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Introduction to SAS Grid

SAS Grid is a type of manager support. It is most widely used to distribute or spread the user tasks across multiple computers through the network connection, enabling the workload balance to accelerate the data process. The job schedules is more flexible and sophisticated on the grid computing, which already has a centralized environment with peak areas, and computing demands cost efficiency and reliability.

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Key Takeaways

It distributes the n number of tasks to multiple computers on the same network.

It enabled the workforce load balancing algorithm to process the accelerated jobs and schedules.

It is a more flexible and centralized one.

It has faster data processing in the migrated environment.

To increase power and save money.

What is SAS Grid?

In SAS Enterprise guide, the architecture is mainly called and used for sharing multiple computer resources via a network. It acts as the manager role, so it’s named SAS Grid Manager, which provides:

The load balancing algorithm.

Application connectivity access like Policy enforcement and more resource allocation.

Prioritization is highly available in the analytical environment.

It needs several types of machines, which looks like a cluster setup across the same network, and it has several software products. Using the server-side load balancing algorithm, it used the workspace server, which sends the job at a busy set of nodes through config via if the grid is available, the project is configured, or else the grid is configured to a specific set of tasks that already run on the grid table.

SAS Grid Computing

SAS Grid manager, which delivers the load balancing algorithm available on all the set of work tasks and high availability with faster when compared to other computing environments. The cluster is the set or group of computers with their efficiency and specifications across the networks. Workload is also split up of each computer, and it is called the tasks with the help of Workload balancing algorithm for sharing the resource pool and accelerated processing for allowing multiple users with the same sharing datas.

The workload distribution helps to enable the functionality of the SAS grid as the below:

Workload Balancing:

 Mainly, it enables the n number of users that are more than 1 user to be performed in the SAS environment, which distributes the data workload to the shared resource pool.

Accelerated Processing:

 It helps to distribute tasks similar to subtask child processes, so it splits the SAS single job into a shared resources pool.

Scheduling Jobs:

 The jobs allowed users to route the shared resources pool at the exact scheduled time.

SAS Grid Legacy

The grid legacy mainly enabled the users for computing to develop the shared environment and control the larger volume of data that processed and analyzed the program code.

It helps fast the code, which accomplishes the user dynamics to reload the data load resources to balance the split and multiple nodes.

Steps to Create SAS Grid

Given below are the steps mentioned:

1. Navigate to the below URL.

2. And paste the below code for to create the grid table.

3. %MACRO First(August11=);


5. create table &August11. (inp1 CHAR(100),inp2 CHAR(1));

6. QUIT;

7. %IF %SYSFUNC(grdsvc_enable(_all_,Server=SASApp)) NE 0 %THEN

8. %DO;

9. %PUT WARNING: There is no grid in series;

10. %Inputs1(August11=&August11.);

11. %Inputs2(August11=&August11.);

12. %Inputs3(August11=&August11.);

13. %END;

14. %ELSE

16. %END;

17. %MEND;

18. %First(August11=WORK.Test);

19. In the above code we used Macro for initializing and created the table like &August11 with 2 input parameters.

SAS Grid on AWS

SAS on AWS is one of the run time environments for allowing the organizations which deployed the application on either open source or some other feature in the SAS models. We used the data infrastructure to support the wide variety of analytics the patterns will support the AWS devops. For mid-tier architecture, we used Amazon EC2 or r5 instances and types to load the data share client contents by using two or more number of instances in the SAS requirement. Unless we used high availability metadata servers in the EC2 instance types which exceed the minimum requirement from SAS memory recommendations.

The above diagram explains about the AWS cloud in the SAS platform through the Gateway and the amazon VPC[Virtual Private Cloud].

