AWS offers transient instances as a way to sell unused capacity in their data centers. Although these transient instances come with a steep discount at their prices, up to 90% of discount compared to on-demand instances, they do not come with availability guarantees, thus, they are not reliable for regular usage on the Internet. This paper presents PISTIS, a framework that uses reliability engineering models that provides reliable computing resources over transient instances by proactively replacing components that are expected to fail. We evaluated our model and the results show that we can achieve the same reliability of regular on-demand instances but with a price reduction of up to 81% in the best cases compared to on-demand instances.

Connected vehicles, whether equipped with advanced driver-assistance systems or fully autonomous, require human driver supervision and are currently constrained to visual information in their line-of-sight. A cooperative perception system among vehicles increases their situational awareness by extending their perception range. Existing solutions focus on improving perspective transformation and fast information collection. However, such solutions fail to filter out large amounts of less relevant data and thus impose significant network and computation load. Moreover, presenting all this less relevant data can overwhelm the driver and thus actually hinder them. To address such issues, we present Augmented Informative Cooperative Perception (AICP), the first fast-filtering system which optimizes the informativeness of shared data at vehicles to improve the fused presentation. To this end, an informativeness maximization problem is presented for vehicles to select a subset of data to display to their drivers. Specifically, we propose (i) a dedicated system design with custom data structure and lightweight routing protocol for convenient data encapsulation, fast interpretation and transmission, and (ii) a comprehensive problem formulation and efficient fitness-based sorting algorithm to select the most valuable data to display at the application layer. We implement a proof-of-concept prototype of AICP with a bandwidth-hungry, latency-constrained real-life augmented reality application. The prototype adds only 12.6 milliseconds of latency to a current informativeness-unaware system. Next, we test the networking performance of AICP at scale and show that AICP effectively filters out less relevant packets and decreases the channel busy time.

Cloud computing has seen continuous growth over the last decade. The recent rise in popularity of next-generation applications brings forth the question: "Can current cloud infrastructure support the low latency requirements of such apps?" Specifically, the interplay of wireless last-mile and investments of cloud operators in setting up direct peering agreements with ISPs globally to current cloud reachability and latency has remained largely unexplored.

Edge computing aims to enable applications with stringent latency requirements, e.g., augmented reality, and tame the overwhelming data streams generated by IoT devices. A core principle of this paradigm is to bring the computation from a distant cloud closer to service consumers and data producers. Consequentially, the issue of edge computing facilities' placement arises. We present a comprehensive analysis suggesting where to place general-purpose edge computing resources on an Internet-wide scale. We base our conclusions on extensive real-world network measurements. We perform extensive traceroute measurements from RIPE Atlas to datacenters in the US, resulting in a graph of 11K routers. We identify the affiliations of the routers to determine the network providers that can act as edge providers. We devise several edge placement strategies and show that they can improve cloud access latency by up to 30%.

In the early days of cloud computing, datacenters were sparsely deployed at distant locations far from end-users with high end-to-end communication latency. However, today’s cloud datacenters have become more geographically spread, the bandwidth of the networks keeps increasing, pushing the end-users latency down. In this paper, we provide a comprehensive cloud reachability study as we perform extensive global client-to-cloud latency measurements towards 189 datacenters from all major cloud providers. We leverage the well-known measurement platform RIPE Atlas, involving up to 8500 probes deployed in heterogeneous environments, e.g., home and offices. Our goal is to evaluate the suitability of modern cloud environments for various current and predicted applications. We achieve this by comparing our latency measurements against known human perception thresholds and are able to draw inferences on the suitability of current clouds for novel applications, such as augmented reality. Our results indicate that the current cloud coverage can easily support several latency-critical applications, like cloud gaming, for the majority of the world’s population.

The Internet of Vehicles (IoV) enables real-time data exchange among vehicles and roadside units and thus provides a promising solution to alleviate traffic jams in the urban area. Meanwhile, better traffic management via efficient traffic light control can benefit the IoV as well by enabling a better communication environment and decreasing the network load. As such, IoV and efficient traffic light control can formulate a virtuous cycle. Edge computing, an emerging technology to provide low-latency computation capabilities at the edge of the network, can further improve the performance of this cycle. However, while the collected information is valuable, an efficient solution for better utilization and faster feedback has yet to be developed for edge-empowered IoV. To this end, we propose a Decentralized Reinforcement Learning at the Edge for traffic light control in the IoV (DRLE). DRLE exploits the ubiquity of the IoV to accelerate traffic data collection and interpretation towards better traffic light control and congestion alleviation. Operating within the coverage of the edge servers, DRLE aggregates data from neighboring edge servers for city-scale traffic light control. DRLE decomposes the highly complex problem of large area control into a decentralized multi-agent problem. We prove its global optima with concrete mathematical reasoning and demonstrate its superiority over several state-of-the-art algorithms via extensive evaluations.

