Cross-layer Multiuser Session Control for ImprovedSDN Cloud Communications

Helber Silva∗†, Felipe Barbalho∗ and Augusto Neto∗‡

∗ Federal University of Rio Grande do Norte (UFRN), Brazil† Federal Institute of Rio Grande do Norte (IFRN), Brazil‡ Instituto de Telecomunicacoes (IT), Aveiro, Portugal

helber.silva@ifrn.edu.br, barbalho12@ufrn.edu.br, augusto@dimap.ufrn.br

Abstract—The integration of Cloud Computing and Internetof Things (IoT) is foreseen as an enabler to suit a plethoraof novel latency critical applications (e.g, e-health, intelligenttransportation, safety, energy, smart cities, and many others).These applications require multimedia (mainly video) flows to behandled by the underlying network in an efficient and scalableway, as they expect to consume a massive data produced bybillions of things. In view of this, we propose a dynamic multiusersession control plane which leverages 5G’s support of Software-Defined Networking (SDN) substrate to advance beyond todayslimited, per-flow IP-based communication systems. We handlesuch limitations by proposing CLASSICO, a Cross-LAyer SdnSessIon COntrol architecture that exploits SDN to offload the flowstreaming computation operations from the IoT cloud platformto the network edge, affording high timeliness and scalabilityfor the IoT-cloudified system. CLASSICO dynamically buildsApplication Layer multiuser data sessions and maps them intoenhanced group-enabled data paths featuring SDN replication atbranching nodes. We applied our solution to multimedia-alikeuse case, and results show that CLASSICO outperforms typicalSDN-enabled IoT systems in terms to Quality of Service (QoS)and Quality of Experience (QoE) video metrics.

Index Terms—Cloud Computing, Internet of Things, SoftwareDefined Networking, Cross-layer control plane

I. INTRODUCTION

Cloud Computing is envisioned to enable pervasive, openedand on demand transparent access to a variety of resources(e.g., storage, applications and network services), with mini-mal control or service provider management [1]. Cloud plat-forms (e.g., Amazon Web Services, OpenStack and others)can offer virtually unlimited computing and storage servicesthat can bring economical advantages (e.g., decreased opera-tional/renting infrastructure costs) and technical support (e.g.,optimization of hardware and software resources, elasticityand flexibility) to satisfy existing and future user needs [2].The FIWARE project was recently funded to facilitate thedevelopment of innovative IoT smart applications, such asvideoconferencing [3], e-health [4] and smart sensor moni-toring [5], by offering an open cloud-based infrastructure thatincludes a catalog of so called Generic Enablers (GEs).

Despite the benefits on the complementarity of the cloudand IoT, supporting efficient and scalable latency critical IoTapplications over cloud platforms remains a challenge [6].How to solve this problem is of relevant importance knowing

that multimedia (mainly video) data will govern the Internettraffic by 2021 [7]. Further, video traffic is network band-width hungry and has strong QoS requirements to assist userQoE [8].

Recent intra-/inter-cloud networking initiatives fail inproviding network performance and scalability to satisfyQoS/QoE for IoT-enabled multimedia traffic. This is morechallenging in Smart Cities enabled by IoT and cloud plat-forms, as a huge number of IoT multimedia applications areexpected to simultaneously request content from the samecloud-hosted delivery system [9]. In such scenario, the cloudsystem transmits independent (and redundant) data flows foreach request, hence draining link bandwidth of the backhaul.

Existing solutions for Video on Demand (VoD) used byapplications (e.g., YouTube, Netflix and others), try to alleviatethis issue by caching multimedia content at the edge ofthe network. However, this does not allow for realtime, livevideo broadcasting, for example. IP multicast has also beenattempted, but cloud operators expect to use low-end switcheswhich may not support IP multicast. Moreover, IP multicastleads to decreased performance due to high signaling overheadand reduced scalability [10]. Hence, it is necessary to developan improved IoT multimedia data delivery from the cloud,considering applications needs.

