The Dynamic Pivot Mechanism

We consider truthful implementation of the socially efficient allocation in an independent private-value environment in which agents receive private information over time. We propose a suitable generalization of the pivot mechanism, based on the marginal contribution of each agent. In the dynamic pivot mechanism, the ex-post incentive and ex-post participation constraints are satisfied for all agents after all histories. In an environment with diverse preferences it is the unique mechanism satisfying ex-post incentive, ex-post participation and efficient exit conditions. We develop the dynamic pivot mechanism in detail for a repeated auction of a single object in which each bidder learns over time her true valuation of the object. The dynamic pivot mechanism here is equivalent to a modified second price auction.Pivot mechanisms, Dynamic mechanism design, Ex-post equilibrium, Marginal contribution, Multi-armed bandit, Bayesian learning

A Novel Thread Scheduler Design for Polymorphic Embedded Systems

A novel thread scheduler design for polymorphic embedded systems Abstract: The ever-increasing complexity of current day embedded systems necessitates that these systems be adaptable and scalable to user demands. With the growing use of consumer electronic devices, embedded computing is steadily approaching the desktop computing trend. End users expect their consumer electronic devices to operate faster than before and offer support for a wide range of applications. In order to accommodate a broad range of user applications, the challenge is to come up with an efficient design for the embedded system scheduler. Hence the primary goal of the thesis is to design a thread scheduler for a polymorphic thread computing embedded system. This is the first ever novel attempt at designing a polymorphic thread scheduler as none of the existing or conventional schedulers have accounted for thread polymorphism. To summarize the thesis work, a dynamic thread scheduler for a Multiple Application, Multithreaded polymorphic system has been implemented with User satisfaction as its objective function. The sigmoid function helps to accurately model end user perception in an embedded system as opposed to the conventional systems where the objective is to maximize/minimize the performance metric such as performance, power, energy etc. The Polymorphic thread scheduler framework which operates in a dynamic environment with N multithreaded applications has been explained and evaluated. Randomly generated Application graphs are used to test the Polymorphic scheduler framework. The benefits obtained by using User Satisfaction as the objective function and the performance enhancements obtained using the novel thread scheduler are demonstrated clearly using the result graphs. The advantages of the proposed greedy thread scheduling algorithm are demonstrated by comparison against conventional thread scheduling approaches like First Come First Serve (FCFS) and priority scheduling schemes

Libra: An Economy driven Job Scheduling System for Clusters

Clusters of computers have emerged as mainstream parallel and distributed platforms for high-performance, high-throughput and high-availability computing. To enable effective resource management on clusters, numerous cluster managements systems and schedulers have been designed. However, their focus has essentially been on maximizing CPU performance, but not on improving the value of utility delivered to the user and quality of services. This paper presents a new computational economy driven scheduling system called Libra, which has been designed to support allocation of resources based on the users? quality of service (QoS) requirements. It is intended to work as an add-on to the existing queuing and resource management system. The first version has been implemented as a plugin scheduler to the PBS (Portable Batch System) system. The scheduler offers market-based economy driven service for managing batch jobs on clusters by scheduling CPU time according to user utility as determined by their budget and deadline rather than system performance considerations. The Libra scheduler ensures that both these constraints are met within an O(n) run-time. The Libra scheduler has been simulated using the GridSim toolkit to carry out a detailed performance analysis. Results show that the deadline and budget based proportional resource allocation strategy improves the utility of the system and user satisfaction as compared to system-centric scheduling strategies.Comment: 13 page

Social Optimum Equilibrium Selection for Distributed Multi-Agent Optimization

We study the open question of how players learn to play a social optimum pure-strategy Nash equilibrium (PSNE) through repeated interactions in general-sum coordination games. A social optimum of a game is the stable Pareto-optimal state that provides a maximum return in the sum of all players' payoffs (social welfare) and always exists. We consider finite repeated games where each player only has access to its own utility (or payoff) function but is able to exchange information with other players. We develop a novel regret matching (RM) based algorithm for computing an efficient PSNE solution that could approach a desired Pareto-optimal outcome yielding the highest social welfare among all the attainable equilibria in the long run. Our proposed learning procedure follows the regret minimization framework but extends it in three major ways: (1) agents use global, instead of local, utility for calculating regrets, (2) each agent maintains a small and diminishing exploration probability in order to explore various PSNEs, and (3) agents stay with the actions that achieve the best global utility thus far, regardless of regrets. We prove that these three extensions enable the algorithm to select the stable social optimum equilibrium instead of converging to an arbitrary or cyclic equilibrium as in the conventional RM approach. We demonstrate the effectiveness of our approach through a set of applications in multi-agent distributed control, including a large-scale resource allocation game and a hard combinatorial task assignment problem for which no efficient (polynomial) solution exists.Comment: Appears at the 5th Games, Agents, and Incentives Workshop (GAIW 2023). Held as part of the Workshops at the AAMAS 2023 Conferenc