Scalable Resource and Admission Control in Class-Based Differentiated Networks

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Scalable Resource and Admission Control in Class-Based Differentiated Networks

Evariste Logota1,2,*, Carlos Campos1,2, Susana Sargento1,2 and Augusto Neto1,3

1Instituto de Telecomunicações, Aveiro, Portugal 2University of Aveiro, Portugal 3Federal University of Rio Grande do Norte, Natal-RN, Brazil

Received: August 01, 2013 Revised: September 11, 2013 Accepted: September 19, 2013

Abstract: Dynamic aggregate bandwidth over-reservation is a scalable approach for Quality of Service (QoS) control mechanisms, since surplus of reservation allows for admitting several flows without signaling the network. Our recent work, the Advanced Class-based resource Over-Reservation (ACOR), shows interesting results by significantly reducing QoS control signaling overhead with increased resource utilization without incurring QoS violation when compared with related state-of-the-art patented solution. However, ACOR is too sensitive to the number of paths that share bottleneck links. It also resorts to per-flow signaling when links are congested. In view of this, we propose the Extended-ACOR (E-ACOR), which extends ACOR architecture with a new approach, aiming at reducing the performance dependency on paths’ density on bottleneck interfaces. Moreover, it is able to efficiently track congestion information throughout a net-work to prevent unnecessary signaling during network congestion time. Thus, E-ACOR is expected to scale large net-works with reduced signaling. Also, E-ACOR is able to keep all the benefits of ACOR in terms of support for QoS differ-entiation, QoS violation avoidance and resource utilization efficiency. Analytical and simulation results demonstrate the efficiency and cost-effectiveness of E-ACOR over ACOR, by significantly reducing signaling frequency especially during critical periods of congestion while enabling service convergence with differentiation of the control.

Keywords: Quality of Service, class-based bandwidth over-reservation control, distributed network control, congestion.

1. INTRODUCTION

With the overwhelming trend of services and networks integration, there is a high expectation of decentralization control with automated capabilities as alternative to central-ized approaches, which have low fault tolerance capabilities [1]. Moreover, class-based resource provisioning must be carried out carefully to prevent starvation and waste of re-sources, where bandwidth can be reserved to a Class of Serv-ice (CoS) but never effectively used. In our previous work, we designed the Advanced Class-based resource Over-Reservation (ACOR) [2] to deal with the performance issues described above. ACOR implements aggregate resource over-reservation in decentralized net-works, which allows for significant reduction of resource reservation signaling overhead (events and load) with differ-entiated QoS guarantees without incurring waste of re-sources in contrast to related solutions. ACOR achieves these improvements by dynamically controlling and over-reserving, to each CoS, as much resource as it is required in networks through multiple distributed Control Decision Points (CDPs). The CDPs control entities are embedded in cooperating network border nodes (i.e., ingress and egress nodes) and are responsible for correctly synchronizing sha-

*Address correspondence to this author at the Instituto de Telecomunica-ções, Aveiro, Portugal; Tel: +351 234 377 900; Fax: +351 234 377 901; E-mail: [email protected]

red resource utilization statistics inside a network. Moreover, ACOR achieves very low rates of synchronization signaling events through the concept of Virtual Over-PRovisioning (VOPR) [3, 4], which allocates the over-reserved resources of each outgoing interface to each path that uses the inter-face. In this way, every CDP can process several service requests over a path without requiring synchronization of the overall resource utilization statistics on the path as long as the VOPR of the path is not exhausted, and thus allows for reducing synchronization frequency between cooperating CDPs. However, as the VOPR is assigned per path, its per-formance is too limited by the paths’ density on bottleneck outgoing interfaces. In particular, the higher the number of paths that share bottleneck outgoing interface is, the more ACOR incurs signaling events for synchronization as it is demonstrated in [2]. Moreover, ACOR triggers synchroniza-tion signaling whenever it detects that a VOPR of an existing path exhausts. This behavior is very inefficient especially in congestion period of time whereby the VOPR would exhaust upon every service request, and the overall system perform-ance may be drastically damaged. Therefore, this work proposes the Extended ACOR (E-ACOR), a new approach that extends the ACOR architecture to aggregate the VOPR per CDP, not per path, in such a way as to alleviate performance dependency of ACOR on paths’ density on bottleneck links inside a network. In addition, E-ACOR introduces a mechanism for efficiently tracking con-gestion information throughout a network in such a way that

2 Recent Advances in Communications and Networking Technology, 2013, Vol. 1, No. 3 Logota et al.

it allows for preventing unnecessary synchronization signal-ing when paths are congested. E-ACOR is thus able to minimize synchronization frequency, while it keeps all the advantages of ACOR by assuring QoS differentiation with low resource reservation signaling rate without waste of re-sources or QoS violation, and thus allows for overall net-work performance optimization. Analytical and simulation results demonstrate the effectiveness of the E-ACOR and its superiority over the (Legacy) ACOR in terms of signaling overhead minimization.

The paper is organized as follows. Section II presents the related work. Section III presents an overview of the ACOR mechanism and section IV describes the E-ACOR control mechanism. An analytical model of E-ACOR is provided in section V. Then, the numerical and simulation results are presented in section VI, and section VII concludes the paper and introduces future work.

2. RELATED WORK

QoS provisioning strategies have been intensively stud-ied as one of the key functionalities for the success of the next generation of networks [5]. In order to achieve effective support for services convergence, the literature notices a large number of QoS-centric proposals, including network resource and admission control functions, architectures, and protocols.

The aggregate resource reservation control mainly com-bines the QoS guarantee features of a per-flow approach with the scalability and flexibility features of class-based architectures to improve performance [6-9]. Moreover, there is the evidence [6] that aggregation of reservation has no negative effect on the mean delay of the aggregated flows. Rather, it leads to a reduction of delay in the “tail” of the delay distribution (e.g., 99% percentile delay) for the flows. Aggregate resource over-reservation consists of provi-sioning surplus of resources for aggregates (e.g., for each class) so that multiple service requests can be processed without instant signaling events in a network [10, 10]. How-ever, existing over-provisioning centric are not efficient to deal with unpredictable sessions demands, they confront a trade-off between signaling overhead reduction and waste of resources when processing over-reservations [12, 13].

