Computational resource management for data‐driven applications with deadline constraints. (25th October 2016)

• Record Type: Journal Article
• Authors: Tolosana‐Calasanz, Rafael; Diaz‐Montes, Javier; Rana, Omer F.; Parashar, Manish; Xydas, Erotokritos; Marmaras, Charalampos; Papadopoulos, Panagiotis; Cipcigan, Liana
• Is Part Of: Concurrency and computation. Volume 29:Number 8(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2016-10-25
• Subjects: computational resource management -- controlled and distributed EV charging -- hybrid controller -- reactive predictive controller; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4018
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Recent advances in the type and variety of sensing technologies have led to an extraordinary growth in the volume of data being produced and led to a number of streaming applications that make use of this data. Sensors typically monitor environmental or physical phenomenon at predefined time intervals or triggered by user‐defined events. Understanding how such streaming content (the raw data or events) can be processed within a time threshold remains an important research challenge. We investigate how a cloud‐based computational infrastructure can autonomically respond to such streaming content, offering quality of service guarantees. In particular, we contextualize our approach using an electric vehicles (EVs) charging scenario, where such vehicles need to connect to the electrical grid to charge their batteries. There has been an emerging interest in EV aggregators (primarily intermediate brokers able to estimate aggregate charging demand for a collection of EVs) to coordinate the charging process. We consider predicting EV charging demand as a potential workload with execution time constraints. We assume that an EV aggregator manages a number of geographic areas and a pool of computational resources of a cloud computing cluster to support scheduling of EV charging. The objective is to ensure that there is enough computational capacity to satisfy the requirements for managing EV battery charging requests within specific time constraints.