scholarly journals A new load balancing strategy by task allocation in edge computing based on intermediary nodes

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Guangshun Li ◽  
Yonghui Yao ◽  
Junhua Wu ◽  
Xiaoxiao Liu ◽  
Xiaofei Sheng ◽  
...  
Keyword(s):  
Load Balancing ◽  
Real Time ◽  
Task Allocation ◽  
Normal Load ◽  
Task Assignment ◽  
Edge Computing ◽  
Heavy Load ◽  
Light Load ◽  
Assignment Model ◽  
Classification Evaluation

AbstractThe latency of cloud computing is high for the reason that it is far from terminal users. Edge computing can transfer computing from the center to the network edge. However, the problem of load balancing among different edge nodes still needs to be solved. In this paper, we propose a load balancing strategy by task allocation in edge computing based on intermediary nodes. The intermediary node is used to monitor the global information to obtain the real-time attributes of the edge nodes and complete the classification evaluation. First, edge nodes can be classified to three categories (light-load, normal-load, and heavy-load), according to their inherent attributes and real-time attributes. Then, we propose a task assignment model and allocate new tasks to the relatively lightest load node. Experiments show that our method can balance load among edge nodes and reduce the completion time of tasks.

scholarly journals Article Retracted 22/07/2011 - GuST: Guaranteed Services Token Protocol for Real-Time Communications

1970 ◽  
Vol 8 (1-2) ◽  
pp. 197-210
Author(s):  
Simeon Ozuomba ◽  
Gloria A. Chukwudebe
Keyword(s):  
Access Control ◽  
Real Time ◽  
Integrated Services ◽  
Multimedia Applications ◽  
Media Access Control ◽  
Heavy Load ◽  
Integrated Services Networks ◽  
Real Time Traffic ◽  
Light Load ◽  
Guaranteed Services

This Article was RETRACTED on 22/07/2011 at the request of the authors because the paper has already been published in another journal in Nigeria. - Editor, JIEIn this paper, Guaranteed Services Token (GuST) protocol for integrated services networks which can efficiently support diverse traffic consisting of hard and soft real-time traffic along with non-real-time traffic is proposed. This is to meet the increasing demand for better performance of real time communications required by distributed multimedia applications, process control, factory automation, etc.For some time now, timed-token protocols have become the preferred Medium Access Control (MAC) protocol for supporting modern real-time systems. However, the existing timed-token protocols have been studied, and inefficiencies discovered with the way asynchronous traffic is handled. GuST employs the timed-token mechanisms in the Timely-Token protocol along with that of Budget Sharing Token (BuST) protocol. We discussed some bounds on the behavior of GuST protocol. In particular, we show that the token is never late, and the transmission of asynchronous traffic is guaranteed. We also compared GuST protocol against the Timely-Token protocol and the BuST protocol. Our comparison focuses on the ability of those protocols to support synchronous and asynchronous traffic. We demonstrated that the performance achieved by GuST is better than Timely-Toke n and BuST protocols especially for a system with light load of real-time traffic but with heavy load of non-real-time traffic. GuST protocol can be incorporated into the Ethernet network to provide real-time performance guarantees to multimedia applications. It can also be used to improve on the throughput of the Profibus which is a Fieldbus network standard.Keywords: Timed-Token Protocol; Ethernet; Timely-Token Protocol; Budget Sharing Token Protocol; Integrated Services Networks; Real-Time Traffic; Non-Real-Time Traffic; Media Access Control (MAC); GuST: Guaranteed; Services Token protocolDOI: http://dx.doi.org/10.3126/jie.v8i1-2.5112Journal of the Institute of Engineering Vol. 8, No. 1&2, 2010/2011Page: 197-210Uploaded Date: 20 July, 2011

scholarly journals Association Control Based  Load Balancing in Wireless  Cellular Networks Using  Preamble Sequences

2021 ◽  
Author(s):  
◽  
Ankit Chopra
Keyword(s):  
Load Balancing ◽  
Cellular Networks ◽  
Network Capacity ◽  
Correlation Property ◽  
User Association ◽  
Heavy Load ◽  
Implicit Information ◽  
Light Load ◽  
Load Imbalance ◽  
Radio Resources

