A user-centric game selection model based on user preferences for the selection of the best heterogeneous wireless network

2018

ijaa

Abstract. The maximum min utility function (MMUF) problem is an important representative of a large class of cloud computing systems (CCS). Having numerous applications in practice, especially in economy and industry. This paper introduces an effective solution-based search (SBS) algorithm for solving the problem MMUF. First, we suggest a new formula of the utility function in term of the capacity of the cloud.We formulate the capacity in CCS, by using a fractional diffeo-integral equation. This equation usually describes the flow of CCS. The new formula of the utility function is modified recent active utility functions.The suggested technique first creates a high-quality initial solution by eliminating the less promising components, and then develops the quality of the achieved solution by the summation search solution (SSS). This method is considered by the Mittag-Leffler sum as hash functions to determine the position of the agent.Experimental results commonly utilized in the literature demonstrate that the proposed algorithm competes approvingly with the state-of-the-art algorithms both in terms of solution quality and computational efficiency.

“…The utility of the CCS is estimated throughout different time t ∈ [1,5]. Obviously, the maximum utility increases, whenever increasing α → 1.…”

Section: Discussionmentioning

confidence: 99%

“…It is clear that the MMU is converging faster than the utility functions in [1] and [4] (see Fig. 2).…”

Section: Consequently We Obtainmentioning

confidence: 92%

See 1 more Smart Citation

Maximize Minimum Utility Function of Fractional Cloud Computing System Based on Search Algorithm Utilizing the Mittag-Leffler Sum

2018

ijaa

“…Salih et al . proposed an NS model, which is based on the integration of simple additive weighting (SAW) method in the framework of non‐cooperative game theory, and the rank sum or AHP method was used to estimate the weights of the parameters that affect the NS process.…”

Section: Intelligent Heterogeneous Network Selectionmentioning

confidence: 99%

An intelligent selection method based on game theory in heterogeneous wireless networks

Salih

See

Trans. Emerging Tel. Tech.

2016

Self Cite

In next‐generation networks, users can optimize the choice with seamless transfer of different access technologies to maximize cost saving and improve quality of service. In such heterogeneous wireless environments, users equipped with multimode wireless devices that can access rich media services via one or more access networks. These networks may differ in terms of monetary cost, coverage range, technology, available bandwidth, energy usage and so on. The new challenge faced by network operators is to ensure users always connect to the best available network, whenever they want and wherever they are or another word to stay Always‐Best‐Connected. Therefore, a new intelligent method is proposed here to optimize vertical handover in selection processes to ensure Always‐Best‐Connected in heterogeneous wireless networks. The proposed solution identifies a new game network selection model for choosing the best connections from different candidate wireless networks. The selected network should satisfy the typical user demands. The proposed selection model is implemented by using the non‐cooperative game model. The proposed solution is evaluated through in‐depth analysis and simulation‐based testing. Results clearly prove that the proposed solution exhibits the highest performance compared with other solutions. Copyright © 2016 John Wiley & Sons, Ltd.

“…The collection of an optimal technology of these obtainable technologies is necessary and critical in terms of assurance, customer flexibility and service permanency in a mixed wireless cloud environment. In this section, we prepare an application for the above algorithm (see Figure 1) and compare it with the model which is based on the integration of simple additive weighting (SAW) method (see [26]),…”

Section: Applicationsmentioning

confidence: 99%

Cloud Entropy Management System Involving a Fractional Power

Jalab

Gani

2015

Entropy

Cloud computing (CC) capacities deliver high quality, connected with demand services and service-oriented construction. Nevertheless, a cloud service (CS) is normally derived from numerous stages of facilities and concert features, which determine the value of the cloud service. Therefore, it is problematic for the users to estimate these cloud services and select them to appropriate their requirements. In this study, a new algorithm is carried out for a multi-agent system (MAS) based on fractional power. The study depends on a fractional difference equation of type two point boundary value problem (BVP) based on the fractional entropy. We discuss the existence of solutions for the system as well as the stability, utilizing the Hadamard well-posed problem. Experimental results show that the proposed method demonstrates stability and performance.