Joint power-efficient traffic shaping and service provisioning for metro elastic optical networks

Abstract

Considering the time-averaged behavior of a metro elastic optical network, we develop a joint procedure for resource allocation and traffic shaping to exploit the inherent service diversity among the requests for a power-efficient network operation. To support quality of service diversity, we consider minimum transmission rate, average transmission rate, maximum burst size, and average transmission delay as the adjustable parameters of a general service profile. The work evolves from a stochastic optimization problem, which minimizes the power consumption subject to stability, physical, and service constraints. The optimal solution of the problem is obtained using a complex dynamic programming method. To provide a near-optimal fast-achievable solution, we propose a sequential heuristic with a scalable and causal software implementation, according to the basic Lyapunov iterations of an integer linear program. The heuristic method has a negligible optimality gap and a considerable shorter runtime compared to the optimal dynamic programming, and reduces the consumed power by 72% for an offered traffic with unit variation coefficient. The adjustable trade-offs of the proposed scheme offer a typical 10% power saving for an acceptable amount of excess transmission delay or drop rate.

Iterative Configuration in Elastic Optical Networks : (Invited Paper)

“…, the performance of (1) is not mathematically supported. However, even in more relaxed scenarios, the proposed general scheme yields acceptable results, as illustrated in [5] and [9], where we have employed the scheme for special cases of power-and spectrum-efficient QoS-aware resource allocation in EONs, respectively. To further show the generality and applicability of the scheme, we show in the next section how bandwidth limitations can also be considered.…”

“…According to (3a), T W C k b i,k [n] and d i [n] bits are transmitted or dropped, respectively, from the feeding queue of the transmit transponder, where b i,k [n] is the number of assigned frequency slots to connection i having modulation k with spectral efficiency C k . The transponders of connection i with modulation k consumes the power b i,k [n](E + F C k ), where E and F are constant [5]. The objective function f 0 [n] in (3b) is a mixture of the transponder power consumption and number of dropped bits with a weighting coefficient V to adjust optimization priority.…”

“…To properly support these features and exploit the fast controllability of the SDN for iterative reconfiguration of the flexible network elements in an EON, a dynamic resource allocation scheme is required. Such a scheme is usually formulated as a large-scale optimization problem, whose optimal solution imposes a huge computational complexity [5].…”

“…On the other hand, online resource allocation methods provide a better resource utilization, but can lead to severe spectrum fragmentation and consequently, service blockage [6], [7]. In iterative configuration, we involve both the average and instantaneous network behavior in the resource allocation to better handle unexpected conditions and avoid unnecessary reconfigurations that fragment the spectrum [5].…”

“…Stochastic iterative optimization is a powerful tool for iterative reconfiguration in EONs, which allows to simultaneously target average network behavior, handle instantaneous unexpected conditions, avoid inessential reconfigurations, and consider various physical constraints [5]. Inspired by the Lyapunov technique [8] as a main iterative optimization method, a general scheme for iterative configuration in an SDN-based EON is introduced.…”