scholarly journals Fog vs. Cloud Computing: Should I Stay or Should I Go?

2019 ◽  
Vol 11 (2) ◽  
pp. 34
Author(s):  
Flávia Pisani ◽  
Vanderson Martins do Rosario ◽  
Edson Borin
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Execution Time ◽  
Linear Equations ◽  
Test Cases ◽  
Visual Model ◽  
Benchmark Instances ◽  
The Cost ◽  
Time And Energy ◽  
The Internet Of Things

In this article, we work toward the answer to the question “is it worth processing a data stream on the device that collected it or should we send it somewhere else?”. As it is often the case in computer science, the response is “it depends”. To find out the cases where it is more profitable to stay in the device (which is part of the fog) or to go to a different one (for example, a device in the cloud), we propose two models that intend to help the user evaluate the cost of performing a certain computation on the fog or sending all the data to be handled by the cloud. In our generic mathematical model, the user can define a cost type (e.g., number of instructions, execution time, energy consumption) and plug in values to analyze test cases. As filters have a very important role in the future of the Internet of Things and can be implemented as lightweight programs capable of running on resource-constrained devices, this kind of procedure is the main focus of our study. Furthermore, our visual model guides the user in their decision by aiding the visualization of the proposed linear equations and their slope, which allows them to find if either fog or cloud computing is more profitable for their specific scenario. We validated our models by analyzing four benchmark instances (two applications using two different sets of parameters each) being executed on five datasets. We use execution time and energy consumption as the cost types for this investigation.

scholarly journals Task Scheduling Algorithm Based on Bacterial Foraging Optimization (BFO) in Cloud Computing

2018 ◽  
Vol 7 (1) ◽  
pp. 16-19
Author(s):  
Anupama Gupta ◽  
Kulveer Kaur ◽  
Rajvir Kaur
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Execution Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Bacterial Foraging Optimization ◽  
Task Scheduling Algorithm ◽  
Steady Rate ◽  
Time And Energy

Cloud computing is the architecture in which cloudlets are executed by the virtual machines. The most applicable virtual machines are selected on the basis of execution time and failure rate. Due to virtual machine overloading, the execution time and energy consumption is increased at steady rate. In this paper, BFO technique is applied in which weight of each virtual machine is calculated and the virtual machine which has the maximum weight is selected on which cloudlet will be migrated. The performance of proposed algorithm is tested by implementing it in CloudSim and analyzing it in terms of execution time, energy consumption.

scholarly journals Novel load balancing approach based on ant colony optimization technique in cloud computing

2021 ◽  
Vol 10 (4) ◽  
pp. 2320-2326
Author(s):  
Yasameen A. Ghani Alyouzbaki ◽  
Muaayed F. Al-Rawi
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Load Balancing ◽  
Ant Colony Optimization ◽  
Virtual Machine ◽  
Execution Time ◽  
Virtual Machines ◽  
Service Providers ◽  
Ant Colony ◽  
Time And Energy

The cloud is the framework in which communication is connected with virtual machines, data centers, hosts, and brokers. The broker searches for a highly reliable cloudlet virtual machine for execution. Vulnerability can occur in the network because of which framework gets overburden. A research strategy is introduced in this article to expand the fault tolerance of the framework. The proposed approach improvement depends on the algorithm of ant colony optimization (ACO) that can choose the better virtual machine on which is to migrate the cloudlet to reduce the execution time and energy consumption. The efficiency of the proposed approach simulated in terms of execution time, energy consumption and examined with CloudSim. The introduction is provided in this article with a detailed description of cloud computing and, in addition, green cloud computing with its models. This article also discussed the virtual machine (VM) in more depth in the introduction section, which allows cloud service providers to supervise cloud resources competently while dispensing with the need for human oversight. Then the article submitted and explained the related works with their discussion and then it explained the novel proposed load balancing based on ACO technique and concluded that the execution time and energy consumption of the proposed technique is better than the three-threshold energy saving algorithm (TESA) technique that is commonly used in cloud load balancing.

Joint Optimization Offloading Strategy of Execution Time and Energy Consumption of Mobile Edge Computing

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Qingzhu Wang ◽  
Xiaoyun Cui
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Execution Time ◽  
Optimal Solution ◽  
Computation Offloading ◽  
Joint Optimization ◽  
Random Strategy ◽  
Fitness Value ◽  
Cloud Server ◽  
Time And Energy

As mobile devices become more and more powerful, applications generate a large number of computing tasks, and mobile devices themselves cannot meet the needs of users. This article proposes a computation offloading model in which execution units including mobile devices, edge server, and cloud server. Previous studies on joint optimization only considered tasks execution time and the energy consumption of mobile devices, and ignored the energy consumption of edge and cloud server. However, edge server and cloud server energy consumption have a significant impact on the final offloading decision. This paper comprehensively considers execution time and energy consumption of three execution units, and formulates task offloading decision as a single-objective optimization problem. Genetic algorithm with elitism preservation and random strategy is adopted to obtain optimal solution of the problem. At last, simulation experiments show that the proposed computation offloading model has lower fitness value compared with other computation offloading models.

