Efficient Solution for Load Balancing in Fog Computing Utilizing Artificial Bee Colony

Efficient Solution for Load Balancing in Fog Computing Utilizing Artificial Bee Colony

Shivi Sharma, Hemraj Saini
Copyright: © 2019 |Pages: 18
DOI: 10.4018/IJACI.2019100104
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Fog computing is a set of mobile cloudlets which can fulfil the demand of the user who is already considered a mobile job in this architecture. The main aim of Fog computing is to provide the user with an optimal solution which is quick and cost-efficient. This article focuses on a load balancing mechanism for cloudlets along with keeping the cost-effectiveness as an optimal selection parameter. This article utilizes the Artificial Bee Colony (ABC) in order to prioritize the user demand using a fitness function. This work evaluates quality of service (QoS) parameters such as schedule length runtime (SLR), schedule length vm ratio (SLVMR), energy consumed (EC) and energy consumption ratio (ECR) and shows the effectiveness of proposed work.
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1. Introduction

Fog computing, also known as ‘fogging’ or ‘fog networking’, is a decentralized computing framework through which the computers, data storage, and applications are dispersed in the most efficient and logical place among cloud and data sources. The main goal of fog computing is the enhancement of efficiency & decrement of data transportation to cloud for the development, examination, and storage as shown in Figure 1, by the general architecture of fog computing (Roman, Lopez, & Mambo, 2018). In fog, the development takes place in smart devices or data hub on the smart devices, gateways or smart routers. Therefore, it is better to lessen the data amount transferred to the cloud. There is an issue of interoperability for varied hardware nodes in fog computing, so, there is a requirement of a feasible solution for developing unified software and virtual objects on the nodes for representing heterogeneous physical entities.

Figure 1.

Fog computing general architecture


Cloud computing is a promising solution for on-request access to a mutual pool of virtualized arranged assets based on desired Quality of Services (QoS) and pay-as-you-use pricing model. It provides high computational and storage capabilities for IoT (Botta, De Donato, Persico, & Pescapé, 2016). Even though cloud computing can provide so many facilities like storing a large amount of data, but there are challenges for IoT applications such as lack of limited network bandwidth, mobility support, latency, and location awareness. So as to overcome the above issues “Fog computing “turned as a promising foundation to cloud solution as it works on the edge of the system”” (Li, Xiaoguang, Ke, & Ketai, 2011). It provides communication between IoT devices and cloud computing on a large scale to deliver better facilities without the constraints mentioned in Table1 (Hu, Dhelim, Ning, & Qiu, 2017). Table 1 illustrates the requirements of IoT from cloud computing to the fog computing.

Table 1.
Fog-cloud key requirements
Key RequirementsCloud ComputingFog Computing
Delay jitterHighVery low
No. of server nodesFewVery large
Location of serviceWithin the InternetAll the edge of the local network
Location awarenessNoYes
Mobility supportLimitedSupported
Real time interactionsSupportedSupported
Distance between client and serverMultiple hopsOne hop
Type of last mile connectivityLeased lineWireless

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