07-09-2016, 02:44 PM
Designing High Performance Web-Based Computing Services to Promote Telemedicine Database Management System
1453543001-FR.docx (Size: 53.79 KB / Downloads: 5)
ABSTRACT:
Many web computing systems are running real time database services where their information change continuously and expand incrementally. In this context, web data services have a major role and draw significant improvements in monitoring and controlling the information truthfulness and data propagation. Currently, web telemedicine database services are of central importance to distributed systems. However, the increasing complexity and the rapid growth of the real world healthcare challenging applications make it hard to induce the database administrative staff. In this paper, we build an integrated web data services that satisfy fast response time for large scale Tele-health database management systems. Our focus will be on database management with application scenarios in dynamic telemedicine systems to increase care admissions and decrease care difficulties such as distance, travel, and time limitations. We propose three-fold approach based on data fragmentation, database websites clustering and intelligent data distribution. This approach reduces the amount of data migrated between websites during applications’ execution; achieves cost effective communications during applications’ processing and improves applications’ response time and throughput. The proposed approach is validated internally by measuring the impact of using our computing services’ techniques on various performance features like communications cost, response time, and throughput. The external validation is achieved by comparing the performance of our approach to that of other techniques in the literature. The results show that our integrated approach significantly improves the performance of web database systems and outperforms its counterparts.
EXISTING SYSTEM:
Recently, many researchers have focused on designing web medical database management systems that satisfy certain performance levels. Such performance is evaluated by measuring the amount of relevant and irrelevant data accessed and the amount of transferred medical data during transactions’ processing time.
Several techniques have been proposed in order to improve telemedicine database performance, optimize medical data distribution, and control medical data proliferation. These techniques believed that high performance for such systems can be achieved by improving at least one of the database web management services, namely—database fragmentation, data distribution, websites clustering, distributed caching, and database scalability.
DISADVANTAGES OF EXISTING SYSTEM:
• Some of these data records may be overlapped or even redundant, which increase the I/O transactions’ processing time and so the system communications overhead.
• These works have mostly investigated fragmentation, allocation and sometimes clustering problems.
• The transactions should be executed very fast in a flexible load balancing database environment. When the number of sites in a web database system increases to a large scale,
• The intractable time complexity of processing large number of medical transactions and managing huge number of communications make the design of such methods a non-trivial task.
PROPOSED SYSTEM:
Our approach integrates three enhanced computing services’ techniques namely, database fragmentation, network sites clustering and fragments allocation
We propose an estimation model to compute communications cost which helps in finding cost-effective data allocation solutions. We perform both external and internal evaluation of our integrated approach.
In our proposed system we Develop a fragmentation computing service technique by splitting telemedicine database relations into small disjoint fragments. This technique generates the minimum number of disjoint fragments that would be allocated to the web servers in the data distribution phase. This in turn reduces the data transferred and accessed through different websites and accordingly reduces the communications cost.
In the proposed system we introduce a high speed clustering service technique that groups the web telemedicine database sites into sets of clusters according to their communications cost. This helps in grouping the websites that are more suitable to be in one cluster to minimize data allocation operations, which in turn helps to avoid allocating redundant data.
We propose a new computing service technique for telemedicine data allocation and redistribution services based on transactions’ processing cost functions.
Develop a user-friendly experimental tool to perform services of telemedicine data fragmentation, websites clustering, and fragments allocation, as well as assist database administrators in measuring WTDS performance.
Integrate telemedicine database fragmentation, websites clustering, and data fragments allocation into one scenario to accomplish ultimate web telemedicine system throughput in terms of concurrency, reliability, and data availability.
ADVANTAGES OF PROPOSED SYSTEM:
Our integrated approach significantly improves services requirement satisfaction in web systems. This conclusion requires more investigation and experiments.
This technique generates the minimum number of disjoint fragments that would be allocated to the web servers in the data distribution phase.
Introduce a high speed clustering service technique that groups the web telemedicine database sites into sets of clusters according to their communications cost.
