A Review of Distributed Deadlock Detection Techniques based on Diffusion Computation Approach

Singh, Sonia; Tyagi, S. S.

doi:10.5120/7377-0186

Cited by 6 publications

(6 citation statements)

References 16 publications

(20 reference statements)

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“…Anomalies or outliers need to be checked and extracted in all domains, and the present scenario is no exception (Singh & Upadhyaya, 2012). As in most cases, both positive and negative outliers can coexist here.…”

Section: Outlier Removal Through Iqr Techniquementioning

confidence: 94%

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Machine Learning Tool to Predict Student Categories After Outlier Removal

Desarkar

Das

Chaudhuri

2022

Journal of Information Technology Research

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Statistical outlier detection techniques uses academic performance oriented results to find the truly brilliant as well as the weakest amongst a colony of students. Machine Learning allows further partitions within the remaining student community, based on both merit and personality. Present work proposes a decision tree model for predicting three more appropriate categories. It utilizes Text Analytic tools to assess student characteristic traits from their textual responses and feedbacks. The cream of the general pool is chosen to belong to a top class comprising the mentor group, provided they can academically assist the weaker of the lot. But all on the top may not be suited for mentor-ship role - textual assessment data delves to reveal character orientations favouring such decisions. The bulk who can manage their own forms the second class. The bottom of the pool benefits with assistance from the mentor group and comprise the third class.

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Section: Outlier Removal Through Iqr Techniquementioning

confidence: 94%

“…Outlier detection is a technique to identify the presence of unusual patterns within a system, which do not conform to the general expected behavior (Singh & Upadhyaya, 2012). In educational domain, outlier performers refer to the group of students who perform below or above a statistically determined permissible range.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Tool to Predict Student Categories After Outlier Removal

Desarkar

Das

Chaudhuri

2022

Journal of Information Technology Research

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“…As mentioned earlier deadlock occurs whenever waiting transactions want access to the resources held by each other and none of them is able to complete their execution [2]. Most of the reviewed algorithms imply rollback/abort as the solution to deadlocks.…”

Section: Related Workmentioning

confidence: 99%

“…Deadlock is a system state in which every process in some group requests resources from other processes in the group, and then waits indefinitely for these requests to be satisfied [1]. Deadlock is an undesirable situation; some of the consequences of deadlock are: throughput of the system is affected; utilization of the involved resources decreases to zero; deadlock increases with deadlock persistence time; and deadlock cycles do not terminate by themselves until properly detected and resolved [2], [3]. The deadlock problem is intrinsic to distributed database system which employs locking as its concurrency control algorithm.…”

Section: Introductionmentioning

confidence: 99%

Soft Computing for Database Deadlock Resolution

Darwish¹,

El-Zoghabi²,

Hassan³

2015

IJMO

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Abstract-Distributed nature of transactions arising at different sites and needing resources from diverse locations pose various operational problems, such as deadlocks, concurrency and data recovery. A deadlock may occur when a transaction enters into wait state that request resource from other blocked transactions. Deadlock detection and resolving is very difficult in a distributed database system because no controller has complete and current information about the system and data dependencies. In this paper, an enhanced technique for deadlock resolution is presented, which minimizes the abortion or waiting of the selected victim transactions. The proposed system includes the use of fuzzy logic by creating a set of fuzzy rules in order to deal with criticalness and similarity attributes of transactions. By using these rules, fuzzy logic will try to provide an easy conflict resolution method between transactions to diminish transactions wasted restart, and guaranteeing temporal consistency of data and transactions. Furthermore, the presented deadlock handling algorithm does not detect any false deadlock or exclude any really existing deadlocks. Experimental results show performance of the recommended system benefits such as increase in commit rate and decrease in re-execution or waiting of the transactions.

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“…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]. The deadlock cycles 1 do not terminate without outside interference [30].…”

Section: Deadlocksmentioning

confidence: 99%

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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A Review of Distributed Deadlock Detection Techniques based on Diffusion Computation Approach

Cited by 6 publications

References 16 publications

Machine Learning Tool to Predict Student Categories After Outlier Removal

Machine Learning Tool to Predict Student Categories After Outlier Removal

Soft Computing for Database Deadlock Resolution

A distribute deadlock detection and resolution algorithm using agents

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