Explain Server Sprawl and Server Consolidation.

-

 Server sprawl 

  • Server sprawl occurs when many, underutilized servers take up more space and demand resources than their workload justifies. The procurement of a large number of inexpensive, low-end servers, as well as the practice of devoting servers to particular applications, are two common sources of server sprawl. Server sprawl may be restricted to a single server room, but it can also be distributed across many facilities in widely dispersed geographical areas, particularly when a firm acquires another, or two organizations combine. 
  • Server sprawl occurs when one or more servers within a data center are underused in the sense that they aren't being used up to their base capacity. As a concept, server sprawl defines the amount of computing, space, power, and cooling waste within a data center’s cluster of servers.
  • Server sprawl typically exists when an organization houses more servers than it should be based on its current and predicted requirements. These servers can exist within a single server room or data center or can be spread across multiple enterprise-owned and managed computing facilities. The overall waste that server sprawl entails can be considered in terms of underutilization per server, the amount of physical space that's taken up by excess servers, the presence of servers with none or few critical applications deployed over them, and higher maintenance costs. Server sprawl is eliminated through server consolidation or server virtualization, which reduces the number of physical servers and thus their associated maintenance and management costs.


SERVER CONSOLIDATION

  • Many businesses may have many servers, each serving a specific function. These servers include file servers, print servers, e-mail servers, Web servers, database servers, and other application servers. It is fairly rare for apps to demand or highly advise the usage of a dedicated server, generally because the program does not live well with other apps installed on the same operating system. In these cases, organizations will buy small, dedicated servers to host these applications, many of which have a low utilization rate because the application has a small number of users or is only used infrequently, such as once a month. The difficulty with this scenario is that the firm must incur a capital cost of, for example, $6,000 or more, and the new dedicated server's CPU, RAM, and disk storage are significantly underused and virtually squandered. 
  • Over time, the data center may house a large number of tiny, underused servers supporting dedicated applications, incurring data center hosting fees (ping, power, and bandwidth) for each server. Server virtualization can help firms save money in the data center by consolidating servers. compute resources more efficiently and reduce costs. This involves moving multiple, heterogeneous Server consolidation is the practice of reducing the number of servers or server locations in order to use workloads to a single server or combine workloads under a single operating system.
  • Server consolidation is a method of optimizing the use of computer server resources to decrease the total number of servers or server locations required by an organization. The technique evolved in reaction to their capacity, which may not be a sustainable ratio in the present economic environment. Server demands more resources than their workload justifies. Servers in many businesses often run at 15-20% of the issue of server sprawl, which occurs when many, underutilized servers take up more space and consolidation is increasingly being used by businesses to reduce needless expenditures and maximize return on investment (ROI) in the data center.
  • While consolidation can significantly enhance the effective use of server resources, it can also result in complicated data, application, and server settings that are difficult for the typical user to deal with. T address this issue, server virtualization may be utilized to hide server resource specifics from consumers while maximizing resource sharing. Another way to server consolidation is to deploy blade servers to enhance space efficiency.

Advantages of Consolidation

  • Lower hardware and running costs (such as cooling and electricity). Reduce the time it takes to set up new servers.
  • Warranty and software licensing fees will be reduced.
  • Business agility through the use of cloud and shared infrastructure
  • With business continuity and built-in disaster recovery, you can reduce downtime and increase dependability.
  • Provide on-demand IT services independent of hardware, operating systems, applications, or infrastructure providers.

Comments

Post a Comment

Popular posts from this blog

What are different steps used in JDBC? Write down a small program showing all steps.

-
 T he fundamental steps involved in the process of connecting to a database and executing a query consist of the following: Import JDBC packages. Load and register the JDBC driver. Open a connection to the database. Create a statement object to perform a query. Execute the statement object and return a query resultset. Process the resultset. Close the resultset and statement objects. Close the connection. These steps are described in detail in the sections that follow. 1. Import JDBC Packages This is for making the JDBC API classes immediately available to the application program. The following import statement should be included in the program irrespective of the JDBC driver being used: import java.sql.*; Additionally, depending on the features being used, Oracle-supplied JDBC packages might need to be imported. For example, the following packages might need to be imported while using the Oracle extensions to JDBC such as using advanced data types such as BLOB, and so on. import o...
Read more

Explain Parallel Efficiency of MapReduce.

-
Image
 PARALLEL EFFICIENCY OF MAP-REDUCE It is worth trying to figure out what is the parallel efficiency of MapReduce. Now let us assume that the data produced after the map phase is σ times the original data size D (σD), further, we assume that there are P processors which in turn perform map and reduce operations depending on which phase they are used in, so we do not have any wastage of processors. Also, the algorithm itself is assumed to do wD useful work, w is not necessarily a constant it could be D^2 or something like that, but the point is that there is some amount of useful work being done even if you have a single processor and that's wD. Now let us look at the overheads of doing the computation wD using MapReduce. After the map operation, instead of D data items, we have σD data items in P pieces, so each mapper writes data to their local disk. So, P there is some overhead associated with writing this data. Next, this data has to be read by each reducer before it can begin ...
Read more

Suppose that a data warehouse for Big-University consists of the following four dimensions: student, course, semester, and instructor, and two measures count and avg_grade. When at the lowest conceptual level (e.g., for a given student, course, semester, and instructor combination), the avg_grade measure stores the actual course grade of the student. At higher conceptual levels, avg_grade stores the average grade for the given combination. a) Draw a snowflake schema diagram for the data warehouse. b) Starting with the base cuboid [student, course, semester, instructor], what specific OLAP operations (e.g., roll-up from semester to year) should one perform in order to list the average grade of CS courses for each BigUniversity student. c) If each dimension has five levels (including all), such as “student < major < status < university < all”, how many cuboids will this cube contain (including the base and apex cuboids)?

-
Image
a) A Snowflake Shema is shown in figure below: b) The specific OLAP operations to be performed: 1. Roll-up on course from course_id to department. 2. Roll-up on a student from student_id to university. 3. Dice on course, student with department ="CS" and university = "biguniversity" 4. Drill-down on a student from the university to student_name. c) N = 4 dimensions The cube will contain 54 = 625 cuboids.
Read more