Resource Allocation for Outdoor-to-Indoor Multicarrier Transmission with Shared UE-side Distributed Antenna Systems

In this paper, we study the resource allocation algorithm design for downlink multicarrier transmission with a shared user equipment (UE)-side distributed antenna system (SUDAS) which utilizes both licensed and unlicensed frequency bands for improving the system throughput. The joint UE selection and transceiver processing matrix design is formulated as a non-convex optimization problem for the maximization of the end-to-end system throughput (bits/s). In order to obtain a tractable resource allocation algorithm, we first show that the optimal transmitter precoding and receiver post-processing matrices jointly diagonalize the end-to-end communication channel. Subsequently, the optimization problem is converted to a scalar optimization problem for multiple parallel channels, which is solved by using an asymptotically optimal iterative algorithm. Simulation results illustrate that the proposed resource allocation algorithm for the SUDAS achieves an excellent system performance and provides a spatial multiplexing gain for single-antenna UEs.Comment: accepted for publication at the IEEE Vehicular Technology Conference (VTC) Spring, Glasgow, Scotland, UK, May 201

Distributed MPC for Thermal Comfort and Load Allocation with Energy Auction

This paper presents a distributed predictive control methodology for indoor thermal comfort that optimizes the consumption of a limited shared energy resource using an integrated demand-side management approach that involves a power price auction and an appliance loads allocation scheme. The control objective for each subsystem (house or building) aims to minimize the energy cost while maintaining the indoor temperature inside comfort limits. In a distributed coordinated multi-agent ecosystem, each house or building control agent achieves its objectives while sharing, among them, the available energy through the introduction of particular coupling constraints in their underlying optimization problem. Coordination is maintained by a daily green energy auction bring in a demand-side management approach. Also the implemented distributed MPC algorithm is described and validated with simulation studies

Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing

Managing resources in grid computing system is complicated due to the distributed and heterogeneous nature of the resources. Stagnation in grid computing system may occur when all jobs are required or are assigned to the same resources which lead to the resources having high workload or the time taken to process a job is high. This research proposes an Enhanced Ant Colony Optimization (EACO) algorithm that caters dynamic scheduling and load balancing in the grid computing system. The proposed algorithm can overcome stagnation problem, minimize processing time, match jobs with suitable resources, and balance entire resources in grid environment. This research follows the experimental research methodology that consists of problem analysis, developing the proposed framework, constructing the simulation environment, conducting a set of experiments and evaluating the results. There are three new mechanisms in this proposed framework that are used to organize the work of an ant colony i.e. initial pheromone value mechanism, resource selection mechanism and pheromone update mechanism. The resource allocation problem is modeled as a graph that can be used by the ant to deliver its pheromone. This graph consists of four types of vertices which are job, requirement, resource and capacity that are used in constructing the grid job scheduling. The proposed EACO algorithm takes into consideration the capacity of resources and the characteristics of jobs in determining the best resource to process a job. EACO selects the resources based on the pheromone value on each resource which is recorded in a matrix form. The initial pheromone value of each resource for each job is calculated based on the estimated transmission time and execution time of a given job. Resources with high pheromone value are selected to process the submitted jobs. Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources. A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. Experimental results show that EACO produced better grid resource management solution compared to other algorithms

Derandomized Distributed Multi-resource Allocation with Little Communication Overhead

We study a class of distributed optimization problems for multiple shared resource allocation in Internet-connected devices. We propose a derandomized version of an existing stochastic additive-increase and multiplicative-decrease (AIMD) algorithm. The proposed solution uses one bit feedback signal for each resource between the system and the Internet-connected devices and does not require inter-device communication. Additionally, the Internet-connected devices do not compromise their privacy and the solution does not dependent on the number of participating devices. In the system, each Internet-connected device has private cost functions which are strictly convex, twice continuously differentiable and increasing. We show empirically that the long-term average allocations of multiple shared resources converge to optimal allocations and the system achieves minimum social cost. Furthermore, we show that the proposed derandomized AIMD algorithm converges faster than the stochastic AIMD algorithm and both the approaches provide approximately same solutions

Computational Markets to Regulate Mobile-Agent Systems

Mobile-agent systems allow applications to distribute their resource consumption across the network. By prioritizing applications and publishing the cost of actions, it is possible for applications to achieve faster performance than in an environment where resources are evenly shared. We enforce the costs of actions through markets where user applications bid for computation from host machines. \par 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