scholarly journals A computing allocation strategy for Internet of things’ resources based on edge computing

2021 ◽  
Vol 17 (12) ◽  
pp. 155014772110648
Author(s):  
Zengrong Zhang
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Optimization Problem ◽  
Scheduling Algorithm ◽  
Allocation Strategy ◽  
Total Energy Consumption ◽  
Migration Strategy ◽  
Q Learning ◽  
Computing Services ◽  
And Migration

In order to meet the demand for efficient computing services in big data scenarios, a cloud edge collaborative computing allocation strategy based on deep reinforcement learning by combining the powerful computing capabilities of cloud is proposed. First, based on the comprehensive consideration of computing resources, bandwidth, and migration decisions, an optimization problem is constructed that minimizes the sum of all user task execution delays and energy consumption weights. Second, a dynamic offloading scheduling algorithm based on Q-learning is proposed based on the optimization problem. This algorithm makes full use of the computing power for cloud and edge, which effectively meets the demand for efficient computing services in Internet of Things’ scenarios. Finally, facing the environment dynamic changes of edge nodes in edge cloud, the algorithm can adaptively adjust the migration strategy. Experiments show that when the number of Internet of Things’ devices is 30, the total energy consumption of Internet of Things’ devices of proposed algorithm is reduced by 24.67% and 19.44%, respectively, compared with other algorithms. The experimental results show that proposed algorithm can effectively improve the success rate of task offloading and execution, which can reduce the local energy consumption.

scholarly journals A cost efficient model for minimizing energy consumption and processing time for IoT tasks in a Mobile Edge Computing environment

2020 ◽  
Author(s):  
João Luiz Grave Gross ◽  
Cláudio Fernando Fernando Resin Geyer
Keyword(s):  
Energy Consumption ◽  
Cost Minimization ◽  
Cost Model ◽  
Scheduling Algorithm ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Total Energy Consumption ◽  
Data Intensive ◽  
Battery Capacity ◽  
Cost Efficient

In a scenario with increasingly mobile devices connected to the Internet, data-intensive applications and energy consumption limited by battery capacity, we propose a cost minimization model for IoT devices in a Mobile Edge Computing (MEC) architecture with the main objective of reducing total energy consumption and total elapsed times from task creation to conclusion. The cost model is implemented using the TEMS (Time and Energy Minimization Scheduler) scheduling algorithm and validated with simulation. The results show that it is possible to reduce the energy consumed in the system by up to 51.61% and the total elapsed time by up to 86.65% in the simulated cases with the parameters and characteristics defined in each experiment.

scholarly journals Using a vision cognitive algorithm to schedule virtual machines

2014 ◽  
Vol 24 (3) ◽  
pp. 535-550 ◽  
Author(s):  
Jiaqi Zhao ◽  
Yousri Mhedheb ◽  
Jie Tao ◽  
Foued Jrad ◽  
Qinghuai Liu ◽  
...  
Keyword(s):  
Global Optimization ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Optimization Problem ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Primary Concern ◽  
Global Optimization Problem ◽  
Vm Scheduling ◽  
Operation Cost

Abstract Scheduling virtual machines is a major research topic for cloud computing, because it directly influences the performance, the operation cost and the quality of services. A large cloud center is normally equipped with several hundred thousand physical machines. The mission of the scheduler is to select the best one to host a virtual machine. This is an NPhard global optimization problem with grand challenges for researchers. This work studies the Virtual Machine (VM) scheduling problem on the cloud. Our primary concern with VM scheduling is the energy consumption, because the largest part of a cloud center operation cost goes to the kilowatts used. We designed a scheduling algorithm that allocates an incoming virtual machine instance on the host machine, which results in the lowest energy consumption of the entire system. More specifically, we developed a new algorithm, called vision cognition, to solve the global optimization problem. This algorithm is inspired by the observation of how human eyes see directly the smallest/largest item without comparing them pairwisely. We theoretically proved that the algorithm works correctly and converges fast. Practically, we validated the novel algorithm, together with the scheduling concept, using a simulation approach. The adopted cloud simulator models different cloud infrastructures with various properties and detailed runtime information that can usually not be acquired from real clouds. The experimental results demonstrate the benefit of our approach in terms of reducing the cloud center energy consumption

Ada-Things: An adaptive virtual machine monitoring and migration strategy for internet of things applications

2019 ◽  
Vol 132 ◽  
pp. 164-176 ◽  
Author(s):  
Zhong Wang ◽  
Daniel Sun ◽  
Guangtao Xue ◽  
Shiyou Qian ◽  
Guoqiang Li ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Virtual Machine ◽  
Migration Strategy ◽  
Machine Monitoring ◽  
And Migration

scholarly journals Efficient and Energy-Saving Computation Offloading Mechanism with Energy Harvesting for IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yawen Zhang ◽  
Yifeng Miao ◽  
Shujia Pan ◽  
Siguang Chen
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Energy Harvesting ◽  
Convergence Rate ◽  
Energy Saving ◽  
Gradient Descent ◽  
Optimization Problem ◽  
Computation Offloading ◽  
Total Energy Consumption ◽  
Task Processing

