Nov 30

In a perfect world, Application Performance Management (APM) has all the right elements in place, providing value to the business and IT by giving us the metrics we need and showing us the health of our applications. It alerts us to anomalies when slowdowns occur, and shows us trends on performance. But there are other elements in play that can make the operations a little smoother and our days a little brighter.

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Nov 30

The 3rd International @ThingsExpo, co-located with the 16th International Cloud Expo - to be held June 9-11, 2015, at the Javits Center in New York City, NY - announces that it is now accepting Keynote Proposals.
The Internet of Things (IoT) is the most profound change in personal and enterprise IT since the creation of the Worldwide Web more than 20 years ago.
All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020. This number will continue to grow at a rapid pace for the next several decades.

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Nov 30

As cloud gives an opportunity to businesses to buy services externally – how is cloud impacting your customers?
In his General Session at 15th Cloud Expo, Fabio Gori, Director of Worldwide Cloud Marketing at Cisco, provided answers to big questions: Do you see hybrid cloud as where the world is going? What benefits does it bring? And how does Cisco connect all of these clouds? He also discussed Intercloud and Cisco’s investment on it.

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Nov 30

Microservice architectures are the new hotness, even though they aren't really all that different (in principle) from the paradigm described by SOA (which is dead, or not dead, depending on whom you ask).

One of the things this decompositional approach to application architecture does is encourage developers and operations (some might even say DevOps) to re-evaluate scaling strategies. In particular, the notion is forwarded that an application should be built to scale and then infrastructure should assist where necessary.

It was just this notion that led me to a discussion on a particularly useful explanation of scaling strategies called "the scale cube" which is introduced and explained further in The Art of Scalability. Go ahead and open it up and bookmark it; it's a good read and I highly recommend it.

The aforementioned discussion provides an overview of the three axes perspective on scale: x, y and z. Reading the descriptions, it became fairly apparent to me (who lives with one foot in the network and the other in the app) that the use of layer 7 load balancing was a way to implement in some cases and augment in others.

X-axis scaling

X-axis scaling is essentially a typical horizontal (scale out) scaling pattern implemented using a load balancer. Simple and effective for many types of applications, this pattern has been the age old "go to" for quickly scaling out apps that were not perhaps built to scale in the first place. Monolithic applications are almost always scaled out (x-axis) because they were not developed with other scalability models in mind and their reliance on state (via cookies or server-side sessions stored in memory, not databases) makes other scalability models nearly impossible to deploy successfully.

X-axis scalability can easily be implemented using layer 4 (TCP) load balancing if state is not important, but more often than not requires layer 7 (HTTP) load balancing due to the need to examine headers or other variables to ensure persistence to the right application instance (think sticky sessions).

x-axis-scaling

How does this apply to microservices? Consider that instead of apps, each microservice is scaled out (along the x-axis) using an app proxy or application delivery controller (ADC). This allows operations to tune each app proxy or ADC based on the specific purpose of the microservice, improving performance by applying image optimization, compression or even caching where appropriate to the specific service. In a monolithic application, an ADC will be a better choice for this scalability model because of its ability to interpret requests and optimize responses with the benefit of context. In cloud-scale microservice architectures, an app proxy may be the better option when considering cost per service and the relatively simple delivery needs of a given service. 

Y-axis scaling

Y-axis scaling is essentially a layer 7-based sharding pattern when applied to infrastructure. Y-axis scaling relies on the decomposition of applications into services. It is highly appropriate for SOA or RESTful APIs that group like functionality into a service. For example, verb-based decomposition focused on "login" or "checkout" or noun-based decomposition with an emphasis on "customer" or "partner."  The key is that there is some mechanism within each request - either in the URI or in the HTTP headers - that enable the app proxy or ADC to determine to which service the request needs to be forwarded.

y-axis-scaling

Sharding can be implemented in the app, itself, using a routing object to dissect the URI or that functionality can be offloaded to the network and implementing using the data path programmability associated with an app proxy or ADC. This programmability allows operators or developers to implement targeted logic that dissects the URI and determines to which service the request should be directed. This pattern can be (and often is) implemented along with an X-axis scaling strategy for the specific service.

x and y axis scaling pattern

The combination of both Y and X axis scaling is increasingly a good choice for bifurcated networks which split "core" networking from "app" networking. The core network usually provides a significantly capable load balancing service managed by the network team while the app network includes app proxies or virtual ADCs that are managed by operations or developers.

While this pattern can be implemented on monolithic applications, particularly monolithic web applications that rely on URI-based interactions, care must be taken with respect to state. That is, one cannot simply route to service B for "checkout" when it depends on session-level data that may be stored already in service A or C. Shared nothing application architectures do not  lend themselves well to sharding strategies based on application function or content type. Rather, such applications should be scaled using a more traditional approach. Shared session application architectures, however, are very well suited to this type of scalability strategy because the application state is shared across instances, and all services will have access to the necessary data.

Z-axis scaling

Z-axis scaling is a cross between X and Y scaling strategies, using a data sharding-based approach. This strategy is commonly used to shard databases, but can also be used to scale monolithic applications based on some user characteristic.