Example of SAS Grid

Given below are the examples mentioned:


%MACRO Second(vars=, AUgust11= ); PROC SQL; create table &AUgust11. (inps1 CHAR(25),inps2 CHAR(3)); QUIT; %IF %SYSFUNC(grdsvc_enable(_all_,Server=SASApp)) NE 0 %THEN %DO; %PUT WARNING: There is no grid table on this series; %a(AUgust11=&AUgust11.); %b(AUgust11=&AUgust11.); %c(AUgust11=&AUgust11.); %END; %ELSE %DO; %PUT WARNING: Its Grid and used parallel macros; %IF %UPCASE(&vars.) = WORK %THEN %DO; %PUT ERROR: Specified Work is not shared in RSUBMITs; %GOTO Finish; %END; %methd(d=aug11,g=&vars.); %methd(e=aug12,h=&vars.); %methd(f=aug13,i=&vars.); PROC SQL; CREATE TABLE aug11.AUgust11 AS SELECT * FROM &AUgust11.; CREATE TABLE aug12.AUgust11 AS SELECT * FROM &AUgust11.; CREATE TABLE aug13.AUgust11 AS SELECT * FROM &AUgust11.; QUIT; %END; %Finish: %MEND; LIBNAME Sandboxtesting '\MyNetwork'; %Second(vars=Sandboxtesting, AUgust11=WORK.August111);



In the above example, we created SAS grid by using the macro along with procedure SQL.

By using IF and another conditional statement we can validate the inputs.

Table will be created for each session with parallel macros.

Network location is shared at the end of the method.


Given below are the FAQs mentioned:

Q1. What is SAS Grid?


The computing tasks are split into sub-tasks and assigned to multiple PCs with the network.

Q2. How SAS Grid works?


By using the workload the SAS grid will be enabled and operated in the environment.


Load Balancing

Policy Enforcement

Time Saving

Money Saving

Efficient Resource Allocation

Q4. Define Grid Manager.


It’s a web-based tool to monitor the resources, users, and jobs which already scheduled.

Q5. What is SAS Grid Server?


It serves as an intermediate between the SAS application and grid environment.


The SAS grid helps to convert the existing codes to the parallel processing system on the remote sessions like a straightforward approach. By using SAS keywords like RSUBMIT, %SYSLPUT, INHERITLIB are handled, and executing the macros for merging datasets without causing any errors. More complexities exist, and parallel processes will be used by the SAS Grid to perform independent and synchronized data operations.

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Cloud Computing Architecture And Components

What is Cloud Computing Architecture?

Cloud Computing Architecture is a combination of components required for a Cloud Computing service. A Cloud computing architecture consists of several components like a frontend platform, a backend platform or servers, a network or Internet service, and a cloud-based delivery service.

Let’s have a look into Cloud Computing and see what Cloud Computing is made of. Cloud computing comprises two components, the front end, and the back end. The front end consists of the client part of a cloud computing system. It comprises interfaces and applications that are required to access the Cloud computing or Cloud programming platform.

Cloud Computing Architecture

While the back end refers to the cloud itself, it comprises the resources required for cloud computing services. It consists of virtual machines, servers, data storage, security mechanisms, etc. It is under the provider’s control.

In this Cloud Computing Architecture tutorial, you will learn-

Cloud Computing Architecture

In this Cloud Computing Architecture tutorial, you will learn-

The Architecture of Cloud computing contains many different components. It includes Client infrastructure, applications, services, runtime clouds, storage spaces, management, and security. These are all the parts of a Cloud computing architecture.

Front End:

The client uses the front end, which contains a client-side interface and application. Both of these components are important to access the Cloud computing platform. The front end includes web servers (Chrome, Firefox, Opera, etc.), clients, and mobile devices.

Back End:

The backend part helps you manage all the resources needed to provide Cloud computing services. This Cloud architecture part includes a security mechanism, a large amount of data storage, servers, virtual machines, traffic control mechanisms, etc.

