QoSMap: QoS aware Mapping of Virtual Networks for ... - CiteSeerX

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QoSMap: QoS aware Mapping of Virtual Networks for Resiliency and Efficiency Jawwad Shamsi and Monica Brockmeyer Wayne State University Abstract— We describe QoSMap, an efficient and flexible mechanism for constructing virtual networks on a shared Internet substrate for applications having stringent QoS and resiliency requirements. Applications specify desired overlay topology and weighted network characteristics which serve as resource constraints desired by the application in meeting the QoS expectations. QoSMap uses these constraints to select an overlay consisting of high quality direct paths between nodes from a pool of candidate nodes and paths. In addition to the required overlay topology constructed from direct paths between nodes, QoSMap provides path resiliency by constructing alternate one-hop overlay routes via intermediary nodes having paths that meet or exceed the resource constraints. As a case study, we utilized QoSMap to form an overlay for an application requiring constraints on message delay and loss rates. We describe the design of QoSMap and show that it leads to higher quality and more resilient overlays than does a mechanism which addresses only the minimum QoS requirements of the application. Index Terms—Overlay Configuration, Network Management, Quality of Service, Network utilization.

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I. INTRODUCTION

virtualization is widely used for sharing network resources on the Internet. An Internet substrate is mapped to one or more virtual networks, each of which is utilized as an overlay application. The underlying motivation behind network virtualization is to allow multiple and overlapping usage of the Internet substrate. Since the Internet has observed rapid growth from its inception, network virtualization provides a promising technique to allow the deployment and evaluation of a variety of Internet applications while maintaining the current architecture of the Internet [9]. Virtual network assignments are generally requested on the fly and a substrate resource (node or a link) could serve multiple virtual network resources. Therefore, it is essential to effectively map the virtual resources and obtain better utilization of the substrate network. The term “efficient mapping” is general; it could consist of various properties, such as from topological aware construction [10] [12] to application specific bandwidth demands [5]. The task of mapping becomes more challenging with the increased and diversified nature of overlay applications. In ETWORK

 This work is supported in part by the NSF career grant (sponsor and financial support acknowledgment goes here).

addition, many applications such as applications providing QoS have specific requirements and expectations and they require network resources that meet or exceed their constraints. Virtual network assignments are NP-hard problems [14]. Previous efforts on the subject, such as the work proposed by Zhu and Ammar [14] [15], focus on meeting specific requirements for a particular application and assume unlimited overlay hops in connecting two nodes. However, applications that have hop-related constraints such as loss rate and delay could observe degraded performance if paths with multiple overlay hops are utilized. Additionally, many scenarios could exist in which applications have multiple network constraints and have specific preferences for each of them. For instance, an application may desire shorter paths for early delivery of messages and low loss rates for better throughput and require that paths with low loss rates have precedence over paths with shorter delays. Another challenge related to virtual network is that its efficacy depends on the behavior of the underlying Internet substrate. While the network constraints are considered during the mapping of the overlay, the virtual network resource (link or a node) could violate the network constraints if the substrate network experiences congestion or failure. In a similar study, Oppenheimer et al. observed that node placement decisions on PlanetLab become ineffective after 30 minutes [7]. Although reconfiguration of the overlay has been proposed as an alternate option [14] [7], a high cost of service disruption, deployment and re-computation is associated with it. Our thesis is that the resiliency against network failures could be improved and QoS violations could be mitigated by providing alternate routes via an intermediary overlay node which could be utilized by routing or reconfiguration mechanisms. Such mechanisms have already been employed in RON and other similar services [2]. The challenge, however, lies in determining routes via intermediate nodes that meets or exceeds the network constraints. The problem is NP-hard and can be reduced to the virtual network assignment problem. In this paper, we describe QoSMap, a tool that provides an efficient and flexible mechanism for mapping overlay network to the underlying Internet substrate. Applications specify the desired path characteristics and their weights which serve as resource constraints in meeting the QoS demands of an

