Network analytics primarily addresses data packet transmissions sent and received by Layer 3 devices operating under the Open Systems Interconnection (OSI) model established by the International Organization for Standardization (ISO). Layer 2 devices represent the data link layer governed by switches and Layer 4 devices relate to TCP standards associated with web servers. However, Layer 3 devices are specifically concerned with I/O data packet transmissions sent and received through routing devices. Virtualization leads to software-defined networking (SDN) and software-defined data center (SDDC) standards where the Layer 3 functionality is established by software rather than physical devices. This creates a wider range of potential for cloud data center orchestration across “bare metal” hardware resources. In contemporary practice, network analytics software integrates information from Layer 2-4 devices simultaneously due to ecosystem expansion and the introduction of new switch, router, and firewall appliance technology standards. This granularity is further expanded through virtualization, which leads to a greater ability to script and automate data center processes at all layers of the service mesh on the basis of real-time, streaming data packet analytics.
Network analytics can be categorized into historical approaches to recording and reading data from operations that are presented in logs, reports, or graphs by utility software. These reports provide administrators with hourly, daily, monthly, and yearly portraits of events that occur on a particular network or may be based on user and endpoint device activity. IP addresses are the key components of Layer 3 network activity through routers that control data packet transmissions between endpoints based on user request activity. Most network analytics refactor the data available through monitoring Layer 3 processes historically by filtering information into usage patterns or highlighting the data to expose abnormal events. Employees can access network analytics to view the flow of packet data across cloud architecture in real-time due to the increased power of contemporary web server hardware and the expansion of ecosystem software in the data center management sector. Machine learning (ML) techniques are increasingly being introduced as a critical aspect of network analytics that enables better automation of processes in industrial production, telecommunications, eCommerce, and multimedia publishing.
Network analytics are essential to cloud orchestration across all varieties of use cases, but become particularly powerful for enabling the next-generation of data center applications when they can be automated with custom code for industry-specific requirements.
Telecommunications companies implement network analytics at the highest scale of user traffic when managing mobile communications or broadband connections for customers. VMware Smart Experience offers pre-coded tools for user anonymization, subscriber profiles, and cell tower traffic optimization out-of-the-box to carrier companies. These tools are also used by oil and gas companies at scale in monitoring remote IoT devices that regulate pipelines, drilling, and reservoir facilities. Industrial manufacturing companies in the automotive and high tech sectors make the most extensive use of streaming data analytics in the production of self-driving vehicle networks and the AI/ML guidance for autonomous vehicle navigation. Streaming data analytics is opening up new use cases for innovation across all sectors of industry on the basis of “Big Data” applications, Artificial Intelligence (AI), and Machine Learning (ML).
In cloud hosting and enterprise networking, real-time analytics and historical reports are vital tools for maintaining systems health, data security, and optimized I/O transfer speeds between connected devices. Platform providers can quickly identify, isolate, and quarantine incoming malware, viruses, or worms by using real-time packet scanning to identify threats. Infected devices can be detected more quickly through KPI analysis, reports, and security alerts. The ability to configure Layer 3 firewall rules gives network administrators the option to establish restrictions based upon protocol, source or destination IP address, and ports. These rules are enforced through real-time data packet analytics at the device level. Virtualization extends the granularity of control to the VM level through SDN routing functionality, the NSX distributed firewall, and streaming data analytics through Smart Experience. Maps of virtual infrastructure improve connected device discoverability for network resource optimization and can be used to build disaster recovery plans for continuity of service requirements.
For unified network analytics across an entire data center, VMware’s Workspace provides one comprehensive intelligence platform. Workspace ONE Intelligence includes predictive analytics along with the ability to script automation for security in network administration with high-level granularity on rules and filters that extend to the device or VM via SDN routing options and IP addresses. VMware’s Workspace ONE Intelligence integrates with a wide variety of third-party plugins from cloud software development companies to address niche industry requirements or particular security needs. The suite can also output reports for systems administrators that validate compliance with international auditing and data security regulations. One of the primary reasons that businesses of all sizes choose software-defined data center (SDDC) solutions is for the more detailed and advanced streaming data analytics available through real-time monitoring in virtualized environments. vRealize Log Insight creates structured metrics with KPI analysis for business intelligence, network diagnostics, and prediction that makes cloud network management more efficient.
Although there’s no single “right” way of working remotely, there are some general best practices to create the conditions for success. These include:
Problems with remote working tend to surface when the best practices and basic principles of how remote teams work are missing. This leads to challenges such as:
Working from a home office is one form of remote work, but the two terms are not necessarily interchangeable. This is because remote working does not prescribe where someone works; it just means that they rarely go into a traditional office to do their job. Their day-to-day norm is to work from some other location, which may be in their home but is not limited to that location.
Moreover, “working from home” may also refer to a temporary or less frequent version of remote work. This scenario could include, for example, a person who is unexpectedly working from home for a day or two because of a short-term childcare need, but who otherwise would ordinarily work from the company’s office. This style of working is sometimes called telecommuting or telework. Whereas remote workers typically work from an off-site location most or all of the time, telecommuting or teleworking typically means that the person also regularly works on-site in a traditional office.