Storage means of mechanization and automation of managerial and engineering work
Office automation refers to the integration of office functions usually related to managing information. There are many tools used to automate office functions and the spread of electronic processors inside computers as well as inside copiers and printers is at the center of most recent advances in office automation. Raw data storage, electronic data transfer, and the management of electronic business information comprise the basic activities of an office automation system. The modern history of office automation began with the typewriter and the copy machine, which mechanized previously manual tasks. Today, however, office automation is increasingly understood as a term that refers not just to the mechanization of tasks but to the conversion of information to electronic form as well.
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The Evolution of Automation at Google
For SRE, automation is a force multiplier, not a panacea. Of course, just multiplying force does not naturally change the accuracy of where that force is applied: doing automation thoughtlessly can create as many problems as it solves. Therefore, while we believe that software-based automation is superior to manual operation in most circumstances, better than either option is a higher-level system design requiring neither of them—an autonomous system.
Or to put it another way, the value of automation comes from both what it does and its judicious application. What exactly is the value of automation?
Although scale is an obvious motivation for automation, there are many other reasons to use it. Take the example of university computing systems, where many systems engineering folks started their careers. Systems administrators of that background were generally charged with running a collection of machines or some software, and were accustomed to manually performing various actions in the discharge of that duty.
Ultimately, however, this prevalence of manual tasks is unsatisfactory for both the organizations and indeed the people maintaining systems in this way. This inevitable lack of consistency leads to mistakes, oversights, issues with data quality, and, yes, reliability problems. In this domain—the execution of well-scoped, known procedures the value of consistency is in many ways the primary value of automation.
Designed and done properly, automatic systems also provide a platform that can be extended, applied to more systems, or perhaps even spun out for profit.
A platform also centralizes mistakes. In other words, a bug fixed in the code will be fixed there once and forever, unlike a sufficiently large set of humans performing the same procedure, as discussed previously. A platform can be extended to perform additional tasks more easily than humans can be instructed to perform them or sometimes even realize that they have to be done. Depending on the nature of the task, it can run either continuously or much more frequently than humans could appropriately accomplish the task, or at times that are inconvenient for humans.
If automation runs regularly and successfully enough, the result is a reduced mean time to repair MTTR for those common faults. As is well understood in the industry, the later in the product lifecycle a problem is discovered, the more expensive it is to fix; see Testing for Reliability.
Generally, problems that occur in actual production are most expensive to fix, both in terms of time and money, which means that an automated system looking for problems as soon as they arise has a good chance of lowering the total cost of the system, given that the system is sufficiently large. Google has a large amount of automation; in many cases, the services we support could not long survive without this automation because they crossed the threshold of manageable manual operation long ago.
Finally, time saving is an oft-quoted rationale for automation. Although people cite this rationale for automation more than the others, in many ways the benefit is often less immediately calculable. Engineers often waver over whether a particular piece of automation or code is worth writing, in terms of effort saved in not requiring a task to be performed manually versus the effort required to write it.
Therefore, the time savings apply across anyone who would plausibly use the automation. Decoupling operator from operation is very powerful. If we have to staff humans to do the work, we are feeding the machines with the blood, sweat, and tears of human beings. Think The Matrix with less special effects and more pissed off System Administrators.
All of these benefits and trade-offs apply to us just as much as anyone else, and Google does have a strong bias toward automation. Another argument in favor of automation, particularly in the case of Google, is our complicated yet surprisingly uniform production environment, described in The Production Environment at Google, from the Viewpoint of an SRE.
While other organizations might have an important piece of equipment without a readily accessible API, software for which no source code is available, or another impediment to complete control over production operations, Google generally avoids such scenarios.
Even though purchasing software for a particular task would have been much cheaper in the short term, we chose to write our own solutions, because doing so produced APIs with the potential for much greater long-term benefits.
We spent a lot of time overcoming obstacles to automatic system management, and then resolutely developed that automatic system management itself. Of course, although Google is ideologically bent upon using machines to manage machines where possible, reality requires some modification of our approach. Some essential systems started out as quick prototypes, not designed to last or to interface with automation. The previous paragraphs state a maximalist view of our position, but one that we have been broadly successful at putting into action within the Google context.
In general, we have chosen to create platforms where we could, or to position ourselves so that we could create platforms over time. We view this platform-based approach as necessary for manageability and scalability.
In the industry, automation is the term generally used for writing code to solve a wide variety of problems, although the motivations for writing this code, and the solutions themselves, are often quite different. As we implied earlier, there are a number of use cases for automation. Here is a non-exhaustive list of examples:.
However, within Google SRE, our primary affinity has typically been for running infrastructure, as opposed to managing the quality of the data that passes over that infrastructure.