Edge computing has gained attention from both academia and industry by pursuing two significant challenges: 1) moving latency critical services closer to the users, 2) saving network bandwidth by aggregating large flows before sending them to the cloud. While the rationale appeared sound at its inception almost a decade ago, several current trends are impacting it. Clouds have spread geographically reducing end-user latency, mobile phones? computing capabilities are improving, and network bandwidth at the core keeps increasing. In this paper, we scrutinize edge computing, examining its outlook and future in the context of these trends. We perform extensive client-to-cloud measurements using RIPE Atlas, and show that latency reduction as motivation for edge is not as persuasive as once believed; for most applications the cloud is already 'close enough' for majority of the world's population. This implies that edge computing may only be applicable for certain application niches, as opposed to a general-purpose solution.

AWS offers discounted transient virtual instances as a way to sell unused resources in their data-centers, and users can enjoy up to 90% discount as compared to the regular on-demand pricing. Despite the economic incentives to purchase these transient instances, they do not come with regular availability SLAs, meaning that they can be evicted at any moment. Hence, the user is responsible for managing the instance availability to meet the application requirements. In this paper, we present Bricklayer, a software tool that assists users to better use transient resources in the cloud, reducing costs for the same amount of resources, and increasing the overall instance availability. Bricklayer searches for possible combinations of smaller and cheaper instances to compose the requested amount of resources while deploying them into different spot markets to reduce the risk of eviction. We implemented and evaluated Bricklayer using 3 months of historical data from AWS and found out that it can reduce up 54% of the regular spot price and up to 95% compared to the standard on-demand pricing.

Green networking, by making the network more energy efficient and helping reduce environmental impact, is receiving more and more attraction for sustainable ICT. In this paper, we propose a new green approach for Named Data Networking (NDN) where content requests and caching perform towards green content delivery. We design a forwarding and caching strategy, where we first define the greenness of nodes, a quantitative metric for measuring the environmental footprint of the network, based on which we identify corresponding green paths and encourage traffic to aggregate on green paths powered by more eco-friendly renewable energy. We validate our approach with a variety of simulations using real network topology and renewable energy datasets from the US, and the results show that applying the proposed green NDN achieves significant ecofriendly gains.

Mobile Augmented Reality (MAR) applications employ computationally demanding vision algorithms on resource-limited devices. In parallel, communication networks are becoming more ubiquitous. Offloading to distant servers can thus overcome the device limitations at the cost of network delays. Multipath networking has been proposed to overcome network limitations but it is not easily adaptable to edge computing due to the server proximity and networking differences. In this article, we extend the current mobile edge offloading models and present a model for multi-server device-to-device, edge, and cloud offloading. We then introduce a new task allocation algorithm exploiting this model for MAR offloading. Finally, we evaluate the allocation algorithm against naive multipath scheduling and single path models through both a real-life experiment and extensive simulations. In case of sub-optimal network conditions, our model allows reducing the latency compared to single-path offloading, and significantly decreases packet loss compared to random task allocation. We also display the impact of the variation of WiFi parameters on task completion. We finally demonstrate the robustness of our system in case of network instability. With only 70% WiFi availability, our system keeps the excess latency below 9 ms. We finally evaluate the capabilities of the upcoming 5G and 802.11ax.

The sharing economy has made great inroads with services like Uber or Airbnb enabling people to share their unused resources with those needing them. The computing world, however, despite its abundance of excess computational resources has remained largely unaffected by this trend, save for few examples like SETI@home. We present DeCloud, a decentralized market framework bringing the sharing economy to on-demand computing where the offering of pay-as-you-go services will not be limited to large companies, but ad hoc clouds can be spontaneously formed on the edge of the network. We design incentive compatible double auction mechanism targeted specifically for distributed ledger trust model instead of relying on third-party auctioneer. DeCloud incorporates innovative matching heuristic capable of coping with the level of heterogeneity inherent for large-scale open systems. Evaluating DeCloud on Google cluster-usage data, we demonstrate that the system has a near-optimal performance from an economic point of view, additionally enhanced by the flexibility of matching.

