Thus, if a favored project does not request resources, the worker could fetch tasks from other VC projects according to a priority list or any different kind of prioritization. In addition, a worker should always be able to contribute to more than one project at a time. It is then up to the worker to decide to which projects it would like to contribute. The vision is that a worker should only know one IP address of the global VC network to gain information about all other existing and available VC projects and endpoints. Therefore, all VC projects must share their master endpoints in a standardized way. In short, this perspective paper proposes to transform the current situation of isolated VC project networks into a more meshed variant (see Figure 9). Thus, localized decisions per service regarding programming languages, libraries, frameworks, and more are possible and enable best-of-breed approaches.
Because services can be developed and operated by different teams, they not only have a technological but also an organizational impact. Different services are independently scalable based on actual request stimuli. ” Faster delivery, improved scalability, and greater autonomy are often mentioned benefits of microservice architectures.
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This architectural style increases software agility because each microservice becomes an independent unit of development, deployment, operations, versioning, and scaling. Each module-each microservice-is implemented and operated as a small yet independent system, offering access to its internal logic and data through a well-defined network interface. Microservices form “an approach to software and systems architecture that builds on the well-established concept of modularization but emphasizes technical boundaries.
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It took all of April to make full use of the provided volunteer resources (see Figure 2). In cloud computing, we would say that was not elastic enough. The control plane of the project could not scale-out fast enough. The processing pipelines were empty, and the master nodes needed hardware upgrades to handle all these new processing nodes. We first checked whether we had misconfigured the machines, but our machines were operating correctly. However, the effect was that our machines ran at ridiculous low usage rates. In that phase, many teams and individual contributors boarded and the network grew massively. At the end of March, this was even more severe. In other words, we had assigned two machines, but, on average, only one was used. Our machines, although being configured to run at full power, had only usage rates between 40% or 50%. However, these data were so-so (see Figure 2). Nevertheless, we also looked at the usage statistics of our provided machines gathered by our virtualization cluster.
We were impressed by our students and by these numbers. Promising technologies might be containers, serverless architectures, image registries, distributed service registries, and all have one thing in common: They already exist and are all tried and tested in large web-scale deployments.
It turns out that the disclosed resource sharing shortcomings of volunteer computing could be addressed by technologies that have been invented, optimized, and adapted for entirely different purposes by cloud-native companies like Uber, Airbnb, Google, or Facebook. Therefore, this perspective paper reviews the current state of volunteer and cloud computing to analyze what both domains could learn from each other. Notably, efficient resource sharing has been optimized throughout the last ten years in cloud computing. This perspective paper investigates how the resource sharing of future volunteer computing projects could be improved. Consequently, the largest supercomputer had significant idle times. Sadly, processing resources assigned to the corresponding project cannot be shared with other volunteer computing projects efficiently. From close to scratch, the COVID-19 pandemic created the largest volunteer supercomputer on earth.
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