A New Load Balance Scheme for Heterogeneous Entities in Cloud Network Convergence

2021 ◽  
pp. 22-32
Author(s):  
Jiaji Liu ◽  
Zhiwei Zhang ◽  
Wangzhe Xu ◽  
Xinghui Zhu ◽  
Xuewen Dong
Keyword(s):  
Load Balance ◽  
Network Convergence ◽  
Cloud Network

A Review on Load Balancing Model Using Best Partition Technique

2017 ◽  
Vol 7 (8) ◽  
pp. 284
Author(s):  
M. Chaitanya ◽  
K. Durga Charan
Keyword(s):  
Cloud Computing ◽  
Fault Tolerance ◽  
Load Balancing ◽  
Load Balance ◽  
Large Impact ◽  
Cloud Environment ◽  
Public Cloud ◽  
The Public ◽  
Partition Technique ◽  
Textual Content

Load balancing makes cloud computing greater knowledgeable and could increase client pleasure. At reward cloud computing is among the all most systems which offer garage of expertise in very lowers charge and available all the time over the net. However, it has extra vital hassle like security, load administration and fault tolerance. Load balancing inside the cloud computing surroundings has a large impact at the presentation. The set of regulations relates the sport idea to the load balancing manner to amplify the abilties in the public cloud environment. This textual content pronounces an extended load balance mannequin for the majority cloud concentrated on the cloud segregating proposal with a swap mechanism to select specific strategies for great occasions.

Enhancement Load Balance (ELB) Model for Port Ether-Channel Technology

2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Yahya Tareq Hussein ◽  
Ali R. Hashim
Keyword(s):  
Load Balance

Workflow scheduling algorithm based on load balance in grid

2011 ◽  
Vol 30 (12) ◽  
pp. 3184-3186
Author(s):  
Ming-quan WANG ◽  
Jiong YU ◽  
Yuan TIAN ◽  
Yun HAN
Keyword(s):  
Load Balance ◽  
Scheduling Algorithm ◽  
Workflow Scheduling

Allocation of Virtual Machines to the Cloud Network Using the Barking-Deer Approach

2020 ◽  
Author(s):  
Himadri Biswas ◽  
Sudipta Sahana ◽  
Priyajit Sen ◽  
Debabrata Sarddar
Keyword(s):  
Virtual Machines ◽  
Cloud Network

scholarly journals Collision Avoidance Geographic P2P-RPL in Multi-Hop Indoor Wireless Networks

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1484
Author(s):  
Yunyoung Choi ◽  
Jaehyung Park ◽  
Jiwon Jung ◽  
Younggoo Kwon
Keyword(s):  
Energy Consumption ◽  
Collision Avoidance ◽  
Energy Efficient ◽  
Convergence Time ◽  
Location Information ◽  
Route Discovery ◽  
Localization Algorithm ◽  
Control Packet ◽  
Network Convergence ◽  
Routing Control

In home and building automation applications, wireless sensor devices need to be connected via unreliable wireless links within a few hundred milliseconds. Routing protocols in Low-power and Lossy Networks (LLNs) need to support reliable data transmission with an energy-efficient manner and short routing convergence time. IETF standardized the Point-to-Point RPL (P2P-RPL) routing protocol, in which P2P-RPL propagates the route discovery messages over the whole network. This leads to significant routing control packet overhead and a large amount of energy consumption. P2P-RPL uses the trickle algorithm to control the transmission rate of routing control packets. The non-deterministic message suppression nature of the trickle algorithm may generate a sub-optimal routing path. The listen-only period of the trickle algorithm may lead to a long network convergence time. In this paper, we propose Collision Avoidance Geographic P2P-RPL, which achieves energy-efficient P2P data delivery with a fast routing request procedure. The proposed algorithm uses the location information to limit the network search space for the desired route discovery to a smaller location-constrained forwarding zone. The Collision Avoidance Geographic P2P-RPL also dynamically selects the listen-only period of the trickle timer algorithm based on the transmission priority related to geographic position information. The location information of each node is obtained from the Impulse-Response Ultra-WideBand (IR-UWB)-based cooperative multi-hop self localization algorithm. We implement Collision Avoidance Geographic P2P-RPL on Contiki OS, an open-source operating system for LLNs and the Internet of Things. The performance results show that the Collision Avoidance Geographic P2P-RPL reduced the routing control packet overheads, energy consumption, and network convergence time significantly. The cooperative multi-hop self localization algorithm improved the practical implementation characteristics of the P2P-RPL protocol in real world environments. The collision avoidance algorithm using the dynamic trickle timer increased the operation efficiency of the P2P-RPL under various wireless channel conditions with a location-constrained routing space.

scholarly journals Computation offloading through mobile vehicles in IoT-edge-cloud network

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Jun Long ◽  
Yueyi Luo ◽  
Xiaoyu Zhu ◽  
Entao Luo ◽  
Mingfeng Huang
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Low Cost ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Challenging Problem ◽  
Learning Technique ◽  
Cloud Network ◽  
The City ◽  
Task Offloading

AbstractWith the developing of Internet of Things (IoT) and mobile edge computing (MEC), more and more sensing devices are widely deployed in the smart city. These sensing devices generate various kinds of tasks, which need to be sent to cloud to process. Usually, the sensing devices do not equip with wireless modules, because it is neither economical nor energy saving. Thus, it is a challenging problem to find a way to offload tasks for sensing devices. However, many vehicles are moving around the city, which can communicate with sensing devices in an effective and low-cost way. In this paper, we propose a computation offloading scheme through mobile vehicles in IoT-edge-cloud network. The sensing devices generate tasks and transmit the tasks to vehicles, then the vehicles decide to compute the tasks in the local vehicle, MEC server or cloud center. The computation offloading decision is made based on the utility function of the energy consumption and transmission delay, and the deep reinforcement learning technique is adopted to make decisions. Our proposed method can make full use of the existing infrastructures to implement the task offloading of sensing devices, the experimental results show that our proposed solution can achieve the maximum reward and decrease delay.

scholarly journals The NECOS Approach to End-to-End Cloud-Network Slicing as a Service

2021 ◽  
Vol 59 (3) ◽  
pp. 91-97
Author(s):  
Stuart Clayman ◽  
Augusto Neto ◽  
Fabio Verdi ◽  
Sand Correa ◽  
Silvio Sampaio ◽  
...  
Keyword(s):  
Network Slicing ◽  
End To End ◽  
Cloud Network
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