منابع مشابه
Latency-aware Elastic Scaling for Distributed Data Stream Processing
Elastic scaling allows a data stream processing system to react to a dynamically changing query or event workload by automatically scaling in or out. Thereby, both unpredictable load peaks as well as underload situations can be handled. However, each scaling decision comes with a latency penalty due to the required operator movements. Therefore, in practice an elastic system might be able to im...
متن کاملOptimal Operator State Migration for Elastic Data Stream Processing
A cloud-based data stream management system (DSMS) handles fast data by utilizing the massively parallel processing capabilities of the underlying platform. An important property of such a DSMS is elasticity, meaning that nodes can be dynamically added to or removed from an application to match the latter’s workload, which may fluctuate in an unpredictable manner. For an application involving s...
متن کاملAn Elastic Data Stream Processing Ecosystem for Distributed Environments
In the last couple of years, we have observed a trend towards an ever-growing number and volume of data streams. Up to now, these data streams were mainly originating from social media services but today the emergence of the Internet of Things (IoT) also contributes to the growth of data streams. Besides the growth of the data volume, the IoT also introduces several new challenges, like the geo...
متن کاملFUGU: Elastic Data Stream Processing with Latency Constraints
Elasticity describes the ability of any distributed system to scale to a varying number of hosts in response to workload changes. It has become a mandatory architectural property for state of the art cloud-based data stream processing systems, as it allows treatment of unexpected load peaks and cost-efficient execution at the same time. Although such systems scale automatically, the user still ...
متن کاملTowards Elastic Stream Processing: Patterns and Infrastructure
Distributed, highly-parallel processing frameworks as Hadoop are deemed to be state-of-the-art for handling big data today. But they burden application developers with the task to manually implement program logic using lowlevel batch processing APIs. Thus, a movement can be observed that high-level languages are developed which allow to declaratively model dataflows that are automatically optim...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Parallel and Distributed Systems
سال: 2014
ISSN: 1045-9219
DOI: 10.1109/tpds.2013.295