Joint communication and computation resources allocation for cloud-empowered future wireless ...

210 Pages·2017·4.45 MB·English

by  Jessica Oueis

Joint communication and computation resources
allocation for cloud-empowered future wireless networks
Jessica Oueis
To cite this version:
Jessica Oueis. Joint communication and computation resources allocation for cloud-empowered future
wireless networks. Web. Université Grenoble Alpes, 2016. English. ￿NNT: 2016GREAM007￿. ￿tel-
01366449￿
HAL Id: tel-01366449
https://theses.hal.science/tel-01366449
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THE`SE
Pourobtenirlegradede
DOCTEUR DE L’UNIVERSITE´ DE GRENOBLE
Spe´cialite´ : Informatique
Arreˆte´ ministe´rial:7aouˆt2006
Pre´sente´epar
Jessica Oueis
The`sedirige´eparAndrzejDuda
etcodirige´eparSergioBarbarossa
pre´pare´eausein CEA-LETI/DSIS/STCS/LESC-etUMR5217-LIG-Laboratoire
d’InformatiquedeGrenoble
etdeMSTII
Gestion conjointe de ressources de
communication et de calcul pour les
re´seaux sans fils a` base de cloud
The`sesoutenuepubliquementle12Fe´vrier2016,
devantlejurycompose´ de:
M.,JosepVidal
UPCBarcelona,Rapporteur,Pre´sident
M.,KeiSakaguchi
FraunhoferHHI,TokyoInstituteofTechnology,Rapporteur
M.,ValerioFrascolla
Intel,Examinateur
M.,AndrzejDuda
GrenobleINP-Ensimag,Directeurdethe`se
M.,SergioBarbarossa
Universite´ deRome,LaSapienza,Co-Directeurdethe`se
M.,EmilioCalvaneseStrinati
CEA-LETI,Grenoble,Co-Encadrantdethe`se

Abstract
Mobile Edge Cloud brings the cloud closer to mobile users by moving the cloud computational
efforts from the internet to the mobile edge. We adopt a local mobile edge cloud computing ar-
chitecture, where small cells are empowered with computational and storage capacities. Mobile
users’ offloaded computational tasks are executed at the cloud-enabled small cells. We propose
the concept of small cells clustering for mobile edge computing, where small cells cooperate in
order to execute offloaded computational tasks. A first contribution of this thesis is the design of
amulti-parameter computation offloading decision algorithm, SM-POD.Theproposed algorithm
consistsofaseriesoflowcomplexitysuccessiveandnestedclassifications ofcomputationaltasks
atthemobileside, whichleadstoanoffloading decision toeachofthetasks. Thetasks areeither
computed locally using the handset resources, or offloaded to the cloud. To reach the offloading
decision, SM-POD jointly considers computational tasks, handsets, and communication channel
parameters. In the second part of this thesis, we tackle the problem of small cell clusters set up
for mobile edge cloud computing for both single-user and multi-user cases. The clustering prob-
lemisformulated asanoptimization thatjointlyoptimizesthecomputational andcommunication
resource allocation, andthecomputational load distribution onthesmallcellsparticipating inthe
computation cluster. We propose a cluster sparsification strategy, where we trade cluster latency
for higher system energy efficiency. In the multi-user case, the optimization problem is not con-
vex. Inordertocomputeaclusteringsolution,weproposeaconvexreformulationoftheproblem,
andweprovethatbothproblemsareequivalent. Withthegoaloffindingalowercomplexityclus-
tering solution, we propose two heuristic small cells clustering algorithms. The first algorithm is
based on resource allocation on the serving small cells where tasks are received, as a first step.
Then, in a second step, unserved tasks are sent to a small cell managing unit (SCM) that sets up
computational clusters for the execution of these tasks. The main idea of this algorithm is task
scheduling at both serving small cells, and SCM sides for higher resource allocation efficiency.
Thesecondproposed heuristic isaniterative approach inwhichserving smallcellscompute their
desiredclusters,withoutconsideringthepresenceofotherusers,andsendtheirclusterparameters
totheSCM.SCMthenchecks forexcessofresource allocation atanyofthenetworksmallcells.
