. From the test results obtained by the average received users log in, directory access and application by 96% and from the results of tests that have been done on average to CPU usage of 4% and memory usage by 37%. Can reduce from testing carried out to 30 users, using CPU and memory increases along with the number of clients who carry out activities and it takes a pause when logging in so that all users can access the server.

scholarly journals Implementation of Multiprocessing and Multithreading for End Node Middleware Control on Internet of Things Devices

Jurnal INFORM ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 54-60
Author(s):  
Iwan Kurnianto Wibowo ◽  
Adnan Rachmat Anom Besari ◽  
Muh. Rifqi Rizqullah
Keyword(s):  
Internet Of Things ◽  
Raspberry Pi ◽  
Memory Usage ◽  
Database Access ◽  
Sensor Reading ◽  
Cpu Usage ◽  
Educational Robot ◽  
End Node ◽  
Actuator Control ◽  
High Level

Previously, an educational robot system was built by incorporating Internet of Things (IoT) elements. Over time, this educational robot has been implanted with a middleware. Middleware has a role in receiving command data from the real-time database, access sensors, actuators, and sending feedback. Middleware contains protocols that translate commands between high-level programming and Raspberry Pi hardware. The focus of this research is to improve the performance of the middleware to pursue processing time efficiency. For this reason, it is necessary to implement multiprocessing and multithreading in handling several tasks. The CPU division has been adjusted automatically to not work on just one core or block of memory. Several program functions can run in parallel and reduce program execution time efficiently. The tasks handled are sensor reading and actuator control in the form of a motor. Testing has been carried out to perform multiprocessing and multithreading tasks to process six sensors and five actuators. Multiprocessing requires an average of 1.00% to 15.00% CPU usage and 2.70% memory usage. Meanwhile, multithreading involves an average of 1.00% to 71.00% CPU usage and 3.30% memory usage.

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