Lying Your Way to Better Traffic Engineering

To optimize the flow of traffic in IP networks, operators do traffic engineering (TE), i.e., tune routing-protocol parameters in response to traffic demands. TE in IP networks typically involves configuring static link weights and splitting traffic between the resulting shortest-paths via the Equal-Cost-MultiPath (ECMP) mechanism. Unfortunately, ECMP is a notoriously cumbersome and indirect means for optimizing traffic flow, often leading to poor network performance. Also, obtaining accurate knowledge of traffic demands as the input to TE is elusive, and traffic conditions can be highly variable, further complicating TE. We leverage recently proposed schemes for increasing ECMP's expressiveness via carefully disseminated bogus information ("lies") to design COYOTE, a readily deployable TE scheme for robust and efficient network utilization. COYOTE leverages new algorithmic ideas to configure (static) traffic splitting ratios that are optimized with respect to all (even adversarially chosen) traffic scenarios within the operator's "uncertainty bounds". Our experimental analyses show that COYOTE significantly outperforms today's prevalent TE schemes in a manner that is robust to traffic uncertainty and variation. We discuss experiments with a prototype implementation of COYOTE

Multi-capacity bin packing with dependent items and its application to the packing of brokered workloads in virtualized environments

Providing resource allocation with performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, in which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. Existing resource allocation solutions either assume that applications manage their data transfer between their virtualized resources, or that cloud providers manage their internal networking resources. With the increased prevalence of brokerage services in cloud platforms, there is a need for resource allocation solutions that provides predictability guarantees in settings, in which neither application scheduling nor cloud provider resources can be managed/controlled by the broker. This paper addresses this problem, as we define the Network-Constrained Packing (NCP) problem of finding the optimal mapping of brokered resources to applications with guaranteed performance predictability. We prove that NCP is NP-hard, and we define two special instances of the problem, for which exact solutions can be found efficiently. We develop a greedy heuristic to solve the general instance of the NCP problem , and we evaluate its efficiency using simulations on various application workloads, and network models.This work was done while author was at Boston University. It was partially supported by NSF CISE awards #1430145, #1414119, #1239021 and #1012798. (1430145 - NSF CISE; 1414119 - NSF CISE; 1239021 - NSF CISE; 1012798 - NSF CISE

Network-constrained packing of brokered workloads in virtualized environments

Providing resource allocation with performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, in which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. Existing resource allocation solutions either assume that applications manage their data transfer between their virtualized resources, or that cloud providers manage their internal networking resources.With the increased prevalence of brokerage services in cloud platforms, there is a need for resource allocation solutions that provides predictability guarantees in settings, in which neither application scheduling nor cloud provider resources can be managed/controlled by the broker. This paper addresses this problem, as we define the Network-Constrained Packing (NCP)problem of finding the optimal mapping of brokered resources to applications with guaranteed performance predictability. We prove that NCP is NP-hard, and we define two special instances of the problem, for which exact solutions can be found efficiently. We develop a greedy heuristic to solve the general instance of the NCP problem, and we evaluate its efficiency using simulations on various application workloads, and network models.This work is supported by NSF CISE CNS Award #1347522, # 1239021, # 1012798

Application-Aware Deadlock-Free Oblivious Routing

Conventional oblivious routing algorithms are either not application-aware or assume that each flow has its own private channel to ensure deadlock avoidance. We present a framework for application-aware routing that assures deadlock-freedom under one or more channels by forcing routes to conform to an acyclic channel dependence graph. Arbitrary minimal routes can be made deadlock-free through appropriate static channel allocation when two or more channels are available. Given bandwidth estimates for flows, we present a mixed integer-linear programming (MILP) approach and a heuristic approach for producing deadlock-free routes that minimize maximum channel load. The heuristic algorithm is calibrated using the MILP algorithm and evaluated on a number of benchmarks through detailed network simulation. Our framework can be used to produce application-aware routes that target the minimization of latency, number of flows through a link, bandwidth, or any combination thereof

Application-Aware Deadlock-Free Oblivious Routing

Conventional oblivious routing algorithms are either not application-aware or assume that each flow has its own private channel to ensure deadlock avoidance. We present a framework for application-aware routing that assures deadlock-freedom under one or more channels by forcing routes to conform to an acyclic channel dependence graph. Arbitrary minimal routes can be made deadlock-free through appropriate static channel allocation when two or more channels are available. Given bandwidth estimates for flows, we present a mixed integer-linear programming (MILP) approach and a heuristic approach for producing deadlock-free routes that minimize maximum channel load. The heuristic algorithm is calibrated using the MILP algorithm and evaluated on a number of benchmarks through detailed network simulation. Our framework can be used to produce application-aware routes that target the minimization of latency, number of flows through a link, bandwidth, or any combination thereof

Design, Analysis, and Optimization of Traffic Engineering for Software Defined Networks

Network traffic has been growing exponentially due to the rapid development of applications and communications technologies. Conventional routing protocols, such as Open-Shortest Path First (OSPF), do not provide optimal routing and result in weak network resources. Optimal traffic engineering (TE) is not applicable in practice due to operational constraints such as limited memory on the forwarding devices and routes oscillation. Recently, a new way of centralized management of networks enabled by Software-Defined Networking (SDN) made it easy to apply most traffic engineering ideas in practice. \par Toward creating an applicable traffic engineering system, we created a TE simulator for experimenting with TE and evaluating TE systems efficiently as this tool employs parallel processing to achieve high efficiency. The purpose of the simulator is two aspects: (1) We use it to understand traffic engineering, (2) we use it to formulate a new traffic engineering algorithm that is near-optimal and applicable in practice. We study the design of some important aspects of any TE system. In particular, the consequences of achieving optimal TE by solving the multi-commodity flow problem (MCF) and the consequences of choosing single-path routing over multi-path routing. With the help of the TE simulator, we compare many TE systems constructed by combining different paths selection techniques with two objective functions for rate adaptations: load balancing (LB) and average delay (AD). The results confirm that paths selected based on the theoretical approach known as Oblivious Routing combined with AD objective function can significantly increase the performance in terms of throughput, congestion, and delay.\par However, the new proposed system comes with a cost. The AD function has a higher complexity than the LB function. We show that this problem can be tackled by training deep learning models. We trained two models with two different neural network architectures: Multilayer Perceptron (MLP) and Long-Short Term Memory (LSTM), to get a responsive traffic engineering system. The input training data is based on synthetic data obtained from the simulator. The output of the two models is the split ratios that the SDN controller uses to instruct the switching devices about how to forward traffic in the network. The result confirms that both models are effective and can be used to forward traffic in an optimal or near-optimal way. The LSTM model has shown a slightly better result than MLP due to its ability to predict a longer output sequence