SCMreportsanyloadexcesstoservingsmallcellsthatre-distribute thisloadonlessloadedsmall
cells. Whennosmallcellisoverloaded, theSCMvalidates theclusters setupaccordingly. Inthe
finalpartofthisthesis,weproposetheconceptofcomputationcachingforedgecloudcomputing.
Withtheaimofreducing theedgecloudcomputing latency andenergy consumption, wepropose
caching popular computational tasks for preventing their re-execution. Our contribution here is
two-fold: first, wepropose acaching algorithm that is based on requests popularity, computation
size, required computational capacity, and small cells connectivity. This algorithm identifies re-
queststhat,ifcachedanddownloadedinsteadofbeingre-computed,willincreasethecomputation
caching energy and latency savings. Second, we propose a method for setting up a search small
cellsclusterforfindingacachedcopyoftherequests computation. Theclustering policyexploits
therelationshipbetweentaskspopularityandtheirprobabilityofbeingcached,inordertoidentify
i
ii ABSTRACT
possible locations ofthecachedcopy. Theproposed methodreduces thesearchcluster sizewhile
guaranteeing aminimumcachehitprobability.
Keywords
Mobilecloudcomputing, Localcloud, Edgecloud,Smallcellscluster, Resourceallocation, Com-
putation offloading, Computation caching, Loaddistribution
List of Publications
Journal Papers
[J1] J. Oueis, E. Calvanese Strinati, S. Sardellitti, and S. Barbarossa, “Joint Computation and
Communication Resource Allocation in Edge Cloud Computing Clusters”to be submitted,
2015.
Conference Papers
[C1] Oueis, J.; Strinati, E.C.; Barbarossa, S., “Multi-parameter decision algorithm for mobile
computationoffloading,”inWirelessCommunicationsandNetworkingConference(WCNC),
2014IEEE,vol.,no.,pp.3005-3010, 6-9April2014
[C2] Oueis, J.; Calvanese-Strinati, E.; De Domenico, A.; Barbarossa, S., “On the impact of
backhaul network on distributed cloud computing,” in Wireless Communications and Net-
working Conference Workshops (WCNCW), 2014 IEEE , vol., no., pp.12-17, 6-9 April
2014
[C3] Oueis,J.;Strinati,E.C.;Barbarossa,S.,“Smallcellclustering forefficientdistributed cloud
computing,” in Personal, Indoor, and Mobile Radio Communication (PIMRC), 2014 IEEE
25thAnnualInternational Symposiumon,vol.,no.,pp.1474-1479, 2-5Sept. 2014
[C4] Oueis, J.; Strinati, E.C.; Barbarossa, S., “The Fog Balancing: Load Distribution for Small
Cell Cloud Computing,” in Vehicular Technology Conference (VTC Spring), 2015 IEEE
81st,vol.,no.,pp.1-6,11-14May2015
[C5] Oueis, J.; Strinati, E.C.; Sardellitti, S.; Barbarossa, S., “Small Cell Clustering for Effi-
cientDistributedFogComputing: AMulti-userCase,”inVehicularTechnologyConference
(VTCFall),2015IEEE82nd,vol.,no.,pp.,Sept. 2015
[C6] Oueis, J.; Strinati, E.C.; Barbarossa, S., “Distributed Mobile Cloud Computing: A Multi-
user Clustering Solution,” submitted to the International Conference on Communications
(ICC),2016IEEE,23-27May,2016
[C7] Oueis, J.; Strinati, E.C.; Barbarossa, S., “Energy Aware Computation caching on the Edge
cloud,”tobesubmitted, 2015.
[C8] Oueis, J.; Strinati, E.C.;Barbarossa, S.,“Uplink Trafficinfuture mobile networks: pulling
thealarm,”submittedtoCROWNCOM2016.
iii
iv LISTOFPUBLICATIONS
Patents
[P1] J. Oueis, E.Calvanese Strinati, andA.DeDomenico, “Method forModifying TheStateof
LocalAccessPointsinaCellularNetwork,”US20140220994, EP2763469A1.
