绿色智能路由算法：支持多对多组播的灵活QoS

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"这篇研究论文提出了一种支持多对多组播的灵活QoS绿色智能路由算法(GIQM)，旨在解决信息与通信技术(ICT)领域巨大的能源消耗问题，实现网络的绿色化。该算法结合了服务质量(QoS)和功率消耗的考虑，以提高网络效率并降低能耗。在GIQM算法中，首先执行rendezvous点确认阶段(RPCS)，然后通过智能路径选择策略优化路由，确保数据传输的高效和节能。" 本文主要探讨了如何在多对多组播环境下，设计并实现一个兼顾QoS和能源效率的路由算法。随着ICT领域的快速发展，其背后的能源消耗问题日益凸显，这促使学术界和工业界致力于研究绿色网络技术。GIQM算法正是在这种背景下提出的，它不仅关注数据传输的质量，还注重减少网络设备的功耗。 GIQM算法的核心是RPCS阶段，这个阶段的目标是确定一组rendezvous点，这些点作为组播数据流的汇聚点，可以有效地分发信息到多个接收者。在RPCS中，算法会考虑到网络中的拓扑结构、带宽资源、节点的功率状态等因素，以确保rendezvous点的选择既满足QoS要求，又尽可能地降低能量消耗。接下来，GIQM算法通过智能路径选择策略来优化路由。这一策略可能包括基于代价的路由选择、负载平衡机制以及动态调整传输速率等方法。通过这种方式，算法能够在满足不同服务等级协议(SLA)的同时，最小化网络中的能量损耗。此外，GIQM可能还引入了自适应调整机制，根据网络状况的变化实时调整路由决策，以保持最佳性能。该论文的贡献在于提供了一个实际可行的解决方案，将QoS需求与绿色网络理念相结合，为未来互联网的可持续发展提供了新的思路。通过对多对多组播环境下的路由问题进行深入研究，GIQM算法有望在大规模多媒体应用、云计算和物联网等领域发挥重要作用，实现高效、节能的数据传输。这篇研究论文详细介绍了GIQM算法的设计原理和实施过程，对于理解和支持多对多组播的绿色智能路由有重要的理论和实践价值。通过这种算法，网络管理者可以实现更环保、更高效的网络运营，同时保证用户的服务质量需求得到满足。

Some authors devoted themselves to QoS routing algorithms

under the many-to-many multicast scenario. In [40], the

many-to-many multicast QoS routing problem was formulated

as a concave quadratic program and binary Integer Linear

Program (ILP), and a Difference of Convex functions

Algorithm (DCA) and proximal decomposition technique are

proposed to solve it. The delay and delay jitter constrained

many-to-many multicast tree problems were formulated in [41].

A Source-Based Scheme (SBS) is devised, which at first finds a

feasible multicast tree for each source node in the multicast

group and then a minimum cover of these multicast trees so that

there exists at least one feasible multicast tree in the minimum

cover for each source node. A necessary and sufficient

condition is also derived for a core-based multicast tree to be

feasible, and a Core-Based Scheme (CBS) based on the derived

condition is provided. However, in all the aforementioned

literature, only the rigid QoS was taken into account when

routing, while the power consumption factor was ignored.

Some literature embedded power consumption metrics in

their routing decisions. In [42], a new heuristic algorithm based

on the ILP formulation (H

ILP

-GreenRE) was introduced to solve

the energy-aware routing problem. A new energy-aware routing

model with the support of data redundancy elimination (RE)

was proposed. It was enabled within routers and it could

virtually increase capacities of network links. In [43], a novel

energy-efficient routing approach called Safe and Practical

Energy Efficient Detour Routing (SPEED) was proposed to

maximize the number of the aggregated links and then

aggregate traffic for power savings in IP networks without any

changes to the traditional IP packets forwarding diagram and

routing protocols. In [44], a power model that can quantify the

relationship between traffic volume and power consumption

was presented, and three Hop-By-Hop Green Routing

Algorithms (HBHGRA) which guaranteed loop-free routing

and substantially reduced energy footprint in the Internet were

progressively developed. Energy conservation and path stretch

were jointly considered. However, QoS guarantee was not

provided to the user when routing and the multicast scenario

was neglected in [42–44].

Only a few works took both power consumption and QoS

requirements into account in the routing algorithms. For

instance, two routing algorithms subject to routing cost and

rigid QoS constraints were proposed in [45], that is, a

constrained Unicast-Max-Flow-Min-Cost algorithm (UMFMC)

for routing unicast traffic flows and a constrained

Multicast-Max-Flow-Min-Cost algorithm (MMFMC) to

maximize the throughput of a one-to-many multicast tree while

simultaneously minimizing energy costs. However, the flexible

QoS constraints were not considered and the many-to-many

multicast scenario was ignored in [45].

3. Modeling and problem formulation

3.1. Model description

3.1.1. Network model

The network model in this paper is denoted as a connected

graph G = (V, E), as shown in Fig. 1, where V represents a set of

vertices (namely network nodes) and E represents a set of edges

(namely network links).

Fig. 1 Network model

3.1.2. Node model

A node model is proposed and its structure is illustrated in

Fig. 2. It consists of multiple chassis, line cards, a master engine

(ME), a switching fabric, multiple forwarding engines (FEs),

multiple replication engines (REs), and so on. Among them, the

ME is responsible for routing packets and updating routing

table; the switching fabric connects input and output ports

inside the router; the FE is in charge of routing table lookup; the

RE is used for multicast replication. Note that all the engines

are equipped with the buffer of certain size [46]. Power

consumption of network node i can be formulated by Eq. (1).

Data bus

Fig. 2 The proposed node structure

 

(1 ( ) )

chass

port

i k

chass i

i i lc

ford repl k

i i

node ctrl

i p

port p lc

P ChaSt

P P LkdSt

P P

P trf PortSt







 

 

 

 

   

 

  

 

  

 

 



(1)



ctrl

ford

and

repl

represent the power consumption of

the ME, a FE and a RE in node

respectively.



chass

and

port

represent the power consumption of a

chassis and a port in node

respectively.



chass

represents the number of chassis in node

represents the number of line cards in chassis

and

port

represents the number of ports in line card



trf

represents the traffic passing port

in node

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