Soft Computing for Database Deadlock Resolution

Darwish, Saad M.; El-Zoghabi, Adel A.; Hassan, Marwan H.

doi:10.7763/ijmo.2015.v5.429

Cited by 5 publications

(15 citation statements)

References 7 publications

(12 reference statements)

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“…These approaches include (a) prevention, (b) avoidance, (c) detection and resolution of a deadlock. Deadlock prevention protocols guarantee a deadlock-free condition [158] by structuring a database system to prevent at least one of the conditions necessary for a deadlock to occur [30] (i.e., "mutual exclusion, hold and wait, no preemption and circular wait" [139]). These algorithms postpone a transaction execution in case of the existence of one of these conditions; the prevented transaction must restart at a later time.…”

Section: Deadlock Handlingmentioning

confidence: 99%

“…Deadlock avoidance, on the other hand, reduces the system overhead and hence is preferred over the prevention protocols [158]. Deadlock avoidance protocols attempt to predict deadlocks at the time a resource is requested by a transaction and react to the request accordingly to avoid deadlock [30]. Each request is analyzed dynamically in order to achieve efficient transaction execution.…”

Section: Deadlock Handlingmentioning

confidence: 99%

“…The presence of deadlocks is detected by a periodic iteration of a deadlock detection algorithm. When detected, a resolution algorithm selects a transaction to abort, releasing its held resources in order to break the deadlock cycle [24,30,154].…”

Section: Deadlock Handlingmentioning

confidence: 99%

“…A universal example is the traffic deadlock when four cars arrive at a four-way intersection at the same time, each wishing to proceed ahead. Deadlock is a common problem within multitasking concurrent programming systems [30]. Furthermore, the deadlock problem becomes more sophisticated where the underlying system is geographically distributed and the transactions have time constraints, in particular, DRTDBS [61].…”

Section: Deadlocksmentioning

confidence: 99%

“…A deadlock in DRTDBS occurs when a set of transactions are not able to complete their tasks because each transaction is waiting for the resource held by other transactions from this set [77]. A deadlocks drive DRTDBS into an undesirable situation that affects the system negatively [30]. The consequences of deadlocks on the system include decreasing the system throughput, collapsing the utilization of the involved resources to zero, and increasing the number of deadlocked transactions with deadlock persistence time [128].…”

Section: Deadlocksmentioning

confidence: 99%

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A distribute deadlock detection and resolution algorithm using agents

Samani¹

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Deadlock is an intrinsic bottleneck in Distributed Real-Time Database Systems (DRTDBS). Deadlock detection and resolution algorithms are important because in DRTDBS, deadlocked transactions are prone to missing deadlines. We propose an Agent Deadlock Detection and Resolution algorithm (ADCombine), a novel framework for distributed deadlock handling using stationary agents, to address the high overhead suffered by current agent-based algorithms. We test a combined deadlock detection and resolution algorithm that enables the Multi Agent System to adjust its execution based on the changing system load, and that selects its victim transactions more judiciously. We demonstrate the advantages of ADCombine over existing algorithms that use agents or traditional edge-chasing through simulation experiments that measure overhead and performance under a widely vary-

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Section: Deadlock Handlingmentioning

confidence: 99%

Section: Deadlock Handlingmentioning

confidence: 99%

Section: Deadlock Handlingmentioning

confidence: 99%

Section: Deadlocksmentioning

confidence: 99%

Section: Deadlocksmentioning

confidence: 99%

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A distribute deadlock detection and resolution algorithm using agents

Samani¹

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An Efficient Deadlock Handling Model Based on Neutrosophic Logic: Case Study on Real Time Healthcare Database Systems

Hassan¹,

Darwish

Mesbah

2022

IEEE Access

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The adoption of electronic patient records in hospitals will provide numerous benefits, but it will also present new issues. One of these is the creation of deadlocks, which causes delays in conducting tasks such as obtaining patient information. If the underlying system is distributed, the deadlock situation gets even more problematic. The strategy for preventing deadlock requires basic information from the transaction structure and requested resources. However, in most circumstances, this information is either unavailable or ambiguous. Because the cost of processing and storing sophisticated structures (e.g., wait-for graphs) is so high for a system in comparison to an array of structures, most discovery and resolved-deadlock protocols use them. Due to imprecise knowledge in specifying the transactions' attributes, distributed deadlock resolution methods present a new problem in dealing with uncertainty. To address this problem, a variety of fuzzy logic-based techniques have been proposed. However, very little attention has been paid to dealing with ambiguous, vague, incomplete, and inconsistent information about transaction attributes in a single framework. In this paper, we used neutrosophic logic, a generalization of fuzzy logic, to solve the problem of uncertainty in distributed real-time deadlock-resolving systems. The proposed method is structured to reflect multiple types of knowledge and relations among all features and tripartitions the transactions' features include validation factor degree, slackness degree, degree of deadline-missed transaction based on the degree of membership of truthiness, degree of membership of indeterminacy, and degree of membership of falsity. We developed a tool set and conducted experiments using benchmark datasets. There is an increase in detection rate and a large drop in rollback rate when this new strategy is used. Our technology resolved all database deadlocks and significantly increased database performance by up to three orders of magnitude.INDEX TERMS Deadlock recovery, neutrosophic set, healthcare databases, distributed deadlock detection. I. INTRODUCTIONNowadays, we depend heavily on online healthcare systems to ensure that medical treatments are always available. These systems depend largely on an interconnected network for data exchange between diverse entities, including as patients, physicians, and nurses. This results in massive amounts of data being maintained in medical databases, including details of surgeries, drugs, and allergies, among other things. Clinical information systems that are properly developed will allow physicians to obtain pertinent medical information about any patient, anytime they need it. However, one possible issue that users of electronic systems may confront is occasional delays in obtaining information caused by computer freezes. Deadlock is a common cause of a computer seeming to freeze. During the electronic healthcare records (HIMS) pilot phase, and as the number of users increased, the necessity to handle deadlock and concurren...

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