The Internet is largely a self-organizing system that adapts to changes in its operating environment. In this work, we extend these principles to service infrastructure and introduce ICON, standing for intelligent container. Technically, ICON is a container encapsulating a service that is consumed either directly by end-clients or other services. The novelty of ICON is in the ability of containers to adapt to their environment, targeting near-optimal service delivery and requiring only high-level guidance from the application management. Once deployed, containers form an overlay, observe their setting, and migrate or replicate themselves as needed, to the locations e.g., closest to service consumers. ICON captures our long-term vision for self-organizing service overlays that have the potential for global outreach. Bringing intelligence and adaptation to the level of individual containers renders a decentralized solution that has desirable properties, such as scalability, resilience, reliability, and adaptability to volatile environments. We hope that technology like ICON can open the way for more democratized service provisioning, disintermediating service providers from centralized brokers and optimizing orchestrators.

While communication technologies are evolving rapidly, there is still the nontrivial matter of a communication systems being green. Although some energy-aware solutions have been proposed for the telecommunications sector, they are not designed with the ultimate goal of being environment-friendly. In this paper, we investigate the problem of achieving energy efficiency in IP networks by taking into account not only the energy consumption of the network but also the impact of various energy sources, e.g., renewable energies. We propose a new green networking approach in which we classify network nodes into clusters and select one header node in each cluster according to the generation cost and the carbon emission per unit of energy. We develop a routing scheme using IP routing only on header nodes and conducting packet forwarding using a carefully designed identifier on other nodes to achieve a greener communication system. We validate our solution with a variety of simulations using real-world renewable energy statistics, and the results show that our approach is superior to other existing solutions, particularly in terms of energy and cost efficiency.

Edge clouds are an attractive platform to support latency-sensitive applications by providing computations on servers deployed close to end-users. These servers aim to employ MPTCP to leverage multiple connections including wireless over a public network. In this paper, we show that the default MPTCP design does not adequately support reliability in these environments, which makes it unfit for use in edge clouds. We propose RAMPTCP, an extension to MPTCP which focuses on adding reliability over network paths.

Scoped-flooding is used for content discovery in a broad networking context and it has significant impact on the design of caching algorithms in a communication network. Despite its wide usage, a thorough analysis on how scoped-flooding affects a network's performance, e.g., caching and content discovery efficiency, is missing. To develop a better understanding, we first model the behavior of scoped-flooding by the help of a theoretical model on network growth and utility. Next, we investigate the effects of scoped-flooding on various topologies in information-centric networks (ICNs). Using the proposed ring model, we show that flooding can be constrained within a small neighborhood to achieve most of the gains which come from areas with relatively low growth rate, i.e., the network edge. We also study two flooding strategies and compare their behaviors. Given that caching schemes favor more popular items in competition for cache space, popular items are expected to be stored in diverse parts of the network compared to the less popular items. We propose to exploit the resulting divergence in availability along with the routers' topological properties to fine tune the flooding radius. Our results shed light on designing both efficient content discovery mechanism and effective caching algorithms for future ICN.

Green technology for not only reducing energy consumption but also environmental pollution has become a critical factor in ICT industries. However, for the telecommunications sector in particular, most network elements are not usually optimized for power efficiency. In this work, we propose a green routing method in an IP network for the reduction of unnecessary energy consumption. In addition, it can encourage the use of power generated by renewable energy sources instead of using traditional fossil energy. As a green networking approach, we first classify the network nodes into either header or member nodes according to the quantity of the available renewable energies. The member nodes then put the routing related module at layer 3 to sleep based on the assumption that this layer in the OSI model can operate independently. All of the network nodes are then partitioned into clusters consisting of one header node and multiple member nodes. Then, only the header node in a cluster conducts IP routing and its member nodes conduct packet switching using a specially designed identifier, referred to as a tag. To investigate the impact of the proposed scheme, we conducted a number of simulations using real-world renewable energy statistics and results show that our approach outperforms the existing solutions in terms of energy efficiency to a large extent.

With growing data demand and the current dearth of spectrum, mobile operators are looking for new frequency bands to satisfy data-hungry users. One promising avenue of expansion is TV white spaces, which are currently available to secondary users as long as they do not interfere with primary (i.e., incumbent) users. In this work, we explore the benefits of offloading cellular traffic onto TV white spaces. We develop an analytical model and efficient algorithms to assign users to the cellular network or white space channels by considering their channel gains, multi-user interference on white space channels, and the cost of switching between different networks. We perform extensive data-driven simulations in two representative urban scenarios based on publicly available datasets. Our results show that white spaces can increase capacity by 16-62%, depending on the environment, but careful network selection is necessary to ensure that maximum capacity gains are realized. Moreover, we show that white spaces provide a significant benefit in serving indoor users where cellular channel conditions are poor. Specifically, our algorithms can offload up to 40% of cellular traffic to white spaces for indoor scenarios.

