Srinivas Shakkottai

Multi-path TCP: a joint congestion control and routing scheme to exploit path diversity in the internet

We consider the problem of congestion-aware multi-path routing in the Internet. Currently, Internet routing protocols select only a single path between a source and a destination. However, due to many policy routing decisions, single-path routing may limit the achievable throughput. In this paper, we envision a scenario where multi-path routing is enabled in the Internet to take advantage of path diversity. Using minimal congestion feedback signals from the routers, we present a class of algorithms that can be implemented at the sources to stably and optimally split the flow between each source-destination pair. We then show that the connection-level throughput region of such multi-path routing/congestion control algorithms can be larger than that of a single-path congestion control scheme

Network optimization and control

We study how protocol design for various functionalities within a communication network architecture can be viewed as a distributed resource allocation problem. This involves understanding what resources are, how to allocate them fairly, and perhaps most importantly, how to achieve this goal in a distributed and stable fashion. We start with ideas of a centralized optimization framework and show how congestion control, routing and scheduling in wired and wireless networks can be thought of as fair resource allocation. We then move to the study of controllers that allow a decentralized solution of this problem. These controllers are the analytical equivalent of protocols in use on the Internet today, and we describe existing protocols as realizations of such controllers. The Internet is a dynamic system with feedback delays and flows that arrive and depart, which means that stability of the system cannot be taken for granted. We show how to incorporate stability into protocols, and thus, prevent undesirable network behavior. Finally, we consider a futuristic scenario where users are aware of the effects of their actions and try to game the system. We will see that the optimization framework is remarkably robust even to such gaming.

The multicast capacity of large multihop wireless networks

We consider wireless ad hoc networks with a large number of users. Subsets of users might be interested in identical information, and so we have a regime in which several multicast sessions may coexist. We first calculate an upper bound on the achievable transmission rate per multicast flow as a function of the number of multicast sources in such a network. We then propose a simple comb-based architecture for multicast routing, which achieves the upper bound in an order sense under certain constraints. Compared to the approach of constructing a Steiner tree to decide multicast paths, our construction achieves the same order-optimal results while requiring little location information and no computational overhead.

The Price of Simplicity

We study revenue-maximizing pricing by a service provider in a communication network and compare revenues from simple pricing rules to the maximum revenues that are feasible. In particular, we focus on flat entry fees as the simplest pricing rule. We provide a lower bound for the ratio between the revenue from this pricing rule and maximum revenue, which we refer to as the Price of Simplicity. We characterize what types of environments lead to a low Price of Simplicity and show that in a range of environments, the loss of revenue from using simple entry fees is small. We then study the Price of Simplicity for a simple non-linear pricing (price discrimination) scheme based on the Paris Metro Pricing. The service provider creates different service classes and charges differential entry fees for these classes. We show that the gain from this type of price discrimination is small, particularly in environments in which the simple entry fee pricing leads to a low Price of Simplicity.

The Case for Non-Cooperative Multihoming of Users to Access Points in IEEE 802.11 WLANs

In many cases, a mobile user has the option of connecting to one of several IEEE 802.11 access points (APs),each using an independent channel. User throughput in each AP is determined by the number of other users as well as the frame size and physical rate being used. We consider the scenario where users could multihome, i.e., split their traffic amongst all the available APs, based on the throughput they obtain and the price charged. Thus, they are involved in a non-cooperative game with each other. We convert the problem into a fluid model and show that under a pricing scheme, which we call the cost price mechanism, the total system throughput is maximized,i.e., the system suffers no loss of efficiency due to selfish dynamics. We also study the case where the Internet Service Provider (ISP) could charge prices greater than that of the cost price mechanism. We show that even in this case multihoming outperforms unihoming, both in terms of throughput as well as profit to the ISP.

