Acino: Application-Centric Routing And Resource Allocation Algorithms For Online Operation
Creators: name: Klonidis Dimitrios
name: Rozic Ciril
name: Matrakidis Chris
name: Tomkos Ioannis
name: Savi Marco
name: Siracusa Siracusa
Publisher: name: Zenodo
Year: 2017
Subjects: IP
optical
orcherstrator
networks
transport networks
open source
Software-Defined Networks
ACINO
H2020
optimization
Application-centric
resource allocation
online operation

description: Τhe outcomes of the design, development and evaluation of the ACINO application-aware resource allocation and optimization framework are summarized and reported, including also the developed algorithms that apply to both the IP and the optical network layers. The developed framework accommodates the application requirements of each demand in a dynamic way (i.e. online), as such demands (i.e. connection requests) arrive and depart at different times. This work builds on the allocation strategies and application aware polices presented in Deliverable <strong>Cross layer planning and optimization strategies</strong>, which are extended and refined.

The set of possible service requirements that accompany the service demands are first identified. These are: bandwidth, latency, availability, protection, and optical channel sharing (security). A network equipped with the ACINO algorithm must be able to satisfy all service requirements for each demand.

To satisfy the requests, an algorithmic framework has been developed and extensively tested on a reference network with simulations. The framework consists of three modules: IPP (IP layer provisioning), IP-OPT (IP layer optimization) and OPP (optical layer provisioning). IPP tries to accommodate the connection request by finding a path through the network only in the IP layer. IP-OPT moves existing IP connections to accommodate new requests, minimize the number of active IP equipment to save energy, and balance the IP link load. OPP sets up new optical connections. The framework thus calls IPP, IP-OPT, and OPP in a particular sequence to accommodate as many requests as possible, while minimizing energy consumption. The modules are implemented as Java code that runs as an algorithm for the Net2Plan open-<br>
source network simulator. The code can be run in a graphical user interface or in command line for batch runs.

On top of this framework, the ACINO algorithm was developed, which tries to satisfy all service<br>
requirements by allocating the appropriate amount of resources in IP and Optical domains. To investigate the effectiveness of the ACINO algorithm and the related framework, benchmark algorithms have been defined, in which the application awareness or/and the optimization process (implemented by IP-OPT) are deactivated. All benchmark algorithms still include optimal multi-layer allocation, same as the ACINO algorithm. The test scenarios involve an increasing number of traffic classes, each class having a distinctive set of requirements. The percentage of traffic classes and their service requirements are varied to get an insight into their impact on the algorithm performance. The performance is measured in several comparable quantities: the amount of blocked connection requests, the amount of requests whose requirements are not satisfied (referred to as SLA (Service-Level Agreement) violations), and the amount of network resources measured as the number of IP links. It is noted that in the case of ACINO, blocking can occur due to a lack of network resources to satisfy one or more service requirements (including the bandwidth), while in the benchmark application-unaware algorithms blocking is caused only by lack of bandwidth. Also, SLA violations are for established connections which do not satisfy the requirements. ACINO may thus have blocking but no SLA violations, while the application unaware benchmark algorithms may have both.

The numerical results demonstrate the advantage of the ACINO algorithm over the benchmark algorithms. ACINO serves successfully all the service requirements, achieving no SLA violations and, in most scenarios, very small blocking, while requiring almost the same number of network resources compared to the application unaware benchmark case. In the scenarios where ACINO shows some blocking (due mainly to inability to find resources that satisfy the service requirements), this blocking is found to be much lower compared to the sum of blocking and SLA violations in the application-unaware case. Therefore, compared to the application-unaware case, ACINO can provide more service level guarantees. In addition, this<br>
advantage of ACINO is achieved with the use of almost the same network resources. However, it is seen that the ACINO network requires setting up and tearing down optical connections more often, which in turn warrants an efficient ACINO controller. In addition, the role of IP-OPT optimization has been tested on both the ACINO and the application unaware network, showing significant saving in terms of network resources in both cases.

Finally, the software tool developed for the dynamic simulation is demonstrated in network planning scenarios. Here, the same framework and its software implementation are used to predict the impact of a hypothetical connection request and the effects of network component failures. The network operator can thus use the tool to prepare the network for future expected or unexpected events.