Today’s competitions in consumer device market are extremely fierce. New devices, new features, new global players are coming out every day. Domination in any global consumer market becomes increasingly short-lived and difficult. Companies are forced to constantly seek new markets and new ideas to hold onto market shares which may promise new service revenues in the near future. One such strategy is the push to cross of the protected boundary of enterprise vs. consumer market.

Google, with free Android mobile platform and rich assortments of its different price-point models from many manufacturers, has been gaining steady shares from cost-sensitive consumers and school buyers. Today Google has a strong push in cloud-based enterprise productivity software online and on devices with “Google for Work” solutions including Gmail, Google docs, etc. Microsoft, with the year-over-year decline of global PC shipment endangering its once-a-stronghold enterprise software space, on the other hand, is trying hard to gain more shares of the consumer market with its Windows Phone and Surface tablets.

Thanks to Steve Jobs’ visionary leadership, Apple has been very successful in high-end consumer market with its popular iPhone and iPad. However Apple also started observing a slower growth with iPad and more competitions in global smartphone market for iPhone. Of course they turned their attentions to the enterprise space. Apple devices have been flowing nicely into the corporate world in recent years. iPhone has long replaced Blackberry as the mobile choice for enterprises. With the new reality that enterprise solution providers and internal IT departments nowadays must offer applications both online and on mobile, Apple devices are riding on the lucky wave of the corporate changes. iOS is usually the No.1 targeted mobile platform for these developments. For example, Salesforce.com and SAP are observing increased traffic from Apple devices for their mobile versions of the CRM/ERP applications. Apple is also consciously deepening its corporate customer pursuits by allowing its devices to easily connect to enterprise emails and shipping new security features in its newer models such as the Touch ID fingerprint readers and anti-reflective screen coating in Apple Air 2 and Mini 3. This year Apple formed a joint venture with IBM to leverage Big Blue’s enterprise service reach to push more mobile apps running on its devices in enterprise environment. It has already borne fruits after a few months with the first set of mobile apps targeting key industries such as airlines, banking, financial services, insurance, etc. With more apps to come to ease enterprise pain points, they also hope that Apple’s strength in product design and user experience will appease to more enterprise users.

But things are dramatically changed with the new waves of cloud services and Big Data today. Now it becomes everyone’s favorite baby almost overnight. For example, Docker, a small San Francisco startup who just barely released its v1.0 software container solution for cloud about 1.5 years ago [See our August blog on Containers and Cloud], is instantly pursued by all major cloud providers. Google is using container templates directly for its cloud deployment features including autoscaling and load-balancing. Microsoft rushed to sign a new deal with Docker in October in order to allow generic Docker containers to be supported on windows servers and Azure platform in the near future. Today Amazon and Google’s popular cloud platforms have promoted a major chunk of Big Data solutions from open source communities, by either insulating the complexity of management tasks or delivering automation packages to the market. At the meantime, big old telecommunication companies are spending billions to develop open-source-based software-defined network infrastructure. Late for the party but with its own flair, Microsoft started to open its developer source code a few years ago to join the community. This week Microsoft disclosed its intention to open source its re-architected .NET Core as a foundation for both open-source application development and for cross-platform cloud services and application deployment.

On global mobile market, many OEMs and ODMs in emerging markets have been developing and packaging customized mobile applications using open source technologies on their devices, mostly on top of free Android mobile OS. This strategy has proved both fast and effective for many of them. The rocket rise of Chinese local smartphone maker Xiaomi, now #2 in market share in smartphone delivery in China, is a great example. [See October blog on The Fast Shifting Market of Mobile].

