Towards a decentralized architecture for optimization

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Braun and A. Moore and I. BraunA. MooreI. Kroo Published Engineering.

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Collaborative optimization is a new design architecture specifically created for large-scale distributed-analysis applications. In this approach, a problem is decomposed into a user-defined number of subspace optimization problems that are driven towards interdisciplinary compatibility and the appropriate solution by a system-level coordination process.

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Sanchez, D. Morrice Adeel Khalid Nomenclature Introduction. Braun, P. Gage, I.Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

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Comparison – Centralized, Decentralized and Distributed Systems

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Use of the Collaborative Optimization Architecture for Launch Vehicle Design

Hierarchical Decentralized Optimization Architecture for Economic Dispatch: A New Approach for Large-Scale Power System Abstract: In this paper, a new hierarchical decentralized optimization architecture is proposed to solve the economic dispatch problem for a large-scale power system. Conventionally, such a problem is solved in a centralized way, which is usually inflexible and costly in computation. In contrast to centralized algorithms, in this paper we decompose the centralized problem into local problems.

Each local generator only solves its own problem iteratively, based on its own cost function and generation constraint. An extra coordinator agent is employed to coordinate all the local generator agents.

Besides, it also takes responsibility to handle the global demand supply constraint based on a newly proposed concept named virtual agent.

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In this way, different from existing distributed algorithms, the global demand supply constraint and local generation constraints are handled separately, which would greatly reduce the computational complexity. In addition, as only local individual estimate is exchanged between the local agent and the coordinator agent, the communication burden is reduced and the information privacy is also protected.

It is theoretically shown that under proposed hierarchical decentralized optimization architecture, each local generator agent can obtain the optimal solution in a decentralized fashion.

Article :. Date of Publication: 05 September DOI: Need Help?In this article, we will try to understand and compare different aspects of centralized, decentralized and distributed systems.

We start with centralized systems because they are the most intuitive and easy to understand and define. This is the most commonly used type of system in many organisations where client sends a request to a company server and receives the response.

Figure — Centralised system visualisation. Example — Wikipedia. Consider a massive server to which we send our requests and the server responds with the article that we requested. This search term is sent as a request to the Wikipedia servers mostly located in Virginia, U. A which then responds back with the articles based on relevance. In this situation, we are the client node, wikipedia servers are central server. Scaling — Only vertical scaling on central server is possible.

Horizontal scaling will contradict the single central unit characteristic of this system of a single central entity. Architecture of Centralized System — Client-Server architecture. The central node that serves the other nodes in the system is the server node and all the other nodes are the client nodes.

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These are another type of systems which have been gaining a lot of popularity, primarily because of the massive hype of Bitcoin.

Now many organisations are trying to find the application of such systems. In decentralized systems, every node makes its own decision. The final behavior of the system is the aggregate of the decisions of the individual nodes. Note that there is no single entity that receives and responds to the request. Figure — Decentralised system visualisation.

Example — Bitcoin. Lets take bitcoin for example because its the most popular use case of decentralized systems.A complexity that includes not only duration but creation, not only being but becoming, not only geometry but ethics.

It is not the answer we are after, but only how to ask the question. Le Guin, The Dispossessed 1. The late science fiction author Ursula Le Guin describes a dynamic framework of knowledge that prioritizes reflection and self-organization.

The concept of the Singularity imagines human intelligence and knowledge narrowing to, as the term itself suggests, a single point.

The standard model of particle physics while incomplete, it is the most complete model we have for describing the observed physical cosmos suggests that the entire physical cosmos was once a single point, but what came before, we cannot say. The math breaks down. And what comes next, it stands to reason, may be up to the machines to discover without us. If the Singularity indeed connotes a narrowing, in which human minds and actions begin to seem obsolete or worse, the subjects of new oppressionsthen we have ample reason to be cautious, and indeed pessimistic.

