Tolerating Correlated Failures in Massively Parallel Stream Processing Engines

Fault-tolerance techniques for stream processing engines can be categorized into passive and active approaches. A typical passive approach periodically checkpoints a processing task's runtime states and can recover a failed task by restoring its runtime state using its latest checkpoint. On the other hand, an active approach usually employs backup nodes to run replicated tasks. Upon failure, the active replica can take over the processing of the failed task with minimal latency. However, both approaches have their own inadequacies in Massively Parallel Stream Processing Engines (MPSPE). The passive approach incurs a long recovery latency especially when a number of correlated nodes fail simultaneously, while the active approach requires extra replication resources. In this paper, we propose a new fault-tolerance framework, which is Passive and Partially Active (PPA). In a PPA scheme, the passive approach is applied to all tasks while only a selected set of tasks will be actively replicated. The number of actively replicated tasks depends on the available resources. If tasks without active replicas fail, tentative outputs will be generated before the completion of the recovery process. We also propose effective and efficient algorithms to optimize a partially active replication plan to maximize the quality of tentative outputs. We implemented PPA on top of Storm, an open-source MPSPE and conducted extensive experiments using both real and synthetic datasets to verify the effectiveness of our approach

Operating-system support for distributed multimedia

Multimedia applications place new demands upon processors, networks and operating systems. While some network designers, through ATM for example, have considered revolutionary approaches to supporting multimedia, the same cannot be said for operating systems designers. Most work is evolutionary in nature, attempting to identify additional features that can be added to existing systems to support multimedia. Here we describe the Pegasus project's attempt to build an integrated hardware and operating system environment from\ud the ground up specifically targeted towards multimedia

Ethernet - a survey on its fields of application

During the last decades, Ethernet progressively became the most widely used local area networking (LAN) technology. Apart from LAN installations, Ethernet became also attractive for many other fields of application, ranging from industry to avionics, telecommunication, and multimedia. The expanded application of this technology is mainly due to its significant assets like reduced cost, backward-compatibility, flexibility, and expandability. However, this new trend raises some problems concerning the services of the protocol and the requirements for each application. Therefore, specific adaptations prove essential to integrate this communication technology in each field of application. Our primary objective is to show how Ethernet has been enhanced to comply with the specific requirements of several application fields, particularly in transport, embedded and multimedia contexts. The paper first describes the common Ethernet LAN technology and highlights its main features. It reviews the most important specific Ethernet versions with respect to each application field’s requirements. Finally, we compare these different fields of application and we particularly focus on the fundamental concepts and the quality of service capabilities of each proposal

Scalable and fault-tolerant data stream processing on multi-core architectures

With increasing data volumes and velocity, many applications are shifting from the classical “process-after-store” paradigm to a stream processing model: data is produced and consumed as continuous streams. Stream processing captures latency-sensitive applications as diverse as credit card fraud detection and high-frequency trading. These applications are expressed as queries of algebraic operations (e.g., aggregation) over the most recent data using windows, i.e., finite evolving views over the input streams. To guarantee correct results, streaming applications require precise window semantics (e.g., temporal ordering) for operations that maintain state. While high processing throughput and low latency are performance desiderata for stateful streaming applications, achieving both poses challenges. Computing the state of overlapping windows causes redundant aggregation operations: incremental execution (i.e., reusing previous results) reduces latency but prevents parallelization; at the same time, parallelizing window execution for stateful operations with precise semantics demands ordering guarantees and state access coordination. Finally, streams and state must be recovered to produce consistent and repeatable results in the event of failures. Given the rise of shared-memory multi-core CPU architectures and high-speed networking, we argue that it is possible to address these challenges in a single node without compromising window semantics, performance, or fault-tolerance. In this thesis, we analyze, design, and implement stream processing engines (SPEs) that achieve high performance on multi-core architectures. To this end, we introduce new approaches for in-memory processing that address the previous challenges: (i) for overlapping windows, we provide a family of window aggregation techniques that enable computation sharing based on the algebraic properties of aggregation functions; (ii) for parallel window execution, we balance parallelism and incremental execution by developing abstractions for both and combining them to a novel design; and (iii) for reliable single-node execution, we enable strong fault-tolerance guarantees without sacrificing performance by reducing the required disk I/O bandwidth using a novel persistence model. We combine the above to implement an SPE that processes hundreds of millions of tuples per second with sub-second latencies. These results reveal the opportunity to reduce resource and maintenance footprint by replacing cluster-based SPEs with single-node deployments.Open Acces