Asymptotically-Optimal Incentive-Based En-Route Caching Scheme

Content caching at intermediate nodes is a very effective way to optimize the operations of Computer networks, so that future requests can be served without going back to the origin of the content. Several caching techniques have been proposed since the emergence of the concept, including techniques that require major changes to the Internet architecture such as Content Centric Networking. Few of these techniques consider providing caching incentives for the nodes or quality of service guarantees for content owners. In this work, we present a low complexity, distributed, and online algorithm for making caching decisions based on content popularity, while taking into account the aforementioned issues. Our algorithm performs en-route caching. Therefore, it can be integrated with the current TCP/IP model. In order to measure the performance of any online caching algorithm, we define the competitive ratio as the ratio of the performance of the online algorithm in terms of traffic savings to the performance of the optimal offline algorithm that has a complete knowledge of the future. We show that under our settings, no online algorithm can achieve a better competitive ratio than $\Omega(\log n)$, where $n$ is the number of nodes in the network. Furthermore, we show that under realistic scenarios, our algorithm has an asymptotically optimal competitive ratio in terms of the number of nodes in the network. We also study an extension to the basic algorithm and show its effectiveness through extensive simulations

Flexpop: A popularity-based caching strategy for multimedia applications in information-centric networking

Information-Centric Networking (ICN) is the dominant architecture for the future Internet. In ICN, the content items are stored temporarily in network nodes such as routers. When the memory of routers becomes full and there is no room for a new arriving content, the stored contents are evicted to cope with the limited cache size of the routers. Therefore, it is crucial to develop an effective caching strategy for keeping popular contents for a longer period of time. This study proposes a new caching strategy, named Flexible Popularity-based Caching (FlexPop) for storing popular contents. The FlexPop comprises two mechanisms, i.e., Content Placement Mechanism (CPM), which is responsible for content caching, and Content Eviction Mechanism (CEM) that deals with content eviction when the router cache is full and there is no space for the new incoming content. Both mechanisms are validated using Fuzzy Set Theory, following the Design Research Methodology (DRM) to manifest that the research is rigorous and repeatable under comparable conditions. The performance of FlexPop is evaluated through simulations and the results are compared with those of the Leave Copy Everywhere (LCE), ProbCache, and Most Popular Content (MPC) strategies. The results show that the FlexPop strategy outperforms LCE, ProbCache, and MPC with respect to cache hit rate, redundancy, content retrieval delay, memory utilization, and stretch ratio, which are regarded as extremely important metrics (in various studies) for the evaluation of ICN caching. The outcomes exhibited in this study are noteworthy in terms of making FlexPop acceptable to users as they can verify the performance of ICN before selecting the right caching strategy. Thus FlexPop has potential in the use of ICN for the future Internet such as in deployment of the IoT technology

Online algorithms for content caching: an economic perspective

Content Caching at intermediate nodes, such that future requests can be served without going back to the origin of the content, is an effective way to optimize the operations of computer networks. Therefore, content caching reduces the delivery delay and improves the users’ Quality of Experience (QoE). The current literature either proposes offline algorithms that have complete knowledge of the request profile a priori, or proposes heuristics without provable performance. In this dissertation, online algorithms are presented for content caching in three different network settings: the current Internet Network, collaborative multi-cell coordinated network, and future Content Centric Networks (CCN). Due to the difficulty of obtaining a prior knowledge of contents’ popularities in real scenarios, an algorithm has to make a decision whether to cache a content or not when a request for the content is made, and without the knowledge of any future requests. The performance of the online algorithms is measured through a competitive ratio analysis, comparing the performance of the online algorithm to that of an omniscient optimal offline algorithm. Through theoretical analyses, it is shown that the proposed online algorithms achieve either the optimal or close to the optimal competitive ratio. Moreover, the algorithms have low complexity and can be implemented in a distributed way. The theoretical analyses are complemented with simulation-based experiments, and it is shown that the online algorithms have better performance compared to the state of the art caching schemes

