Efficient resource allocation for all-optical multicasting over spectrum-sliced elastic optical networks
01 Aug 2013-IEEE\/OSA Journal of Optical Communications and Networking (Optical Society of America)-Vol. 5, Iss: 8, pp 836-847
TL;DR: This paper investigates how to serve multicast requests over EONs with multicast-capable routing, modulation level, and spectrum assignment (RMSA), and proposes a highly efficient heuristic that is based on an adaptive genetic algorithm (GA) with minimum solution revisits.
Abstract: Recently, optical orthogonal frequency-division multiplexing technology has attracted intensive research interest because spectrum-sliced elastic optical networks (EONs) can be constructed based on it. In this paper, we investigate how to serve multicast requests over EONs with multicast-capable routing, modulation level, and spectrum assignment (RMSA). Both EON planning with static multicast traffic and EON provisioning with dynamic traffic are studied. For static EON planning, we formulate two integer linear programming (ILP) models, i.e., the joint ILP and the separate ILP. The joint ILP optimizes all multicast requests together, while the separate ILP optimizes one request each time in a sequential way. We also propose a highly efficient heuristic that is based on an adaptive genetic algorithm (GA) with minimum solution revisits. The simulation results indicate that the ILPs and the GA provide more efficient EON planning than the existing multicast-capable RMSA algorithms that use the shortest path tree (SPT) and the minimal spanning tree (MST). The results also show that the GA obtains more efficient EON planning results than the separate ILP with much less running time, as it can optimize all multicast requests together in a highly efficient manner. For the dynamic EON provisioning, we demonstrate that the GA is also applicable, and it achieves lower request blocking probabilities than the benchmark algorithms using SPTand MST.
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TL;DR: This paper proposes a layered-auxiliary-graph (LAG) approach that decomposes the physical infrastructure into several layered graphs according to the bandwidth requirement of a virtual optical network request, and designs a novel heuristic for opaque VONE, consecutiveness-aware LRC-K shortest-path-first fit (CaL RC-KSP-FF).
Abstract: Based on the concept of infrastructure as a service, optical network virtualization can facilitate the sharing of physical infrastructure among different users and applications. In this paper, we design algorithms for both transparent and opaque virtual optical network embedding (VONE) over flexible-grid elastic optical networks. For transparent VONE, we first formulate an integer linear programming (ILP) model that leverages the all-or-nothing multi-commodity flow in graphs. Then, to consider the continuity and consecutiveness of substrate fiber links' (SFLs') optical spectra, we propose a layered-auxiliary-graph (LAG) approach that decomposes the physical infrastructure into several layered graphs according to the bandwidth requirement of a virtual optical network request. With LAG, we design two heuristic algorithms: one applies LAG to achieve integrated routing and spectrum assignment in link mapping (i.e., local resource capacity (LRC)-layered shortest-path routing LaSP), while the other realizes coordinated node and link mapping using LAG (i.e., layered local resource capacity(LaLRC)-LaSP). The simulation results from three different substrate topologies demonstrate that LaLRC-LaSP achieves better blocking performance than LRC-LaSP and an existing benchmark algorithm. For the opaque VONE, an ILP model is also formulated. We then design a LRC metric that considers the spectrum consecutiveness of SFLs. With this metric, a novel heuristic for opaque VONE, consecutiveness-aware LRC-K shortest-path-first fit (CaLRC-KSP-FF), is proposed. Simulation results show that compared with the existing algorithms, CaLRC-KSP-FF can reduce the request blocking probability significantly.
326 citations
Cites background from "Efficient resource allocation for a..."
...When the transmission distance of a VOL’s substrate lightpath permits, we always select the highest possible modulation level for it for the highest spectral efficiency [23], [24]....
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TL;DR: In this paper, a deep reinforcement learning framework for routing, modulation and spectrum assignment (RMSA) in elastic optical networks (EONs) is proposed, where deep neural networks (DNNs) are trained with experiences of dynamic lightpath provisioning.
Abstract: This paper proposes DeepRMSA, a deep reinforcement learning framework for routing, modulation and spectrum assignment (RMSA) in elastic optical networks (EONs). DeepRMSA learns the correct online RMSA policies by parameterizing the policies with deep neural networks (DNNs) that can sense complex EON states. The DNNs are trained with experiences of dynamic lightpath provisioning. We first modify the asynchronous advantage actor-critic algorithm and present an episode-based training mechanism for DeepRMSA, namely, DeepRMSA-EP. DeepRMSA-EP divides the dynamic provisioning process into multiple episodes (each containing the servicing of a fixed number of lightpath requests) and performs training by the end of each episode. The optimization target of DeepRMSA-EP at each step of servicing a request is to maximize the cumulative reward within the rest of the episode. Thus, we obviate the need for estimating the rewards related to unknown future states. To overcome the instability issue in the training of DeepRMSA-EP due to the oscillations of cumulative rewards, we further propose a window-based flexible training mechanism, i.e., DeepRMSA-FLX. DeepRMSA-FLX attempts to smooth out the oscillations by defining the optimization scope at each step as a sliding window, and ensuring that the cumulative rewards always include rewards from a fixed number of requests. Evaluations with the two sample topologies show that DeepRMSA-FLX can effectively stabilize the training while achieving blocking probability reductions of more than 20.3% and 14.3%, when compared with the baselines.