What are the reasons for the rebirth and stellar rise of an old baby? First, through time when a disruptive innovation has accumulated enough momentum, reached critical adoption mass and refined enough its solutions to form a sustainable ecosystem, it transforms itself from a smaller player in marginal markets to a powerful player that can compete head-to-head with incumbent dominating technologies in main markets. Second, other disruptive forces of present, triggered by the Second Machine Age innovations, demand new thinking, new ideas, new solutions to brand new problems which incumbents are not prepared to deal with. The disruptive technology thus becomes a more attractive, creative and cost-effective alternative for the experiments of seeking new solutions. Third, once worldwide great minds start gathering around on the subject, everything is possible. To sum up, the current condition is ripe for a broader penetration of a once insignificant disruptive technology to the mainstream market.

Currently, to support broad hybrid-cloud computing suited for most of the enterprise environments, at least two distinct approaches in data center design are competing in the market. One is the hyper-convergence hardware-based cloud appliance approach and the other one is the SDDC approach. Offerings in the first camp include Microsoft’s Dell-based Azure-on-board Cloud Platform System (CPS), VCE Vblock, HP CS700, IBM PureFlex, etc. This approach packages integrated compute, storage and virtual networks together in hardware containers with software management tools. It can also be preloaded with certain platforms or applications. The system can be switched on and linked to enterprise’s existing network to build hybrid cloud on premises almost instantly. The performance and future scalability will be limited by the capacity and numbers of these containers. Companies need to invest on these new appliances, but may save on many of the design, deployment and operation tasks.

On the other side, Google is the pioneer in SDDC camp. From the beginning of their online search business, Google used cheaply collected machines and storage units, bundled them together and programmed software to control everything. From shared compute, storage to virtual networking, all Google global data centers can be managed remotely. Failures from any of the hardware are monitored and switched off instantly without any interruption to the application tier or users. Amazon’s AWS also adopts SDDC with self-designed homogeneous servers.

The SDDC is gaining more steam in cloud industry. The concept has been clarified as distributed virtualization for all elements of the infrastructure – compute, storage, networking and security. It targets on the total abstraction of the application layer from the underlying hardware layers and thus allows service SLAs and automation of the management tasks for each element of the cloud computing. SDDC can promise unlimited scalability, performance and the important self-service to customers. The cheaper hardware scenarios usually attract more attentions, but there are often hidden costs associated with software resources and testing, especially with many of the open-source solutions.

Most of the SDDC solutions today are based on homogenous commodity hardware, but the real needs and challenges from today’s enterprises call for utilizing the existing heterogeneous hardware and network situations. Several companies are trying to come up with more answers, through distributed virtualization by abstraction and encapsulation. For example, VMware NSX extends software-defined network (SDN) concept with vSwitches built in VMware hypervisors to creates virtual networks and encapsulates existing network topology and security configurations, but it still yet to fully support hybrid cloud scenarios.

As the Second Machine Age bringing in unparalleled productivity aided by smart machines, although never raised, TriStrategist asks: could it mean that one day intelligent machines may transcend the time limitation that humans experience? Either by some distorted (or virtual) reality or the co-existence of same human brain power at the same moment but different locations (by robotic surrogates or some sort of scaled-out brain mapping to machines)? All seems likely.

Today besides many industrial applications, newer smart robots can do cognitive captures and basic learning, take instructions, be deployed for dangerous rescues, do domestic chores, and be applied in robotic surgery… Humanoid robots can talk with people through Skype, ask logical-thinking questions and become closer to human-like and human-able. More creative and faster-thinking quantum robotics is also in the making. Very soon, smart robots will prevail in every corner of the human life. More and more office job functions could be carried out by either automation tools or by machines. Humans will be freed to pursue more creative and higher-level jobs that machines yet to be able to simulate or humans will have more leisure time on hands. Many could be displaced too, a possibly serious social and economic issue in the Second Machine Age.

Machines are still machines. The true power behind the machines in the Second Machine Age is no longer some physical engines, but Artificial Intelligence (AI), the “brain power” of the machines. We are currently in a new phase of AI: cognitive computing and deep learning. From traditional data mining, to voice recognition, now to cognitive adaptability, logical reasoning, improvisation and real-time interactions, AI has advanced towards human intelligence in big strides these years with the help from the ever-increasing computational power. Some even predicted that machine intelligence could surpass human intelligence in 15 years by 2030. That sounds very scary indeed.