Software Defined Networks (SDN) [11] play a key role insupporting better network resource allocation, utilization andmanagement of all layers in cloud environments. It has gainedinterest from large-volume data centers and cloud providersand service providers. By using SDN, cloud operators easilyadapt network nodes (e.g., L2 or L3 forwarders) via a centralSDN controller to abstract the actual hardware devices withsoftware programming. Hence, SDN is considered an impor-tant technology to support QoS/QoE over cloud environments.

In this paper we aim at affording efficient and scalablemultiuser IoT session data service transport over cloud plat-forms to improve user QoE by jointly orchestrating per-session control plane and the SDN-enabled data forwarding.We propose CLASSICO, a Cross-LAyer Sdn SessIon COntrolarchitecture that provides the following features: (i) Applica-tion Layer IoT multiuser (i.e., one-to-many) session admissioncontrol to allocate groups of cloud client applications sharingsame content; (ii) intra-/inter-cloud bandwidth-constrained net-

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working service transport provision by dynamically mappingmultiuser sessions into enhanced group-enabled SDN datapaths featuring SDN replication at branching nodes closestto the SDN edge; and (iii) integration of CLASSICO into awidely-used IoT cloud platform to to carry out its evaluationthrough prototyping on a real testbed.

The remaining of this paper is structured as follows. SectionII reviews previous works. In Section III, we provide anoverview of the CLASSICO architecture. Section IV providesthe details with respect to the CLASSICO concepts. SectionV examines the results obtained from CLASSICO through anIoT cloud-SDN compliant evaluation. Finally, in Section VIwe conclude the paper and present future directions.

II. RELATED WORK

Guan and Choi [12] proposed a cloud CDN (ContentDelivery Network)-like solution aiming at minimizing linkbandwidth consumption and delay in the content placementproblem to the cloud provider. They address the joint traffic-latency optimization problem by proposing an algorithm todetermine the optimal content delivery over cloud storage,considering network bandwidth and latency. Jiyan et Al. [13]proposed an application-layer protocol for cloud video systemsthat adapts the transmission rate and FEC coding consideringthe time-varying wireless channel status to optimize the videoquality. In [14], the authors focus on CDNs to address a cloud-based VoD service using the concept of peering CDNs thatcooperate to fulfill a number of requests.

This paper differs from the aforementioned works be-cause we exploit SDN features to improve performanceof programmable network hardware. In particular, we useOpenFlow-GroupMod function to install replication rules atbranching SDN forwarders towards multiple cloud applica-tions sharing multiuser video sessions.

Other works have considered SDN in cloud multimediadelivery. Walter et Al. [15] proposed an SDN session controlthat combines application and communication resources basedon a policy layer. It establishes data sessions using the SIPprotocol, relying the IP/MPLS forwarding features to supportQoS. An SDN framework is presented in [16] to provide QoSfor multimedia services by means of a QoS routing algorithmthat calculates appropriate SDN paths based on network statis-tics to forward packets. Paolo et Al. [17] proposed a near-optimum approximation algorithm to keep minimum delayoverlay SDN paths that satisfy QoS requirements based onmultiple QoS criteria (network capacity, jitter and packet loss).

III. DESCRIPTION OF THE CLASSICO PROPOSAL

The CLASSICO architecture dynamically manages Appli-cation Layer (L7) IoT multiuser data sessions consideringrequests of (possible a huge amount of) cloud client appli-cations for common cloud-hosted content. An IoT multiuserdata session is designed as an 1-to-n channel in which 1 cloud-hosted multimedia server (that stores data collected from IoTdevices) will stream them to n users (i.e., cloud client applica-tions) at the same time. Based on existing multiuser sessions,

CLASSICO invokes the SDN controller to push group-basedflow entries that replicates data packets towards applicationsonly at branching points of a data path. The branching pointsare the L2/L3 forwarders as closest as possible to the SDNedge. This offloads the flow streaming computation operationsfrom the IoT cloud platform, affording high timeliness andscalability for the IoT-cloudified system. To that, a CLASSICOinstance runs over the SDN controller to get the networktopology and the global view of forwarders, physical interfacesand end-to-end data paths.