The QoS-Routing and Resource Control (QoS-RRC) [13] proposal was designed based on the Multi-user Aggregated Resource Allocation (MARA) [11] approach for over-provisioning-centric and load balance-aided QoS-oriented routing functions. Both MARA and QoS-RRC approaches have been designed to exclusively work in networks deploy-ing a single ingress router, which is in charge to control the session admissions, dynamically adjust over-reservations and signals the core routers of the selected path to enforce new resource patterns. In this scheme, there is no need for the ingress routers to synchronize control information, since it assumes that there is no other decision point. However, most today’s network access providers embed multiple ingress routers, and thus they correlate many paths and resources usage. Thus, both QoS-RRC and MARA have very restricted applicability in todays and future networks, although achiev-

ing the performance optimizations of the over-provisioning centric signaling constrained scheme. Our recent work Class-based resource Over-pRovisioning (COR) [12] was designed to overcome key issues of MARA and QoS-RRC. COR introduced specific functions to efficiently compute resource distribution among CoSs implemented on each interface in a way to avoid waste of resources. However, COR prevents reserving too much surplus of resources to each CoS due to session unpredict-ability, and thus fails to optimize signaling overhead reduc-tion. The Advanced Class-based Resource Over-Reservation (ACOR) [2] extends the COR approach with functions to exploit appropriate knowledge of network control data [3, 4]. ACOR significantly reduces controls signaling occurrence without incurring waste of resources. However, a major limi-tation of ACOR is that the solution tightly depends on corre-lated paths’ density on bottleneck links, and synchronization signaling frequency grows rapidly with the increase of the density. Furthermore, ACOR shows serious limitation by resorting to “per-flow” synchronization signaling events when bottleneck links are close to congestion or congested. Therefore, further investigations were still deemed necessary to optimize control performance in class-based resource over-provisioning centric networks. In this sense, our main goal is to extend ACOR architecture with new functionalities aiming at optimizing control overall performance through minimization of signaling frequency without incurring QoS violation, waste of resources or CoS starvation. It is impor-tant to mention that ACOR performance is evaluated in comparison with MARA, since the latter is the most closely related literature as detailed in [2]. Therefore, in this paper, we will focus on the E-ACOR performance against that of ACOR without repeating MARA for the sake of simplicity.

3. OVERVIEW OF ACOR CONTROL MECHANISM

The ACOR [2] is a new control mechanism, which ap-plies differentiated QoS provisioning in class-based net-works by dynamically controlling bandwidth over-reservation, seeking significant reduced control overhead. The intelligence in ACOR approach is pushed to network border where each node (i.e., ingress/egress) hosts a CDP entity and interior/core nodes are left simpler. In a master-client mode, the CDPs are responsible for taking control de-cisions which, in turn, are translated into commands and conveyed in signaling messages to the core nodes to enforce the decisions. All available CDPs cooperate as a means to dynamically exchange appropriate control information for synchronization with changes of network resource states, and therefore, to assist control decisions with accurate informa-tion in distributed manner. ACOR is able to achieve this with significantly low rates of signaling overhead mainly through a two-layer control approach for aggregate resource control, which includes two main parts. On one hand, ACOR imple-ments techniques for dynamic control of aggregate band-width over-reservation, which allows for minimizing the rates of QoS reservation signaling in the sense to optimize related overhead without QoS violation or waste of resources [2]. On the other hand, it uses the VOPR concept [3, 4] which virtually allocates a share of aggregate over-reservations of each outgoing interface to each edge-to-edge path that uses the interface. This way, several service re-

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Scalable Resource and Admission Control Recent Advances in Communications and Networking Technology, 2013, Vol. 1, No. 3 3

quests can be processed over a path without requiring syn-chronization of the overall resource utilization statistics on the path as long as the VOPR of the path is not exhausted. Moreover, the ACOR synchronization operation is selective, that is, only the CDPs, which are correlated with the infor-mation to be updated, are dynamically included in a collabo-ration group such that the information is not broadcasted unnecessarily. The ACOR control architecture and functionalities are implemented using the following five components: 1) Net-work Context Information dataBase (NetCIB) used by each CDP to store and maintain appropriate control information inside the network. The NetCIB of a CDP is mainly com-posed by four information tables, including: PATHS table to store the CDP’s selected trees and the related control in-formation, including a list of correlated CDPs for each tree (two CDPs are considered correlated when their selected trees happen to correlate by sharing outgoing interface(s)); TOPOLOGY table to store the IDs of the outgoing inter-faces of the CDP’s selected trees and the related control in-formation; VOPRS table to store the VOPR of each selected tree in each CoS on each outgoing interface on the tree; and SESSIONS table to store the characteristics and QoS re-quirements of flows composing admitted sessions; 2) Admis-sion Control (AC) to accept or deny a service request de-pending on the service requirements and network resource availability; 3) Synchronization Control (SC) for the coop-eration between CDPs to assure a proper synchronization of topology and the related links’ resource status through the concept of VOPR; 4) Resource Control (RC) responsible for the QoS over-reservation decisions and the dynamic read-justment of the reservations upon need; 5) Basic Functions (BF) such as packet forwarding, QoS reservations and multi-cast trees enforcement, and failure events reports. These functions are implemented in software agents classified as a statefull ACOR-Edge (ACOR-E) and lightweight-state ACOR-Core (ACOR-C). In particular, the ACOR-E implements all the components, such as NetCIB, AC, SC, RC and the BF, and is embedded in each CDP while the ACOR-C only implements the BF component and is embedded in core nodes, enabling the latter to enforce control policies upon instructions received from the CDPs.