In order to effectively extend the lifetime of Internet of Things (IoT) devices, improve the energy efficiency of task processing, and build a self-sustaining and green edge computing system, this paper proposes an efficient and energy-saving computation offloading mechanism with energy harvesting for IoT. Specifically, based on the comprehensive consideration of local computing resource, time allocation ratio of energy harvesting, and offloading decision, an optimization problem that minimizes the total energy consumption of all user devices is formulated. In order to solve such optimization problem, a deep learning-based efficient and energy-saving offloading decision and resource allocation algorithm is proposed. The design of deep neural network architecture incorporating regularization method and the employment of the stochastic gradient descent method can accelerate the convergence rate of the developed algorithm and improve its generalization performance. Furthermore, it can minimize the total energy consumption of task processing by integrating the momentum gradient descent to solve the resource optimization allocation problem. Finally, the simulation results show that the mechanism proposed in this paper has significant advantage in convergence rate and can achieve an optimal offloading and resource allocation strategy that is close to the solution of greedy algorithm.

Research on Communication Scheduling Algorithm for Smart Home in Internet of Things Under Cloud Computing

2019 ◽  
Vol 23 (1) ◽  
pp. 124-128 ◽  
Author(s):  
Jie Zhang ◽  
◽  
Mantao Wang
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Objective Function ◽  
Smart Home ◽  
Data Flow ◽  
Scheduling Algorithm ◽  
Optimal Solution ◽  
Low Latency ◽  
Communication Scheduling

The current communication scheduling algorithm for smart home cannot realize low latency in scheduling effect with unreasonable control of communication throughput and large energy consumption. In this paper, a communication scheduling algorithm for smart home in Internet of Things under cloud computing based on particle swarm is proposed. According to the fact that the transmission bandwidth of any data flow is limited by the bandwidth of network card of sending end and receiving end, the bandwidth limits of network card of smart home communication server are used to predict the maximum practicable bandwidth of data flow. Firstly, the initial value of communication scheduling objective function of smart home and particle swarm is set, and the objective function is taken as the fitness function of particle. Then the current optimal solution of objective function is calculated through predicted value and objective function, current position and flight speed of particle should be updated until the iteration conditions are met. Finally, the optimal solution is output, the communication scheduling of smart home is thus realized. Experiments show that this algorithm can realize low latency with small energy consumption, and the throughput is relatively reasonable.

scholarly journals Energy-Efficient UAV-Enabled MEC System: Bits Allocation Optimization and Trajectory Design

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4521 ◽  
Author(s):  
Linpei Li ◽  
Xiangming Wen ◽  
Zhaoming Lu ◽  
Qi Pan ◽  
Wenpeng Jing and Zhiqun Hu
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Energy Efficient ◽  
High Mobility ◽  
Trajectory Design ◽  
Total Energy Consumption ◽  
Computing Services ◽  
Aerial Vehicle ◽  
Transmission Energy Consumption ◽  
Transmission Energy

The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) system is attracting a lot of attentions for the potential of low latency and low transmission energy consumption, due to the advantages of high mobility and easy deployment. It has been widely applied to provide communication and computing services, especially in Internet of Things (IoT). However, there are still some challenges in the UAV-enabled MEC system. Firstly, the endurance of the UAV is limited and further impacts the performance of the system. Secondly, mobile devices are battery-powered and the batteries of some devices are hard to change. Therefore, in this paper, a UAV-enabled MEC system in which the UAV is empowered to have computing capability and provides tasks offloading service is studied. The total energy consumption of the UAV-enabled system, which includes the energy consumption of the UAV and the energy consumption of the ground users, is minimized under the constraints of the UAV’s energy budget, the number of each task’s bits, the causality of the data and the velocity of the UAV. The bits allocation of uploading data, computing data, downloading data and the trajectory of the UAV are jointly optimized with the goal of minimizing the total energy consumption. Moreover, a two-stage alternating algorithm is proposed to solve the non-convex formulated problem. Finally, the simulation results show the superiority of the proposed scheme compared with other benchmark schemes. Finally, the performance of the proposed scheme is demonstrated under different settings.

scholarly journals A Heuristic Task Scheduling Algorithm for Heterogeneous Virtual Clusters

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Weiwei Lin ◽  
Wentai Wu ◽  
James Z. Wang
Keyword(s):  
Energy Consumption ◽  
Task Scheduling ◽  
Power Efficiency ◽  
High Performance ◽  
Scheduling Algorithm ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Cloud Data ◽  
Virtual Clusters ◽  
Resource Requirements

