A Survey on Delay-Aware Resource Control for Wireless Systems --- Large Deviation Theory, Stochastic Lyapunov Drift and Distributed Stochastic Learning

In this tutorial paper, a comprehensive survey is given on several major systematic approaches in dealing with delay-aware control problems, namely the equivalent rate constraint approach, the Lyapunov stability drift approach and the approximate Markov Decision Process (MDP) approach using stochastic learning. These approaches essentially embrace most of the existing literature regarding delay-aware resource control in wireless systems. They have their relative pros and cons in terms of performance, complexity and implementation issues. For each of the approaches, the problem setup, the general solution and the design methodology are discussed. Applications of these approaches to delay-aware resource allocation are illustrated with examples in single-hop wireless networks. Furthermore, recent results regarding delay-aware multi-hop routing designs in general multi-hop networks are elaborated. Finally, the delay performance of the various approaches are compared through simulations using an example of the uplink OFDMA systems.Comment: 58 pages, 8 figures; IEEE Transactions on Information Theory, 201

Joint delay and power control in single-server queueing systems

Abstract—Many power-aware resource allocation problems in packet networks can be modeled as single-server queueing systems, in which the power consumption depends on the actual service rate. We consider the scenario in which the queue service rate is controlled to minimize server power consumption. We show that power control methods that tune the service rate by using the queue length or the arrival rate exhibit a non-monotonic curve of delay vs. load. This may lead to malfunctioning in endto-end flow/congestion control protocols, which are based on the assumption that delays increase with increasing load. We propose a new policy, in which the service rate is changed while keeping almost flat the delay curve, which permits to achieve a close-tooptimal trade-off between power and delay. I

Final report on the evaluation of RRM/CRRM algorithms

Deliverable public del projecte EVERESTThis deliverable provides a definition and a complete evaluation of the RRM/CRRM algorithms selected in D11 and D15, and evolved and refined on an iterative process. The evaluation will be carried out by means of simulations using the simulators provided at D07, and D14.Preprin

An Adaptive Packet Aggregation Algorithm (AAM) for Wireless Networks

Packet aggregation algorithms are used to improve the throughput performance by combining a number of packets into a single transmission unit in order to reduce the overhead associated with each transmission within a packet-based communications network. However, the throughput improvement is also accompanied by a delay increase. The biggest drawback of a significant number of the proposed packet aggregation algorithms is that they tend to only optimize a single metric, i.e. either to maximize throughput or to minimize delay. They do not permit an optimal trade-off between maximizing throughput and minimizing delay. Therefore, these algorithms cannot achieve the optimal network performance for mixed traffic loads containing a number of different types of applications which may have very different network performance requirements