A replication strategy for distributed real-time object-oriented databases

Peddi, Praveen; DiPippo, Lisa Cingiser

doi:10.1109/isorc.2002.1003670

Cited by 15 publications

(13 citation statements)

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“…Control systems typically experience host crash rates of about 10 5 /hr and link failure rates (causing message loss) in the range of 10 5 /hr (permanent failures) and 10 3 /hr (transient failures) [12], [13]. In fact, we prove that the three properties listed above are impossible to achieve using traditional ways for implementing distributed shared storage [4], [8]- [10], i.e., using algorithms that do not have access to the internals of DCS services, for example, a black-box approach where algorithms are built on top of existing failure detector software blocks (more details in Section III). Yet data storage services for DCSs are imminently needed [6]- [10].…”

Section: Introductionmentioning

confidence: 85%

“…Both algorithms aim at increasing the scalability of real-time databases. Peddi and DiPippo [8] proposed a database replication algorithm for static periodic workloads, where all object locations and client data requirements are known a priori. The algorithm creates transactions from the operations issued to the database objects and feeds these transactions to a scheduling algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…These distributed data services are undeniably demanded in distributed control systems (DCSs) [6]- [11]. For example, power grid DCSs require real-time distributed storage systems (distributed hash tables) to store and retrieve monitoring data for wind and photo-voltaic generation sources [6]; ship-board DCSs require distributed real-time data services to be embedded within the ship [7], [8]; traffic control and agile manufacturing require the presence of distributed fresh data that reflects real-world status [7]- [10]; real-time execution platforms for DCSs, such as [11], rely on real-time replicated data structures for maintaining system and crash detection information.…”

Section: Introductionmentioning

confidence: 99%

“…In summary, the main contributions of this paper are: 1) A first precise derivation of the necessary guarantees that a shared memory abstraction must provide in DCSs. 2) Theoretical proofs showing the impossibility of implementing such guarantees using traditional approaches [4], [5], [8]- [10], e.g., using a black-box approach. 3) TapeWorm, an algorithm that circumvents the above impossibility by following a white-box approach directly utilizing failure detector algorithms of DCSs.…”

Section: Introductionmentioning

confidence: 99%

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Right on Time Distributed Shared Memory

Rachid

Kozhaya

Pignolet

2016

2016 IEEE Real-Time Systems Symposium (RTSS)

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Abstract-The demand for real-time data storage in distributed control systems (DCSs) is growing. Yet, providing realtime DCS guarantees is challenging, especially when more and more sensor and actuator devices are connected to industrial plants and message loss needs to be taken into account.In this paper, we investigate how to build a shared memory abstraction for DCSs as a first step towards implementing different shared storage systems in a DCS context. We first prove that, in the presence of host crashes and message losses, the necessary guarantees of such an abstraction are impossible to implement using a traditional approach that has no access to the internals of existing DCS services, e.g., a modular approach where algorithms are built on top of existing software blocks like failure detectors. We propose a white-box approach that utilizes messages of existing services in any DCS as the sole means of communication. More precisely, we present TapeWorm, an algorithm that attaches itself to the heartbeat messages of the failure detector component in DCSs. We prove that TapeWorm implements the desired shared memory guarantees for applications running on a DCS. We also analyze the performance of TapeWorm and we showcase ways of adapting TapeWorm to various application needs and workloads.