A. Overview of Acor Operations

The ACOR operations are invoked during the network initialization phase and network running phase. An overview of the operations carried out by ACOR approach is defined in the following. 1) ACOR Initialization Phase: As nodes (e.g., CDP or core nodes) boot up, each node publishes its presence with its connection information (e.g., list of neighboring nodes). Then, each CDP creates all possible paths from itself to other CDPs, the edge-to-edge paths, inside the network and selects the best paths, which can be used for service delivery. It is important to mention that a multicast channel is configured on every edge-to-edge path and allows for forcing packets of a flow mapped to a path to follow that particular path [15]. Thus, the terms path and tree are interchangeable in the rest of this paper. Afterwards, the CDPs exchange their trees information, which allows each CDP to build its initial Net-

CIB and the network is set to operate, and ACOR is ready for the running phase. 2) ACOR Running Phase: as the network is running, every CDP is allowed to admit service request(s)

ir in a

CoSi on a tree Tx without synchronization signaling between the correlated CDPs of the Tx as long as the available VOPR AVopr(i,Tx) of the tree Tx in CoSi is sufficient on the bottle-neck outgoing interface (interface with minimum available resource) of the tree Tx, that is, ( ( , )

Vopr xr A i T! ), using the

following function:

A

Vopr(i,T

x) = min Vopr(i, I

e,T

x) !U

BW(i,T

x){ } (1)

In equation (1), Ie is an outgoing interface on the tree Tx, UBW(i,Tx) is the sum of the amount of bandwidth

r

i

jT

x( ) granted to each active flow j mapped in CoSi onto the tree Tx through the interface Ie, and is obtained by:

UBW

(i,Tx) = r

i

j

j=1

!

" Tx( ) (2)

where Φ is the number of flows mapped to Tx in CoSi. Vopr(i,Ie,Tx) in equation (1) is the VOPR of the tree Tx in the CoSi on an outgoing interface Ie and is obtained by:

Vopr(i, Ie,T

x) = U

BW(i,T

x) +

RBW

(i, Ie) !U

BW(i, I

e)

Factor(Ie)

(3)

where RBW(i,Ie) is the current reservation of the CoSi on the interface Ie, UBW(i,Ie) is the total amount of bandwidth gran-ted to the active flows (on all trees) in the CoSi through Ie, and Factor(Ie) is the sharing factor of Ie which is the number of trees that share Ie. Each CDP admits service request(s)

ir in a CoSi on a tree

Tx without issuing QoS reservation signaling into the tree as long as the over-reserved but unused bandwidth ARservBW(i,Tx) of the CoSi is sufficient on the bottleneck outgoing interface of the tree, that is, ( ( , )

RservBW xr A i T! ), using the following

function:

A

RservBW(i,T

x) = min{R

BW(i, I

e) !U

BW(i, I

e)} (4)

Although ACOR significantly decreases the reservation signaling, it triggers synchronization signaling whenever exhaustion of the VOPR of a path is detected, especially in periods of network congestion. As a consequence of this behavior, ACOR places undesired synchronization signaling, and therefore, it will damage performance in prolonged con-gestion scenarios. This situation motivated the research of new schemes to optimize the overall control signaling rate and processing overhead, which resulted in the E-ACOR.

4. E-ACOR CONTROL MECHANISM

This section describes the E-ACOR approach with focus on the main extensions to the functionalities of ACOR.

A. Extension to Vopr Concept

E-ACOR proposes to aggregate VOPRs per CDP, as op-posed to the fine-grained control in ACOR, where the VOPR in a CoS on an outgoing interface is allocated per tree that


uses the interface, to further reduce the frequency of syn-chronization events. To achieve this, E-ACOR redefines the Sharing Factor of an outgoing interface, which denotes the number of ingress CDPs that deploy trees through the inter-face, instead of the number of trees using the interface as in ACOR. The Sharing Factor, FactorCDP(Ie), of any interface Ie in E-ACOR is thus limited to the number of correlated CDPs on the interface Ie. In order to facilitate understanding, our description is based on a simple network topology as in Fig. (1). Hence, one can see that the outgoing interface I1 belongs to 2 trees, all of them with the same root CDP1, and each with leaf nodes CDP3 (tree 0) and CDP2 (tree 1). In this case, the sharing factor of I1 in E-ACOR (FactorCDP(I1)) is set to 1, whilst ACOR sets to 2: all the trees that a CDP deploys through an outgoing interface share the same pool of VOPR allocated to the CDP on the interface. Therefore, the VOPR aggregation allows for further reducing the frequency of VOPR exhaustion or synchronization signaling events where the VOPR per tree would exhaust more often due to highly unpredictable service demands to each tree. It is also worth mentioning that E-ACOR maintains the general archi-tecture of ACOR (see Fig. 1) in which a system initialization phase may be characterized by multicast trees creation in step (a), synchronization between CDPs in step (b), and CDPs’ local databases creation in step (c), following the principles detailed in ACOR [2] and summarised earlier in subsection III-1. Further, E-ACOR pushes control load to network bor-ders: the proposed functions are implemented in a software agent named E-ACOR-Edge (E-ACOR-E) embedded in CDPs (see Fig. 1) and the core nodes remain simpler by keeping the ACOR-C agent described in subsection III). The aggregate VOPR of a CDPA in a CoSi on an outgo-ing interface Ie, denoted as Vopr(i, Ie, CDPA), is therefore, obtained as in the following:

Vopr(i, Ie,CDP

A) = U

Agg(i, I

e,CDP

A) +

RBW

(i, Ie) !U

BW(i, I

e)

FactorCDP

A

(Ie)

(5)

where RBW(i,Ie) and UBW(i,Ie) are the reservation and used bandwidth as in equation (3), ( )

ACDP e

Factor I is the number of correlated CDPs on Ie, and UAgg(i,Ie,CDPA) is the total

amount of bandwidth granted to the currently active flows (aggregate used bandwidth) in CoSi on all the trees Tx deployed by the CDPA through the outgoing interface Ie, and is obtained by:

UAgg

(i, Ie,CDP

A) = U

BW(i,T

x)

x=1

m

! (6)

where m is the number of the trees of the CDPA through the outgoing interface Ie, and UBW(i,Tx) is obtained from equation (2) with x as an integer.

