Abstract

Contention is the main problem in optical burst switching (OBS) systems that lead to loss more burst by dropping the contended burst. To deal with this problem, we propose a new proactive method which is based on the integration of Real-Time Scheduling Techniques: Earliest-deadline-first (EDF) and round-robin (RR). Based on this consideration, this paper proposes a series of novel coherent interdependence methods to propose a novel OBS edge node Architecture: (i) An adaptive priority system to obtain QoS guarantees, (ii) Admission control and bandwidth distribution to maximize the link throughput. The proposed technique classifies the incoming data packets into two categories: granted service (GS) for high priority and best effort (BE) for low priority. The RR technique is used for scheduling different BE and GS packets when they have the same deadlines while the EDF is used to manage GS packets with different deadlines. In addition, the admission and bandwidth assignment policies vary in function of traffic characteristics. For GS traffic, only the bandwidth used is needed, while the remaining bandwidth is assigned to best effort traffic. With the EDF and RR scheduling algorithms, the proposed edge node design obtains a brand new architecture. The proposed OBS architecture has been tested for dynamic traffic in which both BE and GS traffic arrives according to a Poisson distribution for different scenarios based on traffic distribution and switch resources reservation. In scenario#1, 90% GS traffic and 10% BE traffic and reserve 7% of the switch resources for BE traffic are considered. Therefore, the proposed architecture provides a higher grade of service to the end-users with the guaranteed service level of agreements than the existing architecture that does not implement resource reservation protocols. In addition, the modified OBS-edge node is designed and implemented on FPGA Virtex-11C2V40. The hardware performance analysis shows that the network dimension is a key factor in latency measurement. A 2×2 network dimension can serve approximately 230 Mbps throughput and approximately 400 Mbps in the case when the number of flits (segment of burst) is twice.

Highlights

  • Optical networks have gained significant attention since their inception due to the availability of massive bandwidth in THz [1]

  • Contention in the optical burst switching (OBS) system occurs if multiple bursts at the same time from different input ports are destined for the same output port; the contention in traditional electronic packet switching networks is handled through buffering

  • In conventional OBS edge node, the incoming packets are divided according to the arrival times and forwarded to the OBS assembler unit directly without considering the priority of incoming, which leads to the possibility of losing the packets in the output port

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Summary

INTRODUCTION

Optical networks have gained significant attention since their inception due to the availability of massive bandwidth in THz [1]. In OPS networks, the packets are independently routed via switches through the network in the optical domain [2]. In an OBS network, packets are assembled into a larger frame called data bursts (DB). In comparison with OPS network, very less processing operations per packet are required in an OBS network core optical router. The energy consumption and the carbon footprint of a core optical router in an OPS network are likely to be larger than that of an OBS network router for the same amount of data [5]. A burst disassembly unit in the egress unit recovers IP packets from the incoming data burst frame. Contention in the OBS system occurs if multiple bursts at the same time from different input ports are destined for the same output port; the contention in traditional electronic packet switching networks is handled through buffering.

Contention Resolution
OBS-EDGE NODE CONVENTIONAL ARCHITECTURE
DESIGN AND IMPLEMENTATION
Queues virtual channels
Time configuration Controller
POISSON DISTRIBUTION ANALYSIS
HARDWARE PERFORMANCE ANALYSIS
Performance Analysis: latency
Performance Analysis
Findings
VIII. CONCLUSION

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