Related papers: Nezha: A Key-Value Separated Distributed Store wit…
Log-Structured Merge-Trees (LSM-trees) dominate persistent key-value storage but suffer from high write amplification from 10x to 30x under random workloads due to repeated compaction. This overhead becomes prohibitive for large values with…
This paper proposes round-hashing, which is suitable for data storage on distributed servers and for implementing external-memory tables in which each lookup retrieves at most a single block of external memory, using a stash. For data…
Increasingly complex and diverse deep neural network (DNN) models necessitate distributing the execution across multiple devices for training and inference tasks, and also require carefully planned schedules for performance. However,…
Due to individual unreliable commodity components, failures are common in large-scale distributed storage systems. Erasure codes are widely deployed in practical storage systems to provide fault tolerance with low storage overhead. However,…
We propose a framework for distributed robust statistical learning on {\em big contaminated data}. The Distributed Robust Learning (DRL) framework can reduce the computational time of traditional robust learning methods by several orders of…
This paper proposes an algorithm for increasing data persistency in large-scale sensor networks. In the scenario considered here, k out of n nodes sense the phenomenon and produced ? information packets. Due to usually hazardous environment…
In large scale systems, approximate nearest neighbour search is a crucial algorithm to enable efficient data retrievals. Recently, deep learning-based hashing algorithms have been proposed as a promising paradigm to enable data dependent…
Distributed databases often suffer unequal distribution of data among storage nodes, which is known as `data skew'. Data skew arises from a number of causes such as removal of existing storage nodes and addition of new empty nodes to the…
Multi-core processors improve performance, but they can create unpredictability owing to shared resources such as caches interfering. Cache partitioning is used to alleviate the Worst-Case Execution Time (WCET) estimation by isolating the…
Programming with replicated objects is difficult. Developers must face the fundamental trade-off between consistency and performance head on, while struggling with the complexity of distributed storage stacks. We introduce Correctables, a…
Consensus protocols are crucial for reliable distributed systems as they let them cope with network and server failures. For decades, most consensus protocols have been designed as variations of the seminal Paxos, yet in 2014 Raft was…
Introduction. Distributed data processing and storage systems require efficient methods to distribute keys across buckets. While simple and fast, the traditional modulo-based mapping is unstable when the number of buckets changes, leading…
This paper introduces a microservices architecture for the purpose of enhancing the flexibility and performance of an airline reservation system. The architectural design incorporates Redis cache technologies, two different messaging…
Many cloud applications rely on fast and non-relational storage to aid in the processing of large amounts of data. Managed runtimes are now widely used to support the execution of several storage solutions of the NoSQL movement,…
The Raft agreement algorithm is recognized for its ease of understanding and practical implementation, and is currently adopted in systems such as Kubernetes. However, it has some limitations in terms of scalability and performance as it…
We develop a distributed second order optimization algorithm that is communication-efficient as well as robust against Byzantine failures of the worker machines. We propose COMRADE (COMunication-efficient and Robust Approximate Distributed…
Today's massive AI computation loads push heavy data synchronization across sites, i.e., nodes in data centers. Any reduction in such consensus latency can significantly improve the overall performance of desired systems. This consensus…
Parallel programmers face the often irreconcilable goals of programmability and performance. HPC systems use distributed memory for scalability, thereby sacrificing the programmability advantages of shared memory programming models.…
Partitioned DNN inference is a promising approach for latency-sensitive intelligent services in edge networks, since it allows different parts of a model to be executed across end devices, edge servers, and the cloud. However, in a…
Payment channel hub (PCH), by leveraging a powerful hub to reliably provide off-chain payment services, offers an effective enhancement to payment channel networks (PCNs). However, existing approaches typically rely on a single hub to relay…