An Adaptive RIO (A-RIO) Queue Management Algorithm

Orozco, Julio; Ros, David

doi:10.1007/978-3-540-45188-4_2

Cited by 13 publications

(4 citation statements)

References 7 publications

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“…Therefore, for maximum utilization of link and fair distribution of traffic, we adopt a concept from A-RIO [43] and calculate min th = max (td*l/2, 5). After binding and finding non-self antigen, the IS activates the lymphocytes and eradicates the antigens that are associated with the pathogen.…”

Section: Immune Based Congestion Detectionmentioning

confidence: 99%

“…To utilize the capacity of link efficiently and distribute traffic fairly, min th must adjust in a resourceful way. Therefore, for maximum utilization of link and fair distribution of traffic, we adopt a concept from A-RIO [43] and calculate min th = max (td*l/2, 5). Here, td shows the target delay, and l illustrates the capacity of link in packets per second.…”

Section: Ql ¼mentioning

confidence: 99%

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Bio‐inspired packet dropping for ad‐hoc social networks

Liaqat

Xia

Yang

et al. 2014

Int J Communication

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Ad-hoc social networks (ASNETs) explore social properties of nodes in communications. The usage of various social applications in a resource-scarce environment and the dynamic nature of the network create unnecessary congestion that might degrade the quality of service dramatically. Traditional approaches use drop-tail or random-early discard techniques to drop data packets from the intermediate node queue. Nonetheless, because of the unavailability of the social properties, these techniques are not suitable for ASNETs. In this paper, we propose a Bio-inspired packet dropping (BPD) algorithm for ASNETS. BPD imitates the matching procedure of receptors and epitopes in immune systems to detect congestions. The drop probability settings depend on the selection of data packets, which is based on node popularity level. BPD selects the most prioritized node through social properties, which is inspired by the B-cell stimulation in immune systems. To fairly prioritize data packets, two social properties are used: (1) similarity and (2) closeness centrality between nodes. Extensive simulations are carried out to evaluate and compare BPD to other existing schemes in terms of mean goodput, mean loss rate, throughput, delay, attained bandwidth, and overhead ratio. The results show that the proposed scheme outperforms these existing schemes.(QoS) and overhead cost. Therefore, to overcome scheduling problems, the consideration of socially aware properties provide high advantages in minimum QoS with less overhead cost [9]. Moreover, another reason for congestion in intermediate node 7 is the unavailability of receivers due to node mobility. Therefore, to avoid congestion-related losses in ASNETs, an accurate active queue management (AQM) policy is required [10]. The systemic constraints, such as poor link utilization and data packet drop rates in networks, result in delay queuing processes with increment of congested intermediate node. However, to solve the issue, some conflicts arise. In trying to reduce queuing delay, the size of buffer should be reduced, whereas it leads to low utilization of link at the same time, resulting in a high packet loss rate. To have an efficient queue management, accurate detection and dropping of targeted data packets are necessary because queue management strongly depends on dropping policies.Additionally, due to social behaviors of nodes in ASNET environments, dropping probabilities also depend on social properties of nodes. Primarily, there are multiple AQM schemes available to identify prioritization of data packets for dropping [11][12][13]. The non-prioritized data packets are dropped in an intermediate node due to less availability of the next node and resource constraint characteristics of nodes. Data packet dropping is broadly used to overcome issues present in the intermediate node, and it provides availability of prioritized data packets. However, every data packet dropping in an intermediate node is a kind of resource consumption. According to the concept of ASNET, dropped data packet...

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Section: Immune Based Congestion Detectionmentioning

confidence: 99%

Section: Ql ¼mentioning

confidence: 99%

Bio‐inspired packet dropping for ad‐hoc social networks

Liaqat

Xia

Yang

et al. 2014

Int J Communication

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show abstract

“…The combination of the Adaptive RED (A-RED) algorithm [35] and the RIO-C algorithm [42], suitable for building an Assured Forwarding (AF) per-hop behaviour (PHB), was proposed in [107] for solving the following tasks: the simplification of the configuration of DiffServ-enabled routers by alleviating the parameter settings problem; the automatic translation a delay parameter into a set of router parameters, the stabilization of the queue occupation around a target value under heavy network load, irrespective of traffic profile. The further study of the proposed algorithm was carried out in [108].…”

mentioning

confidence: 99%

“…In [115,116] two versions of Adaptive RIO algorithm [107] were presented: ARIO-D (Adaptive RIO for Delay) and ARIO-L (Adaptive RIO for Loss). The first algorithm (ARIO-D) takes specific average queue length range as control target, keeps Q max as constant and adaptively adjusts p max for outpackets to meet the expected steady state.…”

mentioning

confidence: 99%