Therefore, the context for our automation is often automation to manage the lifecycle of systems, not their data: for example, deployments of a service in a new cluster. Widely available tools like Puppet, Chef, cfengine, and even Perl, which all provide ways to automate particular tasks, differ mostly in terms of the level of abstraction of the components provided to help the act of automating.
A full language like Perl provides POSIX-level affordances, which in theory provide an essentially unlimited scope of automation across the APIs accessible to the system, 30 whereas Chef and Puppet provide out-of-the-box abstractions with which services or other higher-level entities can be manipulated.
For example, we often assume that pushing a new binary to a cluster is atomic; the cluster will either end up with the old version, or the new version.
Very few abstractions model these kinds of outcomes successfully, and most generally end up halting themselves and calling for intervention. SRE has a number of philosophies and products in the domain of automation, some of which look more like generic rollout tools without particularly detailed modeling of higher-level entities, and some of which look more like languages for describing service deployment and so on at a very abstract level.
Work done in the latter tends to be more reusable and be more of a common platform than the former, but the complexity of our production environment sometimes means that the former approach is the most immediately tractable option. In fact, instead of having a system that has to have external glue logic, it would be even better to have a system that needs no glue logic at all , not just because internalization is more efficient although such efficiency is useful , but because it has been designed to not need glue logic in the first place.
Despite the best of intentions, attempting to more tightly couple the two turnup automation and the core system often fails due to unaligned priorities, as product developers will, not unreasonably, resist a test deployment requirement for every change.
Secondly, automation that is crucial but only executed at infrequent intervals and therefore difficult to test is often particularly fragile because of the extended feedback cycle. Cluster failover is one classic example of infrequently executed automation: failovers might only occur every few months, or infrequently enough that inconsistencies between instances are introduced.
The evolution of automation follows a path:. However, sometimes manual operations are unavoidable. There is additionally a subvariety of automation that applies changes not across the domain of specific system-related configuration, but across the domain of production as a whole. The first case study is about how, due to some diligent, far-sighted work, we managed to achieve the self-professed nirvana of SRE: to automate ourselves out of a job.
Because Ads data obviously has high reliability requirements, an SRE team was charged with looking after that infrastructure. From to , the Ads Database mostly ran in what we considered to be a mature and managed state.
For example, we had automated away the worst, but not all, of the routine work for standard replica replacements. We believed the Ads Database was well managed and that we had harvested most of the low-hanging fruit in terms of optimization and scale.
Unfortunately, this was accompanied by a significant new difficulty. A core operating characteristic of Borg is that its tasks move around automatically. Tasks commonly move within Borg as frequently as once or twice per week. This frequency was tolerable for our database replicas, but unacceptable for our masters.
At that time, the process for master failover took 30—90 minutes per instance. Simply because we ran on shared machines and were subject to reboots for kernel upgrades, in addition to the normal rate of machine failure, we had to expect a number of otherwise unrelated failovers every week.
This factor, in combination with the number of shards on which our system was hosted, meant that:. Therefore, our only choice was to automate failover. Actually, we needed to automate more than just failover. We graduated from optimizing our infrastructure for a lack of failover to embracing the idea that failure is inevitable, and therefore optimizing to recover quickly through automation.
While automation let us achieve highly available MySQL in a world that forced up to two restarts per week, it did come with its own set of costs. All of our applications had to be changed to include significantly more failure-handling logic than before.
Given that the norm in the MySQL development world is to assume that the MySQL instance will be the most stable component in the stack, this switch meant customizing software like JDBC to be more tolerant of our failure-prone environment. However, the benefits of migrating to MoB with Decider were well worth these costs.
Our failovers were automated, so an outage of a single database task no longer paged a human. The main upshot of this new automation was that we had a lot more free time to spend on improving other parts of the infrastructure. Such improvements had a cascading effect: the more time we saved, the more time we were able to spend on optimizing and automating other tedious work. Some might say that we had successfully automated ourselves out of this job. The hardware side of our domain also saw improvement.
Migrating to MoB freed up considerable resources because we could schedule multiple MySQL instances on the same machines, which improved utilization of our hardware.
Our team was now flush with hardware and engineering resources. This example demonstrates the wisdom of going the extra mile to deliver a platform rather than replacing existing manual procedures. The next example comes from the cluster infrastructure group, and illustrates some of the more difficult trade-offs you might encounter on your way to automating all the things. As it turned out, that was approximately the same frequency at which we turned up a new cluster.
Steps 4 and 6 were extremely complex. While basic services like DNS are relatively simple, the storage and compute subsystems at that time were still in heavy development, so new flags, components, and optimizations were added weekly. Some services had more than a hundred different component subsystems, each with a complex web of dependencies.