Location and Provisioning Problems in Cloud Computing Networks

2013 ◽  
pp. 22-45 ◽  
Author(s):  
Federico Larumbe ◽  
Brunilde Sansò
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Data Centers ◽  
Optimization Problems ◽  
Virtual Machine Placement ◽  
Future Research ◽  
Network Efficiency ◽  
Placement Problem ◽  
The Cost

This chapter addresses a set of optimization problems that arise in cloud computing regarding the location and resource allocation of the cloud computing entities: the data centers, servers, software components, and virtual machines. The first problem is the location of new data centers and the selection of current ones since those decisions have a major impact on the network efficiency, energy consumption, Capital Expenditures (CAPEX), Operational Expenditures (OPEX), and pollution. The chapter also addresses the Virtual Machine Placement Problem: which server should host which virtual machine. The number of servers used, the cost, and energy consumption depend strongly on those decisions. Network traffic between VMs and users, and between VMs themselves, is also an important factor in the Virtual Machine Placement Problem. The third problem presented in this chapter is the dynamic provisioning of VMs to clusters, or auto scaling, to minimize the cost and energy consumption while satisfying the Service Level Agreements (SLAs). This important feature of cloud computing requires predictive models that precisely anticipate workload dimensions. For each problem, the authors describe and analyze models that have been proposed in the literature and in the industry, explain advantages and disadvantages, and present challenging future research directions.

A Hybrid Resource Reservation Method for Workflows in Clouds

2012 ◽  
Vol 4 (4) ◽  
pp. 1-21
Author(s):  
Tyng-Yeu Liang ◽  
Fu-Chun Lu ◽  
Jun-Yao Chiu
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
High Performance ◽  
Resource Reservation ◽  
Scientific Workflows ◽  
Service Oriented ◽  
Time And Energy ◽  
Gpu Architecture ◽  
Performance Computing ◽  
Oriented System

QoS and energy consumption are two important issues for Cloud computing. In this paper, the authors propose a hybrid resource reservation method to address these two issues for scientific workflows in the high-performance computing Clouds built on hybrid CPU/GPU architecture. As named, this method reserves proper CPU or GPU for executing different jobs in the same workflow based on the profile of execution time and energy consumption of each resource-to-program pair. They have implemented the proposed resource reservation method on a real service-oriented system. The experimental results show that the proposed resource reservation method can effectively maintain the QoS of workflows while simultaneously minimizing the energy consumption of executing the workflows.

scholarly journals Exploring Direct Convolution Performance on the Gemmini Accelerator

2020 ◽  
Author(s):  
Caio Vieira ◽  
Arthur Lorenzon ◽  
Lucas Schnorr ◽  
Philippe Navaux ◽  
Antonio Carlos Beck
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Energy Consumption ◽  
Convolutional Neural Network ◽  
Building Block ◽  
Execution Time ◽  
Performance Gap ◽  
Main Building ◽  
Time And Energy ◽  
Lower Frequencies

Convolutional Neural Network (CNN) algorithms are becoming a recurrent solution to solve Computer Vision related problems. These networks employ convolutions as main building block, which greatly impact their performance since convolution is a costly operation. Due to its importance in CNN algorithms, this work evaluates convolution performance in the Gemmini accelerator and compare it to a conventional lightlyand heavily-loaded desktop CPU in terms of execution time and energy consumption. We show that Gemmini can achieve lower execution time and energy consumption when compared to a CPU even for small convolutions, and this performance gap grows with convolution size. Furthermore, we analyze the minimum Gemmini required frequency to match the same CPU execution time, and show that Gemmini can achieve the same runtime while working in much lower frequencies.

The Rise of Big Data, Cloud, and Internet of Things

2018 ◽  
pp. 201-222 ◽  
Author(s):  
Reema Abdulraziq ◽  
Muneer Bani Yassein ◽  
Shadi Aljawarneh
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
End Users ◽  
Structured Data ◽  
The Internet ◽  
Huge Amount ◽  
Volume Velocity ◽  
The Cost ◽  
The Internet Of Things

Big data refers to the huge amount of data that is being used in commercial, industrial and economic environments. There are three types of big data; structured, unstructured and semi-structured data. When it comes to discussions on big data, three major aspects that can be considered as its main dimensions are the volume, velocity, and variety of the data. This data is collected, analysed and checked for use by the end users. Cloud computing and the Internet of Things (IoT) are used to enable this huge amount of collected data to be stored and connected to the Internet. The time and the cost are reduced by means of these technologies, and in addition, they are able to accommodate this large amount of data regardless of its size. This chapter focuses on how big data, with the emergence of cloud computing and the Internet of Things (IOT), can be used via several applications and technologies.

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