Cloud Computing Architecture Diagram

Important Components of Cloud Computing Architecture

Here are some important components of Cloud computing architecture:

1. Client Infrastructure:

Client Infrastructure is a front-end component that provides a GUI. It helps users to interact with the Cloud.

2. Application:

The application can be any software or platform which a client wants to access.

3. Service:

The service component manages which type of service you can access according to the client’s requirements.

Three Cloud computing services are:

Software as a Service (SaaS)

Platform as a Service (PaaS)

Infrastructure as a Service (IaaS)

4. Runtime Cloud:

Runtime cloud offers the execution and runtime environment to the virtual machines.

5. Storage:

Storage is another important Cloud computing architecture component. It provides a large amount of storage capacity in the Cloud to store and manage data.

6. Infrastructure:

It offers services on the host level, network level, and application level. Cloud infrastructure includes hardware and software components like servers, storage, network devices, virtualization software, and various other storage resources that are needed to support the cloud computing model.

7. Management:

This component manages components like application, service, runtime cloud, storage, infrastructure, and other security matters in the backend. It also establishes coordination between them.

8. Security:

Security in the backend refers to implementing different security mechanisms for secure Cloud systems, resources, files, and infrastructure to the end-user.

9. Internet:

Benefits of Cloud Computing Architecture

Following are the cloud computing architecture benefits:

Makes the overall Cloud computing system simpler.

Helps to enhance your data processing.

Provides high security.

It has better disaster recovery.

Offers good user accessibility.

Significantly reduces IT operating costs.

Virtualization and Cloud Computing

The main enabling technology for Cloud Computing is Virtualization. Virtualization is the partitioning of a single physical server into multiple logical servers. Once the physical server is divided, each logical server behaves like a physical server and can run an operating system and applications independently. Many popular companies like VMware and Microsoft provide virtualization services. Instead of using your PC for storage and computation, you can use their virtual servers. They are fast, cost-effective, and less time-consuming.

For software developers and testers, virtualization comes in very handy. It allows developers to write code that runs in many different environments for testing.

Virtualization is mainly used for three main purposes: 1) Network Virtualization, 2) Server Virtualization, and 3) Storage Virtualization

Network Virtualization: It is a method of combining the available resources in a network by splitting up the available bandwidth into channels. Each channel is independent of others and can be assigned to a specific server or device in real time.

Storage Virtualization: It is the pooling of physical storage from multiple network storage devices into what appears to be a single storage device that is managed from a central console. Storage virtualization is commonly used in storage area networks (SANs).

Server Virtualization: Server virtualization is the masking of server resources like processors, RAM, operating system, etc., from server users. Server virtualization intends to increase resource sharing and reduce the burden and complexity of computation from users.

Virtualization is the key to unlock the Cloud system, what makes virtualization so important for the cloud is that it decouples the software from the hardware. For example, PCs can use virtual memory to borrow extra memory from the hard disk. Usually, a hard disk has a lot more space than memory. Although virtual disks are slower than real memory, if managed properly, the substitution works perfectly. Likewise, there is software that can imitate an entire computer, which means 1 computer can perform the functions equals to 20 computers. This concept of virtualization is a crucial element in various types of cloud computing, which you can learn more about in this comprehensive guide.


Cloud Computing Architecture is a combination of components required for a Cloud Computing service.

The front-end part is used by the client that contains client-side interfaces and applications, which are important to access the Cloud computing platforms.

The service provider uses the back-end part to manage all the needed resources to provide Cloud computing services.

Components of Cloud Computers are 1) Client Infrastructure, 2) Application, 3) Service, 4) Runtime Cloud, 5) Storage, 6) Infrastructure, 7) Management, 8) Security, and 9) Internet.

Cloud computing makes a complete Cloud computing system simpler.

Virtualization is the partitioning of a single physical server into multiple logical servers.

