Borrowing Constraint as an Optimal Contract

We study a continuous-time version of the optimal risk-sharing problem with one-sided commitment. In the optimal contract, the agent's consumption is non-decreasing and depends only on the maximal level of the agent's income realized to date. In the complete-markets implementation of the optimal contract, the Alvarez-Jermann solvency constraints take the form of a simple borrowing constraint familiar from the Bewley-Aiyagari incomplete-markets models. Unlike in the incomplete-markets models, however, the asset buffer stock held by the agent is negatively correlated with income.Borrowing constraint, limited commitment

Energy Sharing for Multiple Sensor Nodes with Finite Buffers

We consider the problem of finding optimal energy sharing policies that maximize the network performance of a system comprising of multiple sensor nodes and a single energy harvesting (EH) source. Sensor nodes periodically sense the random field and generate data, which is stored in the corresponding data queues. The EH source harnesses energy from ambient energy sources and the generated energy is stored in an energy buffer. Sensor nodes receive energy for data transmission from the EH source. The EH source has to efficiently share the stored energy among the nodes in order to minimize the long-run average delay in data transmission. We formulate the problem of energy sharing between the nodes in the framework of average cost infinite-horizon Markov decision processes (MDPs). We develop efficient energy sharing algorithms, namely Q-learning algorithm with exploration mechanisms based on the $\epsilon$-greedy method as well as upper confidence bound (UCB). We extend these algorithms by incorporating state and action space aggregation to tackle state-action space explosion in the MDP. We also develop a cross entropy based method that incorporates policy parameterization in order to find near optimal energy sharing policies. Through simulations, we show that our algorithms yield energy sharing policies that outperform the heuristic greedy method.Comment: 38 pages, 10 figure

Energy Management in a Cooperative Energy Harvesting Wireless Sensor Network

In this paper, we consider the problem of finding an optimal energy management policy for a network of sensor nodes capable of harvesting their own energy and sharing it with other nodes in the network. We formulate this problem in the discounted cost Markov decision process framework and obtain good energy-sharing policies using the Deep Deterministic Policy Gradient (DDPG) algorithm. Earlier works have attempted to obtain the optimal energy allocation policy for a single sensor and for multiple sensors arranged on a mote with a single centralized energy buffer. Our algorithms, on the other hand, provide optimal policies for a distributed network of sensors individually harvesting energy and capable of sharing energy amongst themselves. Through simulations, we illustrate that the policies obtained by our DDPG algorithm using this enhanced network model outperform algorithms that do not share energy or use a centralized energy buffer in the distributed multi-nodal case.Comment: 11 pages, 4 figure

Providing VCR Functionality in VOD Servers

Resource-sharing techniques are widely used by VOD servers. Stream merging is one of the most efficient resource-sharing techniques. ERMT is able to achieve merge trees with the closest cost of optimal merge tree. Full VCR support has become a “must have” feature for VOD services. This researcher proposed an algorithm to enable VCR support on ERMT. Furthermore, client local buffer and fixed-interval periodical multicasting were also deployed by the algorithm to improve the stream-client ratio. After thorough runs of simulations and numerous comparisons to BEP, the highly efficient resource- sharing technique, the proposed algorithm with client local buffer utilization and fixed- interval multicasting showed better performance in all simulations. The biggest discovery is that the best-performer is modified ERMT with client local buffer support for VCR without fixed-interval multicasting. Another discovery is that bigger client buffer size hurts the performance of ERMT

Multiplexing regulated traffic streams: design and performance

The main network solutions for supporting QoS rely on traf- fic policing (conditioning, shaping). In particular, for IP networks the IETF has developed Intserv (individual flows regulated) and Diffserv (only ag- gregates regulated). The regulator proposed could be based on the (dual) leaky-bucket mechanism. This explains the interest in network element per- formance (loss, delay) for leaky-bucket regulated traffic. This paper describes a novel approach to the above problem. Explicitly using the correlation structure of the sources’ traffic, we derive approxi- mations for both small and large buffers. Importantly, for small (large) buffers the short-term (long-term) correlations are dominant. The large buffer result decomposes the traffic stream in a stream of constant rate and a periodic impulse stream, allowing direct application of the Brownian bridge approximation. Combining the small and large buffer results by a concave majorization, we propose a simple, fast and accurate technique to statistically multiplex homogeneous regulated sources. To address heterogeneous inputs, we present similarly efficient tech- niques to evaluate the performance of multiple classes of traffic, each with distinct characteristics and QoS requirements. These techniques, applica- ble under more general conditions, are based on optimal resource (band- width and buffer) partitioning. They can also be directly applied to set GPS (Generalized Processor Sharing) weights and buffer thresholds in a shared resource system