MODULES:
Web Architecture and Communications System Model
Fragmentation and Clustering
Fragments Allocation
Data Allocation and Replication
MODULES DESCSRIPTION:
Web Architecture and Communications System Model
In the first module, the telemedicine approach is designed to support web database provider with computing services that can be implemented over multiple servers, where the data storage, communication and processing transactions are fully controlled, costs of communication are symmetric, and the patients’ information privacy and security are met. We propose fully connected sites on a web telemedicine heterogeneous network system with different bandwidths; 128 kbps, 512 kbps, or multiples. In this environment, some servers are used to execute the telemedicine queries triggered from different web database sites. Few servers are run the database programs and perform the fragmentation clustering- allocation computing services while the other servers are used to store the database fragments. Communications cost (ms/byte) is the cost of loading and processing data fragments between any two sites in WTDS. To control and simplify the proposed web telemedicine communication system, we assume that communication costs between sites are symmetric and proportional to the distance between them. Communication costs within the same site are neglected.
Fragmentation and Clustering
Telemedicine queries are triggered from web servers as transactions to determine the specific information that should be extracted from the database. Transactions include but not limited to: read, write, update, and delete. To control the process of database fragmentation and to achieve data consistency in the telemedicine database system, IFCA fragmentation service technique partitions each database relation according to the Inclusion-Integration-Disjoint assumptions where the generated fragments must contain all records in the database relations, the original relation should be able to be formed from its fragments, and the fragments should be neither repeated nor intersected. The logical clustering decision is defined as a Logical value that specifies whether a website is included or excluded from a certain cluster, based on the communications cost range. The communications cost range is defined as a value (ms/byte) that specifies how much time is allowed for the websites to transmit or receive their data to be considered in the same cluster, this value is determined by the telemedicine database administrator.
Fragments Allocation
The allocation decision value ADV is defined as a logical value (1, 0) that determines the fragment allocation status for a specific cluster. The fragments that achieve allocation decision value of (1) are considered for allocation and replication process. The advantage that can be generated from this assumption is that, more communications costs are saved due to the fact that the fragments’ locations are in the same place where it is processed, hence improve the WTDS performance. On the other hand, the fragments that carry out allocation decision value of (0) are considered for allocation process only in order to ensure data availability and fault-tolerant in the WTDS. In this case, each fragment should be allocated to at least one cluster and one site in this cluster. The allocation decision value ADV is assumed to be computed as the result of the comparison between the cost of allocating the fragment to the cluster and the cost of not allocating the fragment to the same cluster. The allocation cost function is composed of the following sub-cost functions that are required to perform the fragment transactions locally: cost of local retrieval, cost of local update to maintain consistency among all the fragments distributed over the websites, and cost of storage, or cost of remote update and remote communications (for remote clusters that do not have the fragment and still need to perform the required transactions on that fragment). The not allocation cost function consists of the following sub-cost functions: cost of local retrieval and cost of remote retrievals required to perform the fragment transactions remotely when the fragment is not allocated to the cluster.
Data Allocation and Replication
Data allocation techniques aim at distributing the database fragments on the web database clusters and their respective sites. We introduce a heuristic fragment allocation and replication computing service to perform the processes of fragments allocation in the WTDS. Initially, all fragments are subject for allocation to all clusters that need these fragments at their sites. If the fragment shows positive allocation decision value (i.e., allocation benefit greater than zero) for a specific cluster, then the fragment is allocated to this cluster and tested for allocation at each of its sites, otherwise the fragment is not allocated to this cluster. This fragment is subsequently tested for replication in each cluster of the WTDS. Accordingly, the fragment that shows positive allocation decision value for any WTDS cluster will be allocated at that cluster and then tested for allocation at its sites. Consequently, if the fragment shows positive allocation decision value at any site of cluster that already shows positive allocation decision value, then the fragment is allocated to that site, otherwise, the fragment is not allocated. This process is repeated for all sites in each cluster that shows positive allocation decision value.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
System : Pentium IV 2.4 GHz.
Hard Disk : 40 GB.
Floppy Drive : 1.44 Mb.
Monitor : 15 VGA Colour.
Mouse : Logitech.
Ram : 512 Mb.
SOFTWARE REQUIREMENTS:
Operating system : Windows XP/7.
Coding Language : JAVA/J2EE
IDE : Netbeans 7.4
Database : MYSQL