A Legacy Of Creativity In Poems And Prose

A Legacy of Creativity in Poems and Prose Poet and activist Grace Paley to read at tonight’s Lowell Memorial Lecture

Grace Paley will read from her work as part of tonight’s Robert Lowell Memorial Lecture. Photo by Gentl and Hyers/Arts Counsel, Inc.

BU’s Creative Writing Program has seen its share of rising stars. Poets Sylvia Plath, Anne Sexton, and George Starbuck sat in Room 222 at 236 Bay State Road in the 1950s for classes taught by visiting lecturer Robert Lowell. In celebration of the Creative Writing Program’s rich legacy, prominent writer, thinker, pacifist, and feminist Grace Paley will read from her work at the Robert Lowell Memorial Lecture tonight at 7:30.

“Though the series emphasizes poetry, we want to include fiction writers as well,” says Robert Pinsky, a College of Arts and Sciences professor of English and three-time U.S. poet laureate. “Grace is a profound, funny writer, elegant and down-to-earth.” The evening will also include readings by Pinsky and Samantha Mineo Myers (GRS’02), a CAS Writing Program lecturer.

“I guarantee that Grace Paley’s reading will be full of life and art,” says Pinsky. “People who attend will be very glad they did.”

Paley is known for her short fiction, her poems, and her political activism. She is the author of three books of short fiction: The Little Disturbances of Man, Enormous Changes at the Last Minute, and Later the Same Day. A compilation of her previously published work, The Collected Stories, was reprinted this month. She has published three books of poetry, Leaning Forward, New and Collected Poems, and Begin Again: Collected Poems, and a book of short stories and poetry, Long Walks and Intimate Talk.

She has received the Edith Wharton Award, the Rea Award for the Short Story, the Vermont Governor’s Award for Excellence in the Arts, and the Jewish Cultural Achievement Award for Literary Arts. She is a member of the National Academy of Arts and Letters and was named New York state’s first official writer.

In addition to her writing, Paley has been active in protesting nuclear proliferation, American militarization, and the Vietnam War. Her one nonfiction work, 365 Reasons Not to Have Another War, was published in 1989.

“In my experience, Grace Paley’s art, her humanity, her wit, and her political ardor are all part of one admirable unit,” Pinsky says.

The semiannual Robert Lowell Memorial Lecture Series was established in 2005 to bring distinguished writers to campus to read their works alongside a member of the Creative Writing Program faculty and a recent graduate of the program. The lecture series is funded by Nancy Livingston (COM’69) and her husband, Fred Levin, through the Shenson Foundation in memory of Ben and A. Jess Shenson. 

“By including brief readings by a recent student and a faculty member before the reading by a visiting poet or fiction writer,” says Pinsky, “we honor the spirit of that class [in Room 222], which we hope endures.”

Pinsky is the author of six books of poetry, four books of criticism, two books of translation, and a prose book. He is a member of the American Academy of Arts and Sciences.

Myers, who earned an M.A. from the Creative Writing Program, will read some of her poems. “It’s an unexpected honor to read, of course,” she says. “Mr. Pinsky’s readings are always engaging, and it will be the first time I’ve heard Ms. Paley read her work.”

An archivist for the Poetry Foundation and a finalist this year in the Massachusetts Cultural Council’s Artist Grant Program, Myers’ poetry has been published in several journals, including New Orleans Review, Washington Square, and Notre Dame Review.

“You will see from the reading that Samantha is a marvelous writer and — as her BU students can confirm — a magnetic personality,” says Pinsky.

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Performing Exploratory Data Analysis With Sas And Python

This article was published as a part of the Data Science Blogathon

Hi all, this is my first blog hope you all like it and can learn, gain some information out of it!


In this blog, we will try to understand the process of EDA(Exploratory Data Analysis) and we will also perform a practical demo of how to do EDA with SAS and Python.

The dataset that I will be using is the bank loan dataset which has 100514 records and 19 columns. I took this big dataset so that we could learn more from it rather than just using a small dataset for our project which would not be so much fun and learning.