B. Extension to NetCIB Database

The E-ACOR architecture contains the Extended NetCIB (ENetCIB) database, extending the TOPOLOGY and VOPRS tables of ACOR’s NetCIB, and introducing a new table, called CONGESTION, to store congestion information about existing trees. The following describes this extension base on the ENetCIB database of CDP1 in Fig. (1). TOPOLOGY Table: in addition to ACOR information, such as the IDs (e.g., IP or MAC address) of each outgoing interface Ie that belongs to the trees rooted at CDP1 (see Ta-ble 2), the interface capacity, the amount of bandwidth re-served for each CoSi on the interface (Rsv RBW(i,Ie)), the total amount of used bandwidth in each CoSi and the trees correlation patterns on the interface, E-ACOR stores the In-terface Sharing Factor as defined in subsection IV-A (see Table 2). Moreover, it maintains the CDP’s aggregate used bandwidth (Own Used UAgg(i,Ie,CDP1)) using equation 6. VOPRS Table: stores the aggregate VOPRs of the CDP, computed using equation 5, for each CoSi on each outgoing interface Ie that lies on the CDP’s trees (that originates at the CDP). CONGESTION Table: illustrated in Table 1, it tracks congestion information about trees inside a network, ena-bling every CDP to exploit congestion information for pre-venting unnecessary synchronization signaling when all can-didate trees are congested. As shown in Table 1, each CDP maintains the ID of the

Fig. (1). E-ACOR use case.


trees created inside a network where the ID of a CDP’s tree is a tuple composed of the CDP’s ID and the tree’s index (CDP_ID, Tree_Index) to assure uniqueness. Note that the indices of trees rooted at different CDPs may overlap since they are assigned by CDPs independently. In Table 1, the indices of all trees in the network in Fig. (1) are 0 or 1, since each CDP only maintains a single tree from itself to each of the remaining 2 CDPs for the sake of simplicity. Further-more, each CDP (e.g., CDP1 in Table 1) maintains the total available bandwidth ABW(Ib,Tx) on the bottleneck outgoing interface Ib of each of its own trees using the following func-tion:

ABW

(Ib,T

x) = min C ! b ! U

BW(i, I

e)

i

k

"#$%&

'()&

(7)

where, C is the capacity of each outgoing interface Ie on its own tree Tx, b is the bandwidth dedicated to the control CoS, k is the number of service CoSs, and UBW(i,Ie) is the total amount of the used bandwidth in each service CoSi on the interface Ie; these parameters are obtained from the TOPOLOGY table (see Table 2) based on Fig. (1), consi-dering that C=1000Mbps, b=1Mbps, and UBW(i,Ie) = 0 at network initialization.

The available bandwidth A

BWI

b,T

x( ) parameter obtained

in equation (7) enables each CDP (e.g., CDP1) to be aware of the congestion level of each of its trees. However, since this parameter is updated only upon synchronization, since multiple trees originated at distributed CDPs under unpre-dictable traffic behaviors share network bandwidth, a CDP cannot simply rely on the available bandwidth shown in the

CONGESTION table to avoid synchronization without tak-ing wrong decisions or wasting resources. E-ACOR ad-dresses this challenge by associating a Congestion Flag to each of the trees: a Congestion Flag of a tree Tx is either ON (Flag(Tx) = 1) to refer that the tree is congested, or it is OFF (Flag(Tx) =0) to indicate that the tree is not congested. The Congestion Flags of trees are jointly exploited with the total available bandwidth of trees to prevent unnecessary syn-chronization signaling without wasting resources as will be detailed in the next sub-sections.

C. Extension to Admission Control Functions

When a network is running and a given ingress CDPA receives an authorized service request r to a CoSi and des-tined to a given egress CDPB in the control domain, CDPA collects the candidate trees of the incoming request, that is, the trees it roots to connect the desired egress CDPB. Then, among the candidate trees, it selects the one (Tx) holding the highest available VOPR. An available VOPR in a CoSi on a CDPA’s tree Tx is the aggregate VOPR of the CDPA which has not yet been granted to any flow in CoSi on the bottle-neck outgoing interface of the tree Tx. It is obtained by the following function:

A

Vopr(i,T

x) = min Vopr(i, I

e,CDP

A) !U

Agg(i, I

e,CDP

A){ } (8)

where Vopr(i, Ie, CDPA) is obtained using the equation (5), UAgg(i,Ie,CDPA) is obtained using the equation (6) and Ie is an outgoing interface on the tree Tx. If the available VOPR is sufficient for the admission ( ( ),

Vopr xr A i T! ), CDPA maps the request to the requested

CoSi on that tree Tx without triggering synchronization sig-naling between the correlated CDPs or issuing QoS reserva-

Table 1. Congestion Table of CDP1.

CDP1 CDP2 CDP3

Trees Index Available Bandwidth on Trees

Congestion

Flag

Congestion

Flag

Congestion

Flag

0 999 0 0 0

1 999 0 0 0

Table 2. Topology Table of CDP1.

CS EF AF BE Interface

IDs Capacity

Interface Sharing Factor Rsv

Total Used

Own

Used Rsv

Total Used

Own

Used Rsv

Total Used

Own

Used

Trees Correlations Paterns

(CDP_ID: Tree_Intex)

I1 1000 1 1 399.6 0 0 299.7 0 0 299.7 0 0 (1:0); (1:1)

I3 1000 1 1 399.6 0 0 299.7 0 0 299.7 0 0 (1:0)

I5 1000 2 1 399.6 0 0 299.7 0 0 299.7 0 0 (1:0); (2:1)

I7 1000 1 1 399.6 0 0 299.7 0 0 299.7 0 0 (1:1)

I11 1000 2 1 399.6 0 0 299.7 0 0 299.7 0 0 (1:1); (3:0)


tion signaling into Tx. Then, the CDP automatically updates its Aggregate Used (Own Used) bandwidth in its TOPOLOGY table according to the bandwidth r granted to the flow. As a result, the available VOPRs are checked in real-time manner using equation (8), which allows each CDP to admit several re-quests without synchronization or QoS reservation signaling, as long as the VOPRs are available. Likewise, whenever a service belonging to a CoSi terminates from a tree Tx, the CDP automatically updates its Aggregate Used bandwidth in its TOPOLOGY table. Note that there is no signaling when a service terminates, since the bandwidth is over-reserved.

However, if the attempt to admit a request based on available VOPR fails (

r > A

Vopri,T

x( ) ), being the unused VOPR insufficient on the bottleneck outgoing interfaces of all candidate trees, ACOR would trigger synchronization among the correlated CDPs of the candidate trees in order to avoid QoS violations or waste of resources, which would include unnecessary synchronization signaling. E-ACOR addresses this challenge by introducing the congestion in-formation tacking mechanism, which is able to exploit trees’ congestion information dynamically to avoid unnecessary synchronization signaling, which will be detailed in the sub-sequent subsection.