Information-centric networking (ICN) is a popular research topic. At its heart is the concept of in-network caching. Various algorithms have been proposed for optimizing ICN caching, many of which rely on collaborative principles, i.e. multiple caches interacting to decide what to store. Past work has assumed altruistic nodes that will sacrifice their own performance for the global optimum. We argue that this assumption is insufficient and oversimplifies the reality. We address this problem by modeling the in-network caching problem as a Nash bargaining game. We develop optimal and heuristic caching solutions that consider both performance and fairness. We argue that only algorithms that are fair to all parties involved in caching will encourage engagement and cooperation. Through extensive simulations, we show our heuristic solution, FairCache, ensures that all collaborative caches achieve performance gains without undermining the performance of others.

According to an estimate several billion smart devices will be connected to the Internet by year 2020. This exponential increase in devices is a challenge to the current Internet architecture, where connectivity is based on host-to-host communication. Information-Centric Networking is a novel networking paradigm in which data is addressed by its name instead of location. Several ICN architecture proposals have emerged from research communities to address challenges introduced by the current Internet Protocol (IP) regarding e.g. scalability. Content-Centric Networking (CCN) is one of the proposals. In this paper we present a way to use CCN in an Internet of Things (IoT) context. We quantify the benefits from hierarchical content naming, transparent in-network caching and other information-centric networking characteristics in a sensor environment. As a proof of concept we implemented a presentation bridge for a home automation system that provides services to the network through CCN.

Novel network systems need to be carefully evaluated before their actual deployments in developing regions. However, large-scale network experiment is a challenging task. Simulations, emulations, and real-world testbeds all have their advantages and disadvantages. In this paper we present LiteLab, a light-weight platform specialized for large-scale networking experiments. We cover in detail its design, key features, and architecture. We also perform an extensive evaluation of Lite-Lab's performance and accuracy and show that it is able to both simulate network parameters with high accuracy, and also able to scale up to very large networks. LiteLab is flexible, easy to deploy, and allows researchers to perform large-scale network experiments with a short development cycle. We have used LiteLab for many different kinds of network experiments and are planning to make it available for others to use as well.

Caching is a core principle of information-centric networking (ICN). Many novel algorithms have been proposed for enabling ICN caching, many of which rely on collaborative principles, i.e. multiple caches interacting to decide what to store. Past work has assumed entirely altruistic nodes that will sacrifice their own performance for the global optimum. In this paper, we argue that this assumption is flawed. We address this problem by modelling the in-network caching problem as a Nash bargaining game. We develop optimal and heuristic caching solutions that explicitly consider both performance and fairness. We argue that only algorithms that are fair to all parties will encourage engagement and cooperation. Through extensive simulations, we show our heuristic solution, FairCache, ensures that all collaborative caches achieve performance gains without undermining the performance of others.

The ICT industry has come under criticism as being one of the major energy consumers to exacerbate high global carbon emissions. Meanwhile, using renewable energy to power ICT infrastructure is becoming an attractive solution and is gaining its momentum due to the recent breakthroughs of converting solar and wind energies as power sources at competitive costs. Although significant amounts of fossil fuel based-energy can be saved by allowing network devices (e.g., routers and line-cards) to be set to sleep, this optimization approach comes at a price of degrading routing performance, i.e., the quality of service. This paper addresses the problem of minimizing fossil fuel consumption in large Internet Service Provider (ISP) networks, by utilizing a novel gradient-based routing protocol, which favors forwarding packets along routers powered by the highest quantity of renewable energies. Besides favoring renewable energy, the proposed routing protocol can support putting routers to sleep in order to optimize energy consumption while ensuring a minimum degradation in routing performance. Through our evaluation utilizing real meteorological data, our proposed solution has demonstrated a massive reduction of fossil fuel usage by the network (> 70%) while maintaining the routing performance to a similar level when no energy optimization is applied.

The Internet is overloading its users with excessive information flows, so that effective content-based filtering becomes crucial in improving user experience and work efficiency. Latent semantic analysis has long been demonstrated as a promising information retrieval technique to search for relevant articles from large text corpora. We build Kvasir, a semantic recommendation system, on top of latent semantic analysis and other state-of-the-art technologies to seamlessly integrate an automated and proactive content provision service into web browsing. We utilize the processing power of Apache Spark to scale up Kvasir into a practical Internet service. In addition, we improve the classic randomized partition tree to support efficient indexing and searching of millions of documents. Herein we present the architectural design of Kvasir, the core algorithms, along with our solutions to the technical challenges in the actual system implementation.