2 application and also contribute in determining the quality of resources, such that QoSMap selects high quality resources while forming an overlay. In order to meet the stringent QoS requirements and provide paths with high quality, QoSMap only considers the direct path between two substrate nodes. In addition, QoSMap strives to construct high quality one-hop alternate routes via intermediary overlay nodes that fulfill application constraints. The alternate routes serve as a back-up for direct overlay routes and provide increased resiliency and durability against changing network conditions. Since both the direct and the indirect paths are high quality paths that meet or exceed application QoS constraints, an application can utilize either of these paths without violating QoS requirements. The QoSMap architecture is a generic platform that could fulfill virtual network assignment problem for a variety of Internet applications. As a case study, we solve overlay network assignment problem for an application that have strict QoS requirements of loss rate and message delay. We compared the performance of QoSMap with a totally random approach of overlay mapping and observed that the QoSMap provides higher quality and more resilient overlays. The main contributions of this paper can be summarized as follows: - It provides a generic mechanism for QoS-based overlay mapping to the Internet substrate, where high quality paths are achieved by only considering the direct edges between two underlay nodes. - In addition to fulfilling the request for the overlay topology, QoSMap also provides high quality alternate routes via an intermediary node that meets or exceeds the application constraints. Such paths increase the resiliency of the overlay against failure of direct paths. QoSMap can simplify network management by selecting nodes and links that fulfill the application QoS requirements. It can be employed by many applications that require use of overlay services and utilize underlying substrate such as the PlanetLab.

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II. VIRTUAL NETWORK ASSIGNMENT PROBLEM A. Assumptions and Considerations QoSMap does not monitor network and collect information about the network parameters. We assume that any application providing QoS will require performance monitoring and that the data yielded by such monitoring can serve as input to the QoSMap. Therefore, QoSMap can be integrated with other monitoring tools such as CoMoN [8]. QoSMap supports asymmetric paths i.e. it considers forward and backward paths between two nodes, as two separate paths. B. Problem Description We describe the virtual network assignment problem as follows: - The underlying topology is represented as a directed graph G , with V nodes and E edges, where each edge has

specific network characteristics (such as bandwidth, delay etc). The application specifies the desired overlay topology that includes the total number of virtual nodes, virtual links (including the source and the destination nodes for each overlay link) and weighted network constraints. In general, an application can specify ‘n’ constraints, represented as P1, P2, P3 …. Pn., each having a weight, w1, w2, w3…..wn. such that, w1+w2 + w3 + …. + wn =1. The assignment problem consists of building an overlay that meets or exceeds the resource constraints requested by the application. Since the purpose of the weighted constraints is to achieve a minimum level of QoS, each characteristic related to the constraint contributes (in proportion to its weight) in determining the quality of the path. Therefore, the goal of QoSMap is to select paths with high quality, where the quality (M) of the path is computed as follows:

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Pi wi ----- (1) Ri

where, Ri is the requested value for the constraint i. M represents the ratio of the QoS received by an application over the QoS requested. We assume that each Pi is increasing - that is that the application favors high values of the metrics. The quality (M) of an overlay path may also be related to the number of overlay hops. If an application has stringent QoS requirements such as loss-rate, then paths with multiple hops could experience low quality. Additionally, due to multiple overlay hops, such paths are vulnerable to transient congestions and network changes. Therefore, an effective solution should favor direct overlay paths in meeting such requirements. The quality of overlay paths could be degraded due to transient or persistent congestion. While reconfiguration of the overlay network has been suggested to recuperate from such behavior [14], a high cost of service disruption is associated with it. In such a scenario, alternate routes with an additional overlay hop between two overlay nodes could be utilized to continue service and avoid service disruption. Such path provides resiliency against changing network conditions. Therefore, an important consideration while mapping overlay nodes is that they facilitate one-hop alternate routes via intermediary nodes. An imperative challenge in computing such routes is that they meet or exceeds the QoS requirements of an application. An application must specify the aggregate function to compute network characteristics for paths via an intermediary node. In addition, intermediary nodes should be selected such that the back up routes exhibits high quality. The provision of alternate routes is likely to bring additional nodes in the overlay. This constitutes the cost of

