Scalable Intra and Inter Domain IPv6 QoS Management and Pricing Scheme

Author(s):  
El-Bahlul Fgee ◽  
Shyamala Sivakumar ◽  
William J. Phillips ◽  
William Robertson

Network multimedia applications constitute a large part of Internet traffic and guaranteed delivery of such traffic is a challenge because of their sensitivity to delay, packet loss and higher bandwidth requirement. The need for guaranteed traffic delivery is exacerbated by the increasing delay experienced by traffic propagating through more than one QoS domain. Hence, there is a need for a flexible and a scalable QoS manager that handles and manages the needs of traffic flows throughout multiple IPv6 domains. The IPv6 QoS manager, presented in this paper, uses a combination of the packets’ flow ID and the source address (Domain Global Identifier (DGI)), to process and reserve resources inside an IPv6 domain. To ensure inter-domain QoS management, the QoS domain manager should also communicate with other QoS domains’ managers to ensure that traffic flows are guaranteed delivery. In this scheme, the IPv6 QoS manager handles QoS requests by either processing them locally if the intended destination is located locally or forwards the request to the neighboring domain’s QoS manager. End-to-end QoS is achieved with an integrated admission and management unit. The feasibility of the proposed QoS management scheme is illustrated for both intra- and inter-domain QoS management. The scalability of the QoS management scheme for inter-domain scenarios is illustrated with simulations for traffic flows propagating through two and three domains. Excellent average end-to-end delay results have been achieved when traffic flow propagates through more than one domain. Simulations show that packets belonging to non-conformant flows experience increased delay, and such packets are degraded to lower priority if they exceed their negotiated traffic flow rates. Many pricing schemes have been proposed for QoS-enabled networks. However, integrated pricing and admission control has not been studied in detail. A dynamic pricing model is integrated with the IPv6 QoS manager to study the effects of increasing traffic flows rates on the increased cost of delivering high priority traffic flows. The pricing agent assigns prices dynamically for each traffic flow accepted by the domain manager. Combining the pricing strategy with the QoS manager allows only higher priority traffic packets that are willing to pay more to be processed during congestion. This approach is flexible and scalable as end-to-end pricing is decoupled from packet forwarding and resource reservation decisions. Simulations show that additional revenue is generated as prices change dynamically according to the network congestion status.

2021 ◽  
Vol 16 (2) ◽  
pp. 30-47
Author(s):  
Dovydas Skrodenis ◽  
Donatas Čygas ◽  
Algis Pakalnis ◽  
Andrius Kairys

Planned special events (PSEs) attract more people than usual to specific areas, which leads to increased traffic flows and congestions on the roads. Roadwork zones are among the most vulnerable areas on the roads, where increased traffic can lead to congestion. In roadwork zones, the vehicle flow capacity is already lower than in the conventional situations without roadworks, but at the time of PSEs, these zones become difficult to pass if no attention is paid to the change of the traffic management scheme. This kind of events poses many threats for road authorities, thus, new traffic management systems should be considered. This paper analyzes 2 PSEs and one national celebration in Lithuania and a significant impact they have on the regular traffic flow. PSEs are taken into consideration as they attract traffic to a known place; however, national celebrations distort traffic along all roads and it is not known exactly, which roads will be congested the most. Since roadwork zones cause congestion problems even in conventional situations, this paper presents traffic capacity calculations at these road stretches during PSEs and considers how they change depending on the traffic management scheme.


Author(s):  
Xiaolong Xu ◽  
Zijie Fang ◽  
Lianyong Qi ◽  
Xuyun Zhang ◽  
Qiang He ◽  
...  

The Internet of Vehicles (IoV) connects vehicles, roadside units (RSUs) and other intelligent objects, enabling data sharing among them, thereby improving the efficiency of urban traffic and safety. Currently, collections of multimedia content, generated by multimedia surveillance equipment, vehicles, and so on, are transmitted to edge servers for implementation, because edge computing is a formidable paradigm for accommodating multimedia services with low-latency resource provisioning. However, the uneven or discrete distribution of the traffic flow covered by edge servers negatively affects the service performance (e.g., overload and underload) of edge servers in multimedia IoV systems. Therefore, how to accurately schedule and dynamically reserve proper numbers of resources for multimedia services in edge servers is still challenging. To address this challenge, a traffic flow prediction driven resource reservation method, called TripRes, is developed in this article. Specifically, the city map is divided into different regions, and the edge servers in a region are treated as a “big edge server” to simplify the complex distribution of edge servers. Then, future traffic flows are predicted using the deep spatiotemporal residual network (ST-ResNet), and future traffic flows are used to estimate the amount of multimedia services each region needs to offload to the edge servers. With the number of services to be offloaded in each region, their offloading destinations are determined through latency-sensitive transmission path selection. Finally, the performance of TripRes is evaluated using real-world big data with over 100M multimedia surveillance records from RSUs in Nanjing China.


2018 ◽  
Vol 7 (2.7) ◽  
pp. 203 ◽  
Author(s):  
Kalathiripi Rambabu ◽  
N Venkatram

The phenomenal and continuous growth of diversified IOT (Internet of Things) dependent networks has open for security and connectivity challenges. This is due to the nature of IOT devices, loosely coupled behavior of internetworking, and heterogenic structure of the networks.  These factors are highly vulnerable to traffic flow based DDOS (distributed-denial of services) attacks. The botnets such as “mirae” noticed in recent past exploits the IoT devises and tune them to flood the traffic flow such that the target network exhaust to response to benevolent requests. Hence the contribution of this manuscript proposed a novel learning-based model that learns from the traffic flow features defined to distinguish the DDOS attack prone traffic flows and benevolent traffic flows. The performance analysis was done empirically by using the synthesized traffic flows that are high in volume and source of attacks. The values obtained for statistical metrics are evincing the significance and robustness of the proposed model


2020 ◽  
Vol 26 (3) ◽  
pp. 266-274
Author(s):  
Uttam Kumar Khedlekar ◽  
Priyanka Singh ◽  
Neelesh Gupta

This paper aims to develop a dynamic pricing policy for deteriorating items with price and stock dependent demand. In declining market demand of items decreases with respect to time and also after a duration items get outdated. In this situation it needs a pricing policy to sale the items before end season. The proposed dynamic pricing policy is applicable for a limited period to clease the stock. Policy decision regarding the selling price could aggressively attracts the costumers. Objectives are to maximize the prot/revenue, pricing strategy and economic order level for such a stock dependent and price sensitive items. We are giving numerical example and simulation to illustrate the proposed model.


2021 ◽  
Author(s):  
Ginno Millan ◽  
manuel vargas ◽  
Guillermo Fuertes

Fractal behavior and long-range dependence are widely observed in measurements and characterization of traffic flow in high-speed computer networks of different technologies and coverage levels. This paper presents the results obtained when applying fractal analysis techniques on a time series obtained from traffic captures coming from an application server connected to the internet through a high-speed link. The results obtained show that traffic flow in the dedicated high-speed network link exhibited fractal behavior since the Hurst exponent was in the range of 0.5, 1, the fractal dimension between 1, 1.5, and the correlation coefficient between -0.5, 0. Based on these results, it is ideal to characterize both the singularities of the fractal traffic and its impulsiveness during a fractal analysis of temporal scales. Finally, based on the results of the time series analyzes, the fact that the traffic flows of current computer networks exhibited fractal behavior with a long-range dependence was reaffirmed.


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