D. Extension to Synchronization Control Functions

This subsection focuses on how the Congestion Flag in-formation is used to achieve scalable performance without wasting resources as further detailed in the following steps: Step 1: VOPR exhausted while available bandwidth is insuf-ficient in all candidate trees, and the congestion Flag of the tree Tx that shows the highest available resources Flag(Tx)=0. In this case, the CDP turns the congestion Flag to 1 and triggers synchronization. In this way, each relevant CDP is enabled to update the status of Tx’s flag accordingly without any extra signaling message. Hence, the control met-rics, such as the total used bandwidth, the VOPRs and the reservations parameters of the outgoing interfaces of certain candidate trees, may be readjusted to allow for service ad-mission. After synchronization, the request may be success-fully admitted, and the congestion Flag is reset to 0 and in-cluded in the synchronization message sent to correlated CDPs after the database is thus processed; otherwise, the status of the Flag remains 1. Step 2: VOPR exhausted while available bandwidth is insufficient in all candidate trees, and the congestion Flag of the tree Tx that shows the highest available resources Flag(Tx)=1. In this case, the CDP considers that the candi-date trees are congested and blocks the incoming request without synchronization signaling. The request may be mapped to a CoS with lower QoS level, depending on the service agreement between customer and service provider. Step 3: VOPR exhausted and available resources are sufficient in a candidate tree Tx upon receiving a request. In this case, the CDP automatically triggers synchroniza-tion to allow for updating the resource status on all relevant interfaces, so that reservation parameters may be readjusted. In this situation, the congestion Flag of the tree Tx is turned 0

if it was 1, and is included in the synchronization message for the remote CDPs to update the Flags status accordingly. Step 4: Service termination from a tree Tx. In this case, if the Flags of the correlated trees of Tx are 1, the CDP turns them to 0 and sends a notification message to the related CDPs carrying the ID of the tree Tx from which the service terminated. The concerned CDPs reset their Flags accord-ingly to allow for future synchronization. However, if none of the Flags of the correlated trees of Tx is 1, the CDP does not issue any notification. Also, a CDP resets the Flag of its tree Tx upon service termination from the tree without issu-ing notification to the correlated CDPs. This way, E-ACOR avoids synchronization without notifying correlated CDPs upon every service termination. Moreover, no service is ad-mitted without assuring a minimum available reservation, and thus, avoiding QoS violations.

5. ANALYTICAL MODEL OF AGGREGATE VOPR

This section provides a model to compare the perform-ance of the E-ACOR aggregate VOPR with that of the ACOR VOPR [2]. For this purpose, we use the network to-pology in Fig. (2) to illustrate bottleneck link sharing scenar-ios: each ingress CDP with several trees to each egress CDP. We suppose that an amount of bandwidth Bi(Ib) is over-reserved to each CoSi on a bottleneck outgoing interface Ib inside a network. Besides, service requests arrival to a CoSi on a given tree Tx that uses the interface Ib are Poisson proc-esses with rate λi,x, and therefore, the sum λi of service re-quests arrival rates to a set of trees is also a Poisson process, with an exponential duration. In this sense, the overall ses-sion requests arrival rate λi,CDP from all m’ trees of a given CDP into the CoSi over the interface Ib is also as Poisson process such that:

!i,CDP

= !i,x

x=1

m '

" (9)

Considering that p trees rooted at q different ingress CDPs share the bottleneck interface Ib, we model the per-formance of the aggregate VOPR and that of the ACOR VOPR as in the following. ACOR: the available VOPR Vi,x of a given tree Tx in a CoSi within surplus of reservation Bi(Ib) on the interface Ib is obtained by:

Vi,x=

Bi

Ib( )

p (10)

The total number n of connections that Vi,x of a CoSi can accommodate simultaneously without synchronization events for a given tree Tx sharing the interface Ib is obtained by:

n =B

iI

b( )r * p

!

"

!!

#

$

##

(11)

E-ACOR: the available aggregate VOPR ( ),AggVoprA i CDP

of a given CDP in a CoSi within Bi(Ib) on the interface Ib is obtained by:


AAggVopr

i,CDP( ) =B

iI

b( )q

(12)

The total number n’ of connections that Vi,CDP can accom-modate without synchronization events is:

n ' =B

iI

b( )r * q

!

"

!!

#

$

##

(13)

Then, we model the ACOR VOPR and the aggregate VOPR of E-ACOR as an M/M/n/n and M/M/n’/n’ queuing systems, respectively. As such, the probability

P

ACOR

i,x that the available VOPR Vi,x (see equation 10) exhausts to trigger synchronization in ACOR is the probability that an incoming request to the CoSi on the tree Tx finds all the n “VOPRed connections” slots occupied, and is obtained using the Erlang B formula as in [2]. Likewise, the probability

P

E!ACOR

i,CDP that the

available aggregate VOPR A

AggVopri,CDP( ) of a CDP (see

equation 12) exhausts to trigger synchronization in E-ACOR systems is the probability that an incoming request to the CoSi on any of the CDP’s tree on the bottleneck interface finds all the n’ “VOPRed sessions” slots occupied, and is also obtained using the Erlang B formula as:

PE!ACOR

i,x=

"i,CDP

µ

#

$%

&

'(

n '

*1

n '!

"i,CDP

µ

#

$%

&

'(

)

*1

) !)=0

n '

* (14)

where α is an integer, µ is a real number (service rate) as in [2], and

!

i,CDPis obtained from equation (9).

6. PERFORMANCE EVALUATION

First, we assess analytically the advantages of VOPRs aggregation of E-ACOR over the per-tree VOPR approach of ACOR. Then, we analyze the overall improvement of E-ACOR over ACOR in terms of minimization of synchroniza-tion overhead through simulations using the Network Simu-

lator version 2 – ns-2 [16] (extended with ACOR and E-ACOR functionalities). The performance characteristics of QoS reservation signaling overhead minimization, avoidance of QoS violations and waste of resources are incorporated in E-ACOR as inherent advantages of ACOR [2], and there-fore, the related results are not repeated in this paper. In or-der to clearly observe the advantages of VOPRs aggregation and the benefits of the congestion tracking mechanism of E-ACOR separately through simulations results, we implement a third control mechanism called C-ACOR. Basically, C-ACOR is equal to ACOR embedding the congestion tracking mechanism, but without the VOPR aggregation. Then, we plot the simulation results for these three approaches (E-ACOR, ACOR and C-ACOR) to analyze each enhancement function of E-ACOR over ACOR.

