Modern datacenter networks rely on multi-root tree topologies, to ensure efficient and reliable connectivity. However, traditional traffic engineering solutions like Equal Cost Multiple Path (ECMP) and Weighted-Cost MultiPath (WCMP) often fall short in addressing dynamic and heterogeneous traffic conditions. Specifically, these approaches struggle with asymmetric topologies and the allocation of elephant flows, which require more granular and agile traffic management. This paper introduces MTS-PolKA, a novel traffic splitting mechanism designed to enhance traffic engineering in datacenter networks. MTS-PolKA enables packet-level traffic distribution across multiple paths by embedding a label in the packet header that dictates the traffic division profile. Unlike existing methods that require reconfiguration of network tables at each switch, MTS-PolKA allows for dynamic and simultaneous adjustments across all switches in the path through simple modifications to the packet header. This approach significantly improves the agility and efficiency of traffic management. The implementation of MTS-PolKA on programmable switches using the P4 language, leveraging the Residue Number System (RNS) and M-PolKA architecture, demonstrates its potential through experiments conducted in the Mininet emulator. The results highlight the ability of MTS-PolKA to maintain flow stability, explore network multipaths, and enable rapid reconfigurations of traffic division profiles, enhancing the performance and efficiency of modern datacenter networks.

not-yet-known not-yet-known not-yet-known unknown This paper investigates the current landscape of option trading platforms for cryptocurrencies, encompassing both centralized and decentralized exchanges. Option contracts in cryptocurrency markets offer functionalities akin to traditional markets, providing investors with tools to mitigate risks, particularly those arising from price volatility, while also allowing them to capitalize on future volatility trends. The paper discusses these applications of option contracts in the context of decentralized finance (DeFi), emphasizing their utility in managing market uncertainties. Despite a recent surge in the trading volume of option contracts on cryptocurrencies, decentralized platforms account for less than 1% of this total volume. Hence, this paper takes a closer look by examining the design choices of these platforms to understand the challenges hindering their growth and adoption. It identifies technical, financial, and adoption-related challenges that decentralized exchanges face and provides commentary on existing platform responses. Subsequently, it introduces a zero-loss liquidity provision strategy on altcoins that utilizes options with automated market makers. These opportunities result in a positive return with no significant risk. It then investigates opportunities in the past using historical on-chain data to emphasize the number of risk-free opportunities that DeFi is missing due to the lack of a functional options exchange on arbitrary ERC20 token pairs on Ethereum. The experiments show 1015 profitable instances in the past three years on 14 token pairs.

As the digital landscape becomes increasingly complex, traditional cybersecurity measures are struggling to keep pace with the growing sophistication of cyber threats. This escalating challenge calls for new, more robust solutions. In this context, Quantum Computing emerges as a powerful tool that can change our approach to network security. Our research addresses this by introducing QuantumNetSec, a novel Intrusion Detection System (IDS) that combines quantum and classical computing techniques. QuantumNetSec employs Quantum Machine Learning (QML) personalized methodologies to analyze network patterns and detect malicious activities. Through detailed experimentation with publicly shared datasets, QuantumNetSec demonstrated superior performance in both binary and multiclass classification tasks. Our findings highlight the significant potential of quantum-enhanced cybersecurity solutions, showcasing QuantumNetSec’s ability to accurately detect a wide range of cyber threats, paving the way for more resilient and effective intrusion detection systems in the age of quantum utility.

As Internet middleboxes become increasingly prevalent, their influence on traffic streams presents significant challenges and opportunities for network management. Among these devices, transparent middleboxes are particularly difficult to detect, as they can modify traffic patterns without altering content, thereby preserving the appearance of normal routing. This paper addresses the challenge of detecting such transparent middleboxes by proposing a generalized detection framework capable of identifying a broad range of middlebox behaviors, including those that operate transparently. In contrast to existing ad hoc approaches that target specific middlebox types, our framework is novel in its ability to detect a wide variety of middleboxes instead. We validate the effectiveness of our approach through a combination of analytical methods, network simulations, and live Internet experiments. Our results demonstrate the framework's capability to accurately detect widely employed middlebox behaviors, such as network compression, traffic prioritization, and traffic shaping.

