Optimal Distribution of Workloads in Cloud-Fog Architecture in Intelligent Vehicular Networks
ABSTARCT :
With the fast growth in network-connected vehicular devices, the Internet of Vehicles (IoV) has many advances in terms of size and speed for Intelligent Transportation System (ITS) applications. As a result, the amount of produced data and computational loads has increased intensely. A solution to handle the vast volume of workload has been traditionally cloud computing such that a substantial delay is encountered in the processing of workload, and this has made a serious challenge in the ITS management and workload distribution. Processing a part of workloads at the edge-systems of the vehicular network can reduce the processing delay while striking energy restrictions by migrating the mission of handling workloads from powerful servers of the cloud to the edge systems with limited computing resources at the same time. Therefore, a fair distribution method is required that can evenly distribute the workloads between the powerful data centers and the light computing systems at the edge of the vehicular network. In this paper, a kind of Genetic Algorithm (GA) is exploited to optimize the power consumption of edge systems and reduce delays in the processing of workloads simultaneously. By considering the battery depreciation, the supporting power supply, and the delay, the proposed method can distribute the workloads more evenly between cloud and fog servers so that the processing delay decreases significantly. Also, in comparison with the existing methods, the proposed algorithm performs significantly better in both using green energy for recharging the fog server batteries and reducing the delay in processing data.
EXISTING SYSTEM :
? In current SG designs, whole data finds its way into cloud storages comprised of a huge number of servers and storage components having peculiar horizontal and vertical elasticity.
? Unfortunately, the existing cloud based security and privacy enforcements are precisely erratic, and many a times the threats may enter from cloud operators’ side.
? In a competitive plus shared cloud SG environments the worry is that the rivals may spy on proprietary data, leading to cyber physical war among nations.
DISADVANTAGE :
? Fog computing is more efficient than edge computing in terms of processing, while it is less potent than cloud computing. The chief issue in fog computing is the high costs of the required electric power.Nowadays, a more challenging problem is providing sustainable energy resources that can afford the long-term energy requirements of fog nodes in IoV .
? The genetic algorithm is a method for finding approximate solutions to search and optimization problems. This algorithm is considered as a kind of evolutionary algorithm due to its use of biological concepts such as inheritance and mutation. Genetics addresses inheritance and the transfer of attributes from one generation to the next.
PROPOSED SYSTEM :
• We evaluate the proposed framework on real world parameters and show that for a network with approximately 50% time critical applications, the overall service latency for FC is nearly half to that of cloud paradigm.
• We also observed that the FC lowers the aggregated power consumption of the generic CC model by more than 44%.
• We present a mathematical framework for defining the cost profiles in both cloud and fog-based execution. Successively, a cost-efficient optimization framework for cumulative assessment of user to Fog Computing Node (FCN) association, workload allocation, and VM placement constraints, is proposed towards viable deployment of FC
ADVANTAGE :
? Their algorithm was able to learn and adapt itself to any system with unknown modeling parameters.
? Despite the undeniable results of their method in achieving acceptable performance in orchestrating the edge computations and using renewable energy sources for mobile edge nodes, their algorithm failed to fairly distribute the workloads among the computing nodes.
? The GLOBE method of Wu et al. tries to optimize the performance processing nodes at the network edge by geographically balancing the distribution of loads, and at the same time, controlling the input load of any edge nodes.
? This method can handle stochastic events concerning the battery status and power limitations. Although the GLOBE is slightly successful in optimizing the battery energy level, it is still so far from perfect.
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