Using the Cuckoo Algorithm to Optimizing the Response Time and Energy Consumption Cost of Fog Nodes by Considering Collaboration in the Fog Layer

2021 ◽  
Author(s):  
Shakoor Vakilian ◽  
Seyed Vahid Moravvej ◽  
Ali Fanian
Keyword(s):  
Energy Consumption ◽  
Response Time ◽  
Consumption Cost ◽  
Time And Energy ◽  
Cuckoo Algorithm

scholarly journals Modeling Multi-Site Computation Offloading in Unreliable Cloud Environments

2021 ◽  
Author(s):  
Marzieh Ranjbar Pirbasti
Keyword(s):  
Energy Consumption ◽  
Response Time ◽  
Mobile Device ◽  
Fault Injection ◽  
Computation Offloading ◽  
Failure Rates ◽  
Recovery Mechanisms ◽  
Efficient Allocations ◽  
Cloud Servers ◽  
Time And Energy

Offloading heavy computations from a mobile device to cloud servers can reduce the power consumption of the mobile device and improve the response time of mobile applications. However, the gains of offloading can be significantly affected by failures of cloud servers and network links. In this thesis, we propose a fault-aware, multi-site computation offloading model capable of finding efficient allocations of tasks to resources. Our model reduces both response time and energy consumption by incorporating the effect of failures and recovery mechanisms for various offloading allocations. In addition, we create a fault-injection framework to evaluate an allocation under various failure rates and recovery mechanisms. The experiments carried out with our fault-injection framework demonstrate that our fault-aware model can determine an allocation—based on the type of failures, failure rates, and the employed recovery mechanisms—that improves both response time and lower energy consumption compared to model without failures.

scholarly journals Modeling Multi-Site Computation Offloading in Unreliable Cloud Environments

2021 ◽  
Author(s):  
Marzieh Ranjbar Pirbasti
Keyword(s):  
Energy Consumption ◽  
Response Time ◽  
Mobile Device ◽  
Fault Injection ◽  
Computation Offloading ◽  
Failure Rates ◽  
Recovery Mechanisms ◽  
Efficient Allocations ◽  
Cloud Servers ◽  
Time And Energy

Offloading heavy computations from a mobile device to cloud servers can reduce the power consumption of the mobile device and improve the response time of mobile applications. However, the gains of offloading can be significantly affected by failures of cloud servers and network links. In this thesis, we propose a fault-aware, multi-site computation offloading model capable of finding efficient allocations of tasks to resources. Our model reduces both response time and energy consumption by incorporating the effect of failures and recovery mechanisms for various offloading allocations. In addition, we create a fault-injection framework to evaluate an allocation under various failure rates and recovery mechanisms. The experiments carried out with our fault-injection framework demonstrate that our fault-aware model can determine an allocation—based on the type of failures, failure rates, and the employed recovery mechanisms—that improves both response time and lower energy consumption compared to model without failures.

scholarly journals Improved Response Time and Energy Management for Mobile Cloud Computing Using Computational Offloading

2020 ◽  
Vol 2 (1) ◽  
pp. 38-49
Author(s):  
Dr. Jennifer S. Raj
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Response Time ◽  
Mobile Cloud Computing ◽  
Cloud Services ◽  
Mobile Cloud ◽  
Processing Cost ◽  
Frame Work ◽  
Computational Offloading ◽  
Time And Energy

The mobile devices capabilities are found to be greater than before by utilizing the cloud services. There are various of service rendered by the cloud paradigm and the mobile devices usually allows the execution of the resource-intensive applications on the resource- constrained mobile device to be offloaded to the cloudlets that are resource rich thus enhancing the its processing capabilities. But accessing the cloud services within the minimum response time and energy consumption still remains as a serious research problem. So the proposed method put forth in the paper scopes in developing a frame work to choose the optimal cloud service provider. The frame work proposed is categorized into two stages where the initial stage engages the classifier to segregate the mobile device according to the fuzzy K-nearest neighbor and cultivates an improved computational offloading employing the Hidden Markov Model and ACO- ant colony optimization. The algorithm proffered is implemented in the MATLAB version 9.1 and the performance is evinced on the basis of the response time, energy consumption and the processing cost. The results obtained through the proposed method proves to provide an 89% better response time, 95 % better energy consumption and 50% enhanced processing cost compared to the few existing computational offloading methods put forth for the mobile cloud computing.

scholarly journals Economical Speed for Optimizing the Travel Time and Energy Consumption in Train Scheduling using a Fuzzy Multi-Objective Model

