Efficient Usage of Network Bandwidth in the Cloud Architecture

2012 ◽  
Author(s):  
Rick C.S. Chen ◽  
Chung-Ting Kao ◽  
Hui-Kuang Chung
Keyword(s):  
Network Bandwidth ◽  
Cloud Architecture

Virtualized Fog Network with Load Balancing for IoT based Fog-to-Cloud

2020 ◽  
Vol 4 (3) ◽  
Author(s):  
Istabraq M. Al-Joboury ◽  
Emad H. Al-Hemiary
Keyword(s):  
Load Balancing ◽  
Packet Loss ◽  
Fog Computing ◽  
Transport Protocol ◽  
Network Simulator ◽  
Huge Number ◽  
Message Queue ◽  
Limited Bandwidth ◽  
Cloud Architecture ◽  
Fog Network

Fog Computing is a new concept made by Cisco to provide same functionalities of Cloud Computing but near to Things to enhance performance such as reduce delay and response time. Packet loss may occur on single Fog server over a huge number of messages from Things because of several factors like limited bandwidth and capacity of queues in server. In this paper, Internet of Things based Fog-to-Cloud architecture is proposed to solve the problem of packet loss on Fog server using Load Balancing and virtualization. The architecture consists of 5 layers, namely: Things, gateway, Fog, Cloud, and application. Fog layer is virtualized to specified number of Fog servers using Graphical Network Simulator-3 and VirtualBox on local physical server. Server Load Balancing router is configured to distribute the huge traffic in Weighted Round Robin technique using Message Queue Telemetry Transport protocol. Then, maximum message from Fog layer are selected and sent to Cloud layer and the rest of messages are deleted within 1 hour using our proposed Data-in-Motion technique for storage, processing, and monitoring of messages. Thus, improving the performance of the Fog layer for storage and processing of messages, as well as reducing the packet loss to half and increasing throughput to 4 times than using single Fog server.

Enhancing the use of Object Based Contents Addressable Storage

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Simab Hasan Rizvi
Keyword(s):  
File System ◽  
Application Performance ◽  
Shared Storage ◽  
Data Intensive ◽  
Network Bandwidth ◽  
Storage Performance ◽  
Efficient Management ◽  
Object Based ◽  
Data Volume ◽  
Potential Benefits

In Today's age of Tetra Scale computing, the application has become more data intensive than ever. The increased data volume from applications, in now tackling larger and larger problems, and has fuelled the need for efficient management of this data. In this paper, a technique called Content Addressable Storage or CAS, for managing large volume of data is evaluated. This evaluation focuses on the benefits and demerits of using CAS it focuses, i) improved application performance via lockless and lightweight synchronization ofaccess to shared storage data, ii) improved cache performance, iii) increase in storage capacity and, iv) increase network bandwidth. The presented design of a CAS-Based file store significantly improves the storage performance that provides lightweight lock less user defined consistency semantics. As a result, this file system shows a 28% increase in read bandwidth and 13% increase in write bandwidth, over a popular file system in common use. In this paper the potential benefits of using CAS for a virtual machine are estimated. The study also explains mobility application for active use and public deployment.

scholarly journals Evaluating the Performance of Three Popular Web Mapping Libraries: A Case Study Using Argentina’s Life Quality Index

2020 ◽  
Vol 9 (10) ◽  
pp. 563
Author(s):  
Alejandro Zunino ◽  
Guillermo Velázquez ◽  
Juan Pablo Celemín ◽  
Cristian Mateos ◽  
Matías Hirsch ◽  
...  
Keyword(s):  
Life Quality ◽  
Web Technologies ◽  
Web Mapping ◽  
Network Bandwidth ◽  
Web Map ◽  
Raster Maps ◽  
Dynamic Web ◽  
Network Usage ◽  
Overall Performance

Recent Web technologies such as HTML5, JavaScript, and WebGL have enabled powerful and highly dynamic Web mapping applications executing on standard Web browsers. Despite the complexity for developing such applications has been greatly reduced by Web mapping libraries, developers face many choices to achieve optimal performance and network usage. This scenario is even more complex when considering different representations of geographical data (raster, raw data or vector) and variety of devices (tablets, smartphones, and personal computers). This paper compares the performance and network usage of three popular JavaScript Web mapping libraries for implementing a Web map using different representations for geodata, and executing on different devices. In the experiments, Mapbox GL JS achieved the best overall performance on mid and high end devices for displaying raster or vector maps, while OpenLayers was the best for raster maps on all devices. Vector-based maps are a safe bet for new Web maps, since performance is on par with raster maps on mid-end smartphones, with significant less network bandwidth requirements.

