Computation Offloading and Scheduling in Edge-Fog Cloud Computing

Resource allocation and task scheduling in the Cloud environment faces many challenges, such as time delay, energy consumption, and security. Also, executing computation tasks of mobile applications on mobile devices (MDs) requires a lot of resources, so they can offload to the Cloud. But Cloud is far from MDs and has challenges as high delay and power consumption. Edge computing with processing near the Internet of Things (IoT) devices have been able to reduce the delay to some extent, but the problem is distancing itself from the Cloud. The fog computing (FC), with the placement of sensors and Cloud, increase the speed and reduce the energy consumption. Thus, FC is suitable for IoT applications. In this article, we review the resource allocation and task scheduling methods in Cloud, Edge and Fog environments, such as traditional, heuristic, and meta-heuristics. We also categorize the researches related to task offloading in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Mobile Fog Computing (MFC). Our categorization criteria include the issue, proposed strategy, objectives, framework, and test environment.

Scheduling of fog networks with optimized knapsack by symbiotic organisms search

Internet of things as a concept uses wireless sensor networks that have limitations in power, storage, and delay when processing and sending data to the cloud. Fog computing as an extension of cloud services to the edge of the network reduces latency and traffic, so it is very useful in healthcare, wearables, intelligent transportation systems and smart cities. Scheduling is the NP-hard issues in fog computing. Edge devices due to proximity to sensors and clouds are capable of processing power and are beneficial for resource management algorithms. We present a knapsack-based scheduling optimized by symbiotic organisms search that is simulated in iFogsim as a standard simulator for fog computing. The results show improvements in the energy consumption by 18%, total network usage by 1.17%, execution cost by 15%, and sensor lifetime by 5% in our scheduling method are better than the FCFS (First Come First Served) and knapsack algorithms

OPTIMAL ENERGY CONSUMPTION AND COST PERFORMANCE SOLUTION WITH DELAY CONSTRAINTS ON FOG COMPUTING

Cloud computing plays an essential role in development of the Internet of Things, which provides data processing and storage services. Fog computing is the evolution of cloud computing, which helps provide solutions to cloud computing challenges such as latency, location awareness, and real-time mobility support. Fog computing fills the gap between the cloud and IoT devices within the close vicinity of IoT devices. So, computation, networking, storage, data management, and decision making occur along the path between the cloud and IoT devices. The automatic and intelligent management of fog node resources and achieving an effective scheduling policy in the computing model is a necessary requirement and will lead to the improvement of the overall performance of fog computing. Some optimization problems are modeled by mixed-integer nonlinear programming (MINLP). In this paper, a model, i.e. an MINLP optimization problem on fog computing, is designed. Our model has two goals: to increase Cost Performance as well as to reduce energy consumption. Cost Performance is the price, users are charged as benefit/revenue. In other words Cost Performance is defined as the ratio of the average data rate of each user to its cost. Then the exact mathematical method with the GAMS program was used to prove its logical process. In the next step, we solved the model with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing+GA (SA+GA), Teaching–Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), and random method. According to the TOPSIS comparison, the SA+GA method with a value of 0.23 is the best compared to other methods. Then GWO, GA, TLBO, PSO, and GOA methods are better, respectively. [JJCIT 2023; 9(2.000): 76-93

Simultaneous power control and power management algorithm with sector-shaped topology for wireless sensor networks

In this paper, we propose a topology control technique to reduce the energy consumption of wireless sensor networks (WSNs). The technique makes use of both power control and power management methods. The algorithm uses the power management technique to put as many idle nodes as possible into the sleep mode while invoking the power control method to adjust the transmission range of the active nodes. On the contrary to earlier works in which both of these methods were used separately, in this algorithm, they are utilized simultaneously to decide about the sleep nodes and the ranges of active nodes. It is an approximation algorithm which is called simultaneous power control and power management algorithm (SPCPM). The performance bound of this centralized algorithm is determined analytically. Then, to make the proposed method practical for WSNs, a distributed algorithm based on SPCPM is introduced. To assess the efficiency of the proposed algorithm, we compare its average energy consumption with those of three existing topology control algorithms for a sector-based WSN. The simulation results which were obtained for different numbers of transmitting sensor nodes reveal less average energy consumptions for SPCPM compared to other algorithms.Peer reviewe

MEA: an energy efficient algorithm for dense sector-based wireless sensor networks

In this article, first the energy efficiency of sector-shaped wireless sensor networks is analytically investigated. Based on this study, it is shown that the efficiency of existing data propagation algorithms which consider equal ring width is not optimal and may be improved further. Then, we introduce an energy efficient algorithm for these networks which is called minimum energy algorithm (MEA). The detailed analysis verifies that the proposed algorithm has the minimum energy consumption. Although the main emphasis of the proposed technique is on minimizing the energy, it somehow balances the energy consumption in the sector-shaped network as well. In addition, it is shown that the proposed idea can be applied to all existing energy balancing algorithms. The efficacy of the proposed algorithm is studied for networks with different sizes and node densities. The results show that, for example, for a network with a radius of 440 m and four rings when the MEA algorithm is combined with an efficient full power control algorithm (based on equal ring width), the energy conservation increases 50% more. Finally, the results show that the energy conservation of the proposed algorithm increases with the network size.Peer reviewe