Grid Agent Research Articles

Abstract 975: A multiscale computational model for spatio-temporal tumor immune response

Abstract When the immune system responds to tumor development, patterns of immune infiltrates emerge, highlighted by expression of immune checkpoint-related molecules such as PD-L1 on cancer cells and its receptor PD-1 on cytotoxic T cells. Pre-treatment tumor spatial heterogeneity could bear information on intrinsic characteristics of the tumor lesion for individual patient, and thus has the potential to comprise biomarkers for anti-tumor therapeutics. We developed a systems biology computational multiscale agent-based model to capture the interactions between immune cells and cancer cells during tumor progression. Cytotoxic T cells and cancer cells are modeled as free-moving agents in a 3-dimensional grid, where each cell acts in response to its local microenvironment and carries out functions such as division, apoptosis, cytotoxic killing and switching between states with different PD-1 or PD-L1 expression levels. Subsequently, we analyzed the emergent behavior of tumor progression by looking at all these local interactions as a whole. Using this model, we are able to reproduce temporal dynamics of cytotoxic T cells and cancer cells during general tumor progression, as well as 3-dimensional spatial distributions of these cells over the time course of the simulation. By varying the characteristics of the neoantigen profile of individual patients, such as mutational burden and immunogenicity, a spectrum of pre-treatment spatial patterns of PD-1/PD-L1 expression is generated in our simulations, resembling immune-architectures obtained via immunohistochemistry from patient biopsies. We evaluate potential prognostic biomarkers by correlating these spatial characteristics with in silico treatment results with immune checkpoint inhibitors. Simulation results demonstrate that the percentage of PD-L1 positive cancer cells which are not in close proximity of the tumor boundary or vasculature is more indicative of successful anti-PD1/anti-PD-L1 treatment. Our findings suggest that tumor spatial heterogeneity, especially its immune-architecture, reflects the course of tumor progression as well as patient-specific properties, and is thus likely to carry important information about tumor susceptibility to treatment such as with immune checkpoint inhibitors. We demonstrated how prognostic biomarkers could be realistically simulated in a general cancer scenario. The model is further refined for use to predict treatment/biomarker combinations in specific cancer types. Citation Format: Chang Gong, Oleg Milberg, Bing Wang, Paolo Vicini, Rajesh Narwal, Lorin Roskos, Aleksander S. Popel. A multiscale computational model for spatio-temporal tumor immune response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 975. doi:10.1158/1538-7445.AM2017-975

Multiagent‐Based Electric Power Interchange between Microgrids

SUMMARYIn this paper, we propose a multiagent‐based electric power interchange method between microgrids (MGs). The proposed multiagent system consists of eight types of agent: a single micro virtual power plant (MVPP) agent , several MG controller agents (MGCs), several load agents, several gas turbine (gas engine) agents, several photovoltaic generation agents, several wind turbine generation agents, several battery agents, and a single grid agent. In the proposed system, the MVPP plays an important role, facilitating electric power exchange between MGCs. Simulation results suggest that the proposed multiagent system could improve the load factor and reduce the MG operation cost.

A novel approach to optimize workflow in grid-based teleradiology applications

Background and objectiveThis study proposes an infrastructure with a reporting workflow optimization algorithm (RWOA) in order to interconnect facilities, reporting units and radiologists on a single access interface, to increase the efficiency of the reporting process by decreasing the medical report turnaround time and to increase the quality of medical reports by determining the optimum match between the inspection and radiologist in terms of subspecialty, workload and response time. MethodsWorkflow centric network architecture with an enhanced caching, querying and retrieving mechanism is implemented by seamlessly integrating Grid Agent and Grid Manager to conventional digital radiology systems. The inspection and radiologist attributes are modelled using a hierarchical ontology structure. Attribute preferences rated by radiologists and technical experts are formed into reciprocal matrixes and weights for entities are calculated utilizing Analytic Hierarchy Process (AHP). The assignment alternatives are processed by relation-based semantic matching (RBSM) and Integer Linear Programming (ILP). ResultsThe results are evaluated based on both real case applications and simulated process data in terms of subspecialty, response time and workload success rates. Results obtained using simulated data are compared with the outcomes obtained by applying Round Robin, Shortest Queue and Random distribution policies. The proposed algorithm is also applied to a real case teleradiology application process data where medical reporting workflow was performed based on manual assignments by the chief radiologist for 6225 inspections. ConclusionsRBSM gives the highest subspecialty success rate and integrating ILP with RBSM ratings as RWOA provides a better response time and workload distribution success rate. RWOA based image delivery also prevents bandwidth, storage or hardware related stuck and latencies. When compared with a real case teleradiology application where inspection assignments were performed manually, the proposed solution was found to increase the experience success rate by 13.25%, workload success rate by 63.76% and response time success rate by 120%. The total response time in the real case application data was improved by 22.39%.

