Partially shared buffers with full or mixed priority

This paper studies a finite-sized discrete-time two-class priority queue. Packets of both classes arrive according to a two-class discrete batch Markovian arrival process (2-DBMAP), taking into account the correlated nature of arrivals in heterogeneous telecommunication networks. The model incorporates time and space priority to provide different types of service to each class. One of both classes receives absolute time priority in order to minimize its delay. Space priority is implemented by the partial buffer sharing acceptance policy and can be provided to the class receiving time priority or to the other class. This choice gives rise to two different queueing models and this paper analyses both these models in a unified manner. Furthermore, the buffer finiteness and the use of space priority raise some issues on the order of arrivals in a slot. This paper does not assume that all arrivals from one class enter the queue before those of the other class. Instead, a string representation for sequences of arriving packets and a probability measure on the set of such strings are introduced. This naturally gives rise to the notion of intra-slot space priority. Performance of these queueing systems is then determined using matrix-analytic techniques. The numerical examples explore the range of service differentiation covered by both models

Dynamic buffer management policy for shared memory packet switches by employing per-queue thresholds

One of the main problems concerning high-performance communications networks is the unavoidable congestion in network nodes. Network traffic is normally characterised as "bursty", which may use up network resources during peak periods. As a consequence end-user applications are subject to end-to-end delays and disruptions. Simultaneous transmission of packets on a finite bandwidth channel might result in contentions, where one or more packets are refrained from entering the transmission channel resulting in packet losses. Hence, the motivations of this thesis are two-fold: investigation and evaluation of switch architectures with electronic and optical buffers, and the development and evaluation of an improved dynamic threshold policy for shared memory switch architecture. In this work, switch architectures based on modular designs are evaluated, with simulation results showing that modular switch structures, i.e. multistage interconnection networks with optical delay line buffers, offer packet loss rate, throughput and average delay time similar to their electronic counterparts. Such optical architectures emulate prime features of shared memory switch architecture under general traffic conditions. Although the shared memory switch architecture is superior to other buffering approaches, but its performance is inadequate under imbalanced input traffic. Here its limiting features are investigated by means of numerical analysis. Different buffer management schemes, namely static thresholds, dynamic thresholds, pre-emptive, adaptive control, are investigated by using the Markov simulation model. An improved dynamic buffer management policy, decay function threshold (DFT) policy, is proposed and it is compared with the dynamic thresholds (DT), partial sharing partial partitioning (PSPP) and dynamic queue thresholds (DQT) buffer management policies by using bursty traffic source models, such as interrupted Poisson process (IPP), by means of simulations. Simulation results show that proposed policy is as good as well-known dynamic thresholds policy in the presence of best-effort traffic and offers improved packet loss performance when multicast traffic is considered. An integration framework for dynamic buffer management and bandwidth scheduling is also presented in this study. This framework employs loosely coupled buffer management and scheduling (weighted round robin, weighted fair queueing etc.) providing support for quality of service traffic. Conducted tests show that this framework matches the best-effort packet loss performance of dynamic thresholds policy

Exploiting Non-Causal CPU-State Information for Energy-Efficient Mobile Cooperative Computing

Scavenging the idling computation resources at the enormous number of mobile devices can provide a powerful platform for local mobile cloud computing. The vision can be realized by peer-to-peer cooperative computing between edge devices, referred to as co-computing. This paper considers a co-computing system where a user offloads computation of input-data to a helper. The helper controls the offloading process for the objective of minimizing the user's energy consumption based on a predicted helper's CPU-idling profile that specifies the amount of available computation resource for co-computing. Consider the scenario that the user has one-shot input-data arrival and the helper buffers offloaded bits. The problem for energy-efficient co-computing is formulated as two sub-problems: the slave problem corresponding to adaptive offloading and the master one to data partitioning. Given a fixed offloaded data size, the adaptive offloading aims at minimizing the energy consumption for offloading by controlling the offloading rate under the deadline and buffer constraints. By deriving the necessary and sufficient conditions for the optimal solution, we characterize the structure of the optimal policies and propose algorithms for computing the policies. Furthermore, we show that the problem of optimal data partitioning for offloading and local computing at the user is convex, admitting a simple solution using the sub-gradient method. Last, the developed design approach for co-computing is extended to the scenario of bursty data arrivals at the user accounting for data causality constraints. Simulation results verify the effectiveness of the proposed algorithms.Comment: Submitted to possible journa

The costs of uncoordinated infrastructure management in multi-reservoir river basins

Though there are surprisingly few estimates of the economic benefits of coordinated infrastructure development and operations in international river basins, there is a widespread belief that improved cooperation is beneficial for managing water scarcity and variability. Hydro-economic optimization models are commonly-used for identifying efficient allocation of water across time and space, but such models typically assume full coordination. In the real world, investment and operational decisions for specific projects are often made without full consideration of potential downstream impacts. This paper describes a tractable methodology for evaluating the economic benefits of infrastructure coordination. We demonstrate its application over a range of water availability scenarios in a catchment of the Mekong located in Lao PDR, the Nam Ngum River Basin. Results from this basin suggest that coordination improves system net benefits from irrigation and hydropower by approximately 3–12% (or US$12-53 million/yr) assuming moderate levels of flood control, and that the magnitude of coordination benefits generally increases with the level of water availability and with inflow variability. Similar analyses would be useful for developing a systematic understanding of the factors that increase the costs of non-cooperation in river basin systems worldwide, and would likely help to improve targeting of efforts to stimulate complicated negotiations over water resources

Decentralized Delay Optimal Control for Interference Networks with Limited Renewable Energy Storage

In this paper, we consider delay minimization for interference networks with renewable energy source, where the transmission power of a node comes from both the conventional utility power (AC power) and the renewable energy source. We assume the transmission power of each node is a function of the local channel state, local data queue state and local energy queue state only. In turn, we consider two delay optimization formulations, namely the decentralized partially observable Markov decision process (DEC-POMDP) and Non-cooperative partially observable stochastic game (POSG). In DEC-POMDP formulation, we derive a decentralized online learning algorithm to determine the control actions and Lagrangian multipliers (LMs) simultaneously, based on the policy gradient approach. Under some mild technical conditions, the proposed decentralized policy gradient algorithm converges almost surely to a local optimal solution. On the other hand, in the non-cooperative POSG formulation, the transmitter nodes are non-cooperative. We extend the decentralized policy gradient solution and establish the technical proof for almost-sure convergence of the learning algorithms. In both cases, the solutions are very robust to model variations. Finally, the delay performance of the proposed solutions are compared with conventional baseline schemes for interference networks and it is illustrated that substantial delay performance gain and energy savings can be achieved