Ensuring order-fairness in distributed data management systems deployed in untrustworthy environments is crucial to prevent adversarial manipulation of transaction ordering, particularly in unpredictable markets where transaction order directly influences financial outcomes. While Byzantine Fault-Tolerant (BFT) consensus protocols guarantee safety and liveness, they inherently lack mechanisms to enforce order-fairness, exposing distributed systems to attacks such as frontrunning and sandwiching. Previous attempts to integrate order-fairness have often introduced substantial performance overhead, largely due to limitations of the underlying consensus protocols. This paper presents DAG of DAGs (DoD), a high-performance order-fairness protocol designed on top of DAG-based BFT consensus protocols. By leveraging the high throughput and resilience of DAG-based protocols, DoD addresses the performance limitations of existing order-fairness solutions. DoD's novel DAG of DAGs architecture enables seamless integration of order fairness with BFT consensus protocols. Through concurrent block proposals and a wave-based leader election mechanism, DoD significantly improves resilience against adversarial manipulation. A prototype implementation and experimental evaluation demonstrate that DoD effectively provides order fairness with minimal performance overhead.

Distributed data management systems use state Machine Replication (SMR) to provide fault tolerance. The SMR algorithm enables Byzantine Fault-Tolerant (BFT) protocols to guarantee safety and liveness despite the malicious failure of nodes. However, SMR does not prevent the adversarial manipulation of the order of transactions, where the order assigned by a malicious leader differs from the order in that transactions are received from clients. While order-fairness has been recently studied in a few protocols, such protocols rely on synchronized clocks, suffer from liveness issues, or incur significant performance overhead. This paper presents Rashnu , a high-performance fair ordering protocol. Rashnu is motivated by the fact that fair ordering among two transactions is needed only when both transactions access a shared resource. Based on this observation, we define the notion of data-dependent order fairness where replicas capture only the order of data-dependent transactions and the leader uses these orders to propose a dependency graph that represents fair ordering among transactions. Replicas then execute transactions using the dependency graph, resulting in the parallel execution of independent transactions. We implemented a prototype of Rashnu where our experimental evaluation reveals the low overhead of providing order-fairness in Rashnu.

While permissioned blockchains enable a family of data center applications, existing systems suffer from imbalanced loads across compute and memory, exacerbating the underutilization of cloud resources. This paper presents FlexChain , a novel permissioned blockchain system that addresses this challenge by physically disaggregating CPUs, DRAM, and storage devices to process different blockchain workloads efficiently. Disaggregation allows blockchain service providers to upgrade and expand hardware resources independently to support a wide range of smart contracts with diverse CPU and memory demands. Moreover, it ensures efficient resource utilization and hence prevents resource fragmentation in a data center. We have explored the design of XOV blockchain systems in a disaggregated fashion and developed a tiered key-value store that can elastically scale its memory and storage. Our design significantly speeds up the execution stage. We have also leveraged several techniques to parallelize the validation stage in FlexChain to further improve the overall blockchain performance. Our evaluation results show that FlexChain can provide independent compute and memory scalability, while incurring at most 12.8% disaggregation overhead. FlexChain achieves almost identical throughput as the state-of-the-art distributed approaches with significantly lower memory and CPU consumption for compute-intensive and memory-intensive workloads respectively.

Modern programmable network hardware enables in-network computing-pushing increasingly-complex logic into the network to improve the performance, flexibility and reliability of network services. But the current network programming paradigm is constrained to programming a single network device at a time. The lack of support for in-network programs that use several and heterogeneous network hardware simultaneously constrains the scale and behaviour of in-network programs. Dataplane Disaggregation is a new paradigm that addresses this problem. It distributes computations across programmable network hardware including switches and smart NICs. This paradigm transforms a monolithic in-network program into a distributed system executing on possibly heterogeneous resources. The goal of this demo is to make an accessible presentation of Dataplane Disaggregation to the wider distributed systems community. This is intended to stimulate discussion on effective ways to program distributed and heterogeneous systems. Our demo is based on the Flightplan system prototype. Flightplan is open-source and comes with detailed documentation and support scripts, yet it requires some effort to set up and run. This impedes its study by others. Our demo runs completely in the browser and does not burden viewers with any installation effort at all. The technical contribution of this demo consists of a customised visualisation of Flightplan experiments. Moreover, the demo is well-suited to virtual events—as is being planned for ICDCS'21—since it can be run independently and asynchronously by viewers of the demo. This is especially helpful for viewers with slow or intermittent Internet connections. We make the demo's source code freely available online for use by others, including researchers who want to build similar demos.

Requesting https://www.wikipedia.org from your browser can start a small storm of exchanges between your device and name servers, caches and ultimately the Wikipedia servers. Data networking involves simple ideas---such as modular decomposition and path redundancy---that produce interesting behavior through the, sometimes subtle, interaction with one another. Through this interaction, networking is able to achieve amazing feats of reliability, availability, and planetary scale. But it is difficult to connect the big ideas---such as reliability---with how they work across different implementations, topologies and in the face of different levels of network disfunction. Making that leap typically requires time spent with unforgiving tools such as 'tcpdump' across several experiments. This is not helped by the hearty alphabet soup that is the set of protocols that one has to learn to begin to understand how networking really works.

There are a wealth of good-quality open-source tools for teaching, learning about, and experimenting with P4-based SDN. But this tooling and its mode of distribution is geared towards usage at the level of "nuts and bolts", requiring effort to setup even for casual or inexperienced users, and does not give "big picture" insight into the network as the system executes. Our poster describes FDP, a portable platform that builds on existing tooling to enable end-to-end experimentation and zero-effort in-browser interactive visualization, which we envision can benefit teaching of P4-based SDN and research demonstration.