Integrated Power Management of Conventional Units and Industrial Loads in China’s Ancillary Services Scheduling

With the development of the smart grid in China, new opportunities for responsive industrial loads to participate in the provision of ancillary services (AS) will become accessible. This paper summarizes AS in China and analyzes the necessary characteristics and advantages of industrial users to provide AS according to their response mechanism. Cement manufacturing and aluminum smelter processes are selected as two representatives of responsive industrial loads. An agent-based model that includes generation, industrial user, and grid agents is proposed. Using two case studies, we analyze the integrated power management of conventional units and industrial loads in day-ahead and real-time AS scheduling based on real device parameters, price mechanisms and production data. The simulation results indicate that the participation of responsive industrial loads in the provision of AS, in China, can improve the coal consumption rate and the system-wide load factor as well as reduce the total system cost for the provision of AS significantly.

A Multiagent‐Based Microgrid Operation Method

SUMMARYIn this paper, we propose an autonomous microgrid operation using a multiagent approach. The proposed multiagent system consists of seven types of agent: a single microgrid controller agent (MGC), several load agents (LAGs), several gas‐turbine (gas‐engine) agents (GAGs), several photovoltaic generation agents (PVAGs), several wind‐turbine generation agents (WTAGs), several battery agents (BAGs), and a single grid agent (GridAG). In a microgrid, LAGs act as consumers or buyers, GAGs, PVAGs, and WTAGs act as producers or sellers, and BAGs act as prosumers or sellers/buyers. In order to verify the performance of the proposed system, it is applied to a simple model system with different electrical power prices. From the simulation results, it can be seen that the proposed multiagent system can perform microgrid operations efficiently.

All-to-all communication with cellular automata agents in 2 $$d$$ d grids: topologies, streets and performances

Solving the all-to-all communication task in the cyclic triangulate and square grids in shortest time with mobile agents was the objective of this work. In order to solve the problem, the multi-agent system is modeled by cellular automata with synchronous updating and the agents' behavior by an embedded finite state machine (FSM). Agents can move or stay, and turn to any direction. An agent is able to leave a trace by setting a color flag on its site. Colors allow indirect communication similar to pheromones, speed up the task and contribute to a better reliability. More reliable agents are found using different initial control states for the agent's FSMs. A simple genetic procedure based on mutation is used to evolve near optimal FSMs for both grids. Agents in the triangulate grid can solve the task in around 2/3 of the time compared with the square grid. The communication time depends also on the density of agents in the field, e.g., agents with density 4/(16 $$\times $$ × 16) turned out to be the slowest.

Designing and Building a Framework for DNA Sequence Alignment Using Grid Computing

Deoxyribonucleic acid (DNA) is a molecule that encodes unique genetic instructions used in the development and functioning of all known living organisms and many viruses. This Genetic information is encoded as a sequence of nucleotides (adenine, cytosine, guanine, and thymine) recorded using the letters A, C, G, and T.DNA querying or alignment of these sequences required dynamic programming tools and very complex matrices and some heuristic methods like FASTA and BLAST that use massive force of processing and highly time consuming. We present a parallel solution to reduce the processing time. Smith waterman algorithm, Needleman-Wunsch, some weighting matrices and a grid of computers are used to find field of similarity between these sequences in large DNA datasets. This grid consists of master computer and unlimited number of agents. The master computer is the user interface for insert the queried sequence and coordinates the processing between the grid agents.

Simulation of Pedestrian Flow based on Multi-agent

Analyzing pedestrian flow characteristics and the interaction mechanism among pedestrians or between pedestrians and environment, we proposed a pedestrian flow simulation model based on the multi-agent technology. In the model, the hierarchical structure of multi agents was designed according to communication relations, and inner structures including network agent, grid agents, pedestrian agents and service interface. Furthermore, a superimposed shortest path algorithm was proposed to cater for the modelling of complex topological network with multiple exits and inner barriers. Pedestrian decision-making rules were established synthetically by considering the distance factor and pedestrian herd mentality factors. A scenario was studied in which pedestrians tried to evacuate from a large room with inner barriers and four exits. The results show that the weight factor of the network edge impacts the crowds shape at exits. In addition, simulation experiments show the trend of evacuation times with respect to the impact factor of pedestrian herd mentality.