Multihoming of Users to Access Points in WLANs: A Population Game Perspective

We consider non-cooperative mobiles, each faced with the problem of which subset of WLANs access points (APs) to connect and multihome to, and how to split its traffic among them. Considering the many users regime, we obtain a potential game model and study its equilibrium. We obtain pricing for which the total throughput is maximized at equilibrium and study the convergence to equilibrium under various evolutionary dynamics. We also study the case where the Internet service provider (ISP) could charge prices greater than that of the cost price mechanism and show that even in this case multihoming is desirable.

Avoiding Interruptions — A QoE Reliability Function for Streaming Media Applications

We take an analytical approach to study fundamental rate-delay-reliability trade-offs in the context of media streaming. We consider the probability of interruption in media playback (buffer underflow) as well as the number of initially buffered packets (initial waiting time) as the Quality of user Experience (QoE) metrics. We characterize the optimal trade-off between these metrics as a function of system parameters such as the packet arrival rate and file size, for different channel models. In the first model, we assume packets arrive according to independent Poisson processes from multiple servers or peers. We use random linear network coding to simplify the packet requests at the network layer and avoid duplicate packet reception. This allows us to model the receivers buffer as a queue with Poisson arrivals and deterministic departures. For this model, we show that for arrival rates slightly larger than the play rate, the minimum initial buffering required to achieve certain level of interruption probability remains bounded as the file size grows. This is not the case when the arrival rate and the play rate match. In the second model, we consider channels with memory, which can be modeled using Markovian arrival processes. We characterize the optimal trade-off curves for the infinite file size case, in such Markovian environments.

Economics of network pricing with multiple ISPs

In this paper we examine how transit and customer prices are set in a network consisting of multiple ISPs. Some ISPs may be geographically co-located so that they compete for the same set of end users. We examine the existence of equilibrium price strategies in this situation and show how positive profit can be achieved using threat strategies. It is shown that if the number of ISPs competing for the same customers is large then it can lead to price wars. ISPs that are not geographically co-located may not directly compete for users, but are nevertheless involved in a non-cooperative game of setting access and transit prices for each other. We study how such ISPs are linked economically through transit ISPs by considering a multi-stage game. We also consider the economics of private exchange points and show that they could become far more wide spread then they currently are.

Demand-aware content distribution on the internet

The rapid growth of media content distribution on the Internet in the past few years has brought with it commensurate increases in the costs of distributing that content. Can the content distributor defray these costs through a more innovative approach to distribution? In this paper, we evaluate the benefits of a hybrid system that combines peer-to-peer and a centralized client-server approach against each method acting alone. A key element of our approach is to explicitly model the temporal evolution of demand. In particular, we employ a word-of-mouth demand evolution model due to Bass to represent the evolution of interest in a piece of content. Our analysis is carried out in an order scaling depending on the total potential mass of customers N in the market. Using this approach, we study the relative performance of peer-to-peer and centralized client-server schemes, as well as a hybrid of the two-both from the point of view of consumers as well as the content distributor. We show how awareness of demand can be used to attain a given average delay target with lowest possible utilization of the central server by using the hybrid scheme. We also show how such awareness can be used to take provisioning decisions. Our insights are obtained in a fluid model and supported by stochastic simulations.

Content-aware caching and traffic management in content distribution networks

The rapid increase of content delivery over the Internet has led to the proliferation of content distribution networks (CDNs). Management of CDNs requires algorithms for request routing, content placement, and eviction in such a way that user delays are small. We abstract the system of frontend source nodes and backend caches of the CDN in the likeness of the input and output nodes of a switch. In this model, queues of requests for different pieces of content build up at the source nodes, which route these requests to a cache that contains the requested content. For each request that is routed to a cache, a corresponding data file is transmitted back to the requesting source across links of finite capacity. Caches are of finite size, and the content of the caches can be refreshed periodically. Our objective is to design policies for request routing, content placement and content eviction with the goal of small user delays. Stable policies ensure the finiteness of the request queues, while good polices also lead to short queue lengths. We first design a throughput-optimal algorithm that solves the routing-placement-eviction problem. The design yields insight into the impact of different cache refresh policies on queue length, and we construct throughput optimal algorithms that engender short queue lengths. We illustrate the potential of our approach through simulations on different CDN topologies.