3 maintaining service in additional nodes. In order to minimize such cost, preference should be given to the nodes that have already been included in the overlay (either as mapped overlay nodes or intermediary nodes), while selecting the new intermediary nodes. Although additional nodes incur cost they are likely to be useful for dynamic network reconfiguration. However, in this paper we do not explore the reconfiguration problem. III. THE QOSMAP - ALGORITHM The basic idea of the QoSMap algorithm is simple. From the unmapped overlay nodes, QoSMap selects an overlay node with highest degree and finds all the possible underlay nodes whose direct edges fulfill the degree requirements of the overlay node. QoSMap then selects an underlay node based on the three factors. 1) Number of alternate routes an underlay node can provide, 2) whether the underlay node is already in the overlay as a part of an alternate route and 3) the quality of the underlay node, computed from Equation 1 by averaging M for all egress and ingress edges. If at any stage, QoSMap cannot find an underlay node that meets the degree requirements of an overlay node, then it backtracks to the preceding stage and selects a different underlay node for the preceding overlay node. Given an underlying topology and overlay mapping request the main steps of the QoSMap algorithm are described below. 1. 2. 3. 4. 5. 6.

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Filter all the direct edges from the underlying topology that do not meet the application constraints. Add one-hop routes and compute their path characteristics using the aggregate function specified by the application. Evict all one-hop routes that do not meet the application constraints and compute the quality of all the direct and intermediary-routes using equation (1). From the list of unmapped overlay nodes, select a node with highest degree. Prepare a list (called Node-List) of underlay nodes that fulfills the degrees requirements of the selected overlay node. Sort the Node-List according to the following criteria: a. The average number of alternate routes a node provides over all the egress and ingress edges, (capping the number of alternate nodes considered to two). b. Whether or not a node has been included in the overlay as an intermediary node for alternate routes (giving preference to such nodes). c. Quality, where the quality of a node is computed by calculating the average quality over all the egress and ingress direct paths. From the sorted list select the next available underlay node and map it as the selected overlay node. Find alternate routes via intermediary nodes. In determining intermediary nodes, preference is given to the

nodes that are already part of the overlay as a mapped overlay node or unmapped intermediary node. If multiple nodes exist with the same criteria then select nodes with the highest quality (M). Up to two intermediary nodes are selected for each overlay path. 9. If at any stage, no underlay node exists which meet the degree requirements then back track to the preceding level and select a different underlay node for the preceding overlay node. 10. Repeat (Step 4) until all the overlay nodes are mapped or all the nodes in the Node-List have been tried for the overlay node at the first level (highest degree). IV. PERFORMANCE EVALUATION In order to evaluate the performance of QoSMap, we utilized it to solve the overlay mapping request for an application with strict QoS requirements minimizing packet loss rate and message delay. We used the inverse of these values to construct increasing QoS metrics and weighted them equally to construct our overall metric. We compared the performance of QoSMap with a random approach, which randomly selects an overlay node which meets the minimum QoS constraints without attempting to maximize the QoS constraints or to build indirect routes. In our simulation, we generated an underlying network of 50 nodes where each node is connected to every other node via a direct edge. While our initial substrate was fully connected, QoS demands by an application will result in a less connected underlay after filtering. We considered six different types of QoS requests with differing requirements of packet loss and delay constraints such that the filtered underlay topology is varied from 100% connectivity to 20% connectivity between nodes. For each level of QoS, we considered five different types of overlay topologies, a completely connected overlay, randomly connected overlays with 50% and 25% connectivity, a tree topology and a ring topology, each having 15 overlay nodes. In our experiments we observed that for the complete overlay topology, a solution could only be found when the underlay was complete, i.e. the QoS requirements of the application were low. When more stringent QoS requirements were modeled, no solution existed. Similarly, for the random overlay topology with 50% connectivity, a solution could only be found when underlay was 100% connected, 90% connected or 80% connected. For evaluation, we used the QoSMap and the random approaches to fulfill the mapping requests and compare the quality and resiliency of the resulting overlay. We outlined the following criteria for comparison between the two approaches. - What is the quality of the mapped overlay? And what is the advantage of using QoSMap over random in terms of quality of the paths?

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Figure 7: Gain – Resiliency vs. Node cost What is the resiliency of the overlay? And what is the advantage of using QoSMap over random in terms of resiliency? - What is the cost (in terms of extra nodes) of obtaining high resiliency? - Is QoSMap feasible for moderate size overlays? Each of these evaluation criteria is explained below.