<p>The efficient allocation and use of radio resources is crucial for achieving the maximum possible throughput and capacity in wireless networks. The conventional strongest signal-based user association in cellular networks generally considers only the strength of the signal while selecting a BS, and ignores the level of congestion or load at it. As a consequence, some BSs tend to suffer from heavy load, while their adjacent BSs may carry only light load. This load imbalance severely hampers the network from fully utilizing the network capacity and providing fair services to users. In this thesis, we investigate the applicability of the preamble code sequence, which is mainly used for cell identification, as an implicit information indicator for load balancing in cellular networks. By exploiting the high auto-correlation and low cross-correlation property among preamble sequences, we propose distributed load balancing schemes that implicitly obtain information about the load status of BSs, for intelligent association control. This enables the new users to be attached to BSs with relatively low load in the long term, alleviating the problem of non-uniform user distribution and load imbalance across the network. Extensive simulations are performed with various user densities considering throughput fair and resource fair, as the resource allocation policies in each cell. It is observed that significant improvement in minimum throughput and fair user distribution is achieved by employing our proposed schemes, and preamble sequences can be effectively used as a leverage for better cell-site selection from the viewpoint of fairness provisioning. The load of the entire system is also observed to be balanced, which consequently enhances the capacity of the network, as evidenced by the simulation results.</p>

AI-enabled IoT-Edge Data Analytics for Connected Living

2021 ◽  
Vol 21 (4) ◽  
pp. 1-20
Author(s):  
Zhihan Lv ◽  
Liang Qiao ◽  
Sahil Verma ◽  
Kavita
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Data Centers ◽  
Edge Computing ◽  
Time Data ◽  
Processing Task ◽  
Light Load ◽  
Hash Algorithm ◽  
Real Time Data ◽  
Real Time Data Processing

As deep learning, virtual reality, and other technologies become mature, real-time data processing applications running on intelligent terminals are emerging endlessly; meanwhile, edge computing has developed rapidly and has become a popular research direction in the field of distributed computing. Edge computing network is a network computing environment composed of multi-edge computing nodes and data centers. First, the edge computing framework and key technologies are analyzed to improve the performance of real-time data processing applications. In the system scenario where the collaborative deployment tasks of multi-edge nodes and data centers are considered, the stream processing task deployment process is formally described, and an efficient multi-edge node-computing center collaborative task deployment algorithm is proposed, which solves the problem of copy-free task deployment in the task deployment problem. Furthermore, a heterogeneous edge collaborative storage mechanism with tight coupling of computing and data is proposed, which solves the contradiction between the limited computing and storage capabilities of data and intelligent terminals, thereby improving the performance of data processing applications. Here, a Feasible Solution (FS) algorithm is designed to solve the problem of placing copy-free data processing tasks in the system. The FS algorithm has excellent results once considering the overall coordination. Under light load, the V value is reduced by 73% compared to the Only Data Center-available (ODC) algorithm and 41% compared to the Hash algorithm. Under heavy load, the V value is reduced by 66% compared to the ODC algorithm and 35% compared to the Hash algorithm. The algorithm has achieved good results after considering the overall coordination and cooperation and can more effectively use the bandwidth of edge nodes to transmit and process data stream, so that more tasks can be deployed in edge computing nodes, thereby saving time for data transmission to the data centers. The end-to-end collaborative real-time data processing task scheduling mechanism proposed here can effectively avoid the disadvantages of long waiting times and unable to obtain the required data, which significantly improves the success rate of the task and thus ensures the performance of real-time data processing.

scholarly journals Edge computing task scheduling strategy based on load balancing

2020 ◽  
Vol 309 ◽  
pp. 03025
Author(s):  
Lintan Sun ◽  
Zigan Li ◽  
Jingxian Lv ◽  
Chenfei Wang ◽  
Yajuan Wang ◽  
...  
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Rapid Development ◽  
Mobile Terminal ◽  
Heavy Load ◽  
Dynamic Selection ◽  
Scheduling Strategy ◽  
Light Load ◽  
Task Scheduling Algorithm

