Editorial Resource Allocation in Communications …downloads.hindawi.com/journals/jece/2013/328395.pdfEditorial Resource Allocation in Communications and Computing YiSu, 1 FangwenFu,

Hindawi Publishing CorporationJournal of Electrical and Computer EngineeringVolume 2013, Article ID 328395, 2 pageshttp://dx.doi.org/10.1155/2013/328395

EditorialResource Allocation in Communications and Computing

Yi Su,1 Fangwen Fu,2 and Shuo Guo3

1 Qualcomm Incorporated, Santa Clara, CA 95051, USA2 Intel Incorporated, Folsom, CA 95630, USA3University of Minnesota at Twin Cities, Minneapolis, MN 55455-0213, USA

Correspondence should be addressed to Yi Su; [email protected]

Received 29 August 2012; Accepted 29 August 2012

Copyright © 2013 Yi Su et al. This is an open access article distributed under the Creative Commons Attribution License, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Communication networks and computing systems havedemonstrated their importance in the past few decadesas a fundamental driver of economic growth. Over theyears, they have not only expanded in their sizes, such asgeographical area and number of terminals, but also in thevariety of services, users, and deployment environments.Thepurpose of resource allocation in such environments is tointelligently assign the limited available resources amongterminals/clients in an efficient way to satisfy end users’service requirements.

With the dramatic developments and fast evolution ofcommunication networks and computing systems, resourceallocation continues to be the fundamental challenge,because better quality of service is required with the increas-ing demand for bandwidth-hungry and/or computation-intensive services. In particular, it has to cope with variousnew emerging system architectures, such as cognitive net-works, mesh networks, multihop networks, peer-to-peer net-works, multistandard networks, cloud computing systems,and data centers, distributed intelligence in a multitude ofdevices operating autonomously enables shifting traditionalcentralized allocationmechanisms into fully distributed solu-tions. In recent years, many tools including optimizationtheory, control theory, game theory, and auction theory havebeen employed to model and solve a variety of practicalresource allocation problems. Therefore, resource allocationin communication networks and computing systems is apressing research topic that has huge applications. It is imper-ative to develop advanced resource allocation techniquesfor ensuring the optimal performance of these systems andnetworks.

The goal of this special issue is to bring together the mostupdated research contributions in this area. Indeed, we see awide range of new analytical techniques andnovel applicationscenarios emerging as evidenced in the papers presentedhere. The nine accepted papers are relevant to resourceallocations optimizations in orthogonal frequency divisionmultiplexing (OFDM-) based communication systems, cog-nitive radio, satellite communications, grid computing, andnetwork virtualization.

J. Y. Baudais et al. in “Robustness maximization of parallelmultichannel systems,” study bit-loading solutions of bothrobustness optimization problems over independent parallelchannels.Their investigation is based on analytical approach,using generalized Lagrangian relaxation tool, and on greedy-type algorithm approach. The asymptotic convergence ofboth robustness optimizations is proved for both analyticaland algorithmic approaches.They also link the SNR-gapmax-imization problem to the conventional power minimizationproblem and prove that the duality does not hold in all cases.In nonasymptotic regime, they show that the resource alloca-tion policies can be interchanged depending on the robust-ness measure and the operating point of the communicationsystem. They propose a low-complexity resource allocationalgorithm based on the analytical approach, which leads to agood tradeoff between performance and complexity.

C. Gueguen and S. Baey, in “Comparison study of resourceallocation strategies for OFDMmultimedia networks,” presentand compare themainOFDMscheduling techniques used formultimedia services in multiuser OFDM wireless networks.They study the influence of bandwidth granularity on theresource allocation strategies performances. They show that

2 Journal of Electrical and Computer Engineering

bandwidth granularity is of major importance for determin-ing the application range of advanced OFDM schedulingtechniques.