This blog that I’m writing is just for making you’ll understand the way things work in both of these tools (i.e SAS and Python), which are undoubtedly the top best tools out in the market for Data Science and Analysis. You can always tweak the code and make it much more dynamic and reliable.

I have seen that there are ample resources available out there for Python, but this is not the same case with SAS, So I thought of giving you a clearer picture of how things work in SAS.

Let’s get started and dive deep into this process…

Exploratory Data Analysis

First, we’ll answer few questions about this process-

What is EDA?-

Why do we use it?-

How to perform this process?

Answer(1)- It is a process in which we get to know our data by summarizing the information and even using graphical charts to better visualize our data.

Answer(2)- It’s a building block of a data science life cycle and will further help us determine the properties, importance of variables, and build a more robust model for our dataset.

Answer(3)- We just need to select our dataset and perform some basics operations on it like Identification of variables and their data types, Descriptive statistics of our dataset, Finding Missing values, Checking for Unique values in our data, Univariate and Bivariate Analysis, etc… 

So now we will start with practical examples for SAS followed by Python and I’ll mention codes for both of the tools simultaneously for each process…..

In the graphical visualizations part, I haven’t used all variables for demonstrating different types of visualizations I just have used a few no. of variables because it increases the processing time as our data is large, and the second and most important thing is that I don’t want to make this blog too long for viewers to read. My main motive is to make them understand how things work so they can relate to it and further scale their knowledge and write code efficiently.

Identification of variables and data types-

Here we will look at the metadata of our dataset with the type of variables present in it.

SAS Code-

/* Checking the contents of the dataset */ proc contents data=blog.credit_train; run; /* Taking a look at the dataset for only 50 records */ proc print data=blog.credit_train obs=50; run;


Contents of the dataset-

Viewing only 100 records from the dataset as the original no is too huge (i.e 1 lakh+)-

Here only 11 records are captured in the image while we can scroll it further when implementing.

Python Code-

# Importing the libraries for the blog's dataset import numpy as np import pandas as pd import matplotlib.pyplot as plt %matplotlib inline import seaborn as sns # Contents of the data and shape of the data # Reading the dataset df = pd.read_csv('C:UsersAspire 5Desktopcreditcredit_train.csv') df df.head(50) Output-

Contents of the data and shape of the dataset-

Reading the dataset-

Descriptive Statistics of our dataset-

Here we will check the mean, median, mode, etc… for our dataset

SAS Code-

/* Descriptive statistics of our dataset */ proc means data=blog.credit_train mean median mode std var min max;



Descriptive stats-

Python Code-

# Descriptive statistics of our dataset df.describe()


Descriptive stats-

Finding missing values-

We will check the no. of missing values in each column.

SAS Code-

/* Missing values in our dataset */ proc means data=blog.credit_train nmiss; run;


Missing Count-

Python Code-

# Total no of missing values in each column df.isnull().sum()


Missing values count in each column-

Unique values in our data-

We are checking for unique values in our dataset to get familiar with how much value a variable truly holds out of all the records, which may help in normalizing the data if the values are too less.

Unique values for columns-

SAS Code-

Here is the unique value for 5 columns but we can scale it for more columns just by adding the count distinct statement…

/* Unique values for 5 columns */ proc sql; select count(distinct 'Loan Status'n) as 'Loan Status'n, count(distinct Bankruptcies) as Bankruptcies, count(distinct Term) as Term, count(distinct 'Credit Score'n) as 'Credit Score'n, count(distinct 'Monthly Debt'n) as 'Monthly Debt'n from blog.credit_train; quit;


Python Code- # Total count of unique values for each column a = pd.DataFrame(df) a.nunique()


Univariate Analysis-

It is a part of statistical analysis where we take into consideration a single variable and perform analysis on it


It is the representation of distribution for numerical data.

