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In data-intensive real-time embedded applications, it is desirable to process data service requests in a timely manner using fresh data, spending less power. However, related work is relatively scarce despite the importance. In this paper, we present an effective approach to reduce both deadline misses and power expenditure in real-time databases with one or more processor by merging similar real-time queries to decrease repeated data accesses and processing, while doing dynamic power management. In a simulation study, our approach substantially decreases deadline misses and power consumption compared to state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

We consider the problem of real-time data collection in wireless sensor networks, in which data need to be delivered to one or more sinks within end-to-end deadlines. To enhance performance with respect to end-to-end deadline miss ratio, existing approaches schedule packets by prioritizing them based on per-packet deadlines and other factors such as the distance to the sink. However, important factors affecting the end-to-end performance such as queuing delays and buffer overruns have largely been ignored in the existing real-time schemes. Packet prioritization by itself cannot assist with these issues, and may in fact, exacerbate them for real-time data collection, since many high priority packets may simultaneously contend for the constrained network resources. In sensor networks, where the channel bandwidth and buffer space are often quite limited, these issues can dramatically impact real-time performance. Based on this observation, we propose Just-in-Time Scheduling (JiTS) strategies where packets are judiciously delayed within their slack time to reduce contention and load balance the use of the network buffers. We explore several policies for delaying data packets at different intermediate nodes considering potential contention. In addition, we also show that the routing protocol has a significant impact on real-time performance. In particular, shortest path routing leads to considerably better performance than geographic forwarding, which is often used for real-time data transmission in wireless sensor networks. Using an extensive simulation study, we demonstrate that JiTS can significantly improve the deadline miss ratio and packet drop ratio compared to two state-of-the-art approaches for real-time packet delivery for sensor networks (RAP and SPEED) under various scenarios. Notably, JiTS requires neither lower layer (e.g., MAC layer) support nor synchronization among the sensor nodes. [ABSTRACT FROM AUTHOR]

The demand for real-time e-commerce data services has been increasing recently. In many e-commerce applications, it is essential to process user requests within their deadlines, i.e., before the market status changes, using fresh data reflecting the current market status. However, current data services are poor at processing user requests in a timely manner using fresh data. To address this problem, we present a differentiated real-time data service framework for e-commerce applications. User requests are classified into several service classes according to their importance, and they receive differentiated real-time performance guarantees in terms of deadline miss ratio. At the same time, a certain data freshness is guaranteed for all transactions that commit within their deadlines. A feedback-based approach is applied to differentiate the deadline miss ratio among service classes. Admission control and adaptable update schemes are applied to manage potential overload. A simulation study, which reflects the e-commerce data semantics, shows that our approach can achieve a significant performance improvement compared to baseline approaches. Our approach can support the specified per-class deadline miss ratios maintaining the required data freshness even in the presence of unpredictable workloads and data access patterns, whereas baseline approaches fail. [ABSTRACT FROM AUTHOR]