M. H. Ahmed et al. in “Analytical evaluation of theperformance of proportional fair scheduling in OFDMA-basedwireless systems,” evaluate the performance of proportionalfair (PF) scheduling in orthogonal frequency division mul-tiple access (OFDMA) wireless systems. They investigatea two-dimensional (time slot and frequency subcarrier)PF scheduling algorithm for OFDMA systems and evalu-ate its performance analytically and by simulations. Theyderive approximate closed-form expressions for the averagethroughput, throughput fairness index, and packet delay.Computer simulations show good accuracy of the analyticalexpressions.

A. Maiga et al. in “Bitrate optimization with MMSEdetector for multicast LP-OFDM system,” propose a newresource allocation algorithm with minimum mean squareerror (MMSE) detector for multicast linear precoded (LP)OFDM systems. They propose to jointly use the LP-OFDMmodulation technique and an adaptation of the OFDM-based multicast approaches to exploit the transmission linkdiversities of users and improve both the bit rate and thefairness among multicast users.

S. Romaszko and P. Mahonen, in “A rendezvous proto-col with the heterogeneous spectrum availability analysis forcognitive radio ad hoc networks,” look into a new challengeproblem of rendezvous (RDV) protocol in cognitive radioad hoc networks (CRANs). In such a frequently changingenvironment, licensed holders channel occupancy, and het-erogeneous spectrum availability result in a need of on-demand searching for a control traffic channel by CR usersin order to be able to initiate a communication and methodsguaranteeing that all nodes meet periodically in reasonableperiods of time should be advocated. They evaluate a torusquorum system (QS) and difference set (DS) based ren-dezvous protocol (MtQS-DSrdv) and show that the nodesmeet multiple times on different channels in a period, whichincreases the chance of successful establishment of a realcommunication.

A. Alsarhan and A. Agarwal, in “Optimizing spectrumtrading in cognitive mesh network using machine learning,”propose a reinforcement learning (RL) model in a cognitivewireless mesh network for licensed users (primary users,PUs) to maximize the revenue of renting surplus spectrumto unlicensed users (secondary users, SUs). They use RLextract the optimal control policy that maximizes the PUs’profit continuously over time. The extracted policy is usedby PUs to manage renting the spectrum to SUs, and it helpsPUs to adapt to the changing network conditions. They alsopropose a new distributed algorithm to manage spectrumsharing among PUs to maximize the total revenue and utilizespectrum efficiently.

S. Wayer and I. Reichman, in “Resource management insatellite communication systems-heuristic schemes and algo-rithms,” study the challenging resource allocation problemin satellite communication due to the high cost of frequencybandwidth. They define a satisfaction measure to estimatethe allocation processes and carry out resource management

according to the requests of subscribers, their priority levels,and assured bandwidths.

M. Abouelela and M. El-Darieby, in “Multi-domain hier-archical resource allocation for grid applications,” proposea hierarchical-based architecture as well as multidomainhierarchical resource allocation approach for geographicallydistributed applications in grid computing environments.They perform the resource allocation in a distributed wayamong different domains such that each participant domainkeeps its internal topology and private data hidden whilesharing abstracted information with other domains.The pro-posed algorithm jointly schedules computing andnetworkingresources while optimizing the application completion timetaking into account data transfer delays.

A. Razzaq et al. in “Virtual network embedding: a hybridvertex mapping solution for dynamic resource allocation,”investigate the problem of virtual network embedding (VNE)in the context of network virtualization. They analyze twoexisting vertexmapping approaches andpropose a newvertexmapping approach which minimizes complete exhaustion ofsubstrate nodes while still providing good overall resourceutilization. They also investigate under which circumstancesthe proposed vertex mapping approach can provide superiorVN embedding properties.

Before closing this editorial, we would like to thankthose who contributed significantly behind the scene towardsthe success of this special issue. We hope that you willenjoy reading this Special Issue devoted to the exciting fast-evolving field of resource allocation in communications andcomputing as much as we have done.

Yi SuFangwen FuShuo Guo

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Editorial Resource Allocation in Communications …downloads.hindawi.com/journals/jece/2013/328395.pdfEditorial Resource Allocation in Communications and Computing YiSu, 1 FangwenFu,