135 citations
TL;DR: This study reviews and analyzes one of the most important topics in EON namely Routing and Spectrum Allocation (RSA), and compares them through both quality of performance and computational complexity aspects for the first time.
115 citations
Cites background from "Efficient resource allocation for a..."
...Therefore, impairment-aware RSA is essentially a RMSA problem [4]....
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...The objective function of GA is the same as that of the joint RMSA [4]....
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...We categorize both of the RSA and RMSA algorithms into two groups: those that support static traffic and those that support dynamic traffic....
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...Performance evaluation results in [4] indicate that GA can reduce request blocking probability....
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...In this article, we review all available RSA and RMSA algorithms proposed for EON, analyze and compare them through both quality of performance and computational complexity aspects for the first time....
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TL;DR: The simulation results indicate that the proposed algorithms can reuse the deployed VNFs efficiently and arrange the spectrum utilization in a much more load-balanced manner.
Abstract: We study how to allocate spectrum and IT resources jointly for realizing efficient virtual network function (VNF) service chaining in inter-datacenter elastic optical networks. We first formulate an integer linear programming model to solve the problem exactly, and then a longest common subsequence-based heuristic is proposed. The simulation results indicate that the proposed algorithms can reuse the deployed VNFs efficiently and arrange the spectrum utilization in a much more load-balanced manner.
115 citations
Cites background from "Efficient resource allocation for a..."
...1 and the 28-node US Backbone topology in [11]....
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TL;DR: This study studies the provisioning algorithms to realize tree-type virtual network function forwarding graphs (VNF-FGs), i.e., multicast NFV trees (M-NFV-Ts), in inter-DC elastic optical networks (IDC-EONs) cost-effectively and designs two additional online algorithms based on AFM-GS and RB to serve M-NFv-Ts in a dynamic IDC- EON, with the consideration of spectrum fragmentation.
Abstract: It is known that by incorporating network function virtualization (NFV) in inter-datacenter (inter-DC) networks, service providers can use their network resources more efficiently and adaptively and expedite the deployment of new services. This paper studies the provisioning algorithms to realize tree-type virtual network function forwarding graphs (VNF-FGs), i.e., multicast NFV trees (M-NFV-Ts), in inter-DC elastic optical networks (IDC-EONs) cost-effectively. Specifically, we try to optimize the VNF placement and multicast routing and spectrum assignment jointly for orchestrating M-NFV-Ts in an IDC-EON with the lowest cost. Our study addresses both static network planning and dynamic network provisioning. For network planning, we first formulate a mixed integer linear programming (MILP) model to solve the problem exactly, and then propose three heuristic algorithms, namely, auxiliary frequency slot matrix (AFM)-MILP, AFM-GS, and RB. Extensive simulations show that AFM-MILP and AFM-GS can approximate the MILP's performance on low-cost M-NFV-T provisioning with much shorter running time. For network provisioning, we design two additional online algorithms based on AFM-GS and RB to serve M-NFV-Ts in a dynamic IDC-EON, with the consideration of spectrum fragmentation.
111 citations
Cites background from "Efficient resource allocation for a..."
..., the six-node topology in [35], and the NSFNET and US Backbone topologies in [30]....
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"Efficient resource allocation for a..." refers background in this paper
...Previous work has already proposed several integer linear programming (ILP) models and heuristic algorithms to solve the problem of EON planning [7–10]....
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Journal Article•
TL;DR: The model is shown to accurately predict the convergence ra te of a GA using tournament select ion in the onemax domain for a wide range of t ournament sizes and noise levels.
Abstract: Abstr act . Tournament select ion is a useful and rob ust select ion mechanism commonly used by genet ic algorithms (GAs). The selecti on pr essure of to urnament select ion direc tly varies wit h the tournam en t size-the more compe t it ors , t he higher the resulting select ion pr essur e. This pap er develops a model, based on order stat ist ics, that can be used to quantita tively predict th e resul ting select ion pr essure of a tournament of a given size. T his mo del is used to pr edict the convergence ra tes of GAs utili zing tournament selection. While to urnament selection is often used in conjunct ion wit h noisy (imperfect) fitness fun cti ons, lit tl e is understood abo ut how the noise affect s the resul ting select ion pr essur e. The model is extended to quantit atively pred ict t he select ion pressure for tournam ent select ion utili zing noisy fitn ess functions . Given the to urnament size and noise level of a noisy fitness fun ct ion , the exte nded mod el is used to pr ed ict t he resu lt ing select ion pr essure of to urnament select ion . T he accuracy of the mod el is verified using a simple test domain, t he onemax (bit-count ing) domain . T he model is shown to accurately predict t he convergence ra te of a GA using tournament select ion in the onemax domain for a wide range of t ournament sizes and noise levels. T he model develop ed in this paper has a number of immediat e pra cti cal uses as well as a number of longer term rami fica tions. Immediately, t he mod el may be used for determ ining appropria te ra nges of cont rol para meters , for est imat ing stopping times to achieve a spec ified level of solution qua lity , and for approximating convergence t imes in impor tant classes offunction evaluatio ns that utilize sampling . Longer term, the approach of this st udy may be applied to bet ter underst an d
1,005 citations