A. Key Components of the Conceptual Architecture

Fig. 1 depicts the architecture of CLASSICO. The SessionManager component is a piece of software (independent onthe cloud platform) over the SDN Control Layer that acts as aproxy between a client application and a cloud server. It exe-cutes the following procedures: (i) dynamic session admissioncontrol (setup, refresh and release) to build L7 multiuser datasession based on application’s interests (e.g., the IoT producer,its location, the requested data, etc.); and (ii) mapping ofmultiuser sessions into best-connected SDN data plane paths,by triggering the SDN controller to install/remove group-basedflow entries rules into branching L2/L3 OpenFlow-enablednodes for packet replication.

Fig. 1. The CLASSICO architecture

We offer an illustrative example on the benefits of CLAS-SICO in comparison to typical one-to-one cloud data ses-sions. Let be 3 multimedia applications, connected to thesame ingress SDN node into the cloud network infrastructure,requesting cloud-hosted content from the same server. WithCLASSICO, an unique multiuser session is built (differentfrom typical solutions, that will keep 3 sessions). Based on thenetwork topology, CLASSICO will trigger the SDN controllerto push a group-based flow entry into the ingress SDN node (socalled branching point). A single copy of each data packet willbe sent from the server, being replicated only at the branchingpoint for the 3 applications. In typical approaches, the serverwould send 3 copies of each data packet through the networkbackhaul towards each application. Hence, the benefits ofCLASSICO in this example are: improved link bandwidthusage at the network core, reduced amount of TCAM (Ternary

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Content-Addressable Memory) entries at SDN nodes (as theykeep less flow entries than with typical one-to-one sessions),and finally better network QoS/user QoE.

Fig. 2 shows how the Session Manager operates. The cloudclient application gets access to cloud-hosted informations(e.g., type of device, data format, location and etc.) using thecloud platforms APIs [18]. An application sends a messageindicating its interests (number 1 in Fig. 2). By default, thefirst SDN node (ingress point) that receives the messageforwards it to the SDN controller. With CLASSICO, theSDN controller triggers the Session Manager, that accessesthe payload (to be aware of the application’s interests) ofthe message which is an input for the IoT multiuser sessionhandler algorithm (number 3). This algorithm dynamically(re)builds the multiuser session table, and invokes the SDNcontroller to send OFPT_GROUP_MOD messages carryingreplication actions only towards branching points (number 4).The cloud provider triggers the stream source for starting thedata session (number 5). Because of the replication rules atbranching points, the stream will be forwarded to all clientapplications allocated to a multiuser data session. When aclient application wants to end its session, it explores the cloudAPI (number 6). The Session Manager redefines the multiusersession table (number 7). In order to reflect this change intothe SDN, the Session Manager runs again the IoT multiusersession handler algorithm (number 8), which triggers the SDNcontroller for the removal of a forwarding rule into affectedbranching points (number 9), and the procedure ends.

Fig. 2. High level operations of the CLASSICO approach

IV. DESIGN DETAILS OF CLASSICO PROPOSAL

A. Per multiuser IoT cloud data session

An IoT multiuser cloud-based data session s is a tuple(id, cs, d,R), where id is a session identification, cs corre-sponds to cloud service that offers the interfaces for IoT dataestablishment/closing from interested applications, d is thesender IoT device (managed by the cloud provider) and acces-sible through the cloud exposed interface, R = {r1, r2, ..., rn}is a set of all n applications (multiple receivers) involved in

the session s that receive the same data. The receiver host(where runs a cloud client application) is identified by its IPaddress. The Session Manager dynamically controls an internalmultiuser session table, allowing it to add, update or removeIoT data sessions in response to users data requests. A datarequest req from a receiver includes at least the interestedcloud service (req.cs) and the sender IoT device (req.d)offered via the cloud client API.

