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These papers relate "mobile agents", that is, running programs that can migrate from host to host in the Internet at times and to hosts of their own choosing. See also Project dagents.Papers are listed in reverse-chronological order;
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We represent applications as collections of mobile agents and introduce a distributed mechanism for allocating general computational priority to mobile agents. We derive a bidding strategy for an agent that plans expenditures given a budget and a series of tasks to complete. We also show that a unique Nash equilibrium exists between the agents under our allocation policy. We present simulation results to show that the use of our resource-allocation mechanism and expenditure-planning algorithm results in shorter mean job completion times compared to traditional mobile-agent resource allocation. We also observe that our resource-allocation policy adapts favorably to allocate overloaded resources to higher priority agents, and that agents are able to effectively plan expenditures even when faced with network delay and job-size estimation error.
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’s optimal completion time.
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’ actions does not catastrophically affect agents’ performance.
Mobile agents represent informational and computational flow. We develop mechanisms that distributively allocate computation among mobile agents in two settings. The first models a situation where users collectively own networked computing resources and require priority enforcement. We extend the allocation mechanism to allow resource reservation to mitigate utility volatility. The second, more general model relaxes the ownership assumption. We apply our computational market to an open setting where a principal’s chief concern is revenue maximization.
Our simulations compare the performance of market-based allocation policies to traditional policies and relate the cost of ownership and consumption separation. We observe that our markets effectively prioritize applications’ performance, can operate under uncertainty and network delay, provide metrics to balance network load, and allow measurement of market-participation risk versus reservation-based computation.
In addition to allocation problems, we investigate resource selection to optimize execution time. The problem is NP-complete if the costs and latencies are constant. Both metrics’ dependence on the chosen set complicates matters. We study how a greedy approach, a novel heuristic, and a shortest-constrained-path strategy perform in mobile-agent applications.
Market-based computational-resource allocation fertilizes applications where previously there was a dearth of motive for or means of cooperation. The rationale behind mobile-agent performance optimization is also useful for resource allocation in general distributed systems where an application has a sequence of dependent tasks or when data collection is expensive.
The incentives of agents in the two markets drastically differ. The open-interest model motivates agents to be less trusting and to not share information. This aspect stems from the model’s greater applicability to resource allocation, but has a deep impact on system efficiency. In this paper, we summarize some economic theory and allegorical evidence from our models and system implementations that support the claim, and conclude with guidelines for system development.
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.
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.
Agent Tcl is a mobile-agent system whose agents can be written in Tcl, Java, and Scheme. Agent Tcl has extensive navigation and communication services, security mechanisms, and debugging and tracking tools. In this article we focus on Agent Tcl’s architecture and security mechanisms, its RPC system, and its docking system, which lets an agent move transparently among mobile computers, regardless of when they are connected to the network.
We create a formal utility model to derive user-demand functions, allowing agents to efficiently plan expenditure and deal with price fluctuations. By quantifying demand and utility, resource owners can precisely set a value for a good. We simulate our model in a mobile agent scheduling environment and show how mobile agents may use server prices to distribute themselves evenly throughout a network.
Agent Tcl is a mobile-agent system whose agents can be written in Tcl, Java, and Scheme. Agent Tcl has extensive navigation and communication services, security mechanisms, and debugging and tracking tools. In this article we focus on Agent Tcl’s architecture and security mechanisms, its RPC system, and its docking system, which lets an agent move transparently among mobile computers, regardless of when they are connected to the network.