SAS Code-

Here I have used only a single variable just to show you, but you all can do it for multiple variables furthermore you can use another procedure for doing so I’ll list both of the procedures below.

/* You can give multiple variables in this procedure to create histograms */ proc univariate data=blog.credit_train novarcontents; histogram 'Current Loan Amount'n 'Credit Score'n / ; run; /* Creating histogram with only one variable (i.e Credit Score) */ ods graphics / reset width=6.4in height=4.8in imagemap; proc sgplot data=BLOG.CREDIT_TRAIN; histogram 'Credit Score'n /; yaxis grid; run;


Here the output is from the second procedure (i.e proc sgplot) for the Credit Score variable…

Python Code-

# Creating histogram for Credit Score variable plt.hist(df['Credit Score']) plt.xlabel('Credit Score') plt.ylabel('Count')


Bivariate Analysis-

It is a part of statistical analysis where we take into consideration multiple variables and perform analysis on them.

Scatter plot-

It is used to check the relationship between numeric variables.

SAS Code-

/* Checking Relationship between two variables by using scatter plot */ ods graphics / reset width=6.4in height=4.8in imagemap; proc sgplot data=BLOG.CREDIT_TRAIN; scatter x='Number of Open Accounts'n y='Current Credit Balance'n /; xaxis grid; yaxis grid; run; ods graphics / reset;


Python Code-

# Creating a scatter plot to observe relationship between numeric variables sc = sns.scatterplot(data=df, x="Number of Open Accounts" ,y="Current Credit Balance") sc



It says how the variables are correlated with one another, which will further help take important variables into account for model building.

SAS Code-

/* Correaltion among numeric variables */ ods noproctitle; ods graphics / imagemap=on; proc corr data=BLOG.CREDIT_TRAIN pearson nosimple noprob plots=none; var 'Current Loan Amount'n 'Credit Score'n 'Annual Income'n 'Monthly Debt'n 'Years of Credit History'n 'Number of Open Accounts'n 'Number of Credit Problems'n 'Current Credit Balance'n 'Maximum Open Credit'n Bankruptcies 'Tax Liens'n; run;


Python Code-

# Correlation among variables df.corr() #Displaying numerical values df_corr = df.corr() #Generating Graphical visualization sns.heatmap(df_corr, xticklabels = df_corr.columns.values, yticklabels = df_corr.columns.values, annot = True);


Displaying numerical values as output-

Generating graphical visualization as output-


It is used to assess the outliers in our dataset for variables as having outlier’s results in degrading our model performance and lack of accuracy.

SAS Code-

/* Box plot for checking outliers in the data */ ods graphics / reset width=6.4in height=4.8in imagemap; proc sgplot data=BLOG.CREDIT_TRAIN; vbox 'Credit Score'n / category='Loan Status'n; yaxis grid; run; ods graphics / reset;


Python Code-

# Creating a boxplot for a variable ax = sns.boxplot(x='Loan Status', y='Credit Score', data=df ) ax


Let me give you all a short summary about myself so I’m a graduate in Statistics and now aspiring to become a SAS Data Scientist while I have already gained some skills and Certifications around this domain such as Big Data Professional, Machine Learning Specialist, etc…

End Notes

I am now looking forward to writing blogs and further expand my knowledge and help other people to understand the things hovering around this domain. I’m also an active sports player(football) who likes to play and stay fit.

Here’s my LinkedIn id if you all want to connect… till then happy learning!

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.


Making The Case For Grid

Spawned in the mainframe days of computing, grid today is being taken out of the realms of academia and research and being used by enterprises in an attempt to ease the process of homogenizing heterogeneous and siloed compute environments.

Because grid computing puts a layer of virtualization, or abstraction, between applications and the operating systems (OS) those applications run on, it can be used to tie together all a corporation’s CPUs and use them for compute-intensive application runs without the need to for stacks and stacks of new hardware.