Hop count limitation helps controlling the spread of messages as well as the protocol complexity and overhead in a distributed network. For a mobile opportunistic network, we examine how the paths between any two nodes change with increasing number of hops a message can follow. Using the all hops optimal path (AHOP) problem, we represent the total delay of a route from a source node to a destination node as additive weight and use the number of encounters as a representation of bottleneck weight. First, we construct a static (contact) graph from the meetings recorded in a human contact trace and then analyze the change in these two weights with increasing hop count. Alternatively, we aggregate all the contact events in a time interval and construct several time-aggregated graphs over which we calculate the capacity metrics. Although, we observe differences in the properties of the static and the time-aggregated graphs (e.g., higher connectivity and average degree in static graph), our analysis shows that second hop brings most of the benefits of multi-hop routing for the studied networks. However, the optimal paths —path that provides the most desirable bottleneck/additive weight— are achieved at further hops, e.g, hop count ≈ 4. Our finding, which is also verified by simulations, is paramount as it puts an upper bound on the hop count for the hop-limited routing schemes by discovering the optimal hop count for both additive and bottleneck weights.

While there is a drastic shift from host-centric networking to content-centric networking, how to locate and retrieve the relevant content efficiently, especially in a mobile network, is still an open question. Mobile devices host increasing volume of data which could be shared with the nearby nodes in a multi-hop fashion. However, searching for content in this resource-restricted setting is not trivial due to the lack of a content index, as well as, desire for keeping the search cost low. In this paper, we analyze a lightweight search scheme, hop-limited search, that forwards the search messages only till a maximum number of hops, and requires no prior knowledge about the network. We highlight the effect of the hop limit on both search performance (i.e., success ratio and delay) and associated cost along with the interplay between content availability, tolerated waiting time, network density, and mobility. Our analysis, using the real mobility traces, as well as synthetic models, shows that the most substantial benefit is achieved at the first few hops and that after several hops the extra gain diminishes as a function of content availability and tolerated delay. We also observe that the return path taken by a response is on average longer than the forward path of the query and that the search cost increases only marginally after several hops due to the small network diameter.

Searching content in mobile opportunistic networks is a difficult problem due to the dynamically changing topology and intermittent connections. Moreover, due to the lack of global view of the network, it is arduous to determine whether the best response is discovered or search should be spread to other nodes. A node that has received a search query has to take two decisions: (i) whether to continue the search further or stop it at the current node (current search depth) and, independently of that, (ii) whether to send a response back or not. As each transmission and extra hop costs in terms of energy, bandwidth and time, a balance between the expected value of the response and the costs incurred must be sought. In order to better understand this inherent trade-off, we assume a simplified setting where both the query and response follow the same path. We formulate the problem of optimal search for the following two cases: a node holds (i) exactly matching content with some probability, and (ii) some content partially matching the query. We design static search in which the search depth is set at query initiation, dynamic search in which search depth is determined locally during query forwarding, and learning dynamic search which leverages the observations to estimate suitability of content for the query. Additionally, we show how unreliable response paths affect the optimal search depth and the corresponding search performance. Finally, we investigate the principal factors affecting the optimal search strategy.

Content-Centric Network (CCN) shifts the Internet from point-to-point paradigm to receiver-driven data-centric paradigm. While it tries to solve many problems in the current Internet and opens the door to many novel applications, it also leaves many challenges unanswered, e.g., mobility support and mobile content publishing and dissemination. In this paper, we show how a greedy routing can be implemented in CCN architecture to support mobility. This allows for efficient content publisher mobility and supports seamless handoffs for interactive connections. We present our solution - MobiCCN, and evaluate it thoroughly in realistic network topologies to show it outperforms other popular mobility schemes.

Peer-to-peer networks have been quite thoroughly measured over the past years, however it is interesting to note that the BitTorrent Mainline DHT has received very little attention even though it is by far the largest of currently active overlay systems, as our results show. As Mainline DHT differs from other systems, existing measurement methodologies are not appropriate for studying it. In this paper we present an efficient methodology for estimating the number of active users in the network. We have identified an omission in previous methodologies used to measure the size of the network and our methodology corrects this. Our method is based on modeling crawling inaccuracies as a Bernoulli process. It guarantees a very accurate estimation and is able to provide the estimate in about 5 seconds. Through experiments in controlled situations, we demonstrate the accuracy of our method and show the causes of the inaccuracies in previous work, by reproducing the incorrect results. Besides accurate network size estimates, our methodology can be used to detect network anomalies, in particular Sybil attacks in the network. We also report on the results from our measurements which have been going on for almost 2.5 years and are the first long-term study of Mainline DHT.

