Low complexity content replication through clustering in Content-Delivery Networks

Contemporary Content Delivery Networks (CDN) handle a vast number of content items. At such a scale, the replication schemes require a significant amount of time to calculate and realize cache updates, and hence they are impractical in highly-dynamic environments. This paper introduces cluster-based replication, whereby content items are organized in clusters according to a set of features, given by the cache/network management entity. Each cluster is treated as a single item with certain attributes, e.g., size, popularity, etc. and it is then altogether replicated in network caches so as to minimize overall network traffic. Clustering items reduces replication complexity; hence it enables faster and more frequent caches updates, and it facilitates more accurate tracking of content popularity. However, clustering introduces some performance loss because replication of clusters is more coarse-grained compared to replication of individual items. This tradeoff can be addressed through proper selection of the number and composition of clusters. Due to the fact that the exact optimal number of clusters cannot be derived analytically, an efficient approximation method is proposed. Extensive numerical evaluations of time-varying content popularity scenarios allow to argue that the proposed approach reduces core network traffic, while being robust to errors in popularity estimation

Client and server games in peer-to-peer networks

We consider a content sharing network of noncooperative peers. The strategy set of each peer comprises, (i) client strategies, namely feasible request load splits to servers, and (ii) server strategies, namely scheduling disciplines on requests. First, we consider the request load splitting game for given server strategies such as First-In-First-Out or given absolute priority policies. A peer splits its request load to servers to optimize its performance objective. We consider the class of best response load splitting policies residing between the following extremes: a truly selfish, or egotistic one, where a peer optimizes its own delay, and a pseudo-selfish or altruistic one, where a peer also considers incurred delays to others. We derive conditions for Nash equilibrium points (NEPs) and discuss convergence to NEP and properties of the NEP. For both the egotistic cases, the NEP is unique. For the altruistic case, each of the multiple NEPs is an optimum, a global one for the FIFO case and a local one otherwise. Next, we include scheduling in peer strategies. With its scheduling discipline, a peer cannot directly affect its delay, but it can affect the NEP after peers play the load splitting game. The idea is that peer i should offer high priority to (and thus attract traffic from) higher-priority peers that cause large delay to i at other servers. We devise two-stage game models, where, at a first stage, a peer selects a scheduling rule in terms of a convex combination of absolute priorities, and subsequently peers play the load splitting game. In the most sophisticated rule, a peer selects a scheduling discipline that minimizes its delay at equilibrium, after peers play the load splitting game. We also suggest various heuristics for picking the scheduling discipline. Our models and results capture the dual client-server peer role and aim at quantifying the impact of selfish peer interaction on equilibria. ©2009 IEEE

Low complexity algorithms for relay selection and power control in interference-limited environments

We consider an interference-limited wireless network, where multiple source-destination pairs compete for the same pool of relay nodes. In an attempt to maximize the sum rate of the system, we address the joint problem of relay assignment and power control. Initially, we study the autonomous scenario, where each source greedily selects the strategy (transmission power and relay) that maximizes its individual rate, leading to a simple one-shot algorithm of linear complexity. Then, we propose a more sophisticated algorithm of polynomial complexity that is amenable to distributed implementation through appropriate message passing. We evaluate the sum rate performance of the proposed algorithms and derive conditions for optimality. Our schemes incorporate two of the basic features of the LTE-Advanced broadband cellular system, namely interference management and relaying. We also provide guidelines on how our algorithms can be incorporated in such multichannel systems

On-line storage management with distributed decision making for content-centric networks

Content traffic proliferation in Internet makes more dire than ever the development of radical new network architectures, where information will be addressed by semantic attributes rather than the origin and destination identities. In this direction, content-centric networking appears as a flexible communication model that meets the requirements of the content distribution trends of the future Internet. In such networks, information will reside at various locations/nodes (the Content Delivery Network surrogate servers) and the requests of the users for some piece of information will be directed to the closest replica. Since the location of the users and the popularity of the content varies over time, the problem of finding the optimal replication pattern for the available content, given the storage constraints, comes into the foreground. In this paper, we propose two on-line storage management algorithms of gradient descent type, designed specifically for content-centric networks. The proposed algorithms are of polynomial complexity and thus adapt easily to any environmental changes. Each node re-assigns its information items with the aim to minimize the overall traffic cost of the content delivery as the popularity and locality of users' requests change. While both the proposed algorithms operate in a distributed way, differ in the amount of information required for the decision making. Thus, we identify the inherent information performance tradeoff and compare them in terms of network traffic, convergence speed and amount of circulated information. © 2011 IEEE

Client-server games and their equilibria in peer-to-peer networks

In peer-to-peer networks, each peer acts simultaneously as client and server, by issuing and satisfying content requests respectively. In this work, we use concepts from non-cooperative game theory to model the interaction of autonomous peers. The client strategy set consists of feasible request load splits towards servers, while the server strategy set is formed out of possible service disciplines on received requests. The performance metric of interest is the average retrieval delay of content requests. First, we assume preassigned fixed server policies (FIFO and priority) and study the emerging client request load splitting game. Peers are either egotistic (i.e. interested only in optimizing their own delay) or altruistic ones that also take into account delay incurred to other peers. We consider best response updates to model iterative peer interaction. For egotistic peers, we show that the sequence of best responses always converges to the unique Nash Equilibrium Point (NEP). For altruistic peers best response updates converge to one of the multiple NEPs exist, with each one being a global optimum for the FIFO case and a local optimum for any other service strategy profile. We also consider mixed swarms consisting of both egotistic and (partially-) altruistic peers and show an interesting transition from one to multiple NEPs. Next, we include service strategies in the peer strategy set. Though with its service policy a peer cannot directly affect its delay, it can affect the resulting NEP. We devise two-level game models, where, at a first level, a peer selects its favorable service rule and then peers play a client load splitting game. (C) 2014 Elsevier B.V. All rights reserved