1) Session setup: An interested application can invoke acloud client API (e.g., RESTful, Websocket, etc.) to receivecontent from IoT cloud-hosted devices. To this aim, the appli-cation (i.e., the IoT data receiver) sends a data request includ-ing its interests (e.g., the IoT device, the type of data content,etc.) to the cloud service provider. An ingress SDN OpenFlow-enabled node firstly receives the request, encapsulates it intoan OFPT_PACKET_IN message, and forwards to the SDNcontroller. Instead of directly install forwarding rules into theSDN node (as default), the SDN controller desencapsulates therequest and sends it to the Session Manager. Upon receivinga data request, the Session Manager starts the Algorithm 1to dynamically (re)build the IoT multiuser session table. Theinput of the algorithm is the existing IoT multiuser sessiontable T and the data request req. The output is a reducedsession table Tnew, considering the interests of the clientapplication.

At the system bootstrap, the IoT multiuser session tableT is empty. A new multiuser data session s is added intothe updated table Tnew (lines 2-3). After that, the algorithmmaps s into SDN data plane paths. This occurs by triggeringthe SDN controller to send OFPT_GROUP_MOD messages tobranching points, carrying replication actions towards eachr ∈ R (line 9). Finally, the algorithm returns the updated IoTmultiuser session control Tnew (line 10).

Algorithm 1: IOT MULTIUSER CLOUD SESSION CON-TROL ALGORITHMInput: Multiuser session table T ; user request reqOutput: Updated, enhanced multiuser session table Tnew

1 if T = φ then2 s← (1, cs, d,R ∪ req.srcIP );3 update Tnew = T ∪ s;4 else5 if ∃s | s.cs = req.cs then6 update s← (id, cs, d,R ∪ req.srcIP );7 else8 s← (id+ 1, cs, d,R ∪ req.srcIP );

9 call SDN controller to install replication flow rules intothe SDN branching nodes towards s.R;

10 return Tnew;

B. Session Refreshing

When a request is received and the IoT multiuser sessionset S is not empty, the algorithm compares, in each s ∈ S,

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whether the application’s interests match the same data carriedby s or not (line 5). If so, it updates s by allocating theapplication (line 6). On the contrary, a new session s isincluded into S (lines 7-8). After that, the Session Managertriggers the SDN controller to install the replication entriesat branching points (line 9), hence providing the mapping ofthe multiuser data session into best-connected data paths. Thealgorithm ends after returning the updated session (line 10).

C. Session Releasing

The Session Manager releases sessions from S in responseto application’s requests. It searches into S all existing IoTmultiuser data sessions matching the request for closing thesession. If it finds a session s in which the application isattached, the Session Manager updates the session in S byremoving the application, and invokes the SDN controller tosend OFPT_GROUP_MOD messages to all branching pointsinvolved in the data path towards the application’s host. Thisavoids keeping out-of-date flow entries into TCAM memoriesand enhancing resource usage.

V. PERFORMANCE EVALUATION

Our evaluation model is set in a real SDN testbed composedof 4 L2/L3 OpenFlow-capable switches connected throughIEEE 802.3 Ethernet LAN cables, as modeled in Fig. 3. EachEthernet link features 10 Mbps of bandwidth capacity. Cloudclient applications (1 to 10) run in different hosts, which canbe seen as different applications connected over the Internetto the cloud-SDN environment.

Fig. 3. Testbed that features the evaluation model

The Session Manager of the CLASSICO architecture isimplemented as a module of the Floodlight SDN controller.Hence, it is able to invoke Northbound/OpenFlow APIs andafford enforcing replication rules through OFPT_GROUP_MODmessages into branching points. In order to assess CLASSICOfeatures integrated to an open source IoT platform, the widelyused FIWARE platform is adopted. We highlight that CLAS-SICO is as a generic architecture, as portability to anotherIoT platform is a matter of open API support. Our applicationscenario considers the emulation of video sessions to denotehigh quality-sensitive and resource-demanding case. A cloud

multimedia server is set in compliance to the Kurento MediaServer, a FIWARE GE that implements an abstraction layer toafford video streaming to interested clients.

