CATS: linearizability and partition tolerance in scalable and self-organizing key-value stores

Distributed key-value stores provide scalable, fault-tolerant, and self-organizing storage services, but fall short of guaranteeing linearizable consistency in partially synchronous, lossy, partitionable, and dynamic networks, when data is distributed and replicated automatically by the principle of consistent hashing. This paper introduces consistent quorums as a solution for achieving atomic consistency. We present the design and implementation of CATS, a distributed key-value store which uses consistent quorums to guarantee linearizability and partition tolerance in such adverse and dynamic network conditions. CATS is scalable, elastic, and self-organizing; key properties for modern cloud storage middleware. Our system shows that consistency can be achieved with practical performance and modest throughput overhead (5%) for read-intensive workloads

BaseFs - Basically Acailable, Soft State, Eventually Consistent Filesystem for Cluster Management

A peer-to-peer distributed filesystem for community cloud management. https://github.com/glic3rinu/basef

On consistency maintenance in service discovery

Communication and node failures degrade the ability of a service discovery protocol to ensure Users receive the correct service information when the service changes. We propose that service discovery protocols employ a set of recovery techniques to recover from failures and regain consistency. We use simulations to show that the type of recovery technique a protocol uses significantly impacts the performance. We benchmark the performance of our own service discovery protocol, FRODO against the performance of first generation service discovery protocols, Jini and UPnP during increasing communication and node failures. The results show that FRODO has the best overall consistency maintenance performance

Distributed Service Discovery for Heterogeneous Wireless Sensor Networks

Service discovery in heterogeneous Wireless Sensor Networks is a challenging research objective, due to the inherent limitations of sensor nodes and their extensive and dense deployment. The protocols proposed for ad hoc networks are too heavy for sensor environments. This paper presents a resourceaware solution for the service discovery problem, which exploits the heterogeneous nature of the sensor network and alleviates the high-density problem from the flood-based approaches. The idea is to organize nodes into clusters, based on the available resources and the dynamics of nodes. The clusterhead nodes act as a distributed directory of service registrations. Service discovery messages are exchanged among the nodes in the distributed directory. The simulation results show the performance of the service discovery protocol in heterogeneous dense environments

Modeling Routing Overhead Generated by Wireless Proactive Routing Protocols

In this paper, we present a detailed framework consisting of modeling of routing overhead generated by three widely used proactive routing protocols; Destination-Sequenced Distance Vector (DSDV), Fish-eye State Routing (FSR) and Optimized Link State Routing (OLSR). The questions like, how these protocols differ from each other on the basis of implementing different routing strategies, how neighbor estimation errors affect broadcast of route requests, how reduction of broadcast overhead achieves bandwidth, how to cope with the problem of mobility and density, etc, are attempted to respond. In all of the above mentioned situations, routing overhead and delay generated by the chosen protocols can exactly be calculated from our modeled equations. Finally, we analyze the performance of selected routing protocols using our proposed framework in NS-2 by considering different performance parameters; Route REQuest (RREQ) packet generation, End-to-End Delay (E2ED) and Normalized Routing Load (NRL) with respect to varying rates of mobility and density of nodes in the underlying wireless network

On-line planning and scheduling: an application to controlling modular printers

We present a case study of artificial intelligence techniques applied to the control of production printing equipment. Like many other real-world applications, this complex domain requires high-speed autonomous decision-making and robust continual operation. To our knowledge, this work represents the first successful industrial application of embedded domain-independent temporal planning. Our system handles execution failures and multi-objective preferences. At its heart is an on-line algorithm that combines techniques from state-space planning and partial-order scheduling. We suggest that this general architecture may prove useful in other applications as more intelligent systems operate in continual, on-line settings. Our system has been used to drive several commercial prototypes and has enabled a new product architecture for our industrial partner. When compared with state-of-the-art off-line planners, our system is hundreds of times faster and often finds better plans. Our experience demonstrates that domain-independent AI planning based on heuristic search can flexibly handle time, resources, replanning, and multiple objectives in a high-speed practical application without requiring hand-coded control knowledge