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We introduce a novel pricing and resource allocation approach for batch jobs on cloud systems. In our economic model, users submit jobs with a value function that specifies willingness to pay as a function of job due dates. The cloud provider in response allocates a subset of these jobs, taking into advantage the flexibility of allocating resources to jobs in the cloud environment. Focusing on social-welfare as the system objective (especially relevant for private or in-house clouds), we construct a resource allocation algorithm which provides a small approximation factor that approaches 2 as the number of servers increases. An appealing property of our scheme is that jobs are allocated non-preemptively, i.e., jobs run in one shot without interruption. This property has practical significance, as it avoids significant network and storage resources for checkpointing. Based on this algorithm, we then design an efficient truthful-in-expectation mechanism, which significantly improves the running complexity of black-box reduction mechanisms that can be applied to the problem, thereby facilitating its implementation in real systems. [ABSTRACT FROM AUTHOR]

We consider an uplink wireless collision channel, shared by multiple mobile users. The medium access protocol incorporates channel reservation that relies on RTS (request-to-send) and CTS (clear-to-send) control packets. Consequently, collisions are reduced to the relatively short periods where mobiles request channel use. In our model, users individually schedule their channel requests, and the objective of each user is to minimize its own power investment subject to a minimum-throughput demand. Our analysis reveals that for feasible throughput demands, there exist exactly two Nash equilibrium points in stationary strategies, with one superior to the other uniformly over all users. We then show how this better equilibrium point can be obtained through distributed best-response mechanisms. Finally, we quantify and discuss the effect of the relative length of data and control periods on capacity, power and delay. [ABSTRACT FROM AUTHOR]

Reinforcement Learning (RL) is an approach for solving complex multi-stage decision problems that fall under the general framework of Markov Decision Problems (MDPs), with possibly unknown parameters. Function approximation is essential for problems with a large state space, as it facilitates compact representation and enables generalization. Linear approximation architectures (where the adjustable parameters are the weights of pre-fixed basis functions) have recently gained prominence due to efficient algorithms and convergence guarantees. Nonetheless, an appropriate choice of basis function is important for the success of the algorithm. In the present paper we examine methods for adapting the basis function during the learning process in the context of evaluating the value function under a fixed control policy. Using the Bellman approximation error as an optimization criterion, we optimize the weights of the basis function while simultaneously adapting the (non-linear) basis function parameters. We present two algorithms for this problem. The first uses a gradient-based approach and the second applies the Cross Entropy method. The performance of the proposed algorithms is evaluated and compared in simulations. [ABSTRACT FROM AUTHOR]