To carry out QoE assessment (described in Subsection V-B),we consider that a Video Server transmits video streaming todifferent cloud Client applications. We set 10 video sessionsfrom the cloud video server to multiple videoconferencingapplications. With the prospect to verify how CLASSICOis able to improve the communication system performancethrough multiuser session channels, we setup experimentsaccording to a varying sharing-interest ratio, namely 10%,20% and 30%. On the basis that the scenario embodies 10total sessions, 1 client application is connected to the a givensession when the sharing-interest ratio is of 10%, 2 clientapplications are connected to the same session when thesharing-interest ratio is of 20% and 3 for 30%. We fixed 30%for the maximum amount of sharing-interest ratio in the setof experiments because amounts beyond this exhibit exactlysame results (100% video quality).

The video distribution is controlled by the Evalvid toolusing RTP/UDP. The video media has the following charac-teristics: format MPEG-4, file size 1.86MB, (high) resolution1920x1080, frame rate 25fps and bitrate 1024Kbps. We usethe Full Reference (FR) video evaluation model, in which twoinputs files (the original and the received videos) are usedto calculate the QoE metrics. The input files are processedby the Video Quality Measurement Tool (VQMT). We setUDP background traffic using the Iperf tool to featuring over-saturated SDN links resource patterns. Thus, summing up boththe background traffic with the video session rates allows for110% of the SDN bandwidth capacity. Such offered load rateallows to accomplish the evaluation sets.

A. Benchmarking of QoS Networking Behavior

In order to assess the quality of the delivered video stream-ing, we take the QoS network perspective into account. Sincevideo is a delay-sensitive media, we evaluate both CLASSICOand the typical cloud-SDN solution using the end-to-end delayand jitter along the evaluation time, following the recommen-dations in [19]. The End-to-End Delay is the time a packettakes to traverse the network from the server to the client. TheJitter refers to variance of the end-to-end delay that occurs inthe network links.

In Fig. 4, we note that CLASSICO offers better results ondelay independently on the percentage of client applications(i.e., 10%, 20% or 30%) connected to a same multiusersession. The average end-to-end delay of the typical solutionremains about 115ms. The CLASSICO average delay is on63ms when 10% of the client applications share the samesession. When the percentage is 20%, the average end-to-endis 90ms. The better scenario for CLASSICO is when 30%of client applications are connected to a same video session(10ms on average delay), being 91% better than the typicalsolution. This occurs because the multiuser session mapped tooptimized SDN paths eliminates redundant traffic and reduceslinks congestion.

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Fig. 4. End-to-End Delay during the evaluation time

Fig. 5 shows the results of jitter along the evaluation time.The jitter yielded by CLASSICO is better that the typicalsolution for all percentages of client applications sharingthe same multiuser video session. On the other hand, thejitter in a typical system is about 110ms on average. Theimprovements in jitter of CLASSICO when compared to thetypical approach is about 90% when a percentage of 30% ofclient applications shares a common session. The reason forthe improved behavior in jitter by CLASSICO is due to itshigher residual SDN links bandwidth, which contributes to anenhanced network performance.

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Fig. 5. Jitter behavior along the evaluation time

B. Benchmarking of QoE Behavior

Given the cross-layer nature of CLASSICO, we providea QoE analysis to assess users perception considering twokey QoE metrics for video streaming quality in the user’sperception based on the ITU-T recommendations [20]. TheStructural Similarity Metric (SSIM) index is a milestone mea-surement of structural similarity between the original and thereproduced video. The SSIM is a value between [0..1]. WhenSSIM reaches 1, the reproduced video corresponds exactlyto the original video, whilst 0 stands for zero correlation.The Video Quality Metric (VQM) considers frames distortion(comparing brightness, contrast, pixels, etc.) to assess video

quality. The best possible VQM score is 0, meaning maximumuser satisfaction, and in real situations it can reach about 12.