And because the grid simply looks for CPU cycles that are made available to the grid though open grid services architecture (OGSA) APIs, applications simply interact with the CPU via the grid’s abstraction layer irregardless of OS, said Tom Hawk, IBM’s general manager of Grid Computing. In this way, Windows applications can run on Unix and Unix applications can run on Windows and so on.

“We’re exploiting existing infrastructure through some fairly sophisticated algorithmic scheduling functions — knowing which machines are available, pooling machines into a broader grouping of capacity on our way towards exploiting those open APIs so that we really, truly do separate the application from the infrastructure,” he said.

Virtualized Environment

Basically, grid can be thought of as similar to the load balancing of a single server but extended to all the computers in the enterprise. Everything from the lowliest PC to the corporate mainframe can be tied together in a virtualized environment that allows applications to run on disparate operating systems, said Hawk.

“The way I like to think about it really simply is the internet and TCP/IP allow computers to communicate with each other over disparate networks,” he said. “Grid computing allows those computers to work together on a common problem using a common open standards API.”

Some companies in the insurance industry, for example, are utilizing grid to cut the run-time of actuarial programs from hours to minutes, allowing this group to use risk analysis and exposure information many times a day verses just once. In one example, IBM was able to cut a 22-hour run-time down to just 20 minutes by grid enabling the application, said Hawk.

By utilizing the compute resources of the entire enterprise, CPU downtime is put to productive work running programs that once had to wait until nightfall before enough CPU time was available. Servers, which typically have a very low CPU utilization rate, can be harnessed to run more applications more frequently and faster. But this can get addictive, said Ryan.

“Our biggest customers go into this to drive up their asset utilization and what ends up happening is their end-user customers get hooked on having more compute power to solve their problems,” he said.

What this means to the average CIO, who typically has stacks of hardware requests waiting for attention in the inbox, is they can provide this power while throwing most of the new hardware requests into the circular file.

Even data retrieval and integration is being targeted by at least one firm for grid enablement. Avaki is taking grid to a new level by using it as a enterprise information integration (EII) engine that can either work with or by-pass altogether current EII efforts, said Craig Muzilla, vice president of Strategic Marketing for Avaki.

In fact, Avaki’s founder is confident grid will become so pervasive in the coming years it will be commoditized as just a standard part of any operating system. That is why Dr. Andrew Grimshaw founded Avaki as a EII vendor.

“For the CPU cycles it’s maybe a little bit more straightforward,” said Muzilla. “Instead of having to go buy more servers to speed things up or do analysis faster, to run the application faster I can go harvest the untapped CPU cycles. We think eventually that kind of compute grid technology will be embedded in the operating system so we don’t think long-term it’s that attractive for ISVs.”

Grid also plays right into the hands of companies looking to implement on-demand, utility or service-orientated architectures (SOA) since it enables the integration of disparate, heterogeneous compute resources by its very nature. Therefore, on-demand environments can piggy-back on the grid to achieve the integration and productivity promises of those methodologies, said IBM’s Hawk.

“Right now, I’d say the No. 1 reason customers are deploying this technology is to gain resolution or to fix specific business problems they’re having around either computing throughput or customer service,” he said. “The real cool thing here, long-term, is about integration and about collaboration and that’s why I keep harping on this concept of productivity.”

Intel’s Jeff Klaus: Edge Computing And Data Center Management

Clearly, managing a data center is much harder than it used to be. Emerging technologies like artificial intelligence and Big Data and IoT have increased the workload, boosting expense and complexity.

To provide guidance on these trends, I spoke with Jeff Klaus, General Manager, Data Center Software at Intel. We discussed:

Why it’s harder to run a data center than ever before.

The tools and solutions that can help the efficiency of data center management. The role of DCIM (data center infrastructure management) tools.

The mega trend toward collecting and processing data at the edge. While more than 90 percent of data is processed in data centers today, Gartner predicts that by 2023 about 75 percent will be handled at the edge.

