Intelligent Network Communications for Distributed Database Systems

Hababeh, Ismail

doi:10.1109/dbkda.2010.11

Cited by 2 publications

(6 citation statements)

References 16 publications

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“…Notice that the total communication cost between all sites is 594 ms (calculated from table1) while highest total communication after our algorithm is 206 ms, that means; the introduced algorithm can reduce around 65% from the total communication cost with communication average with 0.88 in each communication. On the other hand, Hababeh in [20] used the clustering performance evaluation (CPE) to evaluate his distributed database clustering algorithm. He defined CPE as the result of the reduced costs of communications divided by the sum of communication costs between sites.…”

Section: ) Module 3: Obtaining Total Shard Data Objectsmentioning

confidence: 99%

“…The number of clusters obtained is another criteria to evaluate the clustering algorithm. Hababeh in [20]compared his results with the studies in [21] and [22] as they were the most closest to his clustering technique. The author in the work [21] defined a basic parameter to identify the clustering.…”

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confidence: 92%

“…In contrast, the author in [22] used the natural logarithm function to determine the approximation of the average cheapest path length between network nodes, as more network sites added, more clusters are generated due to the nature of logarithmic function used. The resulted number of clusters from [20] , [21]and [22] are compared with the proposed clustering technique results. Fig.…”

mentioning

confidence: 99%

“…As we can see more clusters are generated by our clustering method. On the other hand, less number of clusters is generated from [20], [21] and [22] .…”

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confidence: 99%

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Clustering Algorithm for Distributed Real-Time Database sites

Ahmed

Salem

Saleh

2015

IJCI. International Journal of Computers and Information

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The demand for real-time database is increasing. Indeed, most real-time systems are inherently distributed in nature and need to handle data in a timely fashion. Obtaining data from remote sites may take long time making the temporal data invalid. This results in large number of tardy transactions with their catastrophic effect. Clustering the database sites nodes can help distributed real-time database systems to face the challenges meeting their time requirements. Reducing the large number of network sites into many clusters with smaller number of sites will effectively decrease the response time, resulting in better meeting of time constraints. In this paper, we introduce a clustering algorithm for distributed real-time database that depend on both the communication time cost and the timing properties of data. The results show the effectiveness of the proposed approach via achieving lower communication time, higher database performance and better meeting of timing requirements. Keywords-clustering; database; real-time; distributed systems I. INTRODUCTION Recently, the demand for real-time database is increasing. Many applications such as e-commerce, mobile communication, accounting, information services, medical monitoring, nuclear reactor control, traffic control systems and telecommunications are some examples of application which require real-time data support [1]. A real-time database system (RTDBS) is defined in [2] as a database system that includes all features of traditional database system, while enforcing real-time constraints or deadlines. According to [3], the time constraints can be on the data level in the form of time validation attribute making temporal data whose validity is lost after the elapse of some pre_specified time interval, or on the transaction level in the form of deadline used by the real-time scheduling and concurrency control. Real-time systems are often classified depending on the value of the deadline. There are three types of deadlines, hard, firm or soft, depending on the resulting value of the computation when missing a deadline. If the hard deadline is missed, a large or infinity penalty returns. when a firm deadline is missed, no value returns. while some value from the computation may be still for some time if the soft deadline is lost [1]. A transaction that executes outside of the deadline boundaries has less value or may damage the system, depending on the type of deadline associated with it [4]. Like any information systems, database is the main component of real-time information system. However, most real-time systems are inherently distributed in nature. Such critical systems always require to deal with their data in a timely fashion [5]. Sometimes data which is required at a particular location is not available, and it has to be obtained from remote site. This may take long time which consumes the validation duration of data make them invalid. This leads to large number of tardy transactions (transactions that miss their deadline).

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Section: ) Module 3: Obtaining Total Shard Data Objectsmentioning

confidence: 99%

mentioning

confidence: 92%

mentioning

confidence: 99%

“…As we can see more clusters are generated by our clustering method. On the other hand, less number of clusters is generated from [20], [21] and [22] .…”

mentioning

confidence: 99%

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Clustering Algorithm for Distributed Real-Time Database sites

Ahmed

Salem

Saleh

2015

IJCI. International Journal of Computers and Information

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“…But the work by Hababeh in [20] generated disjoint groups of network sites according to their communication costs and clustering range. They prevented the overlapping between clusters and allowed the clusters interconnection.…”

Section: Experimental Studymentioning

confidence: 99%

CDRT: An efficient clustering algorithm for distributed real-time database sites

Abdelkader

Salem

Saleh

2014

2014 9th International Conference on Informatics and Systems

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Recently, the demand for real-time database is increasing. Most real-time systems are inherently distributed in nature. They need data to be obtained and updated in a timely fashion. Sometimes required data are at a particular location is not available, and needed to be obtained from remote site. This may take long time that make the temporal data invalid resulting in large number of tardy transactions with their fetal effect.Clustering the database sites nodes can help distributed real-time database systems to face the challenges meeting their time requirements. Reducing the large number of network sites into many clusters with smaller number of sites will effectively decrease the response time, resulting in better meeting of time constraints. In this work, we introduce a novel clustering algorithm for distributed real-time database that depend on both the communication time cost and the timing properties of data. The results showed lower communication time, higher database performance and better meeting of timing requirements.

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Intelligent Network Communications for Distributed Database Systems

Cited by 2 publications

References 16 publications

Clustering Algorithm for Distributed Real-Time Database sites

Clustering Algorithm for Distributed Real-Time Database sites

CDRT: An efficient clustering algorithm for distributed real-time database sites

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