Software development can be an arduous endeavor with numerous steps spanning a few programmers to a couple hundred developers. And keeping that software running smoothly can be even more costly and time-consuming as a few features are added, bug fixes applied, or third party tools integrated. A recent post from Gartner estimated the cost of downtime could be as high as $5,600 per minute which translates to over $300K per hour! Of course this figure varies based on the industry, size of the organization, and other factors but no organization can afford extended downtime in today’s competitive marketplace.

Your Dev/Ops team needs the right application monitoring tools, processes, and culture to avoid downtime and support aggressive revenue and customer retention targets.

So how can you simplify your development process to deliver tangible savings and minimize downtime? Here are 15 simple steps you can implement to save time and money for your Dev/Ops teams.

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SIOS Technology Corp. has announced that SIOS Senior Technical Evangelist and Microsoft Clustering MVP, David Bermingham will present a session about protecting business critical applications in cloud environments at Cloud Expo taking place next week in New York, NY.
Titled, “Protecting Mission Critical Applications in Cloud Environments,” David’s session is scheduled for Thursday, June 11 at 4:50 pm at the Javits Center, New York, NY. For more information about the Cloud Expo or to register, visit here.
Gartner predicts that the bulk of new IT spending by 2016 will be for cloud platforms and applications and that nearly half of large enterprises will have cloud deployments by the end of 2017. The benefits of the cloud may be clear for applications that can tolerate brief periods of downtime, but for critical applications like SQL Server, Oracle and SAP, companies need a strategy for HA and DR protection. By adding SANless clustering software as an ingredient to Windows Server Failover Clustering, companies can provide HA and DR protection in a cloud where traditional shared-storage clusters may be impractical or impossible. Attendees at David’s session will learn the truths and myths of HA and DR in cloud deployments that can dramatically reduce data center costs and risks.

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Let’s just nip the conflation of these terms in the bud, shall we?

“MIcro” is big these days. Both microservices and microsegmentation are having and will continue to have an impact on data center architecture, but not necessarily for the same reasons. There’s a growing trend in which folks – particularly those with a network background – conflate the two and use them to mean the same thing.

They are not.

One is about the application. The other, the network. There is a relationship, but it’s a voluntary one. They are two very different things and we need to straighten out the misconceptions that are rapidly becoming common.

Microservices

Microservices are the resulting set of services (mini applications, if you will) that arise from the process of decomposing an application into smaller pieces. If you take a monolithic application and segment it into many pieces, you end up with microservices. It is an application architecture; an approach to designing applications.

This architectural approach has a significant impact on the network architecture, as it forces broader distribution of application-affine services like load balancing, caching and acceleration to be located closer to the individual service. Microservices as an approach is a forcing factor in the bifurcation of the network as it separates application-affine services from corporate-affine services.

Microservice architectures are beneficial in that they are highly efficient; it separates functional or object domains and thus lends itself well to a more targeted and efficient scalability model. It is particularly useful when designing APIs, as in addition to the scalability benefits it also localizes capabilities and enables isolated upgrades and new features without necessarily disrupting other services (and the teams developing other services). This lends itself well to agile methodologies while enabling a greater focus on API development as it relates to other services as well as the applications that will use the service.

Microsegmentation

Microsegmentation is about the network; to be precise, at the moment it’s about the security functions in the network and where they reside. It’s a network architecture that, like microservices, breaks up a monolithic approach to something (in this case security) and distributes it into multiple services. You could say that microsegmentation is micro-security-services, in that it decomposes a security policy into multiple, focused security policies and distributes them in an resource-affine manner. That is, security policies peculiar to an application are physically located closer to that application, rather than at the edge of the network as part of a grandiose, corporate policy.

This approach, while initially focusing on security, can be applied to other services as well. As noted above, a result of a microservice approach to applications the network naturally bifurcates and application-affine services (like security) move closer to the application. Which is kind of what microsegmentation is all about; smaller, distributed “segments” of security (and other application-affine services like load balancing and caching) logically deployed close to the application.

Thus, if there is any relationship between the two approaches, it is that microservices tend to create an environment in which microsegmentation occurs.

There are other reasons for microsegmentation, including the reality that the scale required at the edge to support every application-specific service is simply pushing IT to the edge of its wits (pun only somewhat intended). The other driving factor (or maybe it’s a benefit?) is that of service isolation, which provides for fewer disruptions in the event of changes occurring in a single service. For example, a change to the core firewall is considered potentially highly disruptive because if it goes wrong, every thing breaks. Changing the firewall rules on a localized, isolated service responsible for serving two or three applications, has a much lower rate of disruption should something go wrong.