Cloud computing provides on-demand computing and storage services with high performance and high scalability. However, the rising energy consumption of cloud data centers has become a prominent problem. In this paper, we first introduce an energy-aware framework for task scheduling in virtual clusters. The framework consists of a task resource requirements prediction module, an energy estimate module, and a scheduler with a task buffer. Secondly, based on this framework, we propose a virtual machine power efficiency-aware greedy scheduling algorithm (VPEGS). As a heuristic algorithm, VPEGS estimates task energy by considering factors including task resource demands, VM power efficiency, and server workload before scheduling tasks in a greedy manner. We simulated a heterogeneous VM cluster and conducted experiment to evaluate the effectiveness of VPEGS. Simulation results show that VPEGS effectively reduced total energy consumption by more than 20% without producing large scheduling overheads. With the similar heuristic ideology, it outperformed Min-Min and RASA with respect to energy saving by about 29% and 28%, respectively.

scholarly journals Energy-Aware Scheduling Based on Marginal Cost and Task Classification in Heterogeneous Data Centers

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2382
Author(s):  
Kaixuan Ji ◽  
Ce Chi ◽  
Fa Zhang ◽  
Antonio Fernández Anta ◽  
Penglei Song ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Marginal Cost ◽  
Optimization Problem ◽  
Data Centers ◽  
State Transition ◽  
Energy Aware ◽  
Total Energy Consumption ◽  
Cooling Systems ◽  
Task Classification ◽  
And Task

The energy consumption problem has become a bottleneck hindering further development of data centers. However, the heterogeneity of servers, hybrid cooling modes, and extra energy caused by system state transitions increases the complexity of the energy optimization problem. To deal with such challenges, in this paper, an Energy Aware Task Scheduling strategy (EATS) utilizing marginal cost and task classification method is proposed that cooperatively improves the energy efficiency of servers and cooling systems. An energy consumption model for servers, cooling systems, and state transition is developed, and the energy optimization problem in data centers is formulated. The concept of marginal cost is introduced to guide the task scheduling process. The task classification method is incorporated with the idea of marginal cost to further improve resource utilization and reduce the total energy consumption of data centers. Experiments are conducted using real-world traces, and energy reduction results are compared. Results show that EATS achieves more energy-savings of servers, cooling systems, state transition in comparison to the other two techniques under a various number of servers, cooling modules and task arrival intensities. It is validated that EATS is effective at reducing total energy consumption and improving the resource utilization of data centers.

scholarly journals TLFW: A Three-Layer Framework in Wireless Rechargeable Sensor Network with a Mobile Base Station

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Anwen Wang ◽  
Xianjia Meng ◽  
Lvju Wang ◽  
Xiang Ji ◽  
Hao Chen ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Internet Of Things ◽  
Sensor Network ◽  
Cluster Head ◽  
Mobile Station ◽  
Base Station ◽  
Wireless Sensor ◽  
Total Energy Consumption

Wireless sensor networks as the base support for the Internet of things have been a large number of popularity and application. Such as intelligent agriculture, we have to use the sensor network to obtain the growing environment data of crops and others. However, the difficulty of power supply of wireless nodes has seriously hindered the application and development of Internet of things. In order to solve this problem, people use low-power sleep scheduling and other energy-saving methods on the nodes. Although these methods can prolong the working time of nodes, they will eventually become invalid because of the exhaustion of energy. The use of solar energy, wind energy, and wireless signals in the environment to obtain energy is another way to solve the energy problem of nodes. However, these methods are affected by weather, environment, and other factors, and they are unstable. Thus, the discontinuity work of the node is caused. In recent years, the development of wireless power transfer (WPT) has brought another solution to this problem. In this paper, a three-layer framework is proposed for mobile station data collection in rechargeable wireless sensor networks to keep the node running forever, named TLFW which includes the sensor layer, cluster head layer, and mobile station layer. And the framework can minimize the total energy consumption of the system. The simulation results show that the scheme can reduce the energy consumption of the entire system, compared with a Mobile Station in a Rechargeable Sensor Network (MSiRSN).

Optimization of multiple autonomous underwater vehicle cooperative system communication network topology based on total energy consumption

2020 ◽  
Vol 234 (3) ◽  
pp. 668-675
Author(s):  
Qingwei Liang ◽  
Junlin Ou ◽  
Liang Shi ◽  
Xin Zhang
Keyword(s):  
Communication Network ◽  
Energy Consumption ◽  
Total Energy ◽  
Network Topology ◽  
Optimization Problem ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Cooperative Systems ◽  
Total Energy Consumption ◽  
Topology Optimization Problem

Given the importance of the communication energy consumption of multiple autonomous underwater vehicle cooperative systems in practical work, this work optimizes the network topology to reduce total energy consumption. In accordance with the characteristics of underwater communication, the energy consumption of communication links is obtained, thereby obtaining the total communication energy consumption of multiple autonomous underwater vehicle cooperative systems. Taking the all-terminal reliability of the communication network as a constraint and the total energy consumption of network communication as the optimization goal, this work puts forward an optimization model for the communication network topology of multiple autonomous underwater vehicle cooperative systems. Furthermore, this work creatively describes the network topology optimization problem as a special path optimization problem suitable for the ant colony optimization algorithm presented to solve the optimization problem and shown to be effective and efficiency on this problem.

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