As of today, TCP remains the de-facto transport protocol in the Internet. However, TCP may incur high delays, especially when retransmitting lost packets as they have to be retransmitted only by the source and after a timeout that is roughly equal to a round trip time. To reduce such delay, recent work [1–3] proposed to deploy a special network function, called Transport Assistant (TA), that is able to detect and retransmit lost TCP packets from inside the network rather than the source, and thereby, reduces retransmission delays. Unfortunately, there is no study on the impact of the placement of the TA on its performance benefits in terms of packet delivery delay. In this paper, we focus on the TA placement problem. We discuss the trade-offs and parameters to be considered to select the best placement for the TA. We first mathematically model the TCP packet delivery delay, i.e., the time needed to deliver TCP packets, when the TA is deployed. We also formulate, as an Integer Linear Program (ILP), the problem of placing multiple TAs in order to reduce TCP packet delivery delays while minimizing their deployment costs. We consider use-cases, one where a TA could handle a single flow and another where a TA could handle multiple flows. We then propose two heuristics to solve the problem with minimal execution time. Through experiments, we demonstrated that the deployment of TAs could reduce TCP packet delivery delays by up to 30% and could be leveraged to guide routing and load balancing. Moreover, we show that using the proposed heuristics for placing TAs could lead to performance that is close to optimal solutions obtained with the ILP but with lower execution time.

This paper presents Deeper, a design for a decentralized exchange that enhances liquidity via reserve sharing. By doing this, it addresses the problem of shallow liquidity in low trading volume token pairs. Shallow liquidity impairs the functioning of on-chain markets by creating room for unwanted phenomena such as high slippage and sandwich attacks. Deeper solves this by allowing liquidity providers of multiple trading pairs against a common token to share liquidity. This is achieved by creating a common reserve pool for the shared token that is accessible by each trading pair. Independent from the shared liquidity, providers are free to add liquidity to individual token pairs without any restriction. The trading between one token pair does not affect the price of other token pairs even though the reserve of the shared token changes. The proposed design is an extension of concentrated liquidity automated market maker DEXs that is simple enough to be implemented on smart contracts. This is demonstrated by providing a template for a hook-based smart contract that adds our custom functionality to Uniswap V4. Experiments on historical prices show that for a batch consisting of 8 trading pairs, Deeper enhances liquidity by over 2.6–5.9×. The enhancement in liquidity can be increased further by increasing the participating tokens in the shared pool. While providing shared liquidity, Liquidity Providers should be cautious of certain risks and pitfalls, which are described. Overall, Deeper enables the creation of fair markets for low trading volume token pairs.

Financial services that are not only related to crypto-currencies but rely on blockchain for security and integrity are jointly referred to as Decentralized Fiance (DeFi) and are evolving rapidly. Given their novel applications of DLT and sophistical economical designs, the distinction between DeFi services and understanding the involved risk are often complex. This paper systematically studies the major classes of DeFi protocols, including risk and security. The selection of DeFi categories is based on a quantitative approach, covering over 80% of total value locked (TVL) in DeFi. Further, a structured methodology is provided to differentiate between DeFi protocols based on the algorithmic design and blockchain-network architecture. The findings indicate that every DeFi protocol falls into one of the three classes of DeFi algorithms: liquidity pool, synthetic asset or aggregator protocol. This work concludes with the risk analysis that is derived from the DeFi protocol, underlying tokens and agents. Certain DeFi assets, such as crypto-backed stablecoins, liquid staking tokens, and wrapped tokens of bridges, are synthetic assets, similar to derivatives in traditional finance, and bore similar risk exposure.