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Section: Introductionmentioning

confidence: 85%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Right on Time Distributed Shared Memory

Rachid

Kozhaya

Pignolet

2016

2016 IEEE Real-Time Systems Symposium (RTSS)

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“…Distributed real-time databases (DRTDBs) have drawn research attention in recent years [13] [14]. Wei et.…”

Section: Related Workmentioning

confidence: 99%

Data Services in Distributed Real-Time Embedded Systems

Kang

Son

2008

Software Technologies for Embedded and Ubiquitous Systems

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The computing systems are becoming deeply embedded into ordinary life and interact with physical processes and events. They monitor the physical world with sensors and provide appropriate reaction and control over it. This cyber-physical interaction, which occurs through ubiquitous embedded systems, has the potential to transform how humans interact with and control the physical world. Applications of such systems include medical and healthcare systems, agile manufacturing, military applications, and environmental monitoring and control. For these applications, the demand for real-time data services is increasing since they are inherently data-intensive. Without real-time data service support, a significant amount of data coming from the physical world may not be properly handled in time, increasing the difficulty of the application development. However, providing real-time data services in such large-scale and geographically distributed environment is a challenging task. In particular, both unpredictable communicational delays and computational workloads of large-scale distributed systems can lead to large number of deadline misses. In this paper, we propose a real-time data service architecture called DRACON (Decentralized data Replication And CONtrol), which is designed to support large-scale distributed real-time applications. DRACON couples cluster-based replicasharing and a decentralized control structure to address communication and computational unpredictability, simultaneously. The cluster-based replica-sharing mechanism not only enables scalable and bounded-delay access to remote data with high probability, but also decouples clusters to have less interaction, allowing a decentralized, thus scalable, QoS control structure.

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Concurrency control for real‐time and mobile transactions: Historical view, challenges, and evolution of practices

Chaudhry

Yousaf

2021

Concurrency and Computation

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Recently, mobile computing has changed the way that spatial data and GIS are processed. Unlike wired and stand-alone GIS, now the trend has been switched from offline to real-time data processing using location aware services, such as GPS technology. The increased usage of location aware services in multiuser real-time environment has made transaction management incredibly significant. If the simultaneous query operations on the same data item are not handled intelligently then this results in data inconsistency issue. Concurrency control protocol is one of the primary aspects that helps in overcoming this issue a in multiuser environment. To the best of our knowledge, the impact of technological advancements on concurrency control has not been thoroughly studied in the literature. In this article, we explored the literature on concurrency control algorithms in depth with respect to real-time applications and the applications with moving objects. We defined a taxonomy of concurrency control solutions and assessed the maturity of these solutions in the light of characteristics of real-time and mobile environment. We compared the most recent developments made in the literature and presented meaningful insights. Challenges are also identified and discussed, which can assist in doing research in this domain in future. K E Y W O R D S concurrency control, mobile, real time 1 INTRODUCTION IoT (Internet of Things) emerged as a promising technology that has revolutionized the way of living. GPS-based devices and sensors are commonly used in majority of the applications. The convergence of multiple domains such as real-time GIS, mobile computing, and IoT has made it necessary to solve the computing problem considering the requirements of each domain. Mobile GIS domain integrates several technologies: GPS, mobile computing, and GIS. In recent times, the data processing environment deals with varying kinds of sensors. The real-time data collected by sensors need to be processed in timely manner to ensure efficient data management. The sensors can be categorized into two types depending on their mobility: fixed and agile/mobile sensors. Fixed sensors are spatially fixed, whereas mobile sensors change their location with respect to time. Mobile sensors collect data on geographical grounds and this data can be referred to as spatiotemporal data. This has given rise to a new research domain of real-time mobile data processing and management. Thus, real-time processing domain emerged with respect to time depending on the nature of sensor technology and evolution of IoT. The future processing systems are expected to meet the requirements of spatiotemporal and real-time data, leading toward a new domain of real-time GIS. Real-time data can be used for both static and dynamic processing. For example, in the first aspect, mobile sensors can process and analyze the real-time spatiotemporal data for dynamic processing. On the other hand, real-time data can be collected and saved in a log for future offline analysis (static processing).

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A replication strategy for distributed real-time object-oriented databases

Abstract: Abstract

Cited by 15 publications

References 13 publications

Right on Time Distributed Shared Memory

Right on Time Distributed Shared Memory

Data Services in Distributed Real-Time Embedded Systems

Concurrency control for real‐time and mobile transactions: Historical view, challenges, and evolution of practices

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