But artificial intelligence, as Ito observes, might also augment the human capacity for reason, establishing a symbiosis that enriches the entire ecosystem. And they are still just tools. Alongside their capacity for computation is their role in enhancing coordinationor the ability of humans to communicate, transact, and investigate productively, together.

Peer-to-peer digital tools such as blockchain technology offer an opportunity for an opening, a dispersion of power and information, and profound possibilities for collaboration on as-yet-unseen scales. Peer-to-peer networks are participatory systems that resist control by a single or outside power.

The participants establish agreed-on rules that evolve as need or complexity arises. By distributing power and value across global systems, the exchange of information and value can become more efficient, equitable, and open: more collaborative. Blockchain technology arguably could make the digital universe look more like a complex adaptive system of the kinds found in nature, if it becomes the best version of itself.

This is a big if, and it will be many years in the future, if it comes to pass at all. This essay will explore the possibilities for blockchain to facilitate for the first time complex coordination between strangers on a global scale, a human-machine ecosystem that emerges to bring about abundance without excess, multiplicity without superfluity, complexity without chaos.

No single tool or technology ever can be. First, the financial monopolization by Silicon Valley of all the data and value contained in the Web 2. Third, blockchains are not controlled by a central authority, but by the entire network of participants, who establish the rules for participation themselves and can elect to evolve the system according to consensus; this makes them censorship-resistant and inherently more elastic than most other decision-making mechanisms for large groups of people.

More importantly, blockchain-supported technologies can potentially facilitate decentralized coordination and alignment of human incentives on a scale that only top-down, command-and-control structures previously could.

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Decentralization is the process of dispersing functions and power away from a central location or authority. In a decentralized architecture, it is difficult if not impossible to discern a particular center. The World Wide Web was originally developed as a decentralized platform. Blockchain technologies such as Bitcoin and Ethereum are examples of decentralized architectures and systems. The challenge of coordinating groups of humans and getting them to behave in productive, peaceable ways has been the central story of civilization.

A common claim about the potential for blockchain technology to facilitate social decentralization is that it could move power from centers—major metropolises, governments, large hierarchical organizations and companies—to the edges. Decentralization is also a social challenge: Everyone alive on Earth today has lived under the paradigm of hierarchy and top-down command and control, so we tend to default to them as organizational modes. The temptation to return to these familiar modes of coordination is great at times, and the transition to a less centralized social paradigm with natively digital tools will need to be a conscious one, made many times over, by the participants in the network.

towards a decentralized architecture for optimization

They are not working toward a singular vision or goal, an endpoint of optimization—a Singularity—as much as they are searching for productive pathways and ways to transact freely. By this logic, a network of people trying to build software tools to facilitate decentralization ought to be decentralized themselves: diverse, interacting randomly, coalescing around projects, conducting experiments, cultivating or abandoning them in a fluid state of co-relation.

An emergent ecosystem arises from self-organizing group behaviors. The cognitive challenge of accepting the fundamental impossibility of designing or mapping such a system, of allowing feedback loops their space to loop, and certain objectives sometimes to fail, is significant.

towards a decentralized architecture for optimization

Our brains are inclined to simplify or abstract hyper-complex systems for the sake of coherence. They want to detect signals in the noise, even if they are often false signals.

Online Learning and Optimization in Distributed Energy Systems: Some Problems and Opportunities

But complex adaptive systems are inherently capable of self-regulation and constant evolution.To browse Academia. Skip to main content. Log In Sign Up. Download Free PDF. Towards a decentralized architecture for optimization Marco Biazzini. Mauro Brunato. Alberto Montresor. Towards a decentralized architecture for optimization.

Instead of re- wants to exploit their idle periods to perform optimization lying on custom hardware like dedicated parallel machines tasks. In such systems, high level of churn may be expected: or clustersour approach exploits, in a peer-to-peer fash- nodes may join and leave the system at will, for example ion, the computing and storage power of existing, off-the- when users start or stop to work at their workstations.

