A Congestion Control Framework Based on In-Network Resource Pooling

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 resulting In-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 time alternative sub-paths are 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 condition

A keyword-based ICN-IoT platform

Information-Centric Networking (ICN) has been proposed as a promising solution for the Internet of Things (IoT), due to its focus on naming data, rather than endpoints, which can greatly simplify applications. The hierarchical naming of the Named-Data Networking (NDN) architecture can be used to name groups of data values, for example, all temperature sensors in a building. However, the use of a single naming hierarchy for all kinds of different applications is inflexible. Moreover, IoT data are typically retrieved from multiple sources at the same time, allowing applications to aggregate similar information items, something not natively supported by NDN. To this end, in this paper we propose (a) locating IoT data using (unordered) keywords combined with NDN names and (b) processing multiple such items at the edge of the network with arbitrary functions. We describe and evaluate three different strategies for retrieving data and placing the calculations in the edge IoT network, thus combining connectivity, storage and computing

DEEM: Enabling microservices via DEvice edge markets

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

ChainSoft: Collaborative software development using smart contracts

In recent years, more and more companies require dedicated software to increase the efficiency of their business. However, with rapidly changing technologies it is often inefficient to maintain a dedicated team of developers. On the other hand, outsourcing software development requires considerable effort and trust between involved parties to ensure the quality of the code and adequate payment. We present ChainSoft - a platform for outsourcing software development and automatic payments between parties that distrust each other, by means of blockchain technology. ChainSoft allows any developer to create software and submit software, includes automatic code verification and enforce users' proper behavior. We implement our system using Ethereum Smart Contracts and Github/Travis CI and present first evaluation proving its security and low usage cost

A Congestion Control Framework Based on In-Network Resource Pooling

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 resulting In-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 time alternative sub-paths are 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

On-Demand Routing for Scalable Name-Based Forwarding

Information-centric Networking (ICN) is a future Internet architecture design, where application-level names are directly used to route interests to fetch a copy of the desired content/data from any location. Following the conventions of the Internet Protocol to store the pre-computed routing/forwarding state for all prefixes at the network nodes raises scalability concerns in ICN (where content name prefixes need to be stored), especially at the inter-domain level. Instead, we consider the other extreme; that is, On-Demand Routing (ODR) computation for content name prefixes as interests arrive. ODR makes use of domain-level, per-prefix routing instructions usable by all the forwarders in a domain, named Routing Information Objects (RIO). Forwarders discover and retrieve RIOs in a similar way as content and can be cached in a new data structure called Route Information Store (RIS). RIOs are handed to a routing strategy module to perform a routing decision before relaying the packets. We demonstrate through extensive simulations that ODR scales the storage of routing/forwarding information through caching and information discovery-two mechanisms inherent to the ICN design. We propose our design as an extension of the Named Data Networking (NDN) architecture and discuss all the proposed enhancements in detail

Mobile Data Repositories at the Edge.

In a future IoT-dominated environment the majority of data will be produced at the edge, which may be moved to the network core. We argue that this reverses today’s “core-to-edge” data flow to an “edge-to-core” model and puts severe stress on edge access/cellular links. In this paper, we propose a data-centric communication approach which treats storage and wire the same as far as their ability to supply the requested data is concerned. Given that storage is cheaper to provide and scales better than wires, we argue for enhancing network connectivity with local storage services (e.g., in WiFi Access Points, or similar) at the edge of the network. Such local storage services can be used to buffer IoT and user-generated data at the edge, prior to data-cloud synchronization

FogSpot: Spot Pricing for Application Provisioning in Edge/Fog Computing

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. 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 comes 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 spot price based on current application requests. The proposed mechanism's design takes into account the characteristics of cloudlets' resources, such as their limited elasticity, and LLAs' attributes, like the expected QoS gain and engagement duration. Lastly, FogSpot guarantees end users' requests truthfulness while focusing in maximising either each cloudlet's revenue or resource utilisation

Shard scheduler: object placement and migration in sharded account-based blockchains

We propose Shard Scheduler, a system for object placement and migration in account-based sharded blockchains. Our system calculates optimal placement and decides on object migrations across shards. It supports complex multi-account transactions caused by smart contracts. Placement and migration decisions made by Shard Scheduler are fully deterministic, verifiable, and can be made part of the consensus protocol. Shard Scheduler reduces the number of costly cross-shard transactions, ensures balanced load distribution and maximizes the number of processed transactions for the blockchain as a whole. To this end, it leverages a novel incentive model motivating miners to maximize the global throughput of the entire blockchain rather than the throughput of a specific shard. In our simulations, Shard Scheduler can reduce the number of costly cross-shard transactions by half while ensuring equal load and increasing throughput more than 2 fold when using 60 shards. We also implement and evaluate Shard Scheduler on Chainspace, more than doubling its throughput and reducing user-perceived latency by 70% when using 10 shards

Content Censorship in the InterPlanetary File System

The InterPlanetary File System (IPFS) is currently the largest decentralized storage solution in operation, with thousands of active participants and millions of daily content transfers. IPFS is used as remote data storage for numerous blockchain-based smart contracts, Non-Fungible Tokens (NFT), and decentralized applications. We present a content censorship attack that can be executed with minimal effort and cost, and that prevents the retrieval of any chosen content in the IPFS network. The attack exploits a conceptual issue in a core component of IPFS, the Kademlia Distributed Hash Table (DHT), which is used to resolve content IDs to peer addresses. We provide efficient detection and mitigation mechanisms for this vulnerability. Our mechanisms achieve a 99.6% detection rate and mitigate 100% of the detected attacks with minimal signaling and computational overhead. We followed responsible disclosure procedures, and our countermeasures are scheduled for deployment in the future versions of IPFS