Service Deployment with Predictive Ability for Data Stream Processing in a Cloud-Edge Environment

Autor:	Xiaohong Li, Zhuofeng Zhao, Han Li, Shouli Zhang, Chen Liu
Rok vydání:	2021
Předmět:	Service (systems architecture) Flow (mathematics) business.industry Software deployment Computer science Real-time computing Latency (audio) Resource allocation Cloud computing Enhanced Data Rates for GSM Evolution business Digital signal processing
Zdroj:	Service-Oriented Computing ISBN: 9783030914301 ICSOC
DOI:	10.1007/978-3-030-91431-8_55
Popis:	Runtime IoT data fluctuation brings challenges for optimizing the resource allocation for a data stream processing (DSP) flow in a cloud-edge environment. It can result in extra high latency for a flow. Optimized strategy of dynamic resource allocation is still hard to design to timely dealing with the IoT data fluctuation. In this paper, the above challenge is abstracted and redefined as the service deployment problem. An improved GA optimization algorithm, integrating with the IoT data fluctuation prediction ability, is proposed to handle IoT data fluctuations during the running of a DSP flow. Effectiveness of the proposed approach is evaluated based on the real datasets from a real application.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::798fdaaacb66336517f4698ccf804bd8 https://doi.org/10.1007/978-3-030-91431-8_55 Zobrazit plný text záznamu