Multicriteria decision making based optimum virtual machine selection technique for smart cloud environment

2021 ◽  
pp. 1-15
Author(s):  
Raman Singh ◽  
Maninder Singh ◽  
Sheetal Garg ◽  
Ivan Perl ◽  
Olga Kalyonova ◽  
...  
Keyword(s):  
Virtual Machines ◽  
Smart Cities ◽  
Multicriteria Decision Making ◽  
Work Load ◽  
Load Factor ◽  
Processing Unit ◽  
Central Processing ◽  
Data Centre ◽  
Smart Data ◽  
Iot Devices

In the popular field of cloud computing, millions of job requests arrive at the data centre for execution. The job of the data centre is to optimally allocate virtual machines (VMs) to these job requests in order to use resources efficiently. In the future smart cities, huge amount of job requests and data will be generated by the Internet of Things (IoT) devices which will influence the designing of optimum resource management of smart cloud environments. The present paper analyses the performance efficiency of the data centre with and without job request consolidation. First, the work load performance of the data centre was analysed without job request consolidation, exhibiting that the job requests to VM assignment was highly imbalanced, and only 5% of VMs were running with a load factor of more than 70%. Then, the technique for order of preference by similarity to ideal solution-based VM selection algorithm was applied, which was able to select the best VM using parameters such as the provisioned or available central processing unit capacity, provisioned or available memory capacity, and state of machine (running, hibernated, or available). The Bitbrains dataset consisting of 1750 VMs was used to analyse the performance of the proposed methodology. The analysis concluded that the proposed methodology was capable of serving all job requests using less than 24% VMs with improved load efficiency. The fewer number of VMs with an improved load factor guarantees energy saving and an increase in the overall running efficiency of the smart data centre environment.

ILIS--An integrated laboratory information system. I. Biochemistry and hematology.

1982 ◽  
Vol 28 (2) ◽  
pp. 271-276 ◽  
Author(s):  
S U Deshpande
Keyword(s):  
Medical Records ◽  
Data Entry ◽  
Large Data ◽  
Work Load ◽  
Processing Unit ◽  
Patient Identification ◽  
Central Processing ◽  
Cathode Ray Tubes ◽  
Clinical Laboratories ◽  
On Line

Abstract IBM System 34 (central processing unit, 128 kilobytes; fixed disks, 128.4 megabytes) with seven cathode-ray tubes has been used by our clinical laboratories for the last 30 months. All data-entry programs are in a conversational mode, for on-line corrections of possible errors in patient identification and results. Daily reports are removed from the medical records after temporary and permanent cumulative weekly reports are received, which keep a three-month track of the results. The main advantages of the system are: (a) the increasing laboratory work load can be handled with the same staff; (b) the volume of the medical record files on the patients is decreased; (c) an easily retrievable large data base of results is formed for research purposes; (d) faster billing; and (e) the computer system is run without engaging any additional staff.

Empirical Performance Assessment of Public Clouds Using System Level Benchmarks

2013 ◽  
Vol 3 (4) ◽  
pp. 81-91 ◽  
Author(s):  
Sanjay P. Ahuja ◽  
Thomas F. Furman ◽  
Kerwin E. Roslie ◽  
Jared T. Wheeler
Keyword(s):  
Virtual Machines ◽  
Service Providers ◽  
Cluster Formation ◽  
Cloud Service ◽  
Cloud Services ◽  
System Level ◽  
Processing Unit ◽  
Central Processing ◽  
Empirical Performance ◽  
Set Up

Amazon's Elastic Compute Cloud (EC2) Service is one of the leading public cloud service providers and offers many different levels of service. This paper looks into evaluating the memory, central processing unit (CPU), and input/output I/O performance of two different tiers of hardware offered through Amazon's EC2. Using three distinct types of system benchmarks, the performance of the micro spot instance and the M1 small instance are measured and compared. In order to examine the performance and scalability of the hardware, the virtual machines are set up in a cluster formation ranging from two to eight nodes. The results show that the scalability of the cloud is achieved by increasing resources when applicable. This paper also looks at the economic model and other cloud services offered by Amazon's EC2, Microsoft's Azure, and Google's App Engine.

scholarly journals Computation of Traffic Time Series for Large Populations of IoT Devices