Negotiated Learning for Smart Grid Agents: Entity Selection based on Dynamic Partially Observable Features

An attractive approach to managing electricity demand in the Smart Grid relies on real-time pricing (RTP) tariffs, where customers are incentivized to quickly adapt to changes in the cost of supply. However, choosing amongst competitive RTP tariffs is difficult when tariff prices change rapidly. The problem is further complicated when we assume that the price changes for a tariff are published in real-time only to those customers who are currently subscribed to that tariff, thus making the prices partially observable. We present models and learning algorithms for autonomous agents that can address the tariff selection problem on behalf of customers. We introduce 'Negotiated Learning', a general algorithm that enables a self-interested sequential decision-making agent to periodically select amongst a variable set of 'entities' (e.g., tariffs) by negotiating with other agents in the environment to gather information about dynamic partially observable entity 'features' (e.g., tariff prices) that affect the entity selection decision. We also contribute a formulation of the tariff selection problem as a 'Negotiable Entity Selection Process', a novel representation. We support our contributions with intuitive justification and simulation experiments based on real data on an open Smart Grid simulation platform.

Multiagent-Based Distributed-Energy-Resource Management for Intelligent Microgrids

Microgrid is a combination of distributed generators, storage systems, and controllable loads connected to low-voltage network that can operate either in grid-connected or in island mode. High penetration of power at distribution level creates such multiple microgrids. This paper proposes a two-level architecture for distributed-energy-resource management for multiple microgrids using multiagent systems. In order to match the buyers and sellers in the energy market, symmetrical assignment problem based on naíve auction algorithm is used. The developed mechanism allows the pool members such as generation agents, load agents, auction agents, grid agents, and storage agents to participate in market. Three different scenarios are identified based on the supply–demand mismatch among the participating microgrids. At the end of this paper, two case studies are presented with two and four interconnected microgrids participating in the market. Simulation results clearly indicate that the agent-based management is effective in resource management among multiple microgrids economically and profitably.

Performance analysis of replication mechanism using mobile agent in computational grid using WADE

Grid computing is a coordinated resource sharing and solving the problems in organisations which are dynamic and virtual in nature. Many grid applications will be running in environments where interaction faults more commonly occur between diverse grid nodes. As resources may also be used outside of organisational boundaries, it becomes iteratively difficult to guarantee that a resource being used is not malicious one. Because of the diverse faults and failure conditions developing, deploying, and executing long running applications over the grid remains a challenge. Hence, fault tolerance is a primary factor for grid computing. The prototype system here is designed using agents to provide service replication, reactivation and avoids the single point of failure in a simulated computational grid environment. The agents and the workflows are provided by a common software platform called WADE. Moreover, workflows in the computational grid can also be generated using WOLF which is embedded in the WADE platform itself.

A Multi-agent based Micro Grid Operation Method

In this paper, we propose an autonomous Micro Grid operation by using multi-agent approach. The proposed multi-agent system consists of seven types of agent: single Micro Grid controller agent (MGC), several Load agents (LAGs), several gas-turbines (gas-engine) agents (GAGs), several photovoltaic generation agents (PVAGs), several wind-turbine generation agents (WTAGs), several battery agents (BAGs), and single grid agent (GridAG). In a Micro Grid, LAGs act as consumers or buyers, GAGs, PVAGs and WTAGs act as producers or sellers, and BAGs act as prosumers or sellers/buyers. In order to verify the performance of the proposed system, it applied to a simple model system with different electrical power prices. From the simulation results, it can be seen the proposed multi-agent system could perform the Smart Grid operation efficiently.

Electricity agents in smart grid markets

This paper presents a novel architecture of electricity agents in smart grid markets. The architecture implements a middleware that allows standard agent plug-ins representing “smart grid” elements in a two-way power flow. Agent operations are automated to allow demand variations and exploring a wider spectrum of trade opportunities in an efficient manner, while coordinating with the system operator for reliability, security, and stability. We give trade and operation computational models of power grid components and their interaction protocols and authentication in a multi-agent framework. Trade models are both pool and multilateral based. Operation models respect power dispatch limits, generation and transmission constraints, and spinning reserve requirements. The models are coded as part of the agent software. The protocols are KQML (Knowledge Query Manipulation Language) based communication. An IEEE 5-bus grid system is used for illustration. The testing results for different load profiles show better distribution of market sales and a market clearing price convergence very close to the marginal cost of generation supply, thus an implication of economic efficiency of energy resources, as well as security and power quality compared to traditional electricity trade models based on Cournot and Bertrand estimated clearing prices.

