On-Demand Resource Allocation for OFDMA Small Cells Overlaying CDMA System

2016 ◽  
Author(s):  
Junjie Tan ◽  
Ying-Chang Liang ◽  
Shiying Han ◽  
Gang Yang
Keyword(s):  
Resource Allocation ◽  
Small Cells ◽  
Cdma System ◽  
On Demand

On Demand Resource Allocation for LTE Uplink Transmission Based on Logical Channel Groups

2017 ◽  
Vol 22 (5) ◽  
pp. 868-879 ◽  
Author(s):  
Fang-Chang Kuo ◽  
Kuo-Chang Ting ◽  
Hwang-Cheng Wang ◽  
Chih-Cheng Tseng
Keyword(s):  
Resource Allocation ◽  
On Demand ◽  
Logical Channel ◽  
Lte Uplink

Distributed Dynamic Resource Allocation for OFDMA-Based Cognitive Small Cell Networks Using a Regret-Matching Game Approach

2015 ◽  
pp. 230-253 ◽  
Author(s):  
Wei-Sheng Lai ◽  
Tsung-Hui Chang ◽  
Ta-Sung Lee
Keyword(s):  
Resource Allocation ◽  
Small Cell ◽  
Small Cells ◽  
Allocation Strategy ◽  
Small Cell Networks ◽  
Distributed Resource Allocation ◽  
Cell Network ◽  
Resource Allocation Strategy ◽  
Matching Game ◽  
Cell Networks

Game theoretical approaches have been used to develop distributed resource allocation technologies for cognitive heterogeneous networks. In this chapter, we present a novel distributed resource allocation strategy for cognitive small cell networks based on orthogonal frequency-division multiple access. In particular, we consider a heterogeneous network consisting of macrocell networks overlaid with cognitive small cells that opportunistically access the available spectrum. We focus on a regret-matching game approach, aiming at maximizing the total throughput of the small cell network subject to cross-tier interference and quality of service (QoS) constraints. The regret-matching game approach exploits a regret procedure to learn the optimal resource allocation strategy from the regrets of the actions of cognitive users. Furthermore, the regret-matching game approach is extended to the joint resource allocation and user admission control problem. Numerical results are presented to demonstrate the effectiveness of the proposed regre-matching approaches.

Resource Allocation Policies in Cloud Computing Environment

2017 ◽  
pp. 115-132 ◽  
Author(s):  
Suvendu Chandan Nayak ◽  
Sasmita Parida ◽  
Chitaranjan Tripathy ◽  
Prasant Kumar Pattnaik
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Utilization ◽  
Service Level Agreement ◽  
Service Level ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
On Demand ◽  
Different Types ◽  
Cloud Datacenter

The basic concept of cloud computing is based on “Pay per Use”. The user can use the remote resources on demand for computing on payment basis. The on-demand resources of the user are provided according to a Service Level Agreement (SLA). In real time, the tasks are associated with a time constraint for which they are called deadline based tasks. The huge number of deadline based task coming to a cloud datacenter should be scheduled. The scheduling of this task with an efficient algorithm provides better resource utilization without violating SLA. In this chapter, we discussed the backfilling algorithm and its different types. Moreover, the backfilling algorithm was proposed for scheduling tasks in parallel. Whenever the application environment is changed the performance of the backfilling algorithm is changed. The chapter aims implementation of different types of backfilling algorithms. Finally, the reader can be able to get some idea about the different backfilling scheduling algorithms that are used for scheduling deadline based task in cloud computing environment at the end.

Stable-Matching-Based Energy-Efficient Context-Aware Resource Allocation for Ultra-Dense Small Cells

2017 ◽  
pp. 29-57
Author(s):  
Zhenyu Zhou ◽  
Zheng Chang ◽  
Chen Xu ◽  
Tapani Ristaniemi
Keyword(s):  
Resource Allocation ◽  
Energy Efficient ◽  
Small Cells ◽  
Context Aware ◽  
Delay Sensitive ◽  
Optimum Energy ◽  
Significant Performance ◽  
Promising Solution ◽  
Nonlinear Fractional Programming ◽  
Efficient Matching

Implementing caching to ultra-densely deployed small cells provides a promising solution for satisfying the stringent quality of service (QoS) requirements of delay-sensitive applications with limited backhaul capacity. With the rapidly increasing energy consumption, in this chapter, the authors investigate the NP-hard energy-efficient context-aware resource allocation problem and formulate it as a one-to-one matching problem. The preference lists in the matching are modeled based on the optimum energy efficiency (EE) under specified matching, which can be obtained by using an iterative power allocation algorithm based on nonlinear fractional programming and Lagrange dual decomposition. Next, on account of the Gale-Shapley algorithm, an energy-efficient matching algorithm is proposed. Some properties of the proposed algorithm are discussed and analyzed in detail. Moreover, the authors extend the algorithm to the matching with indifferent and incomplete preference lists. Finally, the significant performance gain of the proposed algorithm is demonstrated through simulation results.

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