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 78 ◽  
Author(s):  
Mikel Izal ◽  
Daniel Morató ◽  
Eduardo Magaña ◽  
Santiago García-Jiménez
Keyword(s):  
Time Series ◽  
Real Time ◽  
Processing Unit ◽  
Security Breaches ◽  
Central Processing ◽  
Traffic Time Series ◽  
Large Populations ◽  
Analysis Computer ◽  
Iot Devices ◽  
Recursive Search

The Internet of Things (IoT) contains sets of hundreds of thousands of network-enabled devices communicating with central controlling nodes or information collectors. The correct behaviour of these devices can be monitored by inspecting the traffic that they create. This passive monitoring methodology allows the detection of device failures or security breaches. However, the creation of hundreds of thousands of traffic time series in real time is not achievable without highly optimised algorithms. We herein compare three algorithms for time-series extraction from traffic captured in real time. We demonstrate how a single-core central processing unit (CPU) can extract more than three bidirectional traffic time series for each one of more than 20,000 IoT devices in real time using the algorithm DStries with recursive search. This proposal also enables the fast reconfiguration of the analysis computer when new IoT devices are added to the network.

scholarly journals Empirical Performance and Energy Consumption Evaluation of Container Solutions on Resource Constrained IoT Gateways

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1378
Author(s):  
Syed M. Raza ◽  
Jaeyeop Jeong ◽  
Moonseong Kim ◽  
Byungseok Kang ◽  
Hyunseung Choo
Keyword(s):  
Energy Consumption ◽  
Detection System ◽  
Processing Unit ◽  
Memory Usage ◽  
Resource Constrained ◽  
Service Architecture ◽  
Central Processing ◽  
Empirical Performance ◽  
Iot Devices ◽  
Container Orchestration

Containers virtually package a piece of software and share the host Operating System (OS) upon deployment. This makes them notably light weight and suitable for dynamic service deployment at the network edge and Internet of Things (IoT) devices for reduced latency and energy consumption. Data collection, computation, and now intelligence is included in variety of IoT devices which have very tight latency and energy consumption conditions. Recent studies satisfy latency condition through containerized services deployment on IoT devices and gateways. They fail to account for the limited energy and computing resources of these devices which limit the scalability and concurrent services deployment. This paper aims to establish guidelines and identify critical factors for containerized services deployment on resource constrained IoT devices. For this purpose, two container orchestration tools (i.e., Docker Swarm and Kubernetes) are tested and compared on a baseline IoT gateways testbed. Experiments use Deep Learning driven data analytics and Intrusion Detection System services, and evaluate the time it takes to prepare and deploy a container (creation time), Central Processing Unit (CPU) utilization for concurrent containers deployment, memory usage under different traffic loads, and energy consumption. The results indicate that container creation time and memory usage are decisive factors for containerized micro service architecture.

scholarly journals A Parallel Meta-Heuristic Approach to Reduce Vehicle Travel Time in Smart Cities

2021 ◽  
Vol 11 (2) ◽  
pp. 818
Author(s):  
Hector Rico-Garcia ◽  
Jose-Luis Sanchez-Romero ◽  
Antonio Jimeno-Morenilla ◽  
Hector Migallon-Gomis
Keyword(s):  
Travel Time ◽  
Smart City ◽  
Graphics Processing Unit ◽  
Smart Cities ◽  
Travelling Salesman Problem ◽  
Urban Environments ◽  
Processing Unit ◽  
Departure Point ◽  
Iot Devices ◽  
Parallel Graphics

The development of the smart city concept and inhabitants’ need to reduce travel time, in addition to society’s awareness of the importance of reducing fuel consumption and respecting the environment, have led to a new approach to the classic travelling salesman problem (TSP) applied to urban environments. This problem can be formulated as “Given a list of geographic points and the distances between each pair of points, what is the shortest possible route that visits each point and returns to the departure point?”. At present, with the development of Internet of Things (IoT) devices and increased capabilities of sensors, a large amount of data and measurements are available, allowing researchers to model accurately the routes to choose. In this work, the aim is to provide a solution to the TSP in smart city environments using a modified version of the metaheuristic optimization algorithm Teacher Learner Based Optimization (TLBO). In addition, to improve performance, the solution is implemented by means of a parallel graphics processing unit (GPU) architecture, specifically a Compute Unified Device Architecture (CUDA) implementation.