[P2] J.Oueis,E.CalvaneseStrinati,andS.Barbarossa,“MethodforOffloadingtheExecutionof
Computation TasksofaWirelessEquipment,”DD14763SP.
[P3] J. Oueis, and E. Calvanese Strinati, “Method, Devices and System for Cloud Constitution
WithSignalResponseTime,”DD15918SP.
[P4] J.Oueis,andE.CalvaneseStrinati,“Methodfordistributed LocalCloudClusterFormation
withMultiplejointCommunication andComputingUErequests,” DD16683SP.
[P5] J. Oueis, and E. Calvanese Strinati, “Caching and Fog Distributed Clustering Offloading,”
DD16782SP.
Contents
Abstract i
ListofPublications iii
Contents v
ListofFigures ix
ListofTables xi
AbbreviationsandAcronyms xiii
IntroductionandThesisOutline 1
1 TheEvolutionofCloudEnableMobileWireless Networks 7
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.2 5GCellular: TheFutureofMobileNetworks . . . . . . . . . . . . . . . . . . . 9
1.2.1 5G:DesignEssentials . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.2.2 5GRequirements andEnablingTechnologies . . . . . . . . . . . . . . . 10
1.2.2.1 5GRequirements andCapabilities . . . . . . . . . . . . . . . 10
1.2.2.2 5GEnablingTechnologies . . . . . . . . . . . . . . . . . . . 12
1.3 CloudTechnologies andNetworkArchitecture: AJointEvolution . . . . . . . . 18
1.3.1 CloudComputing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
1.3.1.1 DefinitionandCharacteristics . . . . . . . . . . . . . . . . . . 18
1.3.1.2 ServiceandDeployment Models . . . . . . . . . . . . . . . . 19
1.3.1.3 CloudComputingEnablers . . . . . . . . . . . . . . . . . . . 20
1.3.2 CloudTechnologies inCellularNetworks . . . . . . . . . . . . . . . . . 20
1.3.2.1 ClassicBaseStationArchitecture . . . . . . . . . . . . . . . . 21
1.3.2.2 CloudRadioAccessNetwork(C-RAN) . . . . . . . . . . . . 22
1.3.2.3 MobileCloudComputing: RemoteClouds . . . . . . . . . . . 23
1.3.2.4 MobileCloudComputing: Cloudlets . . . . . . . . . . . . . . 27
1.3.2.5 MobileEdgeComputing . . . . . . . . . . . . . . . . . . . . 28
1.3.3 FogComputing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
1.4 UplinkTrafficinFutureMobileNetworks: PullingtheAlarm . . . . . . . . . . . 33
1.4.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
1.4.2 WhyUplinkTrafficisGrowing . . . . . . . . . . . . . . . . . . . . . . 34
1.4.2.1 Increase inNumberofMobileSubscribersandDevices . . . . 34
v
vi CONTENTS
1.4.2.2 Evolution ofCellularNetworks . . . . . . . . . . . . . . . . . 34
1.4.2.3 Emergence of Cloud Technologies and Dense Heterogeneous
Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
1.4.2.4 NewApplications andServicesEcosystem . . . . . . . . . . . 36
1.4.2.5 CrowdedNetworksScenarios . . . . . . . . . . . . . . . . . . 37
1.4.2.6 SensorandMTCNetworks . . . . . . . . . . . . . . . . . . . 37
1.4.3 UplinkImprovementRelatedWork . . . . . . . . . . . . . . . . . . . . 38
1.4.3.1 RangeExtensioninHeterogeneous Networks . . . . . . . . . 38
1.4.3.2 DownlinkandUplinkDecoupling . . . . . . . . . . . . . . . . 39
1.4.3.3 UplinkCoMPTechniques . . . . . . . . . . . . . . . . . . . . 39
1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2 EdgeCloudClusterComputing: ChallengesandTrade-offs 43
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.1.1 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.2 SystemModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.2.1 SmallCellsEdgeComputing . . . . . . . . . . . . . . . . . . . . . . . 44
2.2.2 Computation offloading . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.2.3 Computation SmallCellsCluster . . . . . . . . . . . . . . . . . . . . . 46
2.3 PreliminaryonCommunication Trade-offsinHeterogeneous Networks . . . . . 49
2.4 AdvancedMECtrade-offs: JointCommunication andComputation . . . . . . . 51
2.4.1 Computation OffloadingTrade-offs . . . . . . . . . . . . . . . . . . . . 51
2.4.2 SmallCellCloudClusteringTrade-offs . . . . . . . . . . . . . . . . . . 52
2.4.3 LocalComputingvsComputationOffloading . . . . . . . . . . . . . . . 56
2.4.4 Performance andEnergySavings . . . . . . . . . . . . . . . . . . . . . 56
2.4.5 SystemEnergyEfficiency,CellsDensity,andEMFExposure . . . . . . . 58
2.4.6 SmallCellClusterCloud . . . . . . . . . . . . . . . . . . . . . . . . . . 60
2.5 Extending theImpactofBackhaulNetworkonSmallCellCloudComputing . . . 61