Fog-Integrated Cloud Architecture enabled multi-attribute combinatorial reverse auctioning framework

2021 ◽  
Vol 109 ◽  
pp. 102307
Author(s):  
Anubha Aggarwal ◽  
Neetesh Kumar ◽  
Deo Prakash Vidyarthi ◽  
Rajkumar Buyya
Keyword(s):  
Cloud Architecture

scholarly journals Software-Defined Optimal Computation Task Scheduling in Vehicular Edge Networking

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 955
Author(s):  
Zhiyuan Li ◽  
Ershuai Peng
Keyword(s):  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Dynamic Network ◽  
Parameter Tuning ◽  
Global Optimum ◽  
Research Field ◽  
Edge Computing ◽  
Transmission Delay ◽  
Smart Vehicles ◽  
Network Bandwidth

With the development of smart vehicles and various vehicular applications, Vehicular Edge Computing (VEC) paradigm has attracted from academic and industry. Compared with the cloud computing platform, VEC has several new features, such as the higher network bandwidth and the lower transmission delay. Recently, vehicular computation-intensive task offloading has become a new research field for the vehicular edge computing networks. However, dynamic network topology and the bursty computation tasks offloading, which causes to the computation load unbalancing for the VEC networking. To solve this issue, this paper proposed an optimal control-based computing task scheduling algorithm. Then, we introduce software defined networking/OpenFlow framework to build a software-defined vehicular edge networking structure. The proposed algorithm can obtain global optimum results and achieve the load-balancing by the virtue of the global load status information. Besides, the proposed algorithm has strong adaptiveness in dynamic network environments by automatic parameter tuning. Experimental results show that the proposed algorithm can effectively improve the utilization of computation resources and meet the requirements of computation and transmission delay for various vehicular tasks.

scholarly journals HPC Cloud Architecture to Reduce HPC Workflow Complexity in Containerized Environments

2021 ◽  
Vol 11 (3) ◽  
pp. 923
Author(s):  
Guohua Li ◽  
Joon Woo ◽  
Sang Boem Lim
Keyword(s):  
High Performance ◽  
Cloud Services ◽  
Workload Management ◽  
Job Management ◽  
Security Issues ◽  
Cloud Architecture ◽  
Management Efficiency ◽  
Complexity Problem ◽  
And Performance ◽  
Hpc Cloud

The complexity of high-performance computing (HPC) workflows is an important issue in the provision of HPC cloud services in most national supercomputing centers. This complexity problem is especially critical because it affects HPC resource scalability, management efficiency, and convenience of use. To solve this problem, while exploiting the advantage of bare-metal-level high performance, container-based cloud solutions have been developed. However, various problems still exist, such as an isolated environment between HPC and the cloud, security issues, and workload management issues. We propose an architecture that reduces this complexity by using Docker and Singularity, which are the container platforms most often used in the HPC cloud field. This HPC cloud architecture integrates both image management and job management, which are the two main elements of HPC cloud workflows. To evaluate the serviceability and performance of the proposed architecture, we developed and implemented a platform in an HPC cluster experiment. Experimental results indicated that the proposed HPC cloud architecture can reduce complexity to provide supercomputing resource scalability, high performance, user convenience, various HPC applications, and management efficiency.

scholarly journals Bandwidth-Aware Rescheduling Mechanism in SDN-Based Data Center Networks

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1774
Author(s):  
Ming-Chin Chuang ◽  
Chia-Cheng Yen ◽  
Chia-Jui Hung
Keyword(s):  
Data Center ◽  
Completion Time ◽  
Network Performance ◽  
Data Locality ◽  
Task Completion ◽  
Data Center Networks ◽  
Task Completion Time ◽  
Data Packets ◽  
Network Bandwidth ◽  
Data Process

Recently, with the increase in network bandwidth, various cloud computing applications have become popular. A large number of network data packets will be generated in such a network. However, most existing network architectures cannot effectively handle big data, thereby necessitating an efficient mechanism to reduce task completion time when large amounts of data are processed in data center networks. Unfortunately, achieving the minimum task completion time in the Hadoop system is an NP-complete problem. Although many studies have proposed schemes for improving network performance, they have shortcomings that degrade their performance. For this reason, in this study, we propose a centralized solution, called the bandwidth-aware rescheduling (BARE) mechanism for software-defined network (SDN)-based data center networks. BARE improves network performance by employing a prefetching mechanism and a centralized network monitor to collect global information, sorting out the locality data process, splitting tasks, and executing a rescheduling mechanism with a scheduler to reduce task completion time. Finally, we used simulations to demonstrate our scheme’s effectiveness. Simulation results show that our scheme outperforms other existing schemes in terms of task completion time and the ratio of data locality.

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