Contributions of this paper are Such scenario is not unlike a Grid system [12]; a reason- a description of the generic framework, together with a first able approach could thus be to collect a pool of independent instantiation based on particle swarm optimization PSO.

This can be done either using a centralized scheduler, benchmark functions. An interesting question is whether it is possible to come up with an alternative approach, where a distributed algo- rithm spreads the load of single optimization task among a 1 Introduction group of nodes, in a robust, decentralized and scalable way.

We can rephrase the question as follows: can we make a better use of our distributed resources by making them co- Distributed optimization has a long research history [14]. Two possible mo- Most of the previous work assumes the availability of either tivations for such approach come to mind: we want to obtain a dedicated parallel computing facility, or, in the worst case, a more accurate result by a specific deadline focus on qual- specialized clusters of networked machines that are coor- ityor we are allowed to perform a predefined amount of dinated in a centralized fashion master-slave, coordinator- computation over a function and we want to obtain a quick cohort, etc.

While these approaches simplify manage- answer focus on speed. The idea is Global optimization algorithms are stochastic by na- to adopt recent results in the domain of large-scale decen- ture; in particular, the first evaluation is not driven by tralized systems and peer-to-peer P2P systems, where a prior information, so the earliest stages of the search large collection of loosely-coupled machines cooperate to require some random decision. Different runs of the achieve a common goal.

Instead of requiring a specialized same algorithm can evolve in a very different way, so infrastructure or a central server, such systems self-organize that parallel independent execution of identical algo- themselves in a completely decentralized way, avoiding sin- rithms with different random seeds yields a better ex- gle points of failure and performance bottlenecks.

The ad- pected outcome w. In order for such approach to be effi- In these equations, rand is a random number in the cient, the cost of sharing global information should not range [0, 1], while c1 and c2 are learning factors.

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The pseudo code of the procedure is as uations performed simultaneously. Originated in the context of databases [1], gossip end protocols have proven to be able to deal with the high lev- els of unpredictability associated with P2P systems. Apart Particle speeds on each dimension are bounded to a from the original goal of information dissemination mes- maximum velocity vmaxispecified by the user.

towards a decentralized architecture for optimization

The rest of the paper is organized as follows. Section 2 PSO on incomplete topologies The above-described ver- provides the background of the paper. Section 3 introduces sion of PSO assumes that all particles agree on the global the design of our framework and the decentralized PSO al- best point g found so far, and is often referred to as the gorithm.Search engines are among the most important applications or services on the web.

Most existing successful search engines use a centralized architecture and global ranking algorithms to generate the ranking of documents crawled in their databases, for example, Google's PageRank.

However, global ranking of documents has two potential problems: high computation cost, and potentially poor rankings. Both of the problems are related to the centralized computation paradigm. We propose a decentralized architecture to solve the problem in a P2P fashion. We identify three sub-problems in the big picture: a logical framework for ranking computation, an efficient way of computing dynamic local ranking, and a cooperative approach that bridges distributed local rankings and collective global ranking.

In the paper we summarize the current knowledge and existing solutions for distributed IR systems, and present our new ideas. We also provide initial results, demonstrating that the use of such an architecture can ameliorate the above-mentioned problems for Web and P2P search engines. Search engines for large scale distributed systems, e. The state-of-the-art technologies of dealing with these two problems have big limitations such as high computation cost, potentially poor rankings, etc.

The focus of my PhD thesis work is to develop a decentralized architecture for efficiently searching and ranking documents with returned results of high quality. The work covers three main issues in the big picture of my new decentralized search architecture: firstly, a mechanism inspired by Swarm Intelligence of obtaining more dynamic and more semantically meaningful rankings of documents local to Web sites; secondly, a ranking algebra which provides the algebraic ground of computing document rankings; and finally, the idea of global Web site ranking which is the key to establish the global Web document ranking in a decentralized way, and a decentralized algorithm of computing the global Web site ranking.

