Congestion control has traditionally relied on monitoring packet-level performance (e.g., latency, loss) through feedback signals propagating end-to-end together with various queue management practices (e.g., carefully setting various parameters, such as router buffer thresholds) in order to regulate traffic flow. Due to its end-to-end nature, this approach is known to transfer data according to the path’s slowest link, requiring several RTTs to transmit even a few tens of KB during slow start. In this paper, we take a radically different approach to control congestion, which obviates end-to-end performance monitoring and careful setting of network parameters. The resultingIn-Network Resource Pooling Protocol (INRPP)extends the resource pooling principle to exploit in-network resources such as router storage and unused bandwidth along alternative sub-paths. In INRPP, content caches or large (possibly bloated) router buffers are used as a place of temporary custody for incoming data packets in a store and forward manner. Data senders push data in the network and when it hits the bottleneck link, in-network caches at every hop store data in excess of the link capacity; nodes progressively move/send data (from one cache to the next) towards the destination. At the same timealternative sub-pathsare exploited to move data faster towards the destination. We demonstrate through extensive simulations that INRPP is TCP friendly, and improves flow completion time and fairness by as much as 50% compared to RCP, MPTCP and TCP, under realistic network conditions.

Edge computing has emerged as a new paradigm to bring cloud applications closer to users for increased performance. Unlike back-end cloud systems which consolidate their resources in a centralized data center location with virtually unlimited capacity, edge-clouds comprise distributed resources at various “computation spots”, each with very limited capacity. In this article, we consider Function-as-a-Service (FaaS) edge-clouds whereapplication providersdeploy their latency-critical functions to process user requests with strict response time deadlines. In this setting, we investigate the problem ofresource provisioningandallocation. After formulating the optimal solution, we propose resource allocation and provisioning algorithms across the spectrum of fully-centralized to fully-decentralized. We evaluate the performance of these algorithms in terms of their ability to utilize CPU resources and meet request deadlines under various system parameters. Our results indicate that practical decentralized strategies, which require no coordination among computation spots, achieve performance that is close to the optimal fully-centralized strategy with coordination overheads.

The number of devices that the edge of the Internet accommodates and the volume of the data these devices generate are expected to grow dramatically in the years to come. As a result, managing and processing such massive data amounts at the edge becomes a vital issue. This paper proposes "Store Edge Networked Data" (SEND), a novel framework for in-network storage management realized through data repositories deployed at the network edge. SEND considers different criteria (e.g., data popularity, data proximity from processing functions at the edge) to intelligently place different categories of raw and processed data at the edge based on system-wide identifiers of the data context, called labels. We implement a data repository prototype on top of the Google file system, which we evaluate based on real-world datasets of images and Internet of Things device measurements. To scale up our experiments, we perform a network simulation study based on synthetic and real-world datasets evaluating the performance and trade-offs of the SEND design as a whole. Our results demonstrate that SEND achieves data insertion times of 0.06ms-0.9ms, data lookup times of 0.5ms-5.3ms, and on-time completion of up to 92% of user requests for the retrieval of raw and processed data.

Initiatives such as blockchains and decentralized storage networks are pushing for a decentralized Web3 to replace the current architecture. At the core of Web3 are network resource sharing services, which allow anyone to sell spare network capacity in return for rewards. These services require a way to establish trust, as parties are potentially malicious. This can be achieved by reputation systems. In this paper we make the case for using deep reinforcement learning in Web3 reputation calculation. More specifically, we propose a model which allows for decentralized calculation of scores with high personalization for the user.

Named Data Networking (NDN) is one of promising next generation Internet architectures that aim to realize efficient content distribution. However, in terms of producer anonymity, NDN has a serious problem that adversaries can easily learn who publishes what content due to its feature that content is inherently tied to the producer by the content name and the signature. In this article, we first define producer anonymity rigorously in terms of content-producer unlinkability, and then design a system to achieve it. Our design is based on hidden service, which is an onion routing-based system in IP, however, we improve it to take full advantage of NDN. We demonstrate that our system provides a level of anonymity comparable to hidden service with lower overhead through analysis and experiment.