2021 ◽  
Author(s):  
Ahmad Reza Jafarian-Moghaddam
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Pollutant Emissions ◽  
Scheduling Problems ◽  
Train Scheduling ◽  
Multi Objective ◽  
Consumption Ratio ◽  
Proposed Model ◽  
Objective Model ◽  
Time And Energy

AbstractSpeed is one of the most influential variables in both energy consumption and train scheduling problems. Increasing speed guarantees punctuality, thereby improving railroad capacity and railway stakeholders’ satisfaction and revenues. However, a rise in speed leads to more energy consumption, costs, and thus, more pollutant emissions. Therefore, determining an economic speed, which requires a trade-off between the user’s expectations and the capabilities of the railway system in providing tractive forces to overcome the running resistance due to rail route and moving conditions, is a critical challenge in railway studies. This paper proposes a new fuzzy multi-objective model, which, by integrating micro and macro levels and determining the economical speed for trains in block sections, can optimize train travel time and energy consumption. Implementing the proposed model in a real case with different scenarios for train scheduling reveals that this model can enhance the total travel time by 19% without changing the energy consumption ratio. The proposed model has little need for input from experts’ opinions to determine the rates and parameters.

scholarly journals Efficient Placement of Service Function Chains in Cloud Computing Environments

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 323
Author(s):  
Marwa A. Abdelaal ◽  
Gamal A. Ebrahim ◽  
Wagdy R. Anis
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Response Time ◽  
Optimization Problem ◽  
Service Providers ◽  
Virtual Network ◽  
Placement Problem ◽  
Service Function ◽  
Network Functions ◽  
Bandwidth Consumption

The widespread adoption of network function virtualization (NFV) leads to providing network services through a chain of virtual network functions (VNFs). This architecture is called service function chain (SFC), which can be hosted on top of commodity servers and switches located at the cloud. Meanwhile, software-defined networking (SDN) can be utilized to manage VNFs to handle traffic flows through SFC. One of the most critical issues that needs to be addressed in NFV is VNF placement that optimizes physical link bandwidth consumption. Moreover, deploying SFCs enables service providers to consider different goals, such as minimizing the overall cost and service response time. In this paper, a novel approach for the VNF placement problem for SFCs, called virtual network functions and their replica placement (VNFRP), is introduced. It tries to achieve load balancing over the core links while considering multiple resource constraints. Hence, the VNF placement problem is first formulated as an integer linear programming (ILP) optimization problem, aiming to minimize link bandwidth consumption, energy consumption, and SFC placement cost. Then, a heuristic algorithm is proposed to find a near-optimal solution for this optimization problem. Simulation studies are conducted to evaluate the performance of the proposed approach. The simulation results show that VNFRP can significantly improve load balancing by 80% when the number of replicas is increased. Additionally, VNFRP provides more than a 54% reduction in network energy consumption. Furthermore, it can efficiently reduce the SFC placement cost by more than 67%. Moreover, with the advantages of a fast response time and rapid convergence, VNFRP can be considered as a scalable solution for large networking environments.

scholarly journals Trade-Off between Operating Time and Energy Consumption in Pulsed Electric Field Electrodialysis: A Comprehensive Simulation Study

Membranes ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 43
Author(s):  
Manuel César Martí-Calatayud ◽  
Mario Sancho-Cirer Poczatek ◽  
Valentín Pérez-Herranz
Keyword(s):  
Energy Consumption ◽  
Electric Field ◽  
Simulation Study ◽  
Specific Energy ◽  
Operating Time ◽  
Specific Energy Consumption ◽  
Threshold Value ◽  
Pulsed Electric Field ◽  
Pulse Frequency ◽  
Time And Energy

Electrodialysis (ED) has been recently introduced in a variety of processes where the recovery of valuable resources is needed; thus, enabling sustainable production routes for a circular economy. However, new applications of ED require optimized operating modes ensuring low energy consumptions. The application of pulsed electric field (PEF) electrodialysis has been demonstrated to be an effective option to modulate concentration polarization and reduce energy consumption in ED systems, but the savings in energy are usually attained by extending the operating time. In the present work, we conduct a comprehensive simulation study about the effects of PEF signal parameters on the time and energy consumption associated with ED processes. Ion transport of NaCl solutions through homogeneous cation-exchange membranes is simulated using a 1-D model solved by a finite-difference method. Increasing the pulse frequency up to a threshold value is effective in reducing the specific energy consumption, with threshold frequencies increasing with the applied current density. Varying the duty cycle causes opposed effects in the time and energy usage needed for a given ED operation. More interestingly, a new mode of PEF functions with the application of low values of current during the relaxation phases has been investigated. This novel PEF strategy has been demonstrated to simultaneously improve the time and the specific energy consumption of ED processes.

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