2.5.1 SystemModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
2.5.2 LatencyModels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
2.5.2.1 FullMeshTopology . . . . . . . . . . . . . . . . . . . . . . . 63
2.5.2.2 WirelessLTEBackhaul . . . . . . . . . . . . . . . . . . . . . 63
2.5.2.3 TreeTopology . . . . . . . . . . . . . . . . . . . . . . . . . . 63
2.5.2.4 RingTopology . . . . . . . . . . . . . . . . . . . . . . . . . . 64
2.5.3 PowerConsumption Models . . . . . . . . . . . . . . . . . . . . . . . . 65
2.5.3.1 WirelessLTEBackhaul . . . . . . . . . . . . . . . . . . . . . 65
2.5.3.2 FiberBackhaul . . . . . . . . . . . . . . . . . . . . . . . . . . 66
2.5.3.3 Ringtopology . . . . . . . . . . . . . . . . . . . . . . . . . . 66
2.5.3.4 MicrowaveBackhaul . . . . . . . . . . . . . . . . . . . . . . 66
2.6 NumericalEvaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
2.6.1 ClusterLatency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
2.6.2 ClusterCommunicationPowerConsumption . . . . . . . . . . . . . . . 69
2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
3 Multi-parameterComputationOffloadingDecision 71
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
3.1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
3.1.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
CONTENTS vii
3.1.3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.2 SystemModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
3.3 ProblemStatement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
3.4 Proposed OffloadingDecisionAlgorithm: SM-POD . . . . . . . . . . . . . . . . 77
3.5 NumericalEvaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
4 SmallCellsClusteringforMEC:FromSingle-usertoMulti-user 87
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.1.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
4.1.3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.2 SystemModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
4.3 Single-user Multi-cloud UseCase . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.3.1 ProblemStatement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.3.2 LatencyMinimization . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.3.3 ClusterSparsification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
4.3.4 Minimization ofClusterPowerConsumption . . . . . . . . . . . . . . . 99
4.3.5 Minimization ofSmallCellSelfishPowerConsumption . . . . . . . . . 102
4.3.6 NumericalEvaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
4.4 Multi-user Multi-cloud UseCase . . . . . . . . . . . . . . . . . . . . . . . . . . 104
4.4.1 Multi-user ClusteringOptimization . . . . . . . . . . . . . . . . . . . . 105
4.4.2 NumericalEvaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
5 SmallCellsClusteringApproachesforMEC 113
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
5.1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
5.1.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
5.1.3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
5.2 SystemModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
5.3 SmallCellCloudClustering: AScheduling Approach . . . . . . . . . . . . . . . 117
5.3.1 GeneralAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.3.2 Algorithm Implementations . . . . . . . . . . . . . . . . . . . . . . . . 118
5.4 SmallCellCloudClustering: AnIterativeApproach . . . . . . . . . . . . . . . . 119
5.5 NumericalEvaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
5.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
6 ComputationCachinginCluster-basedCloudComputing 129
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
6.1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
6.1.2 RelatedWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
6.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
6.3 SystemModelandNotations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
6.4 Computation CachingforEdgeComputing . . . . . . . . . . . . . . . . . . . . 134
6.5 Proposed CachingAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
6.6 CachingAlgorithm Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
6.7 SearchClusterSparsification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140