1. Introduction

High-end and critical applications such as scientific visualization, medical imaging, and industrial manufacturing, are increasingly dependent on digital media to enable comprehensive analytical studies. The emergence of Ultra High Definition (UHD) formats with up to four (4K) and sixteen (8K) times the number of pixels as High Definition (HD) will further increase the role and importance of digital media in such applications [1]. UHD formats offer extremely high quality visual effects to high-end applications with greater impression of reality and better sense of presence for end-users. However, the deployment of high-end UHD media applications is not so straightforward. Huge amounts of bandwidth are required for the distribution of UHD media content. For example, 4K and 8K have bitrates of 250-500Mb/s for a compressed stream and 7.2-72Gb/s for an uncompressed stream [1].

Therefore, the deployment of networked media infrastructures for such applications requires optical transport technologies capable of providing high-capacity connectivity with low latency and deterministic Quality-of-Service (QoS). These infrastructures should expose functionalities for dynamic discovery and jointly optimized allocation of media and network infrastructure resources. In addition, they must be able to intuitively identify and deliver the appropriate media service e.g. archiving, rendering, transcoding, and high resolution visualization, tailored to end-user capabilities. The concept of a Media Ecosystem (ME) has been proposed as a means to enable interaction and flexible cooperation between the actors in a networked media infrastructure i.e. end-users, media service providers, media and network infrastructure owners [2]. The ME aims to enable application environments that are media- and network-aware, environments where resource-awareness can be leveraged to provision adaptable context-aware services to ensure high Quality-of-Experience (QoE) is delivered to end-users. A major challenge and an active research area in the implementation and deployment of MEs is the mechanism to enable dynamic and coordinated provisioning of media and network infrastructure resources.

We propose a novel ME architecture for deploying high performance networked media infrastructures over metro and core networks to support high-end UHD media applications. Central to this architecture is a novel Networked Media Controller (NMC) capable of coordinated provisioning of media and network infrastructure resources. The proposed architecture is based on the Software-Defined Networking (SDN) paradigm [3]; in particular the OpenFlow-based SDN approach [4]. The OpenFlow-based SDN approach adopts flow based traffic switching, retains data plane functionalities in network elements, and moves all network control functionalities to centrally managed or distributed controllers i.e. OpenFlow Controller (OFC). In an OpenFlow deployment, the network is managed by a network-wide operating system which is dynamically programmable via software. The operating system runs on top of the OFC and controls the data plane using the OpenFlow protocol.

In this paper, we propose and experimentally evaluate an OpenFlow-enabled test-bed demonstrator of the NMC with implementations of online and offline media- and network-aware schedulers for UHD media applications. To the authors’ best knowledge, no previous work in literature has addressed an SDN-based ME for high-end UHD applications.

2. SDN-based ME architecture

The proposed architecture for deploying an SDN-based ME for UHD media applications is depicted in Fig. 1(a) . It comprises the media and network infrastructure resources which are controlled by the NMC. The schematic diagram of the NMC is depicted in Fig. 1(b). This builds on our earlier work which was presented in [5]. The NMC provides the ME with media- and network-awareness, intelligent context-aware service provisioning, and coordinated provisioning of media and network infrastructure resources. The NMC exposes an overlay Web Services Interface to enable service providers and infrastructure owners publish information about available media services, processing tools, and contents. To support different application domains, the NMC exposes a unified description mechanism to service providers and infrastructure owners. This description mechanism utilizes a semantic ontology based on the Resource Description Framework (RDF) [6]. Semantics provide formal and stringent means for representing information, this enables easier and more intuitive decision making by software programs. By defining application-specific classes and associated properties, the NMC allows service providers and infrastructure owners to represent any type of processing tool, content, format, and media service. Services providers and infrastructure owners can leverage RDF subject-predicate-object triplets to publish comprehensive descriptions of services and resources (subjects), their properties (predicates), and values of these properties (objects) to the NMC’s Registry.

 

Fig. 1 (a) Proposed Media Ecosystem Architecture. (b) Schematic diagram of the Networked Media Controller.

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The Request Handler performs semantic reasoning on the descriptions published to the Registry, it considers end-user requests including end-user contexts to identify the appropriate media processing tools and content (if required). The Workflow Generation Engine is used to create execution schedules to deliver media services which require dynamic composition and integration of multiple media infrastructure resources.

OFCs such as NOX [7] can support control and management modules that discover the underlying network infrastructure. The NMC uses these modules to obtain status information about the network. The Path Computation Engine (PCE) leverages this status information to determine the existence of network connectivity with sufficient capabilities to ensure high QoS and QoE for media services delivered by the ME. If media and network infrastructure resources exist to provision end-users’ requests, the Resource Management Interface co-allocates the chosen media and network infrastructure resources. For network setup, the NMC uses a Standard OpenFlow Agent for packet switched domains and an Extended Optical OpenFlow Agent for circuit switched optical domains. The Extended Optical OpenFlow Agent is based on novel extensions to enable OpenFlow control and manage circuit switched optical networks [8]. Specifically, extensions to the OpenFlow switch features request/reply messages to enable OpenFlow discover optical nodes in the data plane, the CFLOW_MOD i.e. flow specification message was implemented for setup of cross-connections in the optical domain, and optical switching constraint considerations were introduced in the OFC. These agents are used to proactively push OpenFlow flow-rules to the network elements in the data plane to switch traffic on high QoS paths selected by the PCE. To reserve media infrastructure resources, the NMC uses appropriate Media Resource Agents.