Perangkat keras komputer

2020 ◽  
Author(s):  
Roudati jannah
Keyword(s):  
Data Storage ◽  
Central Processing Unit ◽  
External Memory ◽  
Storage Device ◽  
Input Device ◽  
Processing Unit ◽  
Central Processing ◽  
Output Device

Perangkat keras komputer adalah bagian dari sistem komputer sebagai perangkat yang dapat diraba, dilihat secara fisik, dan bertindak untuk menjalankan instruksi dari perangkat lunak (software). Perangkat keras komputer juga disebut dengan hardware. Hardware berperan secara menyeluruh terhadap kinerja suatu sistem komputer. Prinsipnya sistem komputer selalu memiliki perangkat keras masukan (input/input device system) – perangkat keras premprosesan (processing/central processing unit) – perangkat keras luaran (output/output device system) – perangkat tambahan yang sifatnya opsional (peripheral) dan tempat penyimpanan data (storage device system/external memory).

CENTRAL PROCESSING UNIT (CPU)

2020 ◽  
Author(s):  
Ika Milia wahyunu Siregar
Keyword(s):  
Processing System ◽  
Central Processing Unit ◽  
Processing Unit ◽  
Central Processing

Perkembangan IT di dunia sangat pesat, mulai dari perkembangan sofware hingga hardware. Teknologi sekarang telah mendominasi sebagian besar di permukaan bumi ini. Karena semakin cepatnya perkembangan Teknologi, kita sebagai pengguna bisa ketinggalan informasi mengenai teknologi baru apabila kita tidak up to date dalam pengetahuan teknologi ini. Hal itu dapat membuat kita mudah tergiur dan tertipu dengan berbagai iklan teknologi tanpa memikirkan sisi negatifnya. Sebagai pengguna dari komputer, kita sebaiknya tahu seputar mengenai komponen-komponen komputer. Komputer adalah serangkaian mesin elektronik yang terdiri dari jutaan komponen yang dapat saling bekerja sama, serta membentuk sebuah sistem kerja yang rapi dan teliti. Sistem ini kemudian digunakan untuk dapat melaksanakan pekerjaan secara otomatis, berdasarkan instruksi (program) yang diberikan kepadanya. Istilah Hardware komputer atau perangkat keras komputer, merupakan benda yang secara fisik dapat dipegang, dipindahkan dan dilihat. Central Processing System/ Central Processing Unit (CPU) adalah salah satu jenis perangkat keras yang berfungsi sebagai tempat untuk pengolahan data atau juga dapat dikatakan sebagai otak dari segala aktivitas pengolahan seperti penghitungan, pengurutan, pencarian, penulisan, pembacaan dan sebagainya.

Perangkat lunak komputer

2020 ◽  
Author(s):  
Intan khadijah simatupang
Keyword(s):  
Data Storage ◽  
Central Processing Unit ◽  
External Memory ◽  
Storage Device ◽  
Input Device ◽  
Processing Unit ◽  
Central Processing ◽  
Output Device

Komputer adalah serangkaian mesin elektronik yang terdiri dari jutaan komponen yang dapat saling bekerja sama, serta membentuk sebuah sistem kerja yang rapi dan teliti. Sistem ini kemudian digunakan untuk dapat melaksanakan pekerjaan secara otomatis, berdasarkan instruksi (program) yang diberikan kepadanya. Istilah Hardware computer atau perangkat keras komputer, merupakan benda yang secara fisik dapat dipegang, dipindahkan dan dilihat. Software komputer atau perangkat lunak komputer merupakan kumpulan instruksi (program/prosedur) untuk dapat melaksanakan pekerjaan secara otomatis dengan cara mengolah atau memproses kumpulan instruksi (data) yang diberikan. Pada prinsipnya sistem komputer selalu memiliki perangkat keras masukan (input/input device system) – perangkat keras pemprosesan (processing/ central processing unit) – perangkat keras keluaran (output/output device system), perangkat tambahan yang sifatnya opsional (peripheral) dan tempat penyimpanan data (Storage device system/external memory).

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