Grid agent cooperation strategies inspired by Game Theory

Grid agent cooperation strategies inspired by Game Theory

Grid-based implementation of XDS-I as part of image-enabled EHR for regional healthcare in Shanghai

Due to the rapid growth of Shanghai city to 20 million residents, the balance between healthcare supply and demand has become an important issue. The local government hopes to ameliorate this problem by developing an image-enabled electronic healthcare record (EHR) sharing mechanism between certain hospitals. This system is designed to enable healthcare collaboration and reduce healthcare costs by allowing review of prior examination data obtained at other hospitals. Here, we present a design method and implementation solution of image-enabled EHRs (i-EHRs) and describe the implementation of i-EHRs in four hospitals and one regional healthcare information center, as well as their preliminary operating results. We designed the i-EHRs with service-oriented architecture (SOA) and combined the grid-based image management and distribution capability, which are compliant with IHE XDS-I integration profile. There are seven major components and common services included in the i-EHRs. In order to achieve quick response for image retrieving in low-bandwidth network environments, we use a JPEG2000 interactive protocol and progressive display technique to transmit images from a Grid Agent as Imaging Source Actor to the PACS workstation as Imaging Consumer Actor. The first phase of pilot testing of our image-enabled EHR was implemented in the Zhabei district of Shanghai for imaging document sharing and collaborative diagnostic purposes. The pilot testing began in October 2009; there have been more than 50 examinations daily transferred between the City North Hospital and the three community hospitals for collaborative diagnosis. The feedback from users at all hospitals is very positive, with respondents stating the system to be easy to use and reporting no interference with their normal radiology diagnostic operation. The i-EHR system can provide event-driven automatic image delivery for collaborative imaging diagnosis across multiple hospitals based on work flow requirements. This project demonstrated that the grid-based implementation of IHE XDS-I for image-enabled EHR could scale effectively to serve a regional healthcare solution with collaborative imaging services. The feedback from users of community hospitals and large hospital is very positive.

CA Models for Target Searching Agents

Agents in a cellular grid have the task to move from their start positions to their individual target positions as fast as possible. Four models using agents are proposed that can be applied to the problem. These models are conform to the CA paradigm. The agents have either a moving direction (directed agent) or not (undirected agent). The agents behave either in a deterministic way according to a control automaton inside of each agent or they behave randomly. In order to find the best behaving agents, control automata (“algorithms”) were evolved using a genetic island model. Near optimal algorithms were evolved separately for k = 1 to k = 256 agents in a 32 × 32 environment using 20 random initial configurations for each k . Then these algorithms were ranked using another set of 100 initial configurations for each k . It turned out that the agents behave better with respect to speed and reliability in this order: (1) controlled directed agents, (2) random directed agents, (3) random undirected agents, and (4) controlled undirected agents. Although the controlled directed agents (optimized for each k ) can solve all the given 100 initial configurations in the ranking set, it can not be assured that no deadlocks may occur for other initial configurations.

Mechanism Design for Grid Computing

We develop a system for grid computing where the price of computing tasks is determined by an audited market-exchange. We show a method to provide a variable certificate, called \witness, of program execution with the following property: If two different agents running the same program on the same input produce the same witness, that proves with certainty very close to 1 that both agents have executed the program correctly. Using these witnesses, a trusted intermediary audits grid agents by dispatching identical work units to different agents and comparing their results. The results of past audits create a reputation history for agents, which is used to offer different prices to consumers based on the expected reliability corresponding to a reputation history. We allow reputations to be traded, instead of being tied to individual agents, and we show that in such a reputation market only high-type agents would have incentive to purchase a high reputation, and only low-type agents would use low reputations.

An agent architecture for managing data resources in a grid environment

The agent paradigm has been successfully used in a large number of research areas. MAPFS, a parallel file system, constitutes one successful application of agents to the I/O field, providing a multiagent I/O architecture. The use of a multiagent system implies coordination and cooperation among its agents. MAPFS is oriented to clusters of workstations, where agents are applied in order to provide features such as caching or prefetching. The adaptation of MAPFS to a grid environment is named MAPFS-Grid. Agents can help to increase the performance of data-intensive applications running on top of the grid. This paper describes the conceptual agent framework and the communication model used in MAPFS-Grid, which provides the management of data resources in a grid environment. The evaluation of our proposal shows the advantages of using agents in a data grid.

Macroeconomics based Grid resource allocation

Resource allocation is the key technology in Grid computing. While most of the existing literature of economics-based Grid resource allocation relies on the basic microeconomic principle, which concerns the behavior of individual agents in the Grid, this paper provides a novel approach based on macroeconomics, which concerns large aggregate behavior instead of individual actions. First, we propose a macroeconomics-based market framework that is well suited for a service-oriented Grid. Then, some macroeconomics-based resource allocation strategies, which can effectively improve the performance of the whole Grid market, are given. Simulation results prove good performance of the proposed method.

Forming and managing agent teams acting as resource brokers in the Grid — preliminary considerations

Recently, we can observe an increasing interest in utilization of software agents in computational Grids. In our work agent teams play role of Grid resource brokers and managers. Previously we have discussed how to efficiently implement matchmaking services, as well as proposed a way by which agents select a team that will execute their job. In this paper we focus our attention on processes involved in agents joining a team.