Centralized content delivery infrastructure exploiting resource pools : performance models and asymptotics

textWe consider a centralized content delivery infrastructure where a large number of storage-intensive files are replicated across several collocated servers. To achieve scalable delays in file downloads under stochastic loads, we allow multiple servers to work together as a pooled resource to meet individual download requests. In such systems basic questions include: How and where to replicate files? How significant are the gains of resource pooling over policies which use single server per request? What are the tradeoffs among conflicting metrics such as delays, reliability and recovery costs, and power? How robust is performance to heterogeneity and choice of fairness criterion? In this thesis we provide a simple performance model for large systems towards addressing these basic questions. For large systems where the overall system load is proportional to the number of servers, we establish scaling laws among delays, system load, number of file replicas, demand heterogeneity, power, and network capacity.Electrical and Computer Engineerin

Breaking the Economic Barrier of Caching in Cellular Networks: Incentives and Contracts

In this paper, a novel approach for providing incentives for caching in small cell networks (SCNs) is proposed based on the economics framework of contract theory. In this model, a mobile network operator (MNO) designs contracts that will be offered to a number of content providers (CPs) to motivate them to cache their content at the MNO's small base stations (SBSs). A practical model in which information about the traffic generated by the CPs' users is not known to the MNO is considered. Under such asymmetric information, the incentive contract between the MNO and each CP is properly designed so as to determine the amount of allocated storage to the CP and the charged price by the MNO. The contracts are derived by the MNO in a way to maximize the global benefit of the CPs and prevent them from using their private information to manipulate the outcome of the caching process. For this interdependent contract model, the closed-form expressions of the price and the allocated storage space to each CP are derived. This proposed mechanism is shown to satisfy the sufficient and necessary conditions for the feasibility of a contract. Moreover, it is shown that the proposed pricing model is budget balanced, enabling the MNO to cover all the caching expenses via the prices charged to the CPs. Simulation results show that none of the CPs will have an incentive to choose a contract designed for CPs with different traffic loads.Comment: Accepted for publication at Globecom 201

Coupling from the past in hybrid models for file sharing peer to peer systems

International audienceIn this paper we show how file sharing peer to peer systems can be modeled by hybrid systems with a continuous part corresponding to a fluid limit of files and a discrete part corresponding to customers. Then we show that this hybrid system is amenable to perfect simulations (i.e. simulations providing samples of the system states which distributions have no bias from the asymptotic distribution of the system). An experimental study is carried to show the respective influence that the different parameters (such as time-to-live, rate of requests, connection time) play on the behavior of large peer to peer systems, and also to show the effectiveness of this approach for numerical solutions of stochastic hybrid systems

Non-fixation for Biased Activated Random Walks

We prove that the model of Activated Random Walks on Z^d with biased jump distribution does not fixate for any positive density, if the sleep rate is small enough, as well as for any finite sleep rate, if the density is close enough to 1. The proof uses a new criterion for non-fixation. We provide a pathwise construction of the process, of independent interest, used in the proof of this non-fixation criterion

Network Game Theory Models of Services and Quality Competition with Applications to Future Internet Architectures and Supply Chains

The Internet has transformed the way in which we conduct business and perform economic and financial transactions. One key challenge of the Internet is the inefficiency of the mechanisms by which technology is deployed and the business and economic models surrounding these processes (Wolf et al. (2014)). Equilibrium models for the Internet generally assume basic economic relationships. However, in new paradigms for the Internet and in supply chain networks, price is not the only factor; quality of service (QoS) is also of increasing importance. Supply chains networks, which give us the means to manufacture products and deliver them to points of demand across the globe, are also under many pressures to offer differentiated products and services (Nagurney (2014)). It is well-known today that success is determined by how well the entire supply chain performs, rather than the performance of its individual entities. This dissertation contributes to the analysis, design, and management of the future Internet and supply chain networks with a focus on price and quality competition in service-oriented networks. Specifically, I focus on economic models for the Internet of the future by developing both a basic and a general network economic game theory model of a quality-based service-oriented Internet to study competition among service providers. To study and analyze the underlying dynamics of the various economic decision-makers, subsequently, I develop a dynamic network economic model of a service-oriented Internet with price and quality competition using projected dynamical systems theory. Then, to assess the prices for various contract durations at the demand markets, I consider a game theory model of a service-oriented Internet in which the network providers compete in usage service rates, quality levels, and duration-based contracts. Finally, I construct a model that captures the competition among manufacturers and freight service providers in a supply chain network. This model is the first one in the literature that handles both price and quality competition with multiple modes of shipment from both equilibrium and dynamic perspectives. For each model, I derive the governing equilibrium conditions and provide the equivalent variational inequality formulations. In order to illustrate the modeling framework and the algorithm, I present computed solutions to several numerical examples for each model as well as sensitivity analysis results. This dissertation is heavily based on the following papers: Saberi, Nagurney, and Wolf (2014), Nagurney et al. (2014a), Nagurney et al. (2015b), and Nagurney et al. (2015a) as well as additional results and conclusions

Efficient Content Distribution With Managed Swarms

Content distribution has become increasingly important as people have become more reliant on Internet services to provide large multimedia content. Efficiently distributing content is a complex and difficult problem: large content libraries are often distributed across many physical hosts, and each host has its own bandwidth and storage constraints. Peer-to-peer and peer-assisted download systems further complicate content distribution. By contributing their own bandwidth, end users can improve overall performance and reduce load on servers, but end users have their own motivations and incentives that are not necessarily aligned with those of content distributors. Consequently, existing content distributors either opt to serve content exclusively from hosts under their direct control, and thus neglect the large pool of resources that end users can offer, or they allow end users to contribute bandwidth at the expense of sacrificing complete control over available resources. This thesis introduces a new approach to content distribution that achieves high performance for distributing bulk content, based on managed swarms. Managed swarms efficiently allocate bandwidth from origin servers, in-network caches, and end users to achieve system-wide performance objectives. Managed swarming systems are characterized by the presence of a logically centralized coordinator that maintains a global view of the system and directs hosts toward an efficient use of bandwidth. The coordinator allocates bandwidth from each host based on empirical measurements of swarm behavior combined with a new model of swarm dynamics. The new model enables the coordinator to predict how swarms will respond to changes in bandwidth based on past measurements of their performance. In this thesis, we focus on the global objective of maximizing download bandwidth across end users in the system. To that end, we introduce two algorithms that the coordinator can use to compute efficient allocations of bandwidth for each host that result in high download speeds for clients. We have implemented a scalable coordinator that uses these algorithms to maximize system-wide aggregate bandwidth. The coordinator actively measures swarm dynamics and uses the data to calculate, for each host, a bandwidth allocation among the swarms competing for the host's bandwidth. Extensive simulations and a live deployment show that managed swarms significantly outperform centralized distribution services as well as completely decentralized peer-to-peer systems