Decentralized Management Framework Research Articles

Using Images to Detect, Plan, Analyze, and Coordinate a Smart Contract in Construction

Reliable construction workflow relies on timely discovery, analysis, and checking of compliance with contract terms, which are time consuming and inefficient tasks. Smart contracts enabled by blockchain technology have demonstrated promise in addressing the inefficiencies of data communications due to their merits of traceability, immutability, transparency, and self-enforceability. However, a smart contract’s inability to interact with real-world data is the main issue that impedes further implementation. Today’s increasing availability of as-built data provides automatic condition assessments that have great potential to automate smart contract executions. This research area is uncharted territory for the industry. This research selects a case study to present an automatic decentralized management framework by exploring image-based deep learning solutions to automate and decentralize the conditioning of smart contract executions enabled by a web3.js-based decentralized blockchain application. It was found that the model can automate management intelligence with minimal workflow interruptions by timely identification of bottleneck activities and enforcement of mitigation strategies. Project managers can use the blockchain prototype to enhance information sharing, remove key risks, and enable a reliable workflow with minimal management efforts.

Decentralized Management of Commercial HVAC Systems

With the growth of commercial building sizes, it is more beneficial to make them “smart” by controlling the schedule of the heating, ventilation, and air conditioning (HVAC) system adaptively. Single-building-based scheduling methods are more focused on individual interests and usually result in overlapped schedules that can cause voltage deviations in their microgrid. This paper proposes a decentralized management framework that is able to minimize the total electricity costs of a commercial microgrid and limit the voltage deviations. The proposed scheme is a two-level optimization where the lower level ensures the thermal comfort inside the buildings while the upper level consider system-wise constraints and costs. The decentralization of the framework is able to maintain the privacy of individual buildings. Multiple data-driven building models are developed and compared. The effect of the building modeling on the overall operation of coordinated buildings is discussed. The proposed framework is validated on a modified IEEE 13-bus system with different connected types of commercial buildings. The results show that coordinated optimization outperforms the commonly used commercial controller and individual optimization of buildings. The results also show that the total costs are greatly affected by the building modeling.

G2G

Grid systems integrate distributed resources to form self-organization and self-management autonomies. With the widespread development of grid systems around the world, grid collaboration for large-scale computing has become a prevalent research topic. In this paper, the authors propose a meta-grid framework, named the Grid-to-Grid (G2G) framework, to harmonize autonomic grids in realizing a grid federation. The G2G framework is a decentralized management framework that is built on top of existing autonomic grid systems. This paper further adopts a super-peer network in a separate layer to coordinate distributed grid systems. A super-peer overlay network is constructed for communication among super-peers, thus enabling collaboration among grid systems. This study proposes the G2G framework for use in a Grid-to-Grid federation and implements a preliminary system as a demonstration. Experimental results show that the proposed meta-grid framework can improve system performance with little overhead.

A probabilistic scheduling heuristic for computational grids

Computational grids are large scale distributed networks of peer clusters of computing resources bounded by a decentralized management framework for the purpose of providing computing services, called grid services. The scheduling problem consists in finding the clusters that host the required set of grid services with a sufficient available capacity to handle user service requests in compliance with some specified quality of service. The interplay of intermittent resource participation, resource load dynamics, network latency and processing delay, and random subsystem failures creates a ubiquitous uncertainty on the state of the grid capacity to handle user requests. In addition to the need to account for this uncertainty, the scheduling strategy has to be decentralized since computational grids span distinct management domains. In this paper, we propose a decentralized scheduling strategy that views the dynamics of the grid service capacity as a stochastic process modeled by a Markov chain. The proposed scheduling scheme uses this model to predict the future local availability of resources. This is consolidated by a confidence model that approximates the future ability of peer clusters to successfully handle delegated service requests. The scalability of the proposed scheduling strategy is illustrated through simulation.