Enterprise Computing Term Questions

Question 1

This first section will discuss the nature of Google’s monitoring systems developed through a formal model for the various systems. This framework is useful in developing, evaluating and adjusting monitoring programs at Google, which is suitable for operating with range. The role of model is to promote a wider discussion into the nature of monitoring and the evolvement of the current solutions into more effective ways of large systems operation.

Google undertakes monitoring in order to maintain the reliability and quality experience for the end users. The robust monitoring has also been important in incorporating the various open and proprietary services. Considering the increasing complexity of its applications, Google Inc requires a comprehensive monitoring system to deal with the increased reliance on distributed architectures. Some of the metrics used in monitoring include resolution, latency, and diversity. Such metrics usually monitor large quantities and data. Dickson, an Engineer at Google Inc., emphasized on a monitoring model that comprises of the various elements including measurement, collection, analysis, escalation and visualization. These processes are interconnected with configuration and storage (Juncai & Shao, 2011). Dickson’s model effectively explains the nature of monitoring systems that continuously review and manage the Google applications.

In addition, Google monitoring systems provide reliable insights into the various source servers with minimal configurations. The system makes it easy to understand the unique trends into the various servers. Google also designed the monitoring systems to analyze the performance and availability of the diverse applications. Monitoring empowers the network operators to understand complications before even they develop into real problems. Thus, the company is able to make informed decisions regarding the automation of the infrastructures. It also makes it possible for the company to review the performance metrics and logs for its IT platform services, such as the common source components Apache and Nginx.

The Google storage system plays a crucial role in enabling Caskey to collect the time series metrics used as the basis for managing the Google application. The system enables Caskey to store application/server data that can be used in the monitoring of the various metrics. When storing metrics in the Google storage system, Caskey suggests a unique way of tracking each metric (Pokorny, 2013). The tracking of each metric is used for collecting data and features of every unit. Such characteristics make it possible to manage the Google application by collecting and managing diverse information and data. Such multiple data and information is very sensitive within the Google application.

Storage is important in the monitoring systems at Google, as it involves placement of time series in an accessible format. It also enables the raw, aggregated and post computation metrics. Most importantly, the storage structure seems to limit the visualization options available to the company’s monitoring systems. Dickson recognized the importance of the storage system at the company collects and organizes the various time series metrics. For instance, storage helped in keeping long-term views on the nature of product management. This makes it easy to understand visualization obtained from such metrics.

The presence of Google storage system would enable the company to deal with the complex information systems. The metrics are reliable sources of quantitative information and could facilitate the evaluation of results. This implies that the automation of processes uses real time data obtained from monitoring system. The Google storage system is keen to group and collect incoming data inputs based on their properties and metrics. The system retrieves information from the metrics and summarizes it to yield time series (Shang, Zhang & Chen, 2012). The resulting time series information are submitted and checked for resulting anomalous conditions. The collection and organization of the time series data are made possible by the Google storage system. This is because it detects the occurrence of the anomalous conditions that inform the operator on the suitable conditions to collect and organize time series data.

In addition, Caskey relied on the Google storage system to manage the Google application by recording the outcomes of the time series metrics. This enables Google’s monitoring system to manage its applications from its adverse and slowed processes. An important consideration is that the data model at Google Inc assumes that all components of a metrics are known when they are retrieved. This must also be considered in the management of the Google applications. Therefore, the Google monitoring system relies heavily on the nature and design of the Google storage system that handles the metric storage units at Google Inc.

Question 2

The databases also make it easy to capture and assess data taken in the third party websites. For instance, individuals can access personal user information, social graphs and generated content from the diverse websites. It is unsurprising that the developers rely on the databases to obtain value in leveraging data and information in order to enrich the existing applications. The availability of data is also changing the way of communication and other processes in the society. All these processes rely on the database to capture large data. The databases are necessary in providing a highly flexible approach that easily accommodates a new form of data that are not disrupted by the content structure changes from the diverse data providers.

Most importantly, the databases enable the storage of both the unstructured and semi structured data in the web applications. This is significant in the overall management of the data in the various organizational websites. Apart from all, the use of the databases is inevitable due to the rising significance of processing data. Since, the databases help in dealing with the mismatch between the object-oriented model and the relational database model used in the writing of web applications. Therefore, the databases forms are an important part of the web scale applications.

There are three potential architectures including the distributed application architecture, tiered application and the windows DNA that can be used to scale an e-commerce database to fit the requirements of a large-scale application. The first architecture design is the distributed application architecture that could fit a large-scale application. The distributed application uses resources from multiple machines by separating the application’s functionality into more manageable roles that can be handled in various configurations. This makes it more suitable and useful to handle the needs of the large-scale e-commerce applications (Shang, Zhang & Chen, 2012). The distributed application architecture is comprised of the system architecture main element. The main element is useful in defining the interaction of the various application elements and their functionality.

The second potential e-commerce architecture is the tiered applications. The tiered applications are characterized by the various layers of information stored in the database. Each layer operates differently than other layers. The two tier applications can also be used in the large-scale application, as it contains significant business logic. With the invention of stored procedures, the Two-tier application enables the conduct of business logic and execution of database server. The tiered applications also adjust properties with the changing business needs and it is dependent on the various factors. Some of the dependent factors include the maximum number of database connections that could hinder the architecture from being operational.

Another tiered application is the three-tier one that includes three main processing services including user services, business service and data services. The main difference between 3-tier architecture and 2-tier application is that the business language is differentiated from the user interface and the sources of data. The breaking up of the tiered applications into the various sections reduces the complexity of the overall application to meet the growing demands among the various businesses. The n-tier applications can also be subdivided to fit the various business needs (Saleh & Abou, 2012). The separation between the various applications can be modified to fit the various business needs and specializations. Thus, the website developers can take advantage of the tiered applications in developing specialized and powerful features. In a large-scale application, the tiered applications are used in making the general-purpose tools more useful and reliable in building and designing the entire applications.

The third potential architecture in e-commerce is the windows distributed architecture. The Windows DNA is one of the largest models that develop and promote the scalable applications in the organizations. The Windows also could enhance the availability of the exiting application and sources to the various clients, web browsers, and internet appliances. The ability of the windows DNA to provide an outline of the organizations is used in developing easier distributed applications. The Windows DNA approach improves the performance, reliability and the cost of operation of the various applications. Microsoft has also continued to develop and enhance its e-commerce applications. Thus, architecture could be used to develop smaller network applications unlike the traditional client server model. As a result, it is recommendable to use to the Windows DNA model as it improves the interoperability through leveraging on the existing and current technology programs. The ecommerce solutions of the large-scale applications can be resolved from the three architectural designs (Pokorny, 2013). Since, it offers fulfillment of customers’ needs and requirements. The frameworks also give the IT developers and managers flexibility in the processing and storage of data.