Trading Risk in Mobile-Agent Computational Markets

Mobile-agent systems allow user programs to autonomously relocate from one host site to another. This autonomy provides a powerful, flexible architecture on which to build distributed applications. The asynchronous, decentralized nature of mobile-agent systems makes them flexible, but also hinders their deployment. We argue that a market-based approach where agents buy computational resources from their hosts solves many problems faced by mobile-agent systems. \par In our earlier work, we propose a policy for allocating general computational priority among agents posed as a competitive game for which we derive a unique computable Nash equilibrium. Here we improve on our earlier approach by implementing resource guarantees where mobile-agent hosts issue call options on computational resources. Call options allow an agent to reserve and guarantee the cost and time necessary to complete its itinerary before the agent begins execution. \par We present an algorithm based upon the binomial options-pricing model that estimates future congestion to allow hosts to evaluate call options; methods for agents to measure the risk associated with their performance and compare their expected utility of competing in the computational spot market with utilizing resource options; and test our theory with simulations to show that option trade reduces variance in agent completion times

Transportable Information Agents

Transportable agents are autonomous programs. They can move through a heterogeneous network of computers under their own control, migrating from host to host. They can sense the state of the network, monitor software conditions, and interact with other agents or resources. The network-sensing tools allow our agents to adapt to the network configuration and to navigate under the control of reactive plans. In this paper we describe the design and implementation of the navigation system that gives our agents autonomy. We also discuss the intelligent and adaptive behavior of autonomous agents in distributed information-gathering tasks

A Market-Based Model for Resource Allocation in Agent Systems

In traditional computational systems, resource owners have no incentive to subject themselves to additional risk and congestion associated with providing service to arbitrary agents, but there are applications that benefit from open environments. We argue for the use of markets to regulate agent systems. With market mechanisms, agents have the abilities to assess the cost of their actions, behave responsibly, and coordinate their resource usage both temporally and spatially. \par We discuss our market structure and mechanisms we have developed to foster secure exchange between agents and hosts. Additionally, we believe that certain agent applications encourage repeated interactions that benefit both agents and hosts, giving further reason for hosts to fairly accommodate agents. We apply our ideas to create a resource-allocation policy for mobile-agent systems, from which we derive an algorithm for a mobile agent to plan its expenditure and travel. With perfect information, the algorithm guarantees the agent\u27s optimal completion time. \par We relax the assumptions underlying our algorithm design and simulate our planning algorithm and allocation policy to show that the policy prioritizes agents by endowment, handles bursty workloads, adapts to situations where network resources are overextended, and that delaying agents\u27 actions does not catastrophically affect agents\u27 performance

Mobile-Agent Planning in a Market-Oriented Environment

We propose a method for increasing incentives for sites to host arbitrary mobile agents in which mobile agents purchase their computing needs from host sites. We present a scalable market-based CPU allocation policy and an on-line algorithm that plans a mobile agent\u27s expenditure over a multihop ordered itinerary. The algorithm chooses a set of sites at which to execute and computational priorities at each site to minimize execution time while preserving a prespecified budget constraint. We present simulation results of our algorithm to show that our allocation policy and planning algorithm scale well as more agents are added to the system

Loss-of-Function of Constitutive Expresser of Pathogenesis Related Genes5 Affects Potassium Homeostasis in Arabidopsis thaliana

Here, we demonstrate that the reduction in leaf K+ observed in a mutant previously identified in an ionomic screen of fast neutron mutagenized Arabidopsis thaliana is caused by a loss-of-function allele of CPR5, which we name cpr5-3. This observation establishes low leaf K+ as a new phenotype for loss-of-function alleles of CPR5. We investigate the factors affecting this low leaf K+ in cpr5 using double mutants defective in salicylic acid (SA) and jasmonic acid (JA) signalling, and by gene expression analysis of various channels and transporters. Reciprocal grafting between cpr5 and Col-0 was used to determine the relative importance of the shoot and root in causing the low leaf K+ phenotype of cpr5. Our data show that loss-of-function of CPR5 in shoots primarily determines the low leaf K+ phenotype of cpr5, though the roots also contribute to a lesser degree. The low leaf K+ phenotype of cpr5 is independent of the elevated SA and JA known to occur in cpr5. In cpr5 expression of genes encoding various Cyclic Nucleotide Gated Channels (CNGCs) are uniquely elevated in leaves. Further, expression of HAK5, encoding the high affinity K+ uptake transporter, is reduced in roots of cpr5 grown with high or low K+ supply. We suggest a model in which low leaf K+ in cpr5 is driven primarily by enhanced shoot-to-root K+ export caused by a constitutive activation of the expression of various CNGCs. This activation may enhance K+ efflux, either indirectly via enhanced cytosolic Ca2+ and/or directly by increased K+ transport activity. Enhanced shoot-to-root K+ export may also cause the reduced expression of HAK5 observed in roots of cpr5, leading to a reduction in uptake of K+