A. Analytical Parameters Configurations

We define a network scenario with m ingress CDPs and m’ egress CDPs and consider a bottleneck outgoing interface Ib inside the network. Moreover, we consider that a certain amount of bandwidth Bi is over-reserved for a CoSi on the interface Ib. To facilitate the understanding of the descrip-tion, the ingress CDPs, which deploy trees through Ib are called correlated ingresses, and the egress CDPs, which are connected through Ib are called correlated egresses. Moreo-ver, each correlated ingress deploys at least 1 tree and at most m’ trees through the interface Ib such that an ingress deploys through Ib at most 1 tree to any of the egresses. This leads to a lower bound and an upper bound of Sharing Fac-tor of the bottleneck interface Ib, called respectively the Smallest Sharing Factor (SSF) and the Highest Sharing Fac-tor (HSF). Basically, SSF corresponds to a scenario where each correlated ingress deploys 1 tree through Ib, and HSF corresponds to a scenario in which each correlated ingress maintains m’ trees through Ib. Hence, given a number c_Ing of correlated ingress CDPs and a number c_Eg of correlated egress CDPs for Ib, the corresponding lower and upper bounds of sharing factors are obtained as:

_ , in ACOR( )

_ , in E-ACORb

c IngSSF I

c Ing

!= "#

(15)

Fig. (2). Topology for analytical study.


_ * _ , in ACOR( )

_ , in E-ACORb

c Ing c EgHSF I

c Ing

!= "#

(16)

Then, we configure the analytical parameters of the sce-nario as summarized in Table 3, and use the SSF and the HSF to compare the lower and upper bounds of synchroniza-tion frequency of E-ACOR and ACOR approaches by vary-ing the number of correlated ingress CDPs – c_Ing – (similar when varying the number of correlated egress CDPs – c_Eg). In Table 3, note that the total over-reserved bandwidth Bi(Ib) in the CoSi on the bottleneck interface Ib is set to 100 Mbps only, the mean service rate to 3 requests/time unit, and the mean request arrival rate to 10 requests/time unit. This is mainly to set the bottleneck link close to congestion to clearly observe the behaviors of ACOR and E-ACOR, espe-cially in Fig. (3) as the synchronization signaling events oc-cur more often during network congestion period of time. Indeed, we vary also the amount of over-reserved bandwidth in CoSi and show results in subsequent subsection (please see Fig. 4).

B. Analytical Results

Fig. (3) is used to plot the probability of synchronization signaling in ACOR and in E-ACOR as a function of the

number of correlated ingresses, which varies between 1 and 20 with a fixed number of 12 correlated egresses. The ana-lytical results confirm that, from upper bound perspective, E-ACOR incurs lower synchronization signaling frequency than ACOR as we expected due to the aggregation of the VOPRs in E-ACOR. Moreover, the synchronization fre-quency grows more rapidly in ACOR than in E-ACOR with the increasing number of ingress CDPs; it reaches about 100% of signaling probability in ACOR when the number of ingress CDPs is beyond 9. This shows the limitation of VOPR granularity per tree in ACOR. E-ACOR and ACOR show the same lower bound synchronization frequency, since in this scenario, every correlated ingress CDP deploys only one tree through the bottleneck interface Ib. Fig. (4) shows the probability of synchronization signal-ing events in ACOR and in E-ACOR as a function of the amount of over-reserved bandwidth for the CoSi on the bot-tleneck-outgoing interface Ib. In this scenario, the amount of over-reserved bandwidth to the CoSi is varied between 50 Mbps and 2Gbps. We observe that E-ACOR shows better performance than ACOR approach. Fig. (4) also confirms that E-ACOR and ACOR experience the same lower bound synchronization frequency as we expected.

C. Simulation Scenario

The simulation scenario considers 4 CoSs configurations in each network interface, as in the following: one Control Signaling class (CS), one Expedited Forwarding (EF), one Assured Forwarding (AF) and one Best Effort (BE). Moreo-ver, each network interface is configured with 1Gbps and random propagation delay. Further, 25k traffic requests are configured with three different traffic patterns (i.e., Constant Bit rate, Pareto and Exponential), belong to various CoSs and ranging from 128Kbps to 8Mbps, and are placed in the simulation experiments following a Poisson distribution. Among the requests generated, 30% are long-lived sessions (active during the whole simulation time), 40% are relatively

Table 3. Parameters Configurations.

Bi(Ib)=100 Over-reserved BW in CoSi on the Interface Ib (Mpbs)

r =1 Mean bandwidth requested per connection (Mbps)

µ =3 Mean service rate per connection (requests/time unit)

λi,x =10 Request arrival rate to a given tree Tx in CoSi on Ib

m=20 Total number of ingress CDPs

m’= 12 Total number of egress CDPs

Fig. (3). Effect of number of ingress CDPs on signaling events.


long-lived sessions (lifetime is equal to 60 minutes) and 30% are short-lived sessions (lifetime is equal to 10 minutes). The simulations comprise 4 randomly generated topolo-gies (number of ingress routers ranging from 3 to 6; core routers from 5 to 15, and egress router from 3 to 6) with dif-ferent degrees of correlations on the links. One of the topol-ogy is illustrated in Fig. (5). In the methodology we adopted, the 4 networks get congested with about 10k connection re-quests. The simulation is run 5 times with different seeds of random mapping of requests to CoSs, CDPs and egress routers, for each topology. Then, the averaging values are plotted for all topologies and seeds with a confidence inter-val of 95%. Then, we collected the number of reservation and synchronization signalling events, and signalling load in E-ACOR, in ACOR, and in C-ACOR. We analyzed also the

minimization of synchronization signalling number of E-ACOR in terms of percentage, to ease the comparison be-tween the different approaches. Moreover, we studied the E-ACOR’s support for services differentiation and QoS violation avoidance through packets-based simulation results (with real traffics activated) and observed the packets dropping statistics per CoS. For this purpose, each network link capacity is configured with 10 Mbps and the bandwidth demand per request ranges between 128 Kbps and 1 Mbps. Then, the BE traffic sources are con-figured to generate packets at higher rate (out-of-profile) than expected. Besides, each traffic that belongs to EF CoS or to AF CoS is configured to comply with the rate granted to it (in-profile). In order to obtain more accurate results, each simulation is run 10 times with different seeds of ran-

Fig. (4). Effect of over-reserved bandwidth on signaling events.

