{"id":37790,"date":"2019-10-31T22:19:46","date_gmt":"2019-10-31T19:19:46","guid":{"rendered":"https:\/\/prohoster.info\/blog\/prikladnye-tehnologii-na-ruinah-blokchejn-lihoradki-ili-o-prakticheskoj-polze-raspredeleniya-resursov\/"},"modified":"2019-10-31T22:19:46","modified_gmt":"2019-10-31T19:19:46","slug":"prikladnye-tehnologii-na-ruinah-blokchejn-lihoradki-ili-o-prakticheskoj-polze-raspredeleniya-resursov","status":"publish","type":"post","link":"https:\/\/prohoster.info\/en\/blog\/administrirovanie\/prikladnye-tehnologii-na-ruinah-blokchejn-lihoradki-ili-o-prakticheskoj-polze-raspredeleniya-resursov","title":{"rendered":"Applied Technologies on the Ruins of Blockchain Fever or About the Practical Benefits of Resource Distribution","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>In recent years, news feeds have been flooded with reports of new types of distributed computing networks emerging seemingly out of nowhere, tackling (or rather trying to tackle) a variety of tasks \u2014 making cities smart, saving the world from copyright violators, or conversely, secretly transmitting information or resources, escaping state control in one sphere or another. Regardless of the area, they all share a number of common features, stemming from the fact that the fuel for their growth has been algorithms and methodologies that surfaced widely during the recent boom in cryptocurrencies and related technologies. Probably every third article on specialized resources at that time had the word \u201cblockchain\u201d in its title \u2014 discussions of new software solutions and economic models became a dominant trend, overshadowing other areas of application for distributed computing systems. <\/p>\n<p>At the same time, visionaries and professionals recognized the essence of the phenomenon: mass distributed computing, related to the construction of networks composed of a large number of disparate and heterogeneous participants, has reached a new level of development. It\u2019s enough to cast aside the hype-driven topics and look at the matter from a different angle: all these networks, assembled from vast pools containing thousands of isolated and diverse participants, did not emerge by themselves. Crypto movement enthusiasts have managed to resolve complex issues of data synchronization and resource and task distribution in a new way, enabling the assembly of such a mass of equipment and creating a new ecosystem designed to solve one narrow-focused task.<br \/>\n<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><br \/>\nOf course, this did not escape the teams and communities involved in the development of free distributed computing, and new projects were quick to emerge.<br \/>\nHowever, despite the significant increase in the volume of available information on developments in the field of network construction and hardware utilization, creators of promising systems will have to address serious challenges.<\/p>\n<h3>The first of these, as strange as it may sound, is the problem of choosing a direction.<\/h3>\n<p>\nThe direction may be correct, or it could lead to a dead end \u2014 there's no escaping that; centralized supplies of visionaries in the IT community are still lagging behind. However, a choice must be made to avoid falling into the traditional trap, which is that the team takes on too broad a scope and attempts to create yet another unspecialized project in wide-ranging distributed computing right from the start. It seems that the workload isn't so daunting; primarily, it involves just applying existing developments: connecting nodes into a network, adapting topology detection algorithms, data exchange, and consistency control, implementing node ranking methodologies and consensus finding, and, of course, simply creating your own query language and its entire linguistic and computational environment. The idea of a universal mechanism is very tempting and constantly resurfaces in one sphere or another, but the result still tends to be one of three outcomes: the created solution either turns out to be a limited prototype with a backlog full of hanging 'ToDos,' or it becomes an unusable monster, ready to drag anyone who touches it into a foul 'Turing swamp,' or simply dies due to the project being pulled in an obscure direction and collapsing under the weight of a 'swank, crab, and pike' scenario.<\/p>\n<p>Let\u2019s avoid making foolish mistakes and choose a direction that has a clear set of tasks and is well-suited for the distributed computing model. One can understand people who try to do everything at once \u2014 there is certainly a lot to choose from. Much looks extremely interesting both from an R&amp;D and development perspective, as well as from an economic standpoint. With the help of a distributed network, one can:<\/p>\n<ul>\n<li>Train neural networks <\/li>\n<li>Process signal streams <\/li>\n<li>Calculate protein structures <\/li>\n<li>Render three-dimensional scenes <\/li>\n<li>Model hydrodynamics <\/li>\n<li>Test trading strategies for stock exchanges <\/li>\n<\/ul>\n<p>\nTo avoid getting carried away with listing interesting things that parallel well, let\u2019s choose distributed rendering as our next topic.