Caching is a key component of information-centric networking, but most of the work in the area focuses on simple en-route caching with limited cooperation between the caches. In this paper we model cache cooperation under a game theoretical framework and show how cache cooperation policy can allow the system to converge to a Pareto optimal configuration. Our work shows how cooperation impacts network caching performance and how it takes advantage of the structural properties of the underlying network.

Distributed hash tables (DHT) are a key building block for modern P2P content-distribution system, for example in implementing the distributed tracker of BitTorrent Mainline DHT. DHTs, due to their fully distributed nature, are known to be vulnerable to certain kinds of attacks and different kinds of defenses have been proposed against these attacks. In this paper, we consider two kinds of attacks on a DHT, one already known attack and one new kind of an attack, and show how they can be targeted against Mainline DHT. We complement them by an extensive measurement study using honeypots which shows that both attacks have been going on for a long time in the network and are still happening. We present numbers showing that the number of sybils in the Mainline DHT network is increasing and is currently around 300,000. We analyze the potential threats from these attacks and propose simple countermeasures against them.

In-network caching of content is a popular technique for eliminating redundant traffic from the network and improve the performance of network applications. In this paper we present a novel cooperative caching strategy to improve performance of in-network caches. Our cooperative scheme is composed of an admission policy for the incoming data and a content exchange protocol between neighbor network caches to improve the search zone. The admission policy enforces that a previously cached data is not unnecessary replicated in other caches, resulting in more space for new data. The content exchange protocol allows for exchange on cached data, increasing the hit rate for incoming requests. The benefits are twofold: first, we reduce the redundant content caching in the network, and second, we improve the hit rate by informing the content cached in the nearby caches. As a proof-of-concept, we have implemented a prototype and evaluated its performance using different large-scale topologies against standard non-cooperative caching algorithms. Our numerical results show that both admission and content exchange policies yield large performance gains over standard algorithms.

This paper proposes an improved version of a fully distributed routing protocol, that is applicable for cloud computing infrastructure. Simulation results shows the protocol is ideal for discovering cloud services in a scalable manner with minimum latency.

In this paper we present an in-network caching architecture based on content routers to improve the traffic efficiency in the Internet. The main idea is to provide a forwarding fabric where data requests are forwarded towards the closest caches in the network path. Conversely, data chunks from the servers are cached in the content routers along the path, serving further requests. In addition, content routers store a neighborhood mapping of available routers, leveraging resource discovery in the network proximity. Some benefits of the architecture include multi-source content retrieval, better traffic efficiency and gradual deployment. As a proof-of-concept, we implemented a content router prototype and evaluated it in different scenarios comparing the bandwidth, latency and neighborhood search. The experimental results show that the content router can leverage multi-source content retrieval with bandwidth reduction without incurring in increased latency.

We consider an opportunistic content sharing system designed to store and distribute local spatio-temporal “floating” information in uncoordinated P2P fashion relying solely on the mobile nodes passing through the area of interest, referred to as the anchor zone. Nodes within the anchor zone exchange the information in opportunistic manner, i.e., whenever two nodes come within each others' transmission range. Outside the anchor zone, the nodes are free to delete the information, since it is deemed relevant only for the nodes residing inside the anchor zone. Due to the random nature of the operation, there are no guarantees, e.g., for the information availability. By means of analytical models, we show that such a system, without any supporting infrastructure, can be a viable and surprisingly reliable option for content sharing as long as a certain criterion, referred to as the criticality condition, is met. The important quantity is the average number of encounters a randomly chosen node experiences during its sojourn time in the anchor zone, which again depends on the communication range and the mobility pattern. The theoretical studies are complemented with simulation experiments with various mobility models showing good agreement with the analytical results.

Content sharing using personal web pages, blogs, or online social networks is a common means for people to maintain contact with their friends, colleagues, and acquaintances. While such means are essential to overcome distances, using infrastructure services for location-based services may not be desirable. In this paper, we analyze a fully distributed variant of an ephemeral content sharing service, solely dependent on the mobile devices in the vicinity using principles of opportunistic networking. The net result is a best effort service for floating content in which: 1) information dissemination is geographically limited; 2) the lifetime and spreading of information depends on interested nodes being available; 3) content can only be created and distributed locally; and 4) content can only be added, but not explicitly deleted. First we present our system design and summarize its analytical modeling. Then we perform extensive evaluation for a map-based mobility model in downtown Helsinki to assess the operational range for floating content, which, at the same time also validate the analytical results obtained for a more abstract model of the system.

