Modified Shortest Job First for Load Balancing in Cloud-Fog Computing

Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 25)


Minimizing the electricity consumption and cost is one of the most demanding needs of today. As with the rapid increase in demand, there is a great need to design new solutions for effective energy management. With the advent of new Information Communication Technologies (ICT) traditional electricity grids, meters, buildings and appliances became Smart Grids (SGs), Smart Meters (SMs), Smart Buildings (SBs) and Smart Appliances (SAs). The SBs consists of a large number of SAs. These smart appliances are constantly sharing, their data with SGs, SMs and SBs. So a huge amount of data is generated every day. This data requires complex computations, faster retrievals and larger storage facilities [1]. Keeping this in view, a new energy management system is designed with the help of Cloud and Fog computing. As the Cloud Computing (CC) provides large number of data computation and permanent storage facilities however, it has limitations in fast data retrieval and causes response delays. On the other hand, Fog Computing (FC) offers faster information retrieval with less response delays with only limitation of temporary storage. The proposed system architecture integrates the qualities of both CC and FC by combining their services. To manage the load between different Virtual Machines (VMs) on Fog servers a new load balancing algorithm Modified Shortest Job First (MSJF) is the proposed. The performance of proposed algorithm is evaluated through different performance parameters. e.g. Processing Time (PT), Response Time (RT) and cost. To validate the performance of proposed scheme simulations are carried out in the Cloud Analyst tool. From the results it is assumed that the proposed technique can not outperforms the Round Robin (RR) and Throttled algorithms, due to its limitations in network delays and RT.


Demand Side Management (DSM) Smart Buildings (SBs) Smart Grids (SGs) Cloud Computing (CC) Fog Computing (FC) 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.COMSATS UniversityIslamabadPakistan

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