Substantial results have been achieved and further work is going on smoothly.

Towards a decentralized architecture with FOAM + the Blockchain

Then we see why these models do not fit well the Web IR systems. The classical model for a centralized IR system is: where is a document collection, is the set of queries, and is the set of mappings which assign every query to a set of relevant documents. Many IR systems use a thesaurus to expand a user query by including synonyms of the keywords in the query.

An example of a valid generalization is. A partial ordering of documents can be defined based on the concept of generalization. Let indicate the list of unique, non-mutual synonymous keywords 2 of document. Partial ordering is defined as:. This is a partial ordering because two documents with terms that have no relationship between any pairs of terms will be unordered. What is mainly used in query processing of IR systems is the so-called property of this model.

An IR system is only when the documents corresponding to a general query must be a superset of all documents corresponding to a more specific query where :. The advantage of being is that, if two queries and are presented such thatit is not necessary to retrieve from the entire document collection for each query. Rather the system can obtain the answer set forand then simply search to obtain the.

A model of decentralized IR can be built by partitioning the centralized IR system into local IR systemswhere are the individual thesaurus, document collection, set of queries, and mapping from queries to document sets of each local IR system. The whole distributed IR system can be redefined as where, and.

Moreover, the partial ordering at each site only pertains to the queries at site. As for each query in the grand system, the document collection for a query contains the documents whose descriptors are at least as specific as the query. Based on this model, the hierarchy represented by is established and partitioned among the different sites. A local site at a lower hierarchy is called a of a higher one if it satisfies several specific criteria.

The local responses are afterwards sent back to the originating site where the final result set is combined from the local ones. For example, if is a ofthen the query results at site contain those found in :.Even on a digitally-native platform like this, architecture in the digital age appears as a representation more than something immanent to clicking, scrolling, swiping. Browsing the web, for example, has spatial ramifications — as does hosting a library of architecture renderings on an overheated server.

But, for a growing contingency of architects, the form of architecture, as a field and object, may be on the brink of a radical mutation. Blockchain technologywhich drives digital currencies like Bitcoin and smart contract platforms like Ethereumnot only offers the possibility of entirely reorganizing the profession, but is itself already a spatial practice.

They helped explain the fundamentals of this promising cryptographic technology, in terms of both its architectural and broader relevance, and detailed their own compelling work with it. As an introduction, could you briefly explain how blockchain technologies work in layman's terms?

The blockchain is a peer-to-peer time stamping technology first introduced by the cryptographic digital currency Bitcoin. The technology is essentially a decentralized public ledger that contains a record of all transactions and activity that occurred on the network in chronological order.

Every node on the network comes into a consensus about the system-wide order of events within a given time interval, and simultaneously publishes this information within a cryptographic block, which is then linked to all previous blocks to form a chain. Thus, the blockchain works as a shared public ledger, which all who use the network can access. The information contained on the blockchain is secured by a decentralized network of powerful computers called miners, which anyone can set up and run.

When a calculation is solved the miner is rewarded with a block. This block contains all network transactions since the last block was found on the network.

A block is found every 10 minutes by one of the miners on the network who receives the intrinsic token, Bitcoin, as a reward for its work, and the block is then added to the blockchain. This serves as the economic incentive for an actor to spend money on the electricity it takes to operate a mining computer. New digital protocols, such as Ethereum, have developed their own blockchains that are able to be programmed for decentralized applications comprised of smart contracts.

These smart contracts make it possible for computer code to execute automatically and transparently, operating as a programmable world computer. Ethereum has a block-time of 12 seconds, as opposed to Bitcoins 10 minute blockchain, allowing transactions to be published much faster. In general, this method of recordkeeping for urban data is being developed for environmental sensors and internet of things devices. How can this technology be implemented in an architectural context?

This technology should already be viewed in an architectural context. The process of securing the blockchain via mining computers has immediate spatial ramifications.


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