Purnima Murali Mohan

TCAM-Aware Local Rerouting for Fast and Efficient Failure Recovery in Software Defined Networks

In Software Defined Networks (SDNs), a reactive approach for failure recovery involves the centralized SDN controller which incurs long delay leading to packet losses. While a proactive approach enables fast failure recovery, it poses a new challenge concerning the number of additional forwarding rules required at every switch traversed by a flow on the primary and backup paths. These forwarding rules are stored in Ternary Content Addressable Memory (TCAM) which is limited in size and can hold only a few thousands of rules at a switch since it is expensive and power hungry. In this paper, we develop and analyze two proactive local rerouting algorithms namely Forward Local Rerouting (FLR) and Backward Local Rerouting (BLR) to compute backup paths for a primary path. By rerouting the failed traffic from the point of failure, local rerouting enables fast recovery. The proposed FLR and BLR algorithms choose backup paths so as to reduce the number of forwarding table entries with improved sharing of forwarding rules at the switches along the primary and backup paths. We evaluate the proposed algorithms through simulations on different topologies. The results show that the proposed algorithms reduce the average number of additional rules required to protect a flow by up to 75% compared to the existing approaches which do not take into account the limited size of TCAM. The results also show that the proposed algorithms are effective in terms of backup bandwidth sharing efficiency.

Performance study of TCP flows with QoS-supported OpenFlow in data center networks

Providing QoS guarantees is an important task in Cloud data centers. Applications running on data centers can achieve predictable performance only if the network resource - bandwidth - is also guaranteed along with the computational and storage resources. While QoS guaranteeing solutions in network has been a subject of intense research in the past decades, in this paper, we focus on studying the capability of software-defined networking architecture in providing QoS guarantees in data centers. In particular, we experiment with the most well-known and standardized OpenFlow, to analyze the goodness of the QoS APIs supported in the recent version (OpenFlow v1.3) in providing rate-guarantees to TCP flows in a data center. We experiment with the commonly implemented TCP congestion control algorithms, namely CUBIC and New Reno. Our study demonstrates that, with rate-limiters, the TCP flows that go through an OpenFlow-enabled switch can experience drops to batches of packets, forcing them to switch to the slow-start phase periodically, thereby leading to inefficient bandwidth utilization. This behaviour can adversely affect the performance of applications generating the flows. Hence, we need to develop better mechanisms that limit flow-rates while allowing the flows to operate in their congestion-avoidance phase.

Fault tolerance in TCAM-limited software defined networks

In Software Defined Networks (SDNs), while a proactive fault tolerance based on the local rerouting approach enables fast failure recovery, it requires to install forwarding rules for the backup paths in the switch Ternary Content Addressable Memory (TCAM) in advance. Since the TCAM size is limited and forwarding rules are long, using large number of forwarding rules for backup paths leads to frequent eviction of flows at the switches. To guarantee the bandwidth requirement for a flow upon a link failure, bandwidth reservation is also required on the backup path. Inefficient backup bandwidth usage will cause rejection of increased number of flows. In this paper, we address the problem of proactive fault tolerance in SDNs, considering the above challenges by adopting the local rerouting approach. We develop an optimization programming formulation that determines the set of backup paths to protect a flow while minimizing the number of additional rules and bandwidth required for the backup paths. Since this problem is computationally prohibitive, we develop two heuristic algorithms, namely Forward Local Rerouting (FLR) and Backward Local Rerouting (BLR) to compute backup paths so as to improve TCAM and bandwidth usage efficiency. We propose a flexible adaptive failure recovery framework that takes the decision of using FLR or BLR based on the network state. We evaluate the proposed algorithms through simulations and compare their performance with the optimal solution. The results show that the proposed algorithms perform very close to the optimal solution and reduce the number of additional rules required to protect a flow by up to 75% compared to the approaches that do not consider the limited size of TCAM. The results also show that the proposed algorithms are effective in terms of backup bandwidth efficiency and meet the carrier-grade requirement with the recovery time below 50ms.

Fragmentation-Based Multipath Routing for Attack Resilience in Software Defined Networks

In this paper, we propose a Fragmentation-based Multipath Routing (FMR) model for Software Defined Networks (SDNs) to enable attack-resilient data transfer. With the use of erasure encoding to fragment a message, the fragments are routed along multiple paths such that no intermediate node receives enough fragments required for message decoding. This ensures that, any intruder on a compromised node does not infer the original data from the received fragments. We develop an optimization programming formulation of the problem to choose reliable paths that provide resilience to attacks. Using FMR, the SDN controller dynamically routes the data fragments along a set of most reliable paths to achieve multipath diversity and hence improve data availability at the destination even in the presence of an attack. We carry out performance studies and demonstrate the effectiveness of our approach in terms of weighted path reliability and blocking performance.

Proportional bandwidth sharing using Bayesian inference in SDN-based data centers

With the evolution of software-defined networking (SDN) paradigm, traffic management in data center networks has become flexible and scalable. The existing solution using OpenFlow, the rate-guaranteeing mechanism, is inefficient as it limits the rate of the flows by dropping batches of packets to achieve the desired throughput. In this paper, we propose BASIS, a solution based on Bayesian inference for providing proportional Quality of Service (QoS) guarantees to tenants in a datacenter network. With BASIS, the bandwidth of an outgoing congested link will be shared among the competing flows in proportion to the weights chosen by them. We use Bayesian inference to capture the history of flow arrival rates and their offered load using a single queue, and estimate the differential drop probabilities of flows in a way that respects the weights assigned to them on arrival. Unlike the rate-limiting approach, BASIS proactively drops a packet of a flow probabilistically to achieve the desired throughput and avoids dropping batches of packets. We evaluate the proposed solution in an emulated SDN platform and show that BASIS achieves the desired throughput with lesser number of packet drops than the existing approaches.

Integrated QoS-aware Resource Provisioning for Parallel and Distributed Applications

With more parallel and distributed applications moving to Cloud and data centers, it is challenging to provide predictable and controllable resources to multiple tenants, and thus guarantee application performance. In this paper, we propose an integrated QoS-aware resource provisioning platform based on virtualization technology for computing, storage and network resources. Coarse-grained CPU mapping and fine-grained CPU scheduling mechanisms are proposed to enable adjustable computing power. A hierarchical distributed scheduling mechanism is implemented on a scalable storage system to guarantee I/O throughput for individual tenants and applications. A network manager has also been developed to guarantee the data transmission rate. Web-based interface enables users to monitor real time resource utilization and to adjust resource QoS levels on the fly. According to our experimental results, the resource cost can be saved up to 45% without degrading the performance of a distributed data processing benchmark, and the performance of a parallel agent-based simulation can be improved by 91% using the same amount of resources.