Simply speaking, the Microservice architecture is following the path of further software granularization to construct each software application as a set of small services, each performing one simple function, running in its own process and communicating with lightweight mechanisms, often through an HTTP resource API such as a REST API. These “micro”-sized services can be deployed independently and grouped together to jointly perform required complex capabilities which traditionally would be handled by one monolithic application with many embedded components and dependencies packaged inside. Errors could be isolated and fixed by re-deploying a few microservices instead of bringing down the entire system. Some startup companies promoting microservices have used the analogy of Apple App Store – so that they can offer PaaS with a rich collection of these microservices for a particular platform.

Differing from the plumbing used in many existing enterprise architectures where complex transformation rules, messaging and interchanges between systems are put on the Enterprise Service Bus in the middle, microservices adopt the idea of “smart endpoints, but dumb pipes”. It emphasizes the ready-deployable nature of the lightweight individual services from the start point to the destination without extra handling in between.

The benefits offered by the fine-grained microservices come with certain cost. For example, instead of a limited number of SLAs for an enterprise application, now numerous SLAs need to be created and maintained at the same time. Each microservice also needs load-balancing and fault-tolerance. Deploying, Managing, communicating, versioning and testing the complexity of the system with a huge number of microservices are demanding tasks.

The most intriguing aspect of microservice concept is that from the decentralized nature of its design patterns, it calls for corresponding changes to the traditional organizational structure by invoking Conway’s Law. Melvin Conway, a computer programmer, in 1968 mentioned that, “organizations which design systems … are constrained to produce designs which are copies of the communication structures of these organizations.” If one reflects carefully, it could be surprisingly true. Thus microservice concept calls for organizations to be organized around business capabilities instead of functions. Because each microservice, although “micro” in context, is an independently deployable service of its own, it must contain all cross-functional service basics as needed and developers must have UI, database, deployment, etc., full stack of skills to build and own the microservices.

Although microservice’s granularization is one form of software modularization, TriStrategist thinks that it may well be short of achieving the goal as tomorrow’s choice for distributed computing. Meaningful architecture abstraction to separate data from mechanisms, separate information from means of delivery will be needed and modularization will be the key for providing the flexibility required for distributed computing, but to meet the desired results, very likely we need both the re-thinking of software application architecture and cloud platform architecture. The future inter-deployable services, micro or not, need to be platform-neutral to truly address the essence of distributed computing. In addition, instead of thinking either smart endpoints or smart pipes, TriStrategist thinks that we need think more towards adding self-managed intelligence at each cross-point in the distributed picture.

Differing from Computational Neuroscience which offers in-depth study of the true complex biological neuronal functions in information processing in the brain, a neural network is more a simplified modeling technique or a set of algorithms in simulating the patterns of stimulations and repetitive learning of the brain by using interconnected parallel computational nodes as artificial neurons that are often organized into inputs, outputs and processing layers. Adaptive weights are used to simulate the connection strength between any two neuron nodes. Theses weights can be adjusted repeatedly by each “learning” cycle instead of being determined beforehand.

There are many college courses designated to the study of Neural Networks. In a simple sense, neural networks offer the possibility of continued learning and corrections in order to eventually fit the models closer to a particular function of the brain by comparing the outcomes to a certain reality. This is a huge deviation from the conventional computational models. Conventional models are deterministic with data and pre-defined instruction sets stored in memory for a centralized processor node to retrieve, compute and store in a sequential manner to generate outcomes. However the processing nodes for neural networks get information from input nodes or external signals to carry out simple weighted computations in parallel and the results are together presented as the outcome. The knowledge of a neural network is in the entire network itself instead of in any single node. Each computational cycle is almost a self-learning and reality-adjusting cycle, similar to the way humans or animals generally learn.