Fig. (5). An example of simulated networks Topology.


dom mapping of requests to CoSs, CDPs and egress routers, for each topology. Then, the mean values are plotted for all topologies with a confidence interval of 95%.

D. Simulation Results

Fig. (6) depicts the number of synchronization signaling events generated by ACOR, E-ACOR and C-ACOR solu-tions in experiments configured with different numbers of connection requests. We observe that E-ACOR allows sig-nificant reduction of the overall synchronization signaling events of both the ACOR and the C-ACOR. Notice that the release notification signaling events of E-ACOR are also counted as synchronization event. To clearly see how much signaling overhead is reduced by means of the E-ACOR congestion tracking mechanism alone, let’s first compare the C-ACOR with the ACOR. One can observe that, under low network resource utilization level with less than 5k requests, there is no noticeable differ-ence between ACOR and the C-ACOR. Indeed, C-ACOR effectively demonstrates increasing superiority over ACOR with the increasing overall resource utilization level (beyond 5k requests). This implies that certain trees may happen to start getting congested when the number of requests is larger than 5k. The difference in performance is even larger during prolonged congestion periods of time with a number of re-quests ranging between 15k and 25k. This proves the effec-tive benefit of the congestion tracking mechanism of E-ACOR. Finally, let’s compare E-ACOR with the C-ACOR. It becomes clear that E-ACOR outperforms the C-ACOR due to the VOPRs aggregation techniques, which makes E-ACOR less sensitive to paths’ density on bottleneck outgo-ing interfaces inside a network to improve performance. Therefore, E-ACOR reduces synchronization signaling fur-ther when compared with ACOR. Fig. (7) is used to plot the signaling messages load which corresponds to the signaling events shown in Fig. (6). The

load is obtained based on our NSIS (Next Steps In Signalling [17]) compliant protocol designed to support the ACOR, C-ACOR and E-ACOR mechanisms. As one can see, the load plots confirm the signaling events results trends seen in Fig. (6). To better appreciate the benefits of E-ACOR, Fig. (8) shows the ratio of synchronization signaling events and load reduction of the several approaches. First, we compare the performance of C-ACOR with that of ACOR. Thus, we observe that, during very low network utilization level (below 2k requests), the C-ACOR stays equal to ACOR (i.e. 0% reduction) since the trees may not have experienced congestion as yet. It is worth recalling that the congestion tracking mechanism incorporated in C-ACOR is mainly to avoid unnecessary synchronization signaling events when some trees are congested. Indeed, the C-ACOR outperforms the ACOR by showing a rapidly rising signaling overhead reduction as the network resource utilization in-creases (some paths would have started getting congested); the benefit of the congestion tracking mechanism. Second, we compare the E-ACOR with the C-ACOR, aiming to solely observe the gain of the VOPRs aggregation over the VOPR per tree. In this way, it turns out that, during very low network utilization level (below 2k requests), the E-ACOR reduces up to 100% of the synchronization events and load overhead of the C-ACOR. As the network utiliza-tion increases with more requests, this performance of the VOPRs aggregation decreases from 100%, but stabilizes around above 22% during the prolonged network congestion periods of time occurred after 16k requests. This demon-strates that the aggregation of VOPRs effectively profits more from resource sharing than the VOPR per tree and E-ACOR is able to maintain its superiority over the C-ACOR by above 22% regardless of the network utilization condi-tions.

Fig. (6). Synchronization signaling events number.


Finally, it becomes easy to understand that E-ACOR al-lows for significantly reducing the synchronization signaling overhead of ACOR above 40%, as the former combines both the VOPRs aggregation and the congestion tracking mecha-nism. Moreover, the signaling overhead (events and load) reduction increases rapidly during prolonged congestion pe-riod of time, which is important to improve system overall performance. Furthermore, Fig. (9) plots the number of resource reservation signaling events generated in ACOR, C-ACOR and E-ACOR. As short-lived and relatively long-lived ses-sions were also simulated, it is worth mentioning that an

were also simulated, it is worth mentioning that an average of 11498 session termination requests have occurred during the simulation. This means that 25000 session establishment requests plus 11498 session termination requests, that is, a total number of 36498 requests were triggered in the simula-tion. However, only about 1839 reservation signaling events (maximum average) were triggered by each system, as in Fig. (9). In other words, ACOR, C-ACOR or E-ACOR each trigger only 5.04% of reservation readjustment signaling while satisfying differentiated QoS for 36498 requests with increased resource utilization.

Fig. (7). Synchronization signalling load.

Fig. (8). Synchronization signaling events and load reduction.


Also, there is no resource release or probing signaling since the resources are over-reserved per CoS and the reser-vation parameters are readjusted upon need using ACOR architecture and protocols. As we expected, ACOR, C-ACOR and E-ACOR show similar performance in terms of QoS reservation signaling events, since E-ACOR and C-ACOR inherit the related functions from ACOR. Also, Fig. (10) is used to show packets loss statistics, which is important to demonstrate that the E-ACOR effec-tively supports differentiated QoS without QoS violation, while allowing for drastically reducing the control signaling overhead. Hence, we observe that only the BE flows have experienced packets loss, between 0% and 20% of their packets, as they were punished for being non-compliance to profile (out-of-profile). This proves that misbehavior of BE

traffic did not affect other CoSs in profile in terms of the packets dropping. The performance is similar in ACOR, C-ACOR and E-ACOR, since E-ACOR and C-ACOR incorpo-rate the related functions from ACOR and mainly focus on reducing ACOR’s synchronization signaling overhead. However, one may argue that, even though the BE traffic flows were configured to be out-of-profile during the simula-tion, there is no dropping when the network paths are not congested (see when session requests number is below 5k in Fig. 10). It is therefore worth noting that WFQ (the schedul-ing discipline used) is a work-conserving scheduler, which means that a network link is never empty as long as there is packet in a queue on the related outgoing interface. Hence, BE flows can benefit more from the network resources when there are less higher priority flows in the network.