With the rapid development and wide application of the Internet of Everything, in order to cope with the increasing amount of data and computational scale of mobile terminal processing, and the imbalance of existing scheduling algorithms and low resource utilization, this paper proposes a task scheduling algorithm based on business priority. The algorithm firstly divides the service according to the priority of the service. Secondly, the standard deviation of the computing task group is used to determine the proportion of long and short services, and the dynamic selection model is established. Finally, according to the idea of secondary allocation, the task of heavy load is assigned to the scheduling strategy of light load resources to execute, and the service redistribution model is established. The simulation results show that compared with the typical algorithm, the proposed algorithm achieves the result of comprehensive consideration of Makespan and load balancing to improve system efficiency.

scholarly journals BP Network Control for Resource Allocation and QoS Ensurance in UAV Cloud

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Ang Gao ◽  
Yansu Hu ◽  
Lixin Li ◽  
Xu Li
Keyword(s):  
Real Time ◽  
Controller Design ◽  
Network Control ◽  
Total Power ◽  
Neuron Network ◽  
Heavy Load ◽  
Bp Network ◽  
Light Load ◽  
Total Power Consumption ◽  
Real Time Applications

Unmanned aerial vehicle (UAV) cloud can greatly enhance the intelligence of unmanned systems by dynamically unloading the compute-intensive applications to cloud. For the uncertain nature of UAV missions and fast-changing environment, different UAV applications may have different quality of service (QoS) requirements. This paper proposes a mixed QoS ensurance and energy-balanced (MQEB) architecture for UAV cloud from a view of control theory, which can support both hard and soft QoS ensurance with the consideration of energy saving. The hard and soft QoS requirements are decoupled by being normalized into a two-level cascaded feedback loop. The former is time slot loop (TS-Loop) to enforce the absolute QoS ensurance for real-time applications, and the latter is contention window loop (CW-Loop) to enforce the plastic QoS ensurance for non-real-time applications. Finally, the back propagating (BP) neuron network is used for parameters’ self-tuning and controller design. The hardware experiments demonstrate the feasibility of MQEB. In heavy load, MQEB has greater throughput and better energy efficiency, and in light load, MQBE has lower total power consumption.

scholarly journals Parallel collaborative planning for the coupled system of underground heavy-load robot

2021 ◽  
Vol 13 (4) ◽  
pp. 168781402110059
Author(s):  
Fang Lixia ◽  
Tong Wang ◽  
Yang Shen ◽  
Pengjiang Wang ◽  
Miao Wu
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Working Conditions ◽  
Task Allocation ◽  
Collaborative Planning ◽  
Coupled System ◽  
Working Environment ◽  
Heavy Load ◽  
Planning Strategy ◽  
Allocation Planning

At present, designing and planning of robots are mainly based on path planning. This mode cannot meet requirements of real-time and precise planning for robots, especially under complex working conditions. Therefore, a parallel collaborative planning strategy is proposed in this paper, which parallel collaborates optimal task allocation planning and optimal local path planning. That is, according to real-time dynamic working environment of robots, the dynamic optimal task allocation planning strategy for coupled system of robot in low coupling state is adopted, to improve real-time working efficiency of underground heavy-load robot. Meanwhile, the parallel elite particle swarm optimization algorithm is adopted to improve accuracy of path tracking and controlling. Finally, the two planning strategies are collaborated parallel to realize intelligent and efficient planning of whole complex coupled system for underground heavy-load robot. The simulation and experiment results show that the parallel collaborative planning algorithm proposed in this paper has perfect controlling effects: Total flow of overall system is saved by 11.03 L, execution time saved by 16.8 s and implementation efficiency has been improved by 10 times. Therefore, the parallel collaborative planning strategy proposed in this paper can not only meet requirements of high efficiency and precision of intelligent robot under complex working conditions, but also greatly improve real-time working effectiveness and robustness of robots, so as to provide a reference for dynamic planning of complex intelligent engineering machinery, and also supply design basis for development of multi-robot collaborative system.

scholarly journals Server Placement and Task Allocation for Load Balancing in Edge-Computing Networks

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 138200-138208
Author(s):  
Ping-Chun Huang ◽  
Tai-Lin Chin ◽  
Tzu-Yi Chuang
Keyword(s):  
Load Balancing ◽  
Task Allocation ◽  
Edge Computing ◽  
Server Placement ◽  
And Task
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