This is highly desirable in a complex environment  in which stability is as important as agility.

COHABITATION

In a nutshell, microservices are to applications what microsegmentation is to network services. Both are about decomposing a monolithic architecture into its core components and distributing them topologically in a way that enables more scalable, secure and isolated domains of control.

The thing to remember is that just because dev has decided to leverage microservices does not in turn mean that the network somehow magically becomes microsegmented or that if microsegmentation is used to optimize the network service architecture that suddenly apps become microservices. Microsegmentation can be used to logically isolate monolithic applications as easily as it can microservices.

Either approach can be used independently of one another, although best practices in networking seem to indicate that if dev decides to go with microservices, microsegmentation is not going to be far behind. But the use of microsegmentation in the network does not mean dev is going to go all in with microservices.

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Python is really a language which has swept the scene in recent years in terms of popularity, elegance, and functionality. Research shows that 8 out 10 computer science departments in the U.S. now teach their introductory courses with Python, surpassing Java. Top-ranked CS departments at MIT and UC Berkeley have switched their introductory courses to Python. And the top three MOOC providers (edX, Coursera, and Udacity) all offer introductory programming courses in Python. Not to mention, Python is known to have an easy syntax that makes learning much more tolerable, and it scales up easily – especially in cloud-based environments.

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Software development, like manufacturing, is a craft that requires the application of creative approaches to solve problems given a wide range of constraints. However, while engineering design may be craftwork, the production of most designed objects relies on a standardized and automated manufacturing process. By contrast, much of moving an application from prototype to production and, indeed, maintaining the application through its lifecycle has often remained craftwork.
In his session at DevOps Summit, Gordon Haff, senior cloud strategy marketing and evangelism manager at Red Hat, discussed the many lessons and processes that DevOps can learn from manufacturing and the assembly line-like tools, such as Platform-as-a-Service, that provide the necessary abstraction and automation to make industrialized DevOps possible.

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While there are hundreds of public and private cloud hosting providers to choose from, not all clouds are created equal. If you’re seeking to host enterprise-level mission-critical applications, where Cloud Security is a primary concern, WHOA.com is setting new standards for cloud hosting, and has established itself as a major contender in the marketplace. We are constantly seeking ways to innovate and leverage state-of-the-art technologies.
In his session at 16th Cloud Expo, Mike Rivera, Senior Solutions Consultant at WHOA.com, will show how a flexible cloud environment enables you to have full visibility into your core infrastructure, enabling you to be proactive and address potential problems before they arise, while providing you with the data for proper capacity planning to support business demand.

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SYS-CON Events announced today that DragonGlass, an enterprise search platform, will exhibit at SYS-CON’s 16th International Cloud Expo®, which will take place on June 9-11, 2015, at the Javits Center in New York City, NY.
After eleven years of designing and building custom applications, OpenCrowd has launched DragonGlass, a cloud-based platform that enables the development of search-based applications. These are a new breed of applications that utilize a search index as their backbone for data retrieval. They can easily adapt to new data sets and provide access to both structured and unstructured data. DragonGlass provides a user-friendly interface to extract, map and create an index store. The platform also supports a simplified REST-based API and a set of widgets to build user-centric applications like dashboards. The product is assembled using some of the leading open source technologies like ElasticSearch and Apache Storm.

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In high-production environments where release cycles are measured in hours or minutes — not days or weeks — there’s little room for mistakes and no room for confusion. Everyone has to understand what’s happening, in real time, and have the means to do whatever is necessary to keep applications up and running optimally. DevOps is a high-stakes world, but done well, it delivers the agility and performance to significantly impact business competitiveness.

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One question I frequently get is: “How do I become a data scientist?” Wow, tough question. There are several new books that outline the different skills, capabilities and technologies that a data scientist is going to need to learn and eventually master. I’ve read several of these books and am impressed with the depth of the content.
Unfortunately, these books spend the vast majority of their time reviewing and/or teaching things such as the data science processes (such as CRISP: Cross Industry Standard Process for Data Mining), and basic and advanced statistics, data mining and data visualization techniques and tools.

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With the advent of micro-services, the application design paradigm has undergone a major shift. The days of developing monolithic applications are over. We are bringing in the principles (read SOA) hereto the preserve of applications or system integration space into the application development world.

Since the micro-services are consumed within the application, the need of ESB is not there. There is no message transformation or mediations required. But service discovery and load balancing of service instance still need to be done, new tools have come up (e.g. Netflix Eureka)

There is no correct answer, depending on the use case, different options can be applied. One can also apply multiple options within the same application depending on the type of the client to be supported or at times, you do not want to expose individual services and an aggregator service might be better.

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