<\/p>\n<p>Distributed rendering on its own is certainly not a new phenomenon. Existing rendering toolkits have long supported load distribution across different machines, without which living in the twenty-first century would be quite gloomy. However, it is important not to think that this topic has been exhausted and there is nothing left to explore \u2014 we will examine a specific current issue: the creation of a tool for forming a rendering network.<\/p>\n<p>Our rendering network is a combination of nodes that need to perform rendering tasks, with nodes that have spare computational resources for processing rendering. Resource owners will connect their stations to the rendering network to receive and execute rendering tasks using one of the supported rendering engines. Task providers will interact with the network as if it were a cloud, autonomously handling resource distribution, correctness control, risk management, and other issues.<\/p>\n<p>Thus, we will consider the creation of a framework that should support integration with a set of popular rendering engines and include components providing tools for organizing a network of heterogeneous nodes and managing task flow.<\/p>\n<p>The economic model for the existence of such a network is not fundamentally significant, so we will adopt a scheme similar to that used in computations in cryptocurrency networks \u2014 resource consumers will send tokens to the providers performing the rendering work. It is much more interesting to understand what properties the framework should have, for which we will consider the main interaction scenario among network participants.<\/p>\n<p>There are three parties involved in the network interaction: resource provider, task provider, and network operator (also referred to as the management center, network, etc. throughout the text).<\/p>\n<p>The network operator provides the resource provider with a client application or an operating system image with a deployed software set that the provider will install on the machine whose resources they want to offer. This will be accessible through a web interface personal cabinet that allows them to set resource access parameters and remotely manage their server landscape: control hardware parameters, perform remote configuration, and reboot.<\/p>\n<p>The network management system analyzes the hardware and specified access parameters upon connecting a new node, ranks it by assigning a specific rating, and places it in the resource registry. In the future, to manage risk, the activity parameters of the node will be analyzed, and the node's rating will be adjusted to ensure network stability. Nobody wants their scene to be sent for rendering on powerful but frequently overheating graphics cards, right?<\/p>\n<p>A user who needs to render a scene can take two paths: either upload the scene to the network repository via the web interface or connect their modeling package or installed renderer to the network using a plugin. In this case, a smart contract is initiated between the user and the network, with the standard condition for completion being the generation of the scene computation result by the network. The user can monitor the task execution process and manage its parameters through the web interface of their personal cabinet.<\/p>\n<p>The task is received at <a class=\"wpil_keyword_link\" href=\"https:\/\/prohoster.info\/en\/server\/\"   title=\"server\" data-wpil-keyword-link=\"linked\">server<\/a>, where the volume of the scene and the number of resources requested by the task initiator are analyzed. After that, the total volume is decomposed into parts adapted for computation on the dedicated quantity and type of resources provided by the network. The overall idea is that visualization can be broken down into many small tasks. Engines take advantage of this by distributing these tasks among multiple resource providers. The simplest way is to render small parts of the scene, called segments. When each segment is ready, the local task is considered complete, and the resource moves on to perform the next unresolved task.<\/p>\n<p>Thus, for the renderer, there is no real difference whether calculations are performed on a single machine or on a grid of multiple individual computing stations. Distributed rendering simply adds more cores to the pool of resources used for the task. It retrieves all the data necessary for rendering a segment over the network, computes it, sends this segment back, and moves on to the next task. Before entering the common network pool, each segment is assigned a set of metadata, allowing worker nodes to select the most suitable computing tasks for them.