3. Experimental demonstration, evaluations and results

The experimental setup depicted in Fig. 2 . was used to evaluate the deployment of our proposed architecture over optical metro and core networks. It comprises an OpenFlow-enabled circuit switched optical domain which has 10Gb/s links, interconnecting two OpenFlow-enabled L2 packet switched domains which have 1Gb/s links. The circuit switched optical domain comprises three commercial ADVA Reconfigurable Optical Add-Drop Multiplexers (ROADMs). One of the packet switched domains interconnecting end-users to the test-bed comprises three NEC IP8800 switches while the other packet switched domain interconnecting a UHD streaming servers to the test-bed comprises one NEC IP8800 switch. The NMC was implemented over the extended NOX controller presented in [8]. The descriptions of 500 media services and infrastructure resources were published to the Registry to emulate a typical real world scenario for high-end UHD media applications.

 

Fig. 2 Test-bed deployment over circuit switched optical network.

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For media and network resource reservations, we experimentally evaluated advanced reservation online (AROn) and offline (AROff) media- and network-aware scheduling algorithms. The AROn scheduler was evaluated for scenarios where end-users’ requests are submitted in advance, in a first-come-first-serve (FCFS) order to the NMC. The operation of the AROn scheduler is as follows:

The BaSPF algorithm modifies the traditional SPF algorithm such that the best path is the shortest path which has sufficient bandwidth for requested UHD format. The AROff scheduler was evaluated for scenarios where all end-user requests are submitted in advance as a single request set. The AROff scheduler is implemented using the Non-adaptive Media and Network-aware (NMNA) algorithm presented in our earlier work [9]. This algorithm is modeled using Integer Linear Programming (ILP) with the objective given in Eq. (1), to maximize the total number of accepted requests; where${\alpha }_j$is a binary acceptance variable for end-users’ requests. The key constraints enforced by the NMNA algorithm include: (i) provisioning an end-user’s request using only a single media infrastructure resource from the set of capable media infrastructure resources; (ii) ensuring the capacity on all reserved links is never exceeded when provisioning end-users’ requests; (iii) traffic flow constraints to ensure traffic traverses the data plane in the right direction; and (iv) loop mitigation constraints to prevent switching loops between network infrastructure resources in the data plane.

The AROn and AROff schedulers were evaluated with end-users requesting for the delivery of UHD (2K and 4K) images encoded at different rates. A request set was randomly generated, and each request specified start and end timeslots with each timeslot set to 120 seconds. Figure 3 . depicts the workflow and service setup times for AROn and AROff schedulers.

 

Fig. 3 Timing results for circuit switched optical network test-bed. (a) AROn scheduler results. (b) AROff scheduler results.

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The AROff scheduler has longer setup timings than the AROn scheduler because it processes the complete end-user request set concurrently while AROn operates on an FCFS basis. For the AROn and AROff schedulers, it took an average of 1551ms and 1329ms respectively to define and push OpenFlow flow-rules to the data plane. For both schedulers, the NMC took an average of less than 17000ms to deliver the first media stream packet to end-users. This included end-to-end delay but was mainly media server processing delay. Table 1 shows the complete end-users’ requests set, and indicates if a request was accepted by the different schedulers. The AROff scheduler accepted more requests i.e.75% than the AROn scheduler which accepted only 38%. The AROff scheduler was able to optimize resource reservation due to its knowledge of the complete end-users’ requests set. However, the objective to maximize the total number of accepted requests causes the AROff scheduler to prioritize the acceptance of a large number of relatively lower bitrate requests.

Table 1. End-User Request Profile for Circuit Switched Optical Network Test-bed

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To evaluate the importance of the interworking relationships promoted by the NMC, the experimental setup depicted in Fig. 4 . was deployed. This OpenFlow-enabled packet switched network comprises five Open vSwitches. The NMC was implemented over the OFC tool in the Open vSwitch framework. Experimental evaluations used the AROn scheduler to compare the performance of a media- and network-aware NMC with a network-unaware NMC. To implement the network-unaware NMC, the interfaces allowing interaction with network modules in the NMC were disabled. In this case, there were no QoS-aware path computations and the data plane used standard MAC learning based switching. End-users simultaneously requested for the delivery of HD images encoded at different bitrates in two scenarios. For the first evaluation scenario, end-users 1 and 2 requested for 50Mb/s and end-user 3 requested for 100 Mb/s. For the second evaluation scenario, end-users 1, 2, and 3 requested for 100Mb/s. The characteristics (percentage of packets lost) of the networked media services delivered by both implementations of the NMC were observed, and are depicted in Fig. 5 .

 

Fig. 4 Test-bed deployment over packet switched network

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Fig. 5 Percentage of Packets Lost. (a) Scenario 1 – 53% network utilization. (b) Scenario 2 – 83% network utilization

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In both scenarios, the complete NMC i.e. with media- and network-awareness outperformed its network-unaware counterpart. The network-unaware NMC was able to identify and reserve the appropriate media infrastructure resources but without an interface to the network, the services delivered experienced very high packet loss. With the network-unaware NMC, all services were delivered along the SW1-SW3-SW5 path because the NMC identified this to be the best path as it had the shortest Time-To-Live (TTL) measurement. For both scenarios, the media- and network-aware NMC delivered adequate QoE to end-users as the interworking relationships present allowed it to not only identify and reserve the appropriate media infrastructure resources, but to also compute high QoS paths in the network. These evaluations highlight the importance of having both media- and network-awareness present.

4. Conclusion

This paper proposed a novel SDN-based architecture for deploying future high-end UHD applications. A novel NMC was proposed to leverage the ability of SDN to tightly integrate application-awareness with network control. Experimental evaluations using an OpenFlow-enabled test-bed demonstrator showed it is possible to deploy the proposed architecture over circuit switched optical networks. Furthermore, results obtained highlighted the importance of coupling media- and network-awareness within a unified control framework to enable the delivery of high QoS and QoE services to end-users. The proposed architecture can provide the foundation for development of collaborative high-end UHD media applications.