Autonomic cache management in Information-Centric Networks

Recent research efforts in the area of future networks indicate Information-Centric Networking (ICN) as the dominant architecture for the Future Internet. The main promise of ICN is that of shifting the communication paradigm of the internetworking layer from machine endpoints to information access and delivery. Optimized content dissemination and efficient caching of information is key to delivering on this promise. Moreover, current trends in management of future networks adopt a more distributed autonomic management architecture where management intelligence is placed inside the network with respect to traditional off-line external management systems. In this paper, we present an autonomic cache management approach for ICNs, where distributed managers residing in cache-enabled nodes decide on which items to cache. We propose three online cache management algorithms with different level of autonomicity and compare them with respect to performance, complexity, execution time and message exchange overhead. Our extensive simulation-based experimentation signifies the importance of network wide knowledge and cooperation. © 2012 IEEE

The Role of Aggregators in Smart Grid Demand Response Markets

The design of efficient Demand Response (DR) mechanisms for the residential sector entails significant challenges, due to the large number of home users and the negligible impact of each of them on the market. In this paper, we introduce a hierarchical market model for the smart grid where a set of competing aggregators act as intermediaries between the utility operator and the home users. The operator seeks to minimize the smart grid operational cost and offers rewards to aggregators toward this goal. Profit-maximizing aggregators compete to sell DR services to the operator and provide compensation to end-users in order to modify their preferable consumption pattern. Finally, end-users seek to optimize the tradeoff between earnings received from the aggregator and discomfort from having to modify their pattern. Based on this market model, we first address the benchmark scenario from the point of view of a cost-minimizing operator that has full information about user demands. Then, we consider a DR market, where all entities are self-interested and non-cooperative. The proposed market scheme captures the diverse objectives of the involved entities and, compared to flat pricing, guarantees significant benefits for each. Using realistic demand traces, we quantify the arising DR benefits. Interestingly, users that are extremely willing to modify their consumption pattern do not derive maximum benefit

Electricity markets meet the home through demand response

Demand response (DR) programs motivate home users through dynamic pricing to shift electricity consumption from peak demand periods. In this paper, we introduce a day ahead electricity market where the operator sets the prices and multiple home users respond by scheduling their demands. The objective of the operator is to minimize electricity generation cost, whereas each user maximizes her utility function that captures the trade-off between timely execution of demands and financial savings. Since the operator is unaware of the users' utility functions, coordination of demands is a challenging task. Our DR model captures the diverse energy characteristics of different home appliances and shows that, in contrast to existing simplified models, in reality optimal demand scheduling is NP-hard. We propose a waterfilling-inspired price setting strategy, which requires only knowledge of the aggregate demand. Based on daily appliance demand traces, we show that our scheme reduces electricity generation cost significantly and derive useful insights regarding the electricity market operation. © 2012 IEEE

An efficient probing mechanism for next generation mobile broadband systems

Opportunistic scheduling exploits multiuser diversity for improving the performance of wireless systems. However, it requires instantaneous channel state information (CSI) to be available at the transmitter side. Since acquiring CSI is resource consuming, it always comes at a cost. We consider the problem of efficient channel state estimation in the context of 4G mobile broadband systems. Our proposed mechanism selects the subcarriers to be probed per user, by estimating the anticipated tentative rate with and without probing. For the latter an informed guess of the channel state is performed. The distinguishing characteristic of this work is that the channel state evolution is assumed Markovian, thus capturing the impact of mobility, contrary to the much simpler i.i.d. case hitherto considered. Based on this probing mechanism we derive a subcarrier allocation algorithm that aims at maximizing the sum rate of the system. Our simulations indicate that the proposed algorithm leads to significant performance benefits. © 2012 IEEE

Distributed cache management in information-centric networks

The main promise of current research efforts in the area of Information-Centric Networking (ICN) architectures is to optimize the dissemination of information within transient communication relationships of endpoints. Efficient caching of information is key to delivering on this promise. In this paper, we look into achieving this promise from the angle of managed replication of information. Management decisions are made in order to efficiently place replicas of information in dedicated storage devices attached to nodes of the network. In contrast to traditional off-line external management systems we adopt a distributed autonomic management architecture where management intelligence is placed inside the network. Particularly, we present an autonomic cache management approach for ICNs, where distributed managers residing in cache-enabled nodes decide on which information items to cache. We propose four on-line intra-domain cache management algorithms with different level of autonomicity and compare them with respect to performance, complexity, execution time and message exchange overhead. Additionally, we derive a lower bound of the overall network traffic cost for a certain category of network topologies. Our extensive simulations, using realistic network topologies and synthetic workload generators, signify the importance of network wide knowledge and cooperation. © 2013 IEEE