In Pursuit of Desirable Equilibria in Large Scale Networked Systems

This thesis addresses an interdisciplinary problem in the context of engineering, computer science and economics: In a large scale networked system, how can we achieve a desirable equilibrium that benefits the system as a whole? We approach this question from two perspectives. On the one hand, given a system architecture that imposes certain constraints, a system designer must propose efficient algorithms to optimally allocate resources to the agents that desire them. On the other hand, given algorithms that are used in practice, a performance analyst must come up with tools that can characterize these algorithms and determine when they can be optimally applied. Ideally, the two viewpoints must be integrated to obtain a simple system design with efficient algorithms that apply to it. We study the design of incentives and algorithms in such large scale networked systems under three application settings, referred to herein via the subheadings: Incentivizing Sharing in Realtime D2D Networks: A Mean Field Games Perspective, Energy Coupon: A Mean Field Game Perspective on Demand Response in Smart Grids, Dynamic Adaptability Properties of Caching Algorithms, and Accuracy vs. Learning Rate of Multi-level Caching Algorithms. Our application scenarios all entail an asymptotic system scaling, and an equilibrium is defined in terms of a probability distribution over system states. The question in each case is to determine how to attain a probability distribution that possesses certain desirable properties. For the first two applications, we consider the design of specific mechanisms to steer the system toward a desirable equilibrium under self interested decision making. The environments in these problems are such that there is a set of shared resources, and a mechanism is used during each time step to allocate resources to agents that are selfish and interact via a repeated game. These models are motivated by resource sharing systems in the context of data communication, transportation, and power transmission networks. The objective is to ensure that the achieved equilibria are socially desirable. Formally, we show that a Mean Field Game can be used to accurately approximate these repeated game frameworks, and we describe mechanisms under which socially desirable Mean Field Equilibria exist. For the third application, we focus on performance analysis via new metrics to determine the value of the attained equilibrium distribution of cache contents when using different replacement algorithms in cache networks. The work is motivated by the fact that typical performance analysis of caching algorithms consists of determining hit probability under a fixed arrival process of requests, which does not account for dynamic variability of request arrivals. Our main contribution is to define a function which accounts for both the error due to time lag of learning the items' popularity, as well as error due to the inaccuracy of learning, and to characterize the tradeoff between the two that conventional algorithms achieve. We then use the insights gained in this exercise to design new algorithms that are demonstrably superior

In Pursuit of Desirable Equilibria in Large Scale Networked Systems

This thesis addresses an interdisciplinary problem in the context of engineering, computer science and economics: In a large scale networked system, how can we achieve a desirable equilibrium that benefits the system as a whole? We approach this question from two perspectives. On the one hand, given a system architecture that imposes certain constraints, a system designer must propose efficient algorithms to optimally allocate resources to the agents that desire them. On the other hand, given algorithms that are used in practice, a performance analyst must come up with tools that can characterize these algorithms and determine when they can be optimally applied. Ideally, the two viewpoints must be integrated to obtain a simple system design with efficient algorithms that apply to it. We study the design of incentives and algorithms in such large scale networked systems under three application settings, referred to herein via the subheadings: Incentivizing Sharing in Realtime D2D Networks: A Mean Field Games Perspective, Energy Coupon: A Mean Field Game Perspective on Demand Response in Smart Grids, Dynamic Adaptability Properties of Caching Algorithms, and Accuracy vs. Learning Rate of Multi-level Caching Algorithms. Our application scenarios all entail an asymptotic system scaling, and an equilibrium is defined in terms of a probability distribution over system states. The question in each case is to determine how to attain a probability distribution that possesses certain desirable properties. For the first two applications, we consider the design of specific mechanisms to steer the system toward a desirable equilibrium under self interested decision making. The environments in these problems are such that there is a set of shared resources, and a mechanism is used during each time step to allocate resources to agents that are selfish and interact via a repeated game. These models are motivated by resource sharing systems in the context of data communication, transportation, and power transmission networks. The objective is to ensure that the achieved equilibria are socially desirable. Formally, we show that a Mean Field Game can be used to accurately approximate these repeated game frameworks, and we describe mechanisms under which socially desirable Mean Field Equilibria exist. For the third application, we focus on performance analysis via new metrics to determine the value of the attained equilibrium distribution of cache contents when using different replacement algorithms in cache networks. The work is motivated by the fact that typical performance analysis of caching algorithms consists of determining hit probability under a fixed arrival process of requests, which does not account for dynamic variability of request arrivals. Our main contribution is to define a function which accounts for both the error due to time lag of learning the items' popularity, as well as error due to the inaccuracy of learning, and to characterize the tradeoff between the two that conventional algorithms achieve. We then use the insights gained in this exercise to design new algorithms that are demonstrably superior