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Figure 8: Execution steps vs. Overlay size A. Quality To compute the overall quality of the overlay, we calculated the average quality over all the direct paths. We observed that QoSMap yields high quality overlays (Figure 1). For instance, even in the extreme scenario in which 80% of the underlay edges are evicted due to stringent QoS requirements on the part of the application (20% connectivity), QoSMap was able

5 to accomplish M of 3.5. Recall that M represents the average ratio between received QoS and requested. In comparison to the QoSMap, the quality of the direct paths of the overlays generated by the random approach is lower. Figure 2, represents the performance gain of the QoSMap over the random approach. The performance gain varies between 1.3 and 2.5, affirming the fact that QoSMap yields better quality paths. We also computed the quality of the indirect paths for the two approaches. However, under many scenarios, the random approach does not provided alternate routes. Figure 3 illustrates that the indirect paths yielded by QoSMap have high quality. B. Resiliency To measure resiliency, we computed the ratio of the number of all the paths (both direct and indirect) to the number of direct paths. For both the QoS and the random approach, we specifically check for the existence of all the one-hop indirect paths that can be utilized as alternate paths because they meet application requirements, not just those explicitly constructed by the algorithm. Figure 4 shows the resiliency of the QoSMap. The resiliency varies from 2.2 to 6.5 and increases with the increase in the connectivity of the underlay. This is due to the fact that in the underlay with higher connectivity, QoSMap was able to find large number of alternate routes. Even under extreme scenario of only 20% underlay connectivity the resiliency is greater than 2, i.e. on average for each overlay path there exist at least one alternate path via an intermediary node. We also computed the resiliency provided by the random algorithm. With the exception of the overlays with complete graph and the random with 50% connectivity, the resiliency of the overlays is maintained around 1, i.e. very few overlay paths have alternate routes with random algorithm. Figure 5 shows the gain of resiliency between QoSMap and the random algorithms. In general, the gain increases with the increase in the underlay connectivity. C. Cost of resiliency To evaluate the cost of resiliency achieved by the addition of indirect routes, we computed the ratio of the total number of nodes (mapped overlay nodes and the intermediary nodes that are not mapped as an overlay node) over the number of nodes requested by the application. Figure 6 illustrates the node cost of different overlays generated by the QoSMap approach. The cost decreases with the increase in the underlay connectivity, as QoSMap was able to utilize mapped overlay nodes as intermediate nodes more easily for more connected overlays. In Figure 7, we examine the ration of resiliency to node cost and demonstrate that the cost of additional nodes contributes to redundancy and that this contribution is especially effective for more connected underlay networks (i.e. those for which the QoS requirements are more modest). D. Feasibility of QoSMap for Medium-sized applications Due to stringent QoS requirements, both the QoSMap and

the random algorithms only considers direct paths between two nodes and therefore requires backtracking to meet the degree requirements of an overlay node. Under some scenarios, backtracking could lead to exponential increase in the total number of execution steps1 and affect the scalability of the two algorithms. Due to strict demands of QoS and the expectation that QoSMap will be used for applications which monitor paths characteristics, we do not target large-size applications. We conducted an experiment to evaluate the performance of QoSMap under increasing overlay sizes and larger underlying topology. We used an underlay network of 100 nodes with 80% connectivity between the nodes and varied the overlay size from 5 nodes to 50 nodes with random connectivity among the overlay nodes. We noted the number of execution steps for each overlay topology and observed that up to the overlay size of 40 nodes the execution step are almost linear to the number of overlay nodes (Figure 8). However, for overlay networks of 45 and 50 nodes, we observed an exponential increase in the number of execution steps due to the rise in the number of backtracks. Therefore, we conclude that QoSMap is more feasible for small to medium size applications with stringent QoS requirements. Note that the cost of executing QoSMap is a one time cost occurring at application deployment. In practice, we found that if an overlay existed, even our un-optimized prototype algorithm returned results with in one minute. V. RELATED WORK Overlay network configuration has received significant consideration from the research community. Zhu and Ammar [14] presented a virtual network assignment solution which reduces link and node stress in the underlying network. Their work is focused on balancing load on substrate nodes and links, in which they do not consider any restriction on the number of overlay hops in computing the end-to-end overlay path. As a result, we do not expect that their approach will satisfy applications having stringent QoS requirements. They also proposed reconfiguration of the overlay due to changing network considerations. Later [15], the authors consider bandwidth and CPU availability as resource constraints to solve the virtual network assignment problem on PlanetLab. In contrast, QoSMap is a generic and flexible mechanism not restricted to any specific network constraint. In order to achieve high quality, we only consider the direct path between two nodes as a mean to connect two overlay nodes. Further, to reduce the complexity and the cost of overlay reconfiguration, we provide alternate routes consisting of an additional hop via intermediary nodes. Such paths increase resiliency against network failures and congestion. The idea of improving path resiliency against network failures is first introduced in RON [2] which utilizes one-hop The total number of execution steps in a solution is equal to the sum of the number of overlay nodes and the number of backtracks. 1