Z-axis scaling is like X-axis scaling in that it relies on cloning of application instances. The difference is that some other component - like an app proxy or ADC - is responsible for distributing requests based on some other information, like the data being requested or the user identity. As long as the data is accessible to the app proxy or the ADC (increasingly iintermediaries have the ability to reach out and query databases or directories to obtain additional information) 

 z-axis-scaling

 

When using Z-axis scaling each server runs an identical copy of the code. In this respect, it’s similar to X-axis scaling. The big difference is that each server is responsible for only a subset of the data. Some component of the system is responsible for routing each request to the appropriate server. One commonly used routing criteria is an attribute of the request such as the primary key of the entity being accessed. Another common routing criteria is the customer type. For example, an application might provide paying customers with a higher SLA than free customers by routing their requests to a different set of servers with more capacity.

This pattern is also useful for premium access policies, where certain users or customers are afforded higher performance guarantees.These instances may be further augmented with additional services or scaled out faster to improve performance. Only certain customers are allowed to access these "gold" instances, and such determinations might be made based on API key, cookie value, or membership in a specific group as determined by a database or directory lookup.

 

The point is that the scaling strategies associated with application architecture can be duplicated and/or augmented by the use of a app proxy or ADC. It is almost always the case that such an intermediary will be necessary to scale an application. That's because reality is that it's just as bad to let network logic (routing) seep into business logic as it is business logic to seep into the presentation (GUI) layer.

Keep your logics separate, and use the tools available to act on the scaling strategy best suited for your application or service.

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Nov 30

Software is eating the world. Companies that were not previously in the technology space now find themselves competing with Google and Amazon on speed of innovation. As the innovation cycle accelerates, companies must embrace rapid and constant change to both applications and their infrastructure, and find a way to deliver speed and agility of development without sacrificing reliability or efficiency of operations.
In her Day 2 Keynote DevOps Summit, Victoria Livschitz, CEO of Qubell, discussed how IT organizations can automate just-in-time assembly of application environments - each built for a specific purpose with the right infrastructure, components, service, data and tools - and deliver this automation to developers as a self-service. Victoria’s keynote will include remarks by Kira Makagon, EVP of Innovation at RingCentral, and Ratnakar Lavu, EVP of Digital Technology at Kohl’s.

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Nov 30

Working with Big Data is challenging, especially when decision makers depend on market insights and intelligence from your data but don’t have quick access to it or find it unusable.
In their session at 6th Big Data Expo, Ian Khan, Global Strategic Positioning & Brand Manager at Solgenia; Zel Bianco, President, CEO and Co-Founder of Interactive Edge of Solgenia; and Ermanno Bonifazi, CEO & Founder at Solgenia, discussed how a revolutionary cloud-based BI along with mobile analytics is already changing the way organizations rely on data for decisions that affect operations as well as strategy. Also learn how combined predictive analytics, data modelling, mobile, and cloud BI are fast changing the key decision-making mechanisms in the enterprise.

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Nov 29

If you thought the Bring Your Own Device (BYOD) craze was a headache, just wait until button cameras, smart watches, and spy glasses (already here) are a daily occurrence in the office. Workplace #Wearables will be a huge challenge in the coming years as more devices, clothing and pretty much any ‘thing’ with a chip or sensor become commonplace in our society. The device explosion with IoT (Internet of Things) will be much larger than any of these mobile phones we carry around.
A couple new reports examine the impact of IoT on businesses.

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Nov 29

Docker has been quickly adopted by nearly everyone and incorporated into everything from cloud technologies, to continuous integration and build systems, to solo developers working exclusively on their laptop. Heck, even Microsoft is getting in on this! It was born in PaaS (dotCloud) and this is the place where it makes a lot of sense. Ephemeral fast-starting single-process containers that can be distributed across a large cluster is where Docker shines.
Docker has been Stackato’s container implementation for a year now, responsible for provisioning and managing the life-cycle of who knows how many Linux containers. The next question is how do we start exposing Docker features to end users, rather than having them as an unexposed implementation detail. These features bring portability with a simple packaging mechanism for building and distributing an application in a consistent way across not only a specific PaaS, but anywhere that Docker runs.

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Nov 29

The Industrial Revolution in the 18th to 19th centuries was a period during which predominantly rural societies in Europe and America became industrial and urban. Advances in steam technology, transportation, mass production and the telegraph collectively transformed industry and society. Today, the Internet of Things (IoT) has the potential to once again transform industry and society just as the Industrial Revolution did. Analyst firm IDC forecasts that the IoT market will grow to $8.9 trillion by 2020 with anywhere between 30 to 50 billion connected autonomous things, making the potential growth opportunities staggering as sensors, technology and networking come together to enable buildings, infrastructure and other resources to swap information. Over the next decade, as much as $3 trillion in global smart technology opportunities will exist for cities.

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Nov 29

jones_supa (887896) writes “A hard to track system lockup bug seems to have appeared in the span of couple of most recent Linux kernel releases. Dave Jones of Red Hat was the one to first report his experience of frequent lockups with 3.18. Later he found out that the issue is present in 3.17 too. The problem was first suspected to be related to Xen. A patch dating back to 2005 was pushed for Xen to fix a vmalloc_fault() path that was similar to what was reported by Dave. The patch had a comment that read “the line below does not always work. Needs investigating!” But it looks like this issue was never properly investigated. Due to the nature of the bug and its difficulty in tracking down, testers might be finding multiple but similar bugs within the kernel. Linus even suggested taking a look in the watchdog code. He also concluded the Xen bug to be a different issue. The bug hunt continues in the Linux Kernel Mailing List.”

Read more of this story at Slashdot.








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