Failing to configure one subsystem, or configuring a system or component differently than other deployments, is a customer-impacting outage waiting to happen. In one case, a multi-petabyte Bigtable cluster was configured to not use the first logging disk on disk systems, for latency reasons. All of the Bigtable data was wiped, instantly. Thankfully we had multiple real-time replicas of the dataset, but such surprises are unwelcome.
Automation needs to be careful about relying on implicit "safety" signals. Early automation focused on accelerating cluster delivery. This approach tended to rely upon creative use of SSH for tedious package distribution and service initialization problems. This strategy was an initial win, but those free-form scripts became a cholesterol of technical debt. As the numbers of clusters grew, some clusters required hand-tuned flags and settings. As a result, teams wasted more and more time chasing down difficult-to-spot misconfigurations.
If a flag that made GFS more responsive to log processing leaked into the default templates, cells with many files could run out of memory under load. Infuriating and time-consuming misconfigurations crept in with nearly every large configuration change.
Benefits of Automation
The U. Edited by the security practitioner and author Lawrence Fennelly, this handbook gathers in a single volume the key information on each topic from eminent subject-matter experts. Taken together, this material offers a range of approaches for defining security problems and tools for designing solutions in a world increasingly characterized by complexity and chaos. The 5e adds cutting-edge content and up-to-the-minute practical examples of its application to problems from retail crime to disaster readiness.
The mechanization of farming practices throughout the world has revolutionized food production, enabling it to maintain pace with population growth except in some less-developed countries, most notably in Africa. Agricultural mechanization has involved the partial or full replacement of human energy and animal-powered equipment e. These two volumes are aimed at the following five major target audiences: University and College students Educators, Professional practitioners, Research personnel and Policy analysts, managers, and decision makers and NGOs. He is an authority on the physical properties of food and biological materials with particular reference to applications in food engineering and agricultural mechanization. In , he received an ASAE Paper Award in recognition of authorship of a contribution to agricultural engineering literature of exceptional merit dealing with the mechanical and physical properties of grasses. He is an authority on grain drying with particular reference to mathematical modeling and numerical simulation in two dimensions. He has taken a particular interest in computer applications in food and environmental engineering and has employed this expertise to enrich his extensive teaching and research portfolios. He co-edited the Proceedings of that Congress which were published by A. Paul McNulty , Patrick M. Strana
Advantages and disadvantages of automation
Related Terms: Inventory Control Systems. The advantages of these systems are numerous. They provide users with increased inventory control and tracking, including greater flexibility to accommodate changing business conditions. They also reduce labor costs, lowering necessary workforce requirements, increasing workplace safety, and removing personnel from difficult working conditions such as cold food storage environments.
Brown , James F. Elsevier , 9. Advances in Biomedical Engineering, Volume 4, is a collection of papers that deals with gas chromatography, mass spectroscopy and the analysis of minute samples, as well as the role of the government in regulating the production, usage, safety, and efficacy of medical devices. One paper reviews the use of mass spectrometry and computer technology in relation to gas-phase analytical methods based on gas chromatograph-mass spectrometer instruments and gas chromatograph-mass spectrometer-computer analytical systems.
For SRE, automation is a force multiplier, not a panacea. Of course, just multiplying force does not naturally change the accuracy of where that force is applied: doing automation thoughtlessly can create as many problems as it solves. Therefore, while we believe that software-based automation is superior to manual operation in most circumstances, better than either option is a higher-level system design requiring neither of them—an autonomous system. Or to put it another way, the value of automation comes from both what it does and its judicious application.
Advantages commonly attributed to automation include higher production rates and increased productivity, more efficient use of materials, better product quality, improved safety , shorter workweeks for labour, and reduced factory lead times. Higher output and increased productivity have been two of the biggest reasons in justifying the use of automation. Despite the claims of high quality from good workmanship by humans, automated systems typically perform the manufacturing process with less variability than human workers, resulting in greater control and consistency of product quality. Also, increased process control makes more efficient use of materials, resulting in less scrap. Worker safety is an important reason for automating an industrial operation. Automated systems often remove workers from the workplace, thus safeguarding them against the hazards of the factory environment.
Industrial robot Autonomous research robot Domestic robot. Home automation Banking automation Laboratory automation Integrated library system Broadcast automation Console automation Building automation. Automated attendant Automated guided vehicle Automated highway system Automated pool cleaner Automated reasoning Automated teller machine Automatic painting robotic Pop music automation Robotic lawn mower Telephone switchboard Vending machine. Automation is the technology by which a process or procedure is performed with minimal human assistance. Automation covers applications ranging from a household thermostat controlling a boiler, to a large industrial control system with tens of thousands of input measurements and output control signals. In control complexity, it can range from simple on-off control to multi-variable high-level algorithms. In the simplest type of an automatic control loop , a controller compares a measured value of a process with a desired set value, and processes the resulting error signal to change some input to the process, in such a way that the process stays at its set point despite disturbances.
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