Marginal Productivity Indices and Linear Programming Relaxations for Dynamic Resource Allocation in Queueing Systems

Many problems concerning resource management in modern communication systems can be simplified to queueing models under Markovian assumptions. The computation of the optimal policy is however often hindered by the curse of dimensionality especially for models that support multiple traffic or job classes. The research focus naturally turns to computationally efficient bounds and high performance heuristics. In this thesis, we apply the indexability theory to the study of admission control of a single server queue and to the buffer sharing problem for a multi-class queueing system. Our main contributions are the following: we derive the Marginal Productivity Index (MPI) and give a sufficient indexability condition for the admission control model by viewing the buffer as the resource; we construct hierarchical Linear Programming (LP) relaxations for the buffer sharing problem and propose an MPI based heuristic with its performance evaluated by discrete event simulation. In our study, the admission control model is used as the building block for the MPI heuristic deployed for the buffer sharing problem. Our condition for indexability only requires that the reward function is concavelike. We also give the explicit non-recursive expression for the MPI calculation. We compare with the previous result of the indexability condition and the MPI for the admission control model that penalizes the rejection action. The study of hierarchical LP relaxations for the buffer sharing problem is based on the exact but intractable LP formulation of the continuous-time Markov Decision Process (MDP). The number of hierarchy levels is equal to the number of job classes. The last one in the hierarchy is exact and corresponds to the exponentially sized LP formulation of the MDP. The first order relaxation is obtained by relaxing the constraint that no buffer overflow may occur in any sample path to the constraint that the average buffer utilization does not exceed the available capacity. Based on the Lagrangian decomposition of the first order relaxation, we propose a heuristic policy based on the concept of MPI. Each one of the decomposed subproblems corresponds to the admission control model we described above. The link to the decomposed sub-problems is the Lagrangian multiplier for the relaxed buffer size constraint in the first order relaxation. Our simulation study indicates the near optimal performance of the heuristic in the (randomly generated) instances investigated

Improving queueing implementation on high speed switches

In this thesis two different conventional shared memory allocation schemes - Dynamic Threshold (DT) and Threshold-based Filtering (TF) - are evaluated under varied traffic conditions in order to determine the optimal configuration for each tested scenario. The effect that a changing ABL, load, and ratio between buffer size and ports have on the packet loss is observed for buffer sharing schemes DT and TF schemes. This allowed to easily determining the Alpha and Thresholds required by DT and TF schemes respectively to obtain an optimal configuration under each of the different tested scenarios. A new shared memory allocation scheme referred to in this thesis as ‘Shortest Queue First Lite’ (SQFL) scheme is evaluated. SQFL scheme aims at decreasing the complexity of SQF in order to facilitate its hardware implementation. Comparisons are drawn between SQFL, SQF, DT and TF in terms of packet loss ratio

Deadlock resolution in flexible manufacturing systems: A Petri nets based approach.

Flexible Manufacturing Systems (FMSs) are characterized by concurrency, resource sharing, routing flexibility, limited buffer sizes, and variety of lot sizes. The sharing of resources and the limitations on buffer sizes may lead to deadlock situations. One of the most challenging problems in FMSs design and operation is to assign the shared resources to jobs efficiently and without causing deadlocks. To date, little has been done to achieve deadlock-free scheduling in FMSs. In this research a new efficient scheduling algorithm for finding an optimal or near-optimal deadlock-free schedule was developed based on the depth-first and backtracking search technique. Two efficient truncation techniques and three heuristic functions were developed and tested using several randomly generated case studies. The performance of flexible manufacturing systems that exhibits deadlocks was analyzed under different levels of routing flexibility and other factors using Petri Nets. It was expected that routing flexibility would complicate the Petri Net model and create new deadlocks, which in turn could negatively affect the system performance. The results showed that increasing routing flexibility improves the system performance, measured by average flow time, in systems exhibiting deadlocks. A novel heuristic deadlock-free rescheduling algorithm based on Petri Nets was developed in order to deal with machine breakdowns in real-time. It guarantees a deadlock-free new schedule and relies on local rather than global rescheduling. The existence of alternative routes, availability of material handling facilities, and the limitations of buffer capacities were considered. In conclusion, the thesis introduces an integrated approach for production scheduling, control and performance evaluation of flexible manufacturing systems that exhibit deadlocks. The first part takes care of optimizing the performance of the manufacturing system, by generating optimal or near optimal schedules, and avoiding the deadlock situations in the same time. The second part could be used in answering the questions of the what-if analysis. Finally, the third part maintains the production control in real-time.Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2001 .E46. Source: Dissertation Abstracts International, Volume: 62-10, Section: B, page: 4716. Adviser: Hoda ElMaraghy. Thesis (Ph.D.)--University of Windsor (Canada), 2001