In Linux, calculating available memory is not always straightforward. This is because Linux treats memory resources differently than other operating systems. Because of this, many Linux server monitoring tools do not properly calculate the true value of the property correctly, because of what Linux is doing with memory resources behind the scenes. So a Linux admin running a Linux system might see that they have zero (0) Memory resources available, when in fact plenty of memory is available.

Linux, by design, uses RAM memory for disk caching to speed up the system. This means that the Mem % Free metric will consistently be low (maybe 5%), when in actuality, the system is only using 50% of the RAM.

It is possible in Sightline EDM to accurately monitor Linux memory usage and generate alerts when the amount of real memory gets too low, as opposed to when the default Mem % Free metric only appears to be too low.

Currently, this needs to be done using an expression, which lets you build and define your own metrics using currently existing metrics. We will make 2 expressions in order to monitor real Linux memory usage.

  1. Mem Real Free GByte: We will make an expression called “Mem Real Free GByte”. It will add the following three metrics together: “Mem Buffers GByte” + “Mem Cached GByte” + “Mem Free GByte“. These metrics added together provide a metric which takes out the cache and buffers from the memory calculation.
  2. Mem Real pct Free: We will then create an expression called “Mem Real pct Free.” This metric provides a percentage value which can be used to create accurate Linux memory usage alerts across systems. This is because a percentage will be accurate whether the system has 4 GB or 40 GB. This is done by the following calculation: (“Mem Real Free GByte” / “Mem Total GByte“)  * 100. This takes the free GBytes and divides by the total GBytes the system has, and then multiplies the result by 100 to get a percentage. For example, if a 16 GB system has a “Mem Real Free GByte” value of 10 GB, then the calculation would be  (10/16) * 100, which equals 62.5%.

By using these expressions, it is possible to create meaningful alerts based on real memory instead of the default Mem % Free across a wide range of Unix systems.

The screenshot below shows the default Linux memory metric, Mem % Free, in the lower blue line hovering around 1% free, in comparison with the expression created for Mem Real pct Free, which shows the upper orange line around 36% free. Although the blue line appears to indicate that the Linux system is out of memory, that memory is actually being used for disk caching, whereas the orange line shows real memory around 36% free, which is a much better metric for creating performance alerts. 


The difference can also be seen at the end of the graph, when an application begins using real memory, causing the orange line to dip down to 5%. The blue line does not reflect this change, however, because the system simply decreases the amount of memory available for disk caching and increases the amount of memory availabile to other applications, which effectively cancels each other out. In this way, it is possible to set up alerts to accurately monitor Linux memory usage in Sightline EDM’s IT Infrastructure monitoring system.

The Industrial Internet of Things (IIoT) is changing the landscape of the U.S. manufacturing industry.  Companies that understand the patterns and trends and position themselves to prepare for the impending advances will most certainly gain a competitive edge in the global marketplace. 

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Companies no longer have the luxury of being anything but data-driven. Data used to be something to simply maintain and manage, but now it’s a valuable asset that companies use to gain a competitive edge. With change happening so rapidly, how are manufacturers preparing to take advantage of the massive amounts of data that is available and more importantly, how are they using that data to really take advantage of the power that IIoT delivers?

When posing the question of how manufacturing companies are preparing for IIoT, many manufacturing leaders think of IIoT as something far off in the distance, but they don’t really understand the full impact that is coming. Many see it as a fad or something that may only have some effect on the way they handle day-to-day operations in the long run.  As we begin to peel back the layers of IIoT, one sees that there is a strong potential for a shift to occur that will change the entire manner in which manufacturing companies operate similar to what the manufacturing industry saw when they were first implementing automation and began using IT and other electronics. As a result, manufacturing leaders are seeking to develop formal and informal IIoT strategies that will position their companies to take advantage of new opportunities to streamline efficiencies, reduce downtime and stimulate profitability sooner rather than later.

Where We’ve Been and Where We’re Going

If one reviews the history of manufacturing, there are four distinct manufacturing industrial revolutions spanning from the initial mechanical production facilities to mass production to use of electronics and IT to IIoT and systems integration. The fourth industrial revolution, or Industry 4.0, will allow manufacturers to leverage the Industrial Internet of Things (IIoT) to collect vast amounts of sensor and network data, apply advanced analytics and further utilize new technology such as robots and 3-D printing to improve quality and output.

While some progressive manufacturers see where the industry is headed, many are only at the starting gate of the next wave of innovation fueled by IIoT applications and solutions. According to a recent study by Smart Industry, many manufacturers are focused on learning and benchmarking to formulate winning strategies. Many will be using the findings to reduce operational costs, optimize asset utilization, improve worker productivity, enhance workplace safety, enhance the customer experience and create new business models and revenue streams.

IIoT Will Produce More “A-Ha” Moments for Manufacturers

The best way for the manufacturing industry to capitalize on IIoT is by gathering more data from sensors and systems and utilizing it to make business-driven decisions. While that may seem as though it is not an easy task, by adding advanced analytics solutions now, manufacturers will most certainly have more “a-ha” moments as they produce insights previously clouded by uncertainty or unattainable due to limited resources and time.

The advanced platforms will enable manufacturers to gather the right data, at the right time which can be leveraged to make well-informed, and most importantly, proactive business decisions. These tools will provide more insight and will enable manufacturers to develop a major engine to identify and create new products, services and profit centers all while simultaneously improving production efficiency, reducing costs, preventing downtime, ensuring quality and enhancing their overall ability to strategically plan business operations.

Manufacturers Aren’t Really Sure Where to Begin

Data is being collected by sensors, PLCs and more to the point some manufacturers are overwhelmed with data and aren’t really sure where to start. With so much data readily available, many manufacturers are wondering how to start implementing IIoT technologies in a thoughtful manner. Many manufacturers are taking a very close look at the data they want to collect and how they will use the information to streamline efficiencies, realize opportunities and produce a sizeable return on investment (ROI).

Manufacturers are concerned by a host of obstacles for adopting IIoT in their companies, with the most notable being cybersecurity. Cybersecurity concerns, lack of overall IIoT knowledge internally, legacy products that do not have obvious IIoT connectivity and lack of senior management support and commitment, just to name a few, are among the most pressing issues that keep manufacturers up at night. In order to wrap their arms around these challenges, proactive manufacturers will need to gain a better understanding of how to leverage advanced analytics. The traditional manufacturing business model is quite reactive and relies on management to be the primary driver of change, production that is driven by a sales forecast, and system improvements if, and only if, it is perceived to be “broken.”  As the manufacturing landscape advances due to IIoT, manufacturers must begin to take a more holistic view of the entire company to better understand how one part of the operation affects other parts in order to take advantage of enormous opportunities for improvement and to proactively gain the competitive edge.

As manufacturers begin to take a more holistic approach, many are working with internal teams, suppliers and consultants to decide the most valuable data to collect, what systems require enhancement, how the data will help them realize opportunities as well as how to gauge the full impact of IIoT changes within and outside the company. The two most critical issues are data management and cybersecurity. These areas will be critical challenges for the company to address as it affects future competitiveness

IIoT is most definitely changing the landscape of the manufacturing industry as we know it. Manufacturers that read the trends, understand data patterns and begin to lay the foundation now to proactively take advantage of the technological advances will be poised to remain viable in the global marketplace throughout the decades to come.