An increasing number of Low Latency Applications (LLAs) in the entertainment, IoT, and automotive domains require response times that challenge the traditional application provisioning using distant Data Centres. The fog computing paradigm extends cloud computing at the edge and middle-tier locations of the network, providing response times an order of magnitude smaller than those that can be achieved by the current “client-to-cloud” network model. Here, we address the challenges of provisioning heavily stateful LLA in the setting where fog infrastructure consists of third-party computing resources, i.e., cloudlets, that come in the form of “data centres in the box”. We introduce FogSpot, a charging mechanism for on-path, on-demand, application provisioning. In FogSpot, cloudlets offer their resources in the form of Virtual Machines (VMs) via markets, collocated with the cloudlets, that interact with forwarded users’ application requests for VMs in real time. FogSpot associates each cloudlet with a price based on applications’ demand. The proposed mechanism’s design takes into account the characteristics of cloudlets’ resources, such as their limited elasticity, and LLAs’ attributes, like their expected QoS gain and engagement duration. Lastly, FogSpot guarantees the end users’ requests truthfulness while focusing in maximising either each cloudlet’s revenue or resource utilisation.

Decentralised cloud computing platforms enable individuals to offer and rent resources in a peer-to-peer fashion. They must assign resources from multiple sellers to multiple buyers and derive prices that match the interests and capacities of both parties. The assignment process must be decentralised, fair and transparent, but also protect the privacy of buyers.

Traversing NAT's remains a big issue in P2P networks, and many of the previously proposed solutions are incompatible with truly decentralised emerging applications. Such applications need a decentralised NAT traversal solution without trusted centralised servers.

We propose a complete framework targeting operator-driven content caching that can be equally applied to both ISP-operated Content Delivery Networks (CDNs) and future Information-Centric Networks (ICNs). In contrast to previous proposals in this area, our solution leverages operators' control on cache placement and content routing, managing to considerably reduce network operating costs by minimizing the amount of transit traffic and balancing load among available network resources. In addition, our solution provides two key advantages over previous proposals. First, it allows for a simple computation of the optimal cache placement. Second, it provides knobs for operators to fine-tune performance. We validate our design through both analytical modeling and trace-driven simulations and show that our proposed solution achieves on average twice as many cache hits in comparison to previously proposed techniques, without increasing delivery latency. In addition, we show that the proposed framework achieves 19-33% better load balancing across links and caching nodes, being also robust to traffic spikes.

Sharding is a method for allocating data items to nodes of a distributed caching or storage system based on the result of a hash function computed on the item's identifier. It is ubiquitously used in key-value stores, CDNs and many other applications. Despite considerable work that has focused on the design and implementation of such systems, there is limited understanding of their performance in realistic operational conditions from a theoretical standpoint. In this paper we fill this gap by providing a thorough modeling of sharded caching systems, focusing particularly on load balancing and caching performance aspects. Our analysis provides important insights that can be applied to optimize the design and configuration of sharded caching systems.

As end-user and edge-network devices are becoming ever more powerful, they are producing ever increasing amounts of data. Pulling all this data into the cloud for processing is impossible, not only due to its enormous volume, but also due to the stringent latency requirements of many applications. Instead, we argue that end-user and edge-network devices should collectively form edge computing swarms and complement the cloud with their storage and processing resources. This shift from centralized to edge clouds has the potential to open new horizons for application development, supporting new low-latency services and, ultimately, creating new markets for storage and processing resources. To realize this vision, we propose Named Functions at the Edge (NFE), a platform where functions can i) be identified through a routable name, ii) be requested and moved (as data objects) to process data on demand at edge nodes, iii) pull raw or anonymized data from sensors and devices, iv) securely and privately return their results to the invoker and v) compensate each party for use of their data, storage, communication or computing resources via tracking and accountability mechanisms. We use an emergency evacuation application to motivate the need for NFE and demonstrate its potential.