<\/p>\n<p>The tasks of segmentation and distribution of calculations must be solved not only from the perspective of optimizing execution time but also from the standpoint of optimal resource usage and energy conservation, as this affects the economic efficiency of the network. In case of an unsuccessful solution, it would be more feasible to assign a miner to the node or turn it off to avoid noise and unnecessary electricity consumption.<\/p>\n<p>However, let's return to the process. When a task is received, a smart contract is also formed between the pool and the node, which is executed upon the correct computation of the task result. As a result of the contract execution, the node may receive a reward in one form or another.<\/p>\n<p>The control center monitors the task execution process by collecting computation results, sending incorrect ones for reprocessing, and ranking the queue, tracking the standard execution time for the task (to ensure that the last segment is not neglected by any node).<\/p>\n<p>The results of the computations undergo a compositing stage, after which the user receives the rendering results and the network may receive a reward.<\/p>\n<p>Thus, the functional composition of the landscape framework intended for building distributed rendering systems emerges:<\/p>\n<ol>\n<li>User personal accounts with web access <\/li>\n<li>Software package for installation on nodes <\/li>\n<li>Management systems:\n<ul>\n<li>Access control subsystem <\/li>\n<li>Task decomposition subsystem for rendering <\/li>\n<li>Task distribution subsystem <\/li>\n<li>Compositing subsystem <\/li>\n<li>Server landscape and network topology management subsystem <\/li>\n<li>Logging and auditing subsystem <\/li>\n<li>Learning expert subsystem <\/li>\n<li>Rest API or another interface for external developers <\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p><\/p>\n<h3>What do you think? What questions does this topic raise, and what answers are you interested in?<\/h3>\n<p>Source: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/post\/466139\/\">habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u0412 \u043f\u043e\u0441\u043b\u0435\u0434\u043d\u0438\u0435 \u0433\u043e\u0434\u044b \u043d\u043e\u0432\u043e\u0441\u0442\u043d\u044b\u0435 \u043b\u0435\u043d\u0442\u044b \u043d\u0430\u0432\u043e\u0434\u043d\u0438\u043b\u0438 \u0441\u043e\u043e\u0431\u0449\u0435\u043d\u0438\u044f \u043e \u043f\u043e\u044f\u0432\u043b\u044f\u044e\u0449\u0438\u0445\u0441\u044f \u0431\u0443\u043a\u0432\u0430\u043b\u044c\u043d\u043e \u0438\u0437 \u043d\u0438\u043e\u0442\u043a\u0443\u0434\u0430 \u0440\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u043d\u044b\u0445 \u0432\u044b\u0447\u0438\u0441\u043b\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0445 \u0441\u0435\u0442\u044f\u0445 \u043d\u043e\u0432\u043e\u0433\u043e \u0442\u0438\u043f\u0430, \u0440\u0435\u0448\u0430\u044e\u0449\u0438\u0445 (\u0442\u043e\u0447\u043d\u0435\u0435, \u043f\u044b\u0442\u0430\u044e\u0449\u0438\u0445\u0441\u044f \u0440\u0435\u0448\u0438\u0442\u044c) \u0441\u0430\u043c\u044b\u0435 \u0440\u0430\u0437\u043d\u043e\u043e\u0431\u0440\u0430\u0437\u043d\u044b\u0435 \u0437\u0430\u0434\u0430\u0447\u0438 \u2014 \u0441\u0434\u0435\u043b\u0430\u0442\u044c \u0433\u043e\u0440\u043e\u0434 \u0443\u043c\u043d\u044b\u043c, \u0441\u043f\u0430\u0441\u0442\u0438 \u043c\u0438\u0440 \u043e\u0442 \u043d\u0430\u0440\u0443\u0448\u0438\u0442\u0435\u043b\u0435\u0439 \u0430\u0432\u0442\u043e\u0440\u0441\u043a\u0438\u0445 \u043f\u0440\u0430\u0432 \u0438\u043b\u0438 \u043d\u0430\u043e\u0431\u043e\u0440\u043e\u0442, \u0442\u0430\u0439\u043d\u043e \u043f\u0435\u0440\u0435\u0434\u0430\u0442\u044c \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u0438\u043b\u0438 \u0440\u0435\u0441\u0443\u0440\u0441\u044b, \u0441\u0431\u0435\u0436\u0430\u0442\u044c \u0438\u0437-\u043f\u043e\u0434 \u043a\u043e\u043d\u0442\u0440\u043e\u043b\u044f \u0433\u043e\u0441\u0443\u0434\u0430\u0440\u0441\u0442\u0432\u0430 \u0432 \u0442\u043e\u0439 \u0438\u043b\u0438 \u0438\u043d\u043e\u0439 \u0441\u0444\u0435\u0440\u0435. \u0412\u043d\u0435 \u0437\u0430\u0432\u0438\u0441\u0438\u043c\u043e\u0441\u0442\u0438 \u043e\u0442 \u0441\u0444\u0435\u0440\u044b, \u0432\u0441\u0435 [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-37790","post","type-post","status-publish","format-standard","hentry","category-administrirovanie"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.1.1 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u0412 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