Search

The demand for real-time data services is increasing in many applications including e-commerce, agile manufacturing, and telecommunications network management. In these applications, it is desirable to execute transactions within their deadlines, i.e., before the real-world status changes, using fresh (temporally consistent) data. However, meeting these fundamental requirements is challenging due to dynamic workloads and data access patterns in these applications. Further, transaction timeliness and data freshness requirements may conflict. In this paper, we define average/transient deadline miss ratio and new data freshness metrics to let a database administrator specify the desired quality of real-time data services for a specific application. We also present a novel QoS management architecture for real-time databases to support the desired QoS even in the presence of unpredictable workloads and access patterns. To prevent overload and support the desired QoS, the presented architecture applies feedback control, admission control, and flexible freshness management schemes. A simulation study shows that our QoS-aware approach can achieve a near zero miss ratio and perfect freshness, meeting basic requirements for real-time transaction processing. In contrast, baseline approaches fail to support the desired miss ratio and/or freshness in the presence of unpredictable workloads and data access patterns. Index Terms--Real-time database, deadline miss ratio, sensor data freshness, QoS management.

Real-time databases (RTDBs) are increasingly being used as an integral part of many computer systems. During normal operation, transactions in RTDBs must be executed in such a way that transaction timing and data time validity constraints can be met. RTDBs must also prepare for possible failures and provide fault tolerance capability. Principles for fault tolerance in RTDBs must take timing requirements into consideration and are distinct from those for conventional databases. We discuss these issues in this paper and describe a logging and recovery technique that is time- cognizant and is suitable for an important class of RTDB applications. The technique minimizes normal runtime overhead caused by logging and has a predictable impact on transaction timing constraints. Upon a failure, the system can recover critical data to a consistent and temporally valid state within predictable time bounds. The system can then resume its major functioning while non- critical data are being recovered in the background. As a result, the recovery time is bounded and shortened. In short, we are providing a bounded and predictable logging and recovery technique for critical transactions accessing critical variant and invariant data, while the database may consist of all kinds of data. Our performance evaluation via simulation shows that logging overhead has a small effect on missing transaction deadlines while adding recovery capability. In addition, our algorithm scales well with respect to large I/O page sizes, indicating that it can be adapted to future generations of storage devices with larger I/O page sizes. We compare our algorithm with another approach that does more frequent checkpointing for data with certain special characteristics. Simulation results disclose conditions under which each approach performs better than the other. Experiments also show that recovery using our approach is 3-6 times faster than traditional recovery. [ABSTRACT FROM AUTHOR]

This paper, presents a modified priority based probe algorithm for deadlock detection and resolution in distributed database systems. The original priority based probe algorithm was presented by Sinha and Natarajan based on work by Chandy, Misra, and Haas. Various examples are used to show that the original priority based algorithm either fails to detect deadlocks or reports deadlocks which do not exist in many situations. A modified algorithm which eliminates these problems is proposed. This algorithm has been tested through simulation and appears to be error free. Finally, the performance of the modified algorithm is briefly discussed. [ABSTRACT FROM AUTHOR]

Quality-aware realtime Embedded DataBase (QeDB) is a database for data-intensive real-time applications running on embedded devices. Currently, databases for embedded systems are best effort, providing no guarantees on their timeliness and data freshness. Existing real-time database (RTDB) technology cannot be applied to these embedded databases since it hypothesizes that the main memory of a system is large enough to hold the entire database. This, however, might not be true in data-intensive real-time applications. QeDB uses a novel feedback control scheme to support QoS in such embedded systems without requiring all data to reside in main memory. In particular, our approach is based on simultaneous control of both I/O and CPU resources to guarantee the desired timeliness. Unlike existing work on feedback control of RTDB performance, we implement and evaluate the proposed scheme on a modern embedded device. The experimental results show that our approach supports the desired timeliness of transactions while still maintaining high data freshness compared to baseline approaches. [ABSTRACT FROM AUTHOR]