Peer-to-peer systems provide a scalable content distribution environment where each peer contributes with a share of resources in the distributed system. The efficiency of peer-to-peer networks comes from the file segmentation procedure, allowing peers to redistribute small pieces of the original file as soon as they finish downloading them instead of waiting for the complete file download. Moreover, the decentralized fashion of the paradigm with multiple sources makes it scalable and robust under high churn. Due to the popularity of such systems, many attacks such as content pollution arose, targeting the content integrity by inserting bogus data in the network to increase the download time and bandwidth consumption. In this paper we present the piece fingerprinting mechanism, a new integrity verification procedure that algorithmically binds all pieces together and relates them to the complete file. Each data block resulted from the segmentation of a large file has a fingerprint indicating whether a block belongs to the content file and also allows the integrity verification of specific parts of the content. The mechanism allows the early detection and correction of the corrupted blocks only, reducing the download time and bandwidth consumption which would be spent re-downloading larger pieces. The analytical evaluation shows that the fingerprinting mechanism has low overhead, usually less than 1% of the file size, and can reduce the bandwidth consumption by 90% in the best case by saving the amount of bandwidth consumed from the re- downloaded pieces.

The Distributed Tree Construction (DTC) algorithm is designed for optimally efficient multicast tree construction over structured peer-to-peer networks. It achieves this by creating a spanning tree over the peers in the multicast group, using only information available locally on each peer. Furthermore, we show that the tree depth has the same upper bound as a regular DHT lookup which in turn guarantees fast and responsive runtime behavior. Our DTC algorithm is DHT-agnostic and works with most existing DHTs. We evaluate the performance of DTC over several DHTs by comparing the performance to existing application-level multicast solutions, we show that DTC sends 30-250% fewer messages than common solutions.

First response communication is tackled by several independent research groups. While there are existing prototypes and simulated results, comparison of first response solutions is hardly possible so far. We have built an universal XML based description format to handle all relevant settings and actions typical for first response scenarios. In addition we implemented a user-friendly movement and environment simulator which interacts with the network simulation on top of the simulated movement. The chosen data structure has proven to be well suited for describing settings and actions found in a first response scenario. The simulator combines movement and network simulation and therefore enables both, fine grained movement models and location aware network models with reciprocal interdependencies. The simulation results of the chosen communication approach are therefore finer grained than using a network or movement simulator separately. In this demo we show different movement and network models, perform movement simulations and show the interface for using mobility simulation within network simulators.

Efficient communication architectures are vital for handling larger scale first response scenarios, and existing mechanisms have several shortcomings due to the heterogeneity of first response groups. In this paper, we consider using peer-to-peer-based communication architectures for first response scenarios. We evaluated different network overlays in environments found in first response -like scenarios. A first response P2P system needs to address both, reliability in high churn situations in network infrastructure mode as well as reasonable usability in wireless ad-hoc networks. Our evaluation shows that a superpeer architecture based on peer capabilities has superior performance when compared with purely unstructured or DHT-based overlays.

A fundamental paradigm in peer-to-peer (P2P) content distribution is that of a large community of intermittently-connected nodes that cooperate to share files. Because nodes are intermittently connected, the P2P community must replicate and replace files as a function of their popularity to achieve satisfactory performance. In this paper, we develop an analytical optimization theory for benchmarking the performance of replication/replacement algorithms, including algorithms that employ erasure codes. We also consider a content management algorithm, the Top-K Most Frequently Requested algorithm, and show that in most cases this algorithm converges to an optimal replica profile. Finally, we present two approaches for achieving an evenly balanced load over all the peers in the community.

No abstract available.

Peer-to-peer systems promise inexpensive scalability, adaptability, and robustness. Thus, they are an attractive platform for file sharing, distributed wikis, and search engines. These applications often store weakly structured data, requiring sophisticated search algorithms. To simplify the search problem, most scalable algorithms introduce structure to the network. However, churn or violent disruption may break this structure, compromising search guarantees.

E-mail is a mission-critical communication function for virtually all institutions. Modern e-mail employs a server-centric design, in which the user is critically dependent on her mail server. We present a peer-to-peer (P2P) email architecture that eliminates the need to rely on a single server and boosts the resilience of email against any kinds of attacks. Our architecture also provides confidential communications for all users. We present how the basic mechanisms of sending and reading email are implemented in our architecture. We also consider additional schemes to improve anonymity in our architecture. We present our prototype implementation and discuss the future of P2P communication architectures.