6 overlay routes in case of failure of direct overlay routes. However, since RON is a fully connected overlay network in which each node is connected to all the other nodes, it does not explicitly constructs alternate routes or assigns virtual network. In contrast, QoSMap is a virtual network solver, which constructs high quality direct and indirect paths that fulfill the QoS requirements and is therefore suitable for applications with more stringent QoS requirements. In another study, Hyman et al. [5] utilizes the notion of resource allocation of virtual circuits to build virtual path with bandwidth considerations. In comparison, QoSMap is developed with a generic framework that can support different path characteristics. Some researchers have also focused on solving virtual network assignment problems for emulation platforms. Liu and et al. [6] presented two algorithms for mapping virtual networks on emulation platforms. Similarly, Ricci and et al. [11] proposed a simulated annealing based solution for mapping virtual networks on the Emulab testbed, an application with different constraints such as meeting hardware requirements and lower level network considerations. Further, our work is different in that we are focused on achieving high quality and resiliency. Different schemes related to graph embedding exist that can be utilized for network mapping [4] [1]. However, the context of such algorithms is different as they consider restricted sets of underlying graphs such as hypercube or Euclidean space. Further, these algorithms are not developed to meet the challenges presented in this paper i.e. meeting stringent QoS requirements and attaining high resiliency. Many researchers have focused on achieving topological aware overlay construction in order to increase the performance of the virtual networks [10] [12]. While, such schemes increases the efficiency of the overlay networks, the perspective of such approaches are different as they are focused on maintaining good connectivity among the overlay nodes and minimizing message delay in the network. Contrary to that, our work is related to solving user specific overlay topology with stringent QoS requirements comprising many considerations related to meeting network constraints and achieving higher resiliency. While QoSMap operates at the application layer of the OSI model, lower level mechanisms such as RSVP-TE based signaling for MPLS [3] can also be utilized to reserve resources for QoS. QoSMap is complementary to these techniques and can be used when they are not available. Moreover, QoSMap also maps virtual networks and is therefore more suitable for overlay applications. VI. CONCLUSION AND FUTURE WORK QoSMap provides a generic platform for mapping overlay networks that meet application-specific stringent QoS demands. To obtain high quality, it only considers direct paths between two nodes for the computation of overlay routes. For

increased resiliency against changing network behavior, it provides high quality one-hop alternate routes via intermediary nodes We compared the performance of QoSMap with a random approach and observed high gains with respect to the quality of paths and the resiliency. We also observed that QoSMap is more feasible for small to medium sized applications. Having demonstrated the effectiveness and efficacy of QoSMap, we would like to extend this research along the following directions. - Evaluate and validate the QoSMap under dynamic network conditions in which network characteristics varies over time. - Evaluate the suitability of QoSMap for the construction of overlay networks with stringent timing constraints such as Predictable Service Overlay Networks (PSON) [13]. - Explore the effectiveness of redundant paths through an application that utilize routing to meet the stringent QoS demands. - Develop a mechanism for overlay re-configuration which exploits the availability of redundant nodes added for alternate routes. - Explore heuristics to reduce the backtracking by permitting some pairs of nodes to be connected by onehop indirect paths instead of requiring only direct paths. - Employ renegotiation of QoS requirements for networks that exhibits low QoS. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]

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