Real-Time Scheduling for Data Stream Management Systems

Abstract

Quality-aware management of data streams is gaining more and more importance with the amount of data produced by streams growing continuously. The resources required for data stream processing depend on different factors and are limited by the environment of the Data Stream Management System (DSMS). Thus, with a potentially unbounded amount of stream data and limited processing resources, some of the data stream processing tasks (originating from different users) may not be satisfyingly answered, and therefore, users should be enabled to negotiate a certain quality for the execution of their stream processing tasks. After the negotiation process, it is the responsibility of the Data Stream Management System to meet the quality constraints by using adequate resource reservation and scheduling techniques. Within this paper, we consider different aspects of real-time scheduling for operations within a DSMS. We propose a scheduling concept which enables us to meet certain time-dependent Quality-of-Service requirements for user-given processing tasks. Furthermore, we describe the implementation of our scheduling concept within a real-time-capable Data Stream Management System, and we give experimental results on that.

Reservation-Based Scheduling for Automotive DSMS under High Overload Condition

“…Schmidt et al [11] introduced the RM scheduling algorithm, which is based on the rate monotonic algorithm and a hard realtime scheduling strategy. It targets electrical train services and presents two different strategies, i.e., minimum output delay and maximum DSMS throughput.…”

“…c 2016 Information Processing Society of Japan [11] ✗ RTSTREAM [12] ✗ ✗ Semantic load shedding [13] ✗ OP-EDF [10] ✗ ✗ D R EDF etc. [24], [25], [26] ✗ CBS etc.…”

“…The objective of OP-EDF is to minimize the deadline miss ratio (DMR). Schmidt et al [12], Wei et al [11], and Ma et al [13] proposed algorithms that can handle hard real-time tasks when systems are not overc 2016 Information Processing Society of Japan loaded. However, the algorithm proposed by Schmidt et al only handles hard real-time tasks with predictable arrival times and may miss deadlines in an unpredictable manner under overload conditions.…”

“…Several studies have adopted real-time scheduling in DSMS to meet timing constraints over data streams [10], [11], [12], [13]. Li et al [10] proposed operator path earliest deadline first (OP-EDF) scheduling to handle huge amounts of sensor data and satisfy real-time requirements.…”

Time-bounded distributed QoS-aware service configuration in heterogeneous cooperative environments

“…The quantity of data produced by a variety of data sources and sent to end systems to further processing is growing significantly, increasingly demanding more processing power and the challenges become even more critical when a coordinated content analysis of data sent from multiple sources is necessary [5]. Thus, with a potentially unbounded amount of stream data and limited resources, some of the processing tasks may not be satisfyingly answered even within the users' minimum acceptable QoS levels [6].…”