Fig. (9). Resource reservation signaling events number.

Fig. (10). Packets loss scenario where EF and AF flows are in-profile and BE flowsare out-of-profile.


It is also important to recall that no service is admitted without sufficient resources for admission, and QoS viola-tion is also avoided.

E. Discussion

The main objective of this subsection is to provide a gen-eral discussion of our findings based on the results obtained in this work. In this sense, the analytical results in Fig. (3) prove that the increase of the number of ingress CDPs forces the synchronization signaling frequency to increase more rapidly in ACOR than in E-ACOR. Also, ACOR is likely to trigger synchronization on a per-request basis (100%) when the number of ingress CDPs goes beyond 9, while E-ACOR clearly shows less sensitivity to the rising number of CDPs. This is due to the fact that ACOR allocates VOPR per indi-vidual tree, while E-ACOR aggregates the VOPRs per CDP to effectively allow for processing more service requests (than in ACOR) without requiring synchronization among the correlated CDPs. As one can imagine, the more CDPs one deploys in a network, the more trees would be created, and the more trees would happen to share bottleneck links inside the network. Also, in Fig. (4), the synchronization signaling frequency decreases rapidly with the increase of the amount of reservation surplus (with E-ACOR clearly outperforming the ACOR), which is a gain to profit with the aggregate VOPR approach, especially in high capacity (fiber optics) future networks. These results are confirmed through simulation in Fig. (6) (signaling events), Fig. (7) (signaling load), and Fig. (8) (events and load reduction in percentage) where E-ACOR outperforms C-ACOR. Furthermore, the simulation results in Fig. (6) (signaling events) and Fig. (7) (signaling load) show a rapidly rising synchronization signaling overhead in ACOR, especially during network congestion period of time (after 10k re-quests). This is due to the fact that ACOR triggers synchro-nization whenever a VOPR exhausts, while the VOPRs would exhaust upon each request during congestion period of time. As a consequence, ACOR generates heavy synchro-nization signaling overhead unnecessarily in congestion situations. This is a crucial problem in ACOR, since exces-sive signaling at congestion time can simply asphyxiate net-work performance. In order to address this challenge, E-ACOR proposes to track congestion information about net-work trees dynamically and enable admission and synchro-nization control subsystems to efficiently exploit the infor-mation to avoid unnecessary synchronization signaling when a requested tree or network is congested. E-ACOR achieves this without incurring undue signaling overhead, as detailed in subsections IV-C and IV-D. The effectiveness of the con-gestion tracking mechanism of E-ACOR is demonstrated through simulation results in Fig. (6) (signaling events), Fig. (7) (signaling load), and Fig. (8) (events and load reduction in percentage) where C-ACOR outperforms ACOR.

This E-ACOR feature of network congestion information monitoring is of paramount importance, not only to reduce synchronization signaling load, but also to enable key net-work control subsystems (e.g., traffic engineering, QoS rout-ing, link capacity planning, etc.) to improve overall perform-ance. Also, as the border nodes are CDPs now, it is assumed that a very large network can be divided into several rea-

sonably smaller control domains (e.g., similarly to Autono-mous Systems - ASs), which can interconnect through the neighboring CDPs, for the sake of scalability. Also, one can add CDPs inside a network and divide the network into areas by following the control principles of the Open Shortest Paths First – OSPF protocol [18]. In addition, let’s suppose that every ingress CDP has a different priority. In this case, the design must enable the CDPs to take the priorities into account for services admissions, especially when the net-work is congested. For example, a CDP should be intelligent enough to force other correlated CDPs (which have lower priorities) to release certain amount of resources so that higher priority services can be accommodated upon need in a dynamic manner. This is important to allow for sharing net-work resources for their optimal utilization. We believe that the ACOR, C-ACOR and E-ACOR approaches can adapt to these scenarios among others, since they provide means to efficiently exploit the correlations between network paths and CDPs (with a good knowledge of network topology and related resource statistics) to improve performance.

To sum up, the E-ACOR demonstrates superiority over the state-of-the-art ACOR solution by allowing significant reduction of the latter’s synchronization signaling overhead. As in Fig. (8), E-ACOR achieves an overall synchronization signaling overhead reduction above 42%, which can reach 100% during low network utilization period of time. Also, the reduction climbs towards 100% during prolonged con-gestion period of time. It is important to mention that, E-ACOR improves performance while being able to incorpo-rate all the advantageous functions of ACOR to guarantee differentiated QoS with increased resource utilization and without QoS violation. We believe that E-ACOR control approach is promising for network and service convergence in the current and future class-based networks (e.g., Inter-net).

7. CONCLUSION & FUTURE WORK

This paper proposed a novel approach, the E-ACOR, which implements mechanisms to control aggregate band-width over-reservation to significantly reduce QoS reserva-tion signaling overhead with increased resource utilization. In this approach, multiple distributed CDPs are efficiently enabled to selectively cooperate in a way to maintain a good knowledge of underlying network topological and related links resource status, keeping low synchronization signaling and related overhead. E-ACOR is able to track congestion information on bottleneck interfaces throughout a network in a way that enables self-controlling CDPs to avoid unneces-sary synchronization signaling during bottleneck interfaces congestion periods of time. The analytical and simulation results demonstrate the advantages of E-ACOR over previ-ous approaches, by allowing significant reduction of the syn-chronization signaling overhead without QoS violation and guaranteeing QoS support and differentiation. In the future, we will study the survivability control of E-ACOR to provide support for stable operations and service continuity in presence of unpredictable links and nodes fail-ures. In addition, we will implement these protocols in test-bed for further evaluations in real network environments.


CONFLICT OF INTEREST

The authors confirm that this article content has no con-flicts of interest.

ACKNOWLEDGEMENTS

The work in this paper is supported by the FCT PTDC/EEA-TEL/104090/2008 GTI-CANE, and FCT PTDC/EEA-TEL/74471/2006 MuMoMgt. Evariste is also supported by FCT scholarship; CNPq supports Augusto Neto.

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DISCLAIMER: The above article has been published in Epub (ahead of print) on the basis of the materials provided by the author. The Editorial Department reserves the right to make minor modifications for further improvement of the manuscript.

Scalable Resource and Admission Control in Class-Based Differentiated Networks

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