Narrow Results

Fog Computing extends storage and computation resources closer to end-devices. In several cases, the Internet of Things (IoT) applications that are time-sensitive require low response time. Thus, reducing the latency in IoT networks is one of the essential tasks. To this end, fog computing is developed with a motive for the data production and consumption to always be within proximity; therefore, the fog nodes must be placed at the edge of the network, which is near the end devices, such that the latency is minimized. The optimal location selection for fog node placement within a network out of a very large number of possibilities, such as minimize latency, is a challenging problem. So, it is a combinatorial optimization problem. Hard combinatorial optimization problems (NP-hard) involve huge discrete search spaces. The fog node placement problem is an NP-hard problem. NP-hard problems are often addressed by using heuristic methods and approximation algorithms. Combinatorial optimization problems can be viewed as searching for the best element of some set of discrete items; therefore in principle, any metaheuristic can be used to solve them. To resolve this, meta-heuristic-based methods is proposed. We apply the Simulated Annealing (SA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) technique to design fog node placement algorithms. Genetic Algorithm is observed to give better solutions. Since Genetic Algorithm may get stuck in local optima, Hybrid Genetic Algorithm, and Simulated Annealing (GA-SA), Hybrid Genetic Algorithm and Particle Swarm Optimization (GA-PSO) were compared with GA. By extensive simulations, it is observed that hybrid GA-SA-based for node placement algorithm outperforms other baseline algorithms in terms of response time for the IoT applications.

An application delivery network (ADN) consists of a set of servers distributed over a large wide area network. In general, ADNs employ two approaches to improve performance in terms of response time: (1) use topological proximity on the network to redirect clients to a closest server, and (2) balance the load of the servers in the original source or the content delivery network using a load balancing algorithm. These two approaches aim to minimize the two major parameters, network latency and server latency, respectively. To accelerate delivery of static content, network latency is the major parameter to minimize. However, to accelerate delivery of dynamically generated content, both parameters are important and a solution requires to achieve a balance between these two approaches. In this paper, we illustrate the need for an integrated approach to this problem. We describe methods for improving the observed performance of an application delivery network by assigning the end user requests to servers. We describe an algorithm which computes such assignments efficiently, so that the assignment task can be performed and adjusted often as the environment changes. The experimental results show that the assignment computation is accurate and close to the optimal.