Key issues and challenges. How can IT managers prepare this move to the edge?

Scroll down below the video to see highlights

“I give [data center managers] a lot of credit for managing the evolving set of technologies. We had this Big Data phenomenon and now it’s AI, and then you’re moving to the edge. And at the same time, the level of interconnects and devices that are connecting to the data center and the speed requirements – it’s a significant challenge.”

“We asked [data center managers] how many remote environments that they’re managing and end points are they managing. And it turned out that close to 60% are managing five plus more, five or more unique environment.

“And it might be the traditional data center that we think of, but it can also be this phenomenon of edge and the continued movement of getting compute resources to the customer closer to the customer and more inexpensive positioning with the customer. And that level of complexity is going to continue, but it certainly has contributed to the challenges of data center operators today.”

“So we [at Intel] are trying to feed additional data to the operators so that they can make more intelligent decisions with this disparate remote environment they’re managing.

And what we’ve seen is that they’re asking us for more analysis tools. We went through this period of time where just getting the data was a struggle, and even in the survey that we commissioned, there were still about 40% of data center operators that are struggling to just get the data they need to manage their environment.”

“Just from talking to customers and understanding some of the complexity, we have a lot of revisions that occur at the OEM space and Intel contributes to some of that complexity because we self-obsolete ourselves every two and a half years or so with a new chipset. And that goes out to all of the OEMs and then they release new servers and new technology to their customers.

And what happens is, the OEM takes a lot of the baseline components that Intel is providing, and they add their firmware layers on top of the chipset or on top of the architecture, and the customization in firmware is really causing some complexity.”

“The edge is faster and it’s cheaper. It’s closer to the customer. And depending on the type of services that are required, it’s a requirement to ensure that the customer’s information is processed that much more quickly in a customer type setting, rather than going back to a traditional data center.

“So the challenge is more remoteness. We talked before about five-plus remote environments, you’re going to see that significantly increase.”

“So there’s generally not going to be a human being there to be able to remediate, fix, turn off and on, or analyze a set of hardware. So you need a lighter tool to remotely establish a link to find out or look and discover what type of issues could be occurring there.

“But you also don’t want a network hog or an analysis tool that requires a lot of network bandwidth or requires a significant amount of people to manage.

“So I think the toolsets, the traditional toolsets, and DCIM, have evolved into a set of buckets that are underneath that larger umbrella that are really just defining customer problems and addressing them. And Edge … has similar issues, but it’s just on a smaller scale. And what we’re seeing is, we see a lot of requests for analysis tools ‘I got all this data, but I want to understand how to interpret that information and what to do with it.’”

“I think there are many tools out there. I think that’s one of the bigger challenges, that I think data center managers are being hit up to evaluate something almost weekly.

“There are customers or partners that are in this space that are doing a good job on the business development by getting into the market and trying to grow. And it’s pretty easy to set up a software tool that can collect some information and evaluate it.

“The IT department discovered [that] really understanding what it’s going take to implement and maintain a solution that really says it’s going to do everything for you, and that’s part of the promises that are made, [requires you to] set aside individuals and resources. Not only to implement, but also to have a much higher level of maintenance resources than you traditionally would believe.

“So, kicking the tires, doing some good diligence, getting some customer referrals, those types of really basic requirements are something that I would encourage all data center managers to do.”

“Then with newer generations, when you look at the evolution of IoT, its sensors are getting smaller and smaller to be able to put inside industrial equipment.

“Well, that’s essentially what’s happened within the IT devices: now there are more sensors that are within your IT devices to help monitor the health, monitor the temperature, and monitor the power utilization.

“So that has blossomed into a whole number of use cases from this information. And how we’ve packaged our sets of tools is, ‘Tell me your top three problems, I have a portfolio of roughly 10 use cases that I can apply to your issues. Let me prove one of those use cases out to you before you make an investment in people or an investment in capital towards the tool.’”

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