Scaling enterprise applications at data layer
Introduction Scalability is an important factor for the organizations which do business on web that may suddenly become demanded. Scalability of an application is its ability to work properly even if its size or context is increased. It just not only means it should work well when its increased in size but also should take full advantage of it. Applications can be scaled at various levels. Be it at a data layer or API layer or the overall architecture of an application. Applications can be scaled up by increasing their processing power or distributing the processing at various nodes. This paper briefly discusses the various technologies for scaling an application at data layer briefly.
Oracle
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Data virtualization enables to release the unused storage to virtual storage when there is a less load. Deploying a database instance in a virtual machine takes less time than traditional way of setting up a database. Database virtualization allows the administrators to add memory, CPU, disk or other instance with ease with very less downtime for users. Database virtualization makes the proper usage of the resources and allows to run the instances based on the demand, policy and constraints. Advantages include pooling unused resources, scalable compute, elastic compute, compute mobility and data mobility. The Oracle RAC conceptually also creates an abstraction layer of database by creating many instances of database without affecting the underlying …show more content…
The I/O operation delays slows down the application and this effects the speed of the application when there are more I/O requests. The database should be able to handle increased requests with ease and good speed. Instead of directly relying on the database, we can use the in memory data nodes or In-Memory Data Grid(IMDG), a layer between the application and the database which provides high availibility, consistency and reliability while isolating the database from the application load and data persistence being handled in the background. The storage implementation and schedule are handled by IMDG and the application need not be aware of anything related to the data. The application can still ask for the synchronous database persistence and in memory persistence for more transient data. The application can achieve big boost in its performance without compromising flexibility. When a new server is introduced in the node then the node has to just connect with the IMDG and everything works as
On 09/29/2015 at 3:18 PM SC received call from Marguerite Pa’s niece who reported that Pa has identified two girls Gabby and Yajaira he wants to be his aide but the agency told them his service were terminated. SC attempted to explain why the service was interrupted and next step; SC end call with Marguerite. SC then called Vital Support and briefly explained to receptionist and requested to speak with a manager. SC was transferred to Vitaliya at Vital Support Nurse manger. Vitaliya stated that the termination date is incorrect and service was provided to Pa beyond termination date and that SC need to change the dates so that they can receive payment.
‘Chubby’ is a unified lock service created by Google to synchronize client activity with loosely coupled distributed systems. The principle objective of Chubby is to provide reliability and availability where as providing performance and storage capacity are considered to be optional goals. Before Chubby Google was using ad-hoc for elections, Chubby improved the availability of systems and reduced manual assistance at the time of failure. Chubby cells usually consist of chubby files, directories and servers, which are also known as replicas. These replicas to select the master use a consensus protocol.
For example, when we run Nginx on all six kernels, the performance of it running on other kernels ranges from 91\% to 97\% of the performance on its own kernel. On the other hand, although the performance of Memcached is generally good while running on other kernels, the performance of other applications running on the Memcached kernel could be as low as 93\% of their best performance. The results show that we have created emph{truly} application-specific Linux kernels for
Hadoop [8] is an open source implementation of MapReduce programming model which runs in a distributed environment. Hadoop consists of two core components namely Hadoop Distributed File System (HDFS) and the MapReduce programming with the job management framework. HDFS and MapReduce both follow the master-slave architecture. A Hadoop program (client) submits a job to the MapReduce framework through the jobtracker which is running on the master node. The jobtracker assigns the tasks to the tasktrackers running on many slave nodes or on a cluster of machines.
What are the exact features of a distributed database? a) Is always connected to the internet b) Always requires more than three machines c) Users see the data in one global schema. d) Have to require the physical location of the data when an update is done
Not only will these innovations improve network strength, but possibly the speeds at which a client can access information from an application server. This has the potential to make cloud computing even more prevalent than it already is today because it would become easier to keep up with mass traffic to the servers. Large server banks would be able to be downsized slightly compared to their current sizes. The computer science techniques used in created Marple show that it is possible to even make an old process useful in modern applications. The hardware of Marple is also programmable making it extremely useful for any network engineers because they will be able to write custom software for Marple-based
FTI leverages native storage and multiple replications and erasures techniques to supply many levels of dependability and performance. FTI provides application-level check inform that enables users to pick out that knowledge must be protected, so as to enhance potency and avoid house, time and energy waste. Figure It offers an on the spot knowledge interface so users don 't have to be compelled to wear down files and/or directory names. All data is managed by FTI in a very clear fashion for the user.
New methods of installing storage to servers are constantly created by SAN’s. Such methods improve both the Availability and performance. Nowadays SAN’s are generally used to connect storage arrays that are shared and tape libraries to generally more than one server. SAN’s can also be used for fail-over clustering. SAN’s are very useful in regards of providing resistance to traditional network bottlenecks as it operates at high speed data transfers between storage devices and servers.
The data processing tasks for all the tools is Map Reduce and it is the Data processing tool which effectively used in the Big Data Analysis[13]. For handling the velocity and heterogeneity of data, tools like Hive, Pig and Mahout are used which are parts of Hadoop and HDFS framework. It is interesting to note that for all the tools used, Hadoop over HDFS is the underlying architecture. Oozie and EMR with Flume and Zoo keeper are used for handling the volume and veracity of data, which are standard Big Data management tools [13].
Today, the skyrocketing number of health care providers that enter the industry in both public and private organizations create a highly competitive market. For this reason, it is necessary for every provider to become competitive to attract customers and overcome the competitors in order to survive in the industry. However, the role of competition is still much debated since the pieces of evidence are mixed and contested (Goddard, 2015). The Kaiser Permanente is one of the healthcare providers that is standing still in the current competitive market since its establishment in 1945. It is established by industrialist and physician named Henry Kaiser and Sidney Garfield, respectively.
Cloud computing is effective in lessening live migration. It is specifically effective for monitoring live migration and can "check live migration being reduced in all Hypervisors mostly in XEN" (Fejzaj, Tafa & Kajo, 2012, p. 460). Given this great efficiency, IT services with reduced live migration. With this being said, cloud computing is therefore, proven as a very useful tool. References Fejzaj, J., Tafa, I., &Kajo, E. (2012), The improvement of live migration in data center in different virtual environment.
Therefore, the database can be any type such as SQL, Not Only SQL (NOSQL), or other. Observation_4: The CSP needs to apply a virtualization technology on storage resources to serve CSUs’ demands efficiently. Therefore, a
Even though organizations hold huge amount of data, they cannot use them effectively as they are unstructured. However new technologies are now available which enable analysis of large, complex, unstructured data. The accessibility of technology has become easy; as a result, there is massive increase in data amounts available with the entrepreneurs. The data usage depends on the ability the way it is stored, managed and then analyzing it adequately. Big data is an upcoming and emerging trend in the field of Information technology.
Relational databases lack relationships this part discusses how relational database lacks relationships or its simplicity. The relational model has a large join table, sparsely populated rows and lots of null-checking logic makes it more complex, difficult and costly. For example, adding foreign key, and its constraints and maintenance required for the database to operate, scattered tables with nullable columns demand special checking in code, and several joins are necessary to perform one command. And finally, to perform reciprocal queries also are expensive because the database has to consider all the rows in the tables. NoSQL databases also lack relationships The method adds relationships/or to use them for connected data to most NoSQL databases such as key-value, document or column oriented is to implant an aggregates identifier inside the field belonging to another aggregate that successfully introducing foreign key.
As compared to other databases this database has a slow extraction of results thus making it a slower database. 2. Memory space: The database uses tables having rows and columns which consumes a lot of physical memory which becomes a disadvantage of the database. P a g e 2 | 5 3.