Because Internet access rates are highly heterogeneous, many video content providers today make available different versions of the videos, with each version encoded at a different rate. Multiple video versions, however, require more server storage and may also dramatically impact cache performance in a traditional cache or in a CDN server. An alternative to versions is layered encoding, which can also provide multiple quality levels. Layered encoding requires less server storage capacity and may be more suitable for caching; but it typically increases transmission bandwidth due to encoding overhead. In this paper we compare video streaming of multiple versions with that of multiple layers in a caching environment. We examine caching and distribution strategies that use both versions and layers. Our analytical results indicate that mixed distribution/caching strategies provide the best overall performance.

The efficient distribution of stored information has become a major concern in the Internet which has increasingly become a vehicle for the transport of stored video. Because of the highly heterogeneous access to the Internet, researchers and engineers have argued for layered encoded video. We investigate delivering layered encoded video using caches. Based on the stochastic knapsack theory, we develop a model for the layered video caching problem. We propose heuristics to determine which videos and which layers in the videos should be cached in order to maximize the revenue from the streaming service. We evaluate the performance of our heuristics through extensive numerical experiments. We find that, for typical scenarios, the revenue increases nearly logarithmically with the cache size and linearly with the link bandwidth that connects the cache to the origin servers. We also consider service models with request queuing and negotiations about the delivered stream quality and find that both extensions provide only small revenue increases.

The efficient distribution of stored information has become a major concern in the Internet which has increasingly become a vehicle for the transport of stored video. Because of the highly heterogeneous access to the Internet, researchers and engineers have argued for layered encoded video. We investigate delivering layered encoded video using caches. Based on a stochastic knapsack model we develop a model for the layered video caching problem. We propose heuristics to determine which videos and which layers in the videos should be cached. We evaluate the performance of our heuristics through extensive numerical experiments. We also consider two intuitive extensions to the initial problem.

Multimedia proxy caching is a client-oriented solution for large-scale delivery of high quality streams over heterogeneous networks such as the Internet. Existing solutions for multimedia proxy caching are unable to adjust quality of cached streams. Thus these solutions either can not maximize delivered quality or exhibit poor caching efficiency. This paper presents the design and implementation ofMocha, a quality adaptive multimedia proxy cache for layered encoded streams. The main contribution of Mocha is its ability to adjust quality of cached streams based on their popularity and on the available bandwidth between proxy and interested clients. Thus Mocha can significantly improve caching efficiency without compromising delivered quality. To perform quality adaptive caching, Mocha implementsfine-grained replacementandfine-grained prefetchingmechanisms. We describe our prototype implementation of Mocha on top of Squid and address various design challenges such as managing partially cached streams. Finally, we validate our implementation and present some of our preliminary results.

We propose a new design for the Domain Name System (DNS) that takes advantage of recent advances in disk storage and multicast distribution technology. In essence, our design consists of geographically distributed servers, called replicated servers, each of which has a complete and up-to-date copy of the entire DNS database. To keep the replicated servers up-to-date, they distribute new resource records over a satellite channel or over terrestrial multicast. The design allows Web sites to dynamically wander and replicate themselves without having to change their URL. The design can also significantly improve the Web surfing experience since it significantly reduces the DNS lookup delay.

Among the different attempts to improve recognition scores and robustness to noise, the recognition of parallel streams of data each one representing partial information on the test signal and the fusion of the decisions have received a great deal of interest. The problem of training such models taking recombination constraints at the level of speech-subunits has not yet been rigorously addressed. This paper shows how equivalence with an extended meta-HMM solves the problem and how reestimation formulas have to be applied to guarantee equivalence between the multistream model and the meta-HMM. 1. INTRODUCTION Since speech is a non-stationary signal, it is analyzed over small time windows where local stationarity is assumed. A typical width for these windows is 10 ms and different kinds of analysis can be conducted on them. The first standard approach is a harmonic analysis that could be obtained using either filter banks or FFT transforms. After elimination of the fundamental period ...

We address practical issues which arise in the implementation of optimized cache replenishment algorithms within a "soft" caching framework. We study the algorithms that have been proposed for optimized soft caching and simulate them using actual proxy traces. Our objective is to determine what compromises have to be made in order to approximate the desired optimal performance while maintaining a complexity level sufficiently low to enable a real-time implementation.