Fig. 6 sketches the SSIM behavior during the simulationtime. The x-axis shows the frame numbers of the video, whilethe y-axis exposes the SSIM value. We observe that the SDNreaches its steady state after the frame number 13 becauseof the OpenFlow signaling process started by the Floodlightcontroller in the beginning of the video traffic.

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Fig. 6. SSIM behavior along the evaluation time in CLASSICO and TypicalCloud SDN set of experiments

The SSIM results show that CLASSICO outperforms thetypical cloud-SDN based solution, keeping a higher correlationbetween the original video and the received files by theclients. The typical cloud-SDN system allows an SSIM of0.9317 on average. On the other hand, the SSIM obtained bythe CLASSICO gets better as the percentage of applicationssharing multiuser session increases. The average SSIM ofCLASSICO is about 0.9355, 0.9517 and 1, when 10%, 20%and 30% of the clients share the same session, respectively,being better than the typical approach in all scenarios. Further,CLASSICO offers the highest correlation (SSIM is 1) when30% of clients are grouped in a multiuser session, hencealleviating traffic in the SDN and allowing better bandwidthusage.

VQM is a relevant metric to assess the video qualitybased on human eye perception. In Fig. 7, the results of theVQM value (in the y-axis) considering the frame numberstransmitted (in the x-axis) are shown.

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Fig. 7. VQM behavior along the evaluation time in CLASSICO and TypicalCloud SDN set of experiments

We note that the VQM yielded by CLASSICO is betterthan the typical scheme during the most part of the evaluationtime. The average VQM obtained by the Typical Cloud-SDNoffers an average value on 7.4192. Differently, the VQMof CLASSICO is better when more clients are grouped.The average VQM obtained by CLASSICO is about 6.8380,5.3570 and 0, respectively, when 10%, 20% and 30% of the

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clients are involved in a common multiuser session. Aside,CLASSICO offers the best quality level (VQM is 0) for 30%of clients sharing a session.

1) Human perception: In order to achieve a quality per-spective on the results of SSIM and VQM, we selected arandom frame to expose how the client users perceive thevideo content in both scenarios, CLASSICO and typical cloud-SDN system. Fig. 8 shows the same frame (number 281) withCLASSICO and the typical system, respectively.

(a) With CLASSICO (b) With Typical Cloud-SDN

Fig. 8. Frame 281 obtained in both set of experiments

The impact of multiple packet transmission over the SDNlinks (by the typical solution) is clearly noticeable. This is dueto increased packet losses in the links during multiple redun-dant flows, knowing that the quality degradation increases withhigher packet losses [21]. On the contrary, it is evident that theper-multiuser session control along with OpenFlow-enabledpacket replication at branching points enable quality transportdelivery through bandwidth-constrained channels, even underthe presence of multiple applications sharing access to samecontent. This visualization also confirms results on SSIM andVQM previously analyzed.

VI. CONCLUSION

This paper proposed CLASSICO, an SDN-enabled cross-layer admission control that exploits SDN substrate to offloadthe flow streaming computation from the IoT cloud platform tothe SDN edge, hence affording high timeliness and scalabilityfor the IoT-cloudified system. CLASSICO dynamically buildsApplication Layer multiuser sessions and maps them intoenhanced group-enabled SDN paths featuring replication atbranching points. The results of the simulation show thatCLASSICO outperforms typical cloud-SDN multimedia sys-tems with improved QoS and QoE. From the QoS networkpoint of view, CLASSICO improves end-to-end delay (by90%) and jitter (about 91%) in comparison to the typicalapproach. In terms of QoE (SSIM and VQM metrics), CLAS-SICO reaches the best quality level (i.e., SSIM equal to 1 andVQM equal to 0) when 30% of client applications share thesame multimedia content (hence the same multiuser session).These results indicate that CLASSICO can support systemsto improve network core bandwidth usage, while providing abetter multimedia transport service. The outcome of this workis enabling CLASSICO to select best SDN paths based onperformance criteria, such as bandwidth and latency, relevantto multimedia applications.

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