Native applications running over handheld devices have an irreplaceable role in users' daily activities. That said, recent studies show that users download on average zero new applications on monthly basis, which suggests that new apps can face discoverability issues. In this work, we aim for a web-based, download/installation-free access to native application features through microservices (μ Services)that are shared between user devices in a peer-to-peer (P2P)manner. Such a P2P approach is self-scalable and requires no investment for μ Service deployment, unlike mobile edge computing or Data Centre. We introduce DEEM, a DEvice Edge Market design that enables device-hosted μServices to end-users. In DEEM, μ Service-based markets act as rendezvous points between available μ Service instances and clients. DEEM ensures the i) assignment of instances to the users that value them the most, in terms of QoS gain, and ii) devices' income maximisation. Our evaluation on synthetic settings demonstrates DEEM's capability in exploiting the pool of device instances for improving the application QoS in terms of latency.

Cloud computing has been tremendously successful in providing a commercial infrastructure for hosting computationally intensive applications. Nevertheless, an increasing number of Low Latency Applications (LLAs) notably in the entertainment, IoT, and automative domains require response times much smaller than the supported ones by the typical “client-to-cloud” network model. Cloudlets have been introduced as “data centres in a box”, for bringing computing resources “closer” to the end users. As a result, LLAs can take advantage of cloudlets to improve their Quality-of-Service (QoS) by reducing the underlying response times between their users and application instances' location. In this work, we study the emerging market of stateful LLAs' provisioning over geo-distributed third-party cloudlets. We assume that cloudlets offer their resources in the form of Virtual Machines (VMs) via collocated markets. Forwarding requests for LLAs interact with cloudlet markets for performing on-path and on-demand resource provisioning. We introduce a pricing scheme where users pay a fixed price for each time unit of their engagement to an LLA instance. Our evaluation on realistic topologies and application requests demonstrate the merits of on-demand provisioning when accompanied by a pay-as-you-go pricing scheme.

The emerging Internet of Things needs edge-computing - this is an established fact. In turn, edge computing needs infrastructure decentralisation. What is not necessarily established yet is that infrastructure decentralisation needs a distributed model of Internet governance and decentralised trust schemes. We discuss the features of a decentralised IoT and edge-computing ecosystem and list the components that need to be designed, as well the challenges that need to be addressed.

New and emerging applications in the entertainment (e.g., Virtual/Augmented Reality), IoT and automotive domains will soon demand response times an order of magnitude smaller than can be achieved by the current “client-to-cloud” network model. Edge-and Fog-computing have been proposed as the promise to deal with such extremely latency-sensitive applications. According to Edge-/Fog-Computing, computing resources are available at the edge of the network for applications to run their virtualised instances. We assume a distributed computing environment, where In-Network Computing Providers (IN CPs) deploy and lease edge resources, while Application Service Providers (AppSPs) have the opportunity to rent those resources to meet their application's latency demands. We build an auction-based resource allocation and provisioning mechanism which produces a map of application instances in the edge computing infrastructure (hence, acronymed Edge-MAP). Edge-MAP takes into account users' mobility (i.e., users connecting to different cell stations over time) and the limited computing resources available in edge micro-clouds to allocate resources to bidding applications. On the micro-level, Edge-MAP relies on Vickrey-English-Dutch (VED) auctions to perform robust resource allocation, while on the macro-level it fosters competition among neighbouring IN CPs. In contrast to related studies in the area, Edge-MAP can scale to any number of applications, adapt to dynamic network conditions rapidly and reallocate resources in polynomial time. Our evaluation demonstrates Edge-MAP's capability of taking into account the inherent challenges of the provisioning problem we consider.

We argue that data communications in dynamic and potentially fragmented networks should not and cannot rely on network-centric resilience schemes, as is the case in today's networks, but should take advantage of techniques that focus on information-centric resilience. We make the case that management and control in disruptive environments should take advantage of information-centricity, rather than focus on node-oriented path recovery routing. This is also essential in the information-centric networking (ICN) paradigm, which is by nature oblivious to network locations. In this context, we build on ICN and enhance the named data networking (NDN) architecture with extra functionality in order to make it resilient to network failures. We introduce an extra interest management routing table, which we call the “satisfied interest table” (SIT) and which points to the direction of already satisfied interests. This way, upon failure of links/nodes toward the content origin, the SIT table can redirect interests toward caches and end-users that have recently received the requested content. Our extensive performance evaluation shows that our simple, yet efficient information resilience scheme can serve most requests made after disruptive effects, e.g., natural disasters, where users are interested in latest updates, dissemination of warnings from first responders and evacuation plans. More generally, we believe that our proposed approach should become part of the main NDN architecture as it can support service resilience in the case of network failures.

Edge computing has emerged as a new paradigm to bring cloud applications closer to users for increased performance. ISPs have the opportunity to deploy private edge-clouds in their infrastructure to generate additional revenue by providing ultra-low latency applications to local users. We envision a rapid increase in the number of such applications for “edge” networks in the near future with virtual/augmented reality (VR/AR), networked gaming, wearable cognitive assistance, autonomous driving and IoT analytics having already been proposed for edge- clouds instead of the central clouds to improve performance. This raises new challenges as the complexity of the resource allocation problem for multiple services with latency deadlines (i.e., which service to place at which node of the edge-cloud in order to satisfy the latency constraints) becomes significant. In this paper, we propose a set of practical, uncoordinated strategies for service placement in edge-clouds. Through extensive simulations using both synthetic and real-world trace data, we demonstrate that uncoordinated strategies can perform comparatively well with the optimal placement solution, which satisfies the maximum amount of user requests.

The vast majority of mobile data transfers today follow the traditional client-server model. Although in the fixed network P2P approaches have been exploited and shown to be very efficient, in the mobile domain there has been limited attempt to leverage on P2P (D2D) for large-scale content distribution (i.e., not DTN-like, point-to-point message transfers). In this paper, we explore the potential of a user-operated, smartphone-centric content distribution model for smartphone applications. In particular, we assume source nodes that are updated directly from the content provider (e.g., BBC, CNN), whenever updates are available; destination nodes are then directly updated by source nodes in a D2D manner. We leverage on sophisticated information-aware and application-centric connectivity techniques to distribute content between mobile devices in densely-populated urban environments. Our target is to investigate the feasibility of an opportunistic content distribution network in an attempt to achieve widespread distribution of heavy content (e.g., video files) to the majority of the destination nodes. We propose ubiCDN as a ubiquitous, user-operated and distributed CDN for mobile applications.

Sharding is a method for allocating data items to nodes of a distributed caching or storage system based on the result of a hash function computed on the item identifier. It is ubiquitously used in key-value stores, CDNs and many other applications. Despite considerable work has focused on the design and the implementation of such systems, there is limited understanding of their performance in realistic operational conditions from a theoretical standpoint. In this paper we fill this gap by providing a thorough modeling of sharded caching systems, focusing particularly on load balancing and caching performance aspects. Our analysis provides important insights that can be applied to optimize the design and configuration of sharded caching systems.

Recent research in Information-Centric Networks has considered various approaches for discovering content in the cache-enabled nodes of the network. Such approaches include scoped flooding and deploying a control plane protocol to disseminate the cache contents in the network, to name a few. In this work, we consider an opportunistic approach that uses trails left behind by data packets from the content origin to the sources in order to discover off-path cached content. We evaluate our approach using an ISP topology for various system parameters. We propose two new forwarding strategies built on top of our approach. Our results indicate that the opportunistic discovery mechanism can significantly increase cache hit rate compared to NDN's default forwarding strategy, while limiting the overhead at acceptable levels.

Named data networking (NDN) has received considerable attention recently, mainly due to its built-in caching, which is expected to enable widespread and transparent operator-controlled caching. One of the important research challenges is to reduce the amount of power consumed by NDN networks as it has been shown that NDN's name prefix matching and caching are power-hungry. As a first step to achieving power-efficient NDN networks, in this paper, we develop a power consumption model of a multicore software NDN router. By applying this model to analyze how caching reduces power, we report that caching can reduce power consumption of an NDN network if the power consumption of routers is in proportion to their load and the computation of caching is as light as that of forwarding.

In-network caching in information centric networking (ICN) is considered as a promising approach to reducing energy consumption of an entire network. However, it is also considered as an energy consuming technique. These contradictory claims lead to one research question: Does caching really reduce the energy consumption of the entire network? To answer the question, we formulate an ICN network as an optimization problem with a realistic energy consumption model for an ICN router. By solving the formulation assuming that ICN forwarding software currently under development is used as a forwarding engine of an ICN router, we reveal that in-network caching alone does not reduce much energy but it enhances a currently developed green networking technique even though the forwarding engine is not fully optimized.

We investigate an information-resilience scheme in the context of Content-Centric Networks (CCN) for the retrieval of content in disruptive, fragmented networks cases. To resolve and fetch content when the origin is not available due to fragmentation, we exploit content cached both in in-network caches and in end-users' devices. Initially, we present the required modifications in the CCN architecture to support the proposed resilience scheme. We also present the family of policies that enable the retrieval of cached content and we derive an analytical expression/lower bound of the probability that an information item will disappear from the network (be absorbed) and the time to absorption when the origin of the item is not reachable. Extensive simulations indicate that the proposed resilience scheme is a valid tool for the retrieval of cached content in disruptive scenarios, since it allows the retrieval of content for a long period after the fragmentation of the network and the “disappearance” of the content origin.

We question the widely adopted view of in-network caches acting as temporary storage for the most popular content in Information-Centric Networks (ICN). Instead, we propose that in-network storage is used as a place of temporary custody for incoming content in a store and forward manner. Given this functionality of in-network storage, senders push content into the network in an open-loop manner to take advantage of underutilised links. When content hits the bottleneck link it gets re-routed through alternative uncongested paths. If alternative paths do not exist, incoming content is temporarily stored in in-network caches, while the system enters a closed-loop, back-pressure mode of operation to avoid congestive collapse.

The proposed shift from host-centric to information-centric networking (ICN) has triggered extensive research in the area of content naming. Efforts have so far focused on the scalability and security properties that can make content objects routable and self-certifying. In this paper, we argue that the information that is exposed through explicitly naming content objects has been overlooked, although several operational and performance issues depend on the information that a name holds. We therefore revisit content naming design decisions taking into account information exposure and deployability of the ICN paradigm.

We introduce the concept of resource management for in-network caching environments. We argue that in Information-Centric Networking environments, deterministically caching content messages at predefined places along the content delivery path results in unfair and inefficient content multiplexing between different content flows, as well as in significant caching redundancy. Instead, allocating resources along the path according to content flow characteristics results in better use of network resources and therefore, higher overall performance. The design principles of our proposed in-network caching scheme, which we call ProbCache, target these two outcomes, namely reduction of caching redundancy and fair content flow multiplexing along the delivery path. In particular, ProbCache approximates the caching capability of a path and caches contents probabilistically to: 1) leave caching space for other flows sharing (part of) the same path, and 2) fairly multiplex contents in caches along the path from the server to the client. We elaborate on the content multiplexing fairness of ProbCache and find that it sometimes behaves in favor of content flows connected far away from the source, that is, it gives higher priority to flows travelling longer paths, leaving little space to shorter-path flows. We introduce an enhanced version of the main algorithm that guarantees fair behavior to all participating content flows. We evaluate the proposed schemes in both homogeneous and heterogeneous cache size environments and formulate a framework for resource allocation in in-network caching environments. The proposed probabilistic approach to in-network caching exhibits ideal performance both in terms of network resource utilization and in terms of resource allocation fairness among competing content flows. Finally, and in contrast to the expected behavior, we find that the efficient design of ProbCache results in fast convergence to caching of popular content items.

We present a new timeout algorithm for TCP, based on the observation that TCP-RTO should not be solely based on RTT estimations. We argue that the design principles of the current timeout algorithm may lead to flow synchronization, unnecessary retransmission effort and unfair resource allocation. WB-RTO exhibits two major properties: (i) it cancels retransmission synchronization which dominates when resource demand exceeds by far resource supply and (ii) reschedules flows on the basis of their contribution to congestion.

We show that TCP timers, based solely on RTT estimations and measurements, cannot capture with precision the level of flow contention. We notice that increased contention may stabilize RTT variation, minimize the deviation and, in turn, shorten the timeout. We show that this behavior is undesirable indeed, since it leads to unfair resource utilization. We propose CA-RTO, an algorithm that incorporates a contention parameter and a randomization technique into the retransmission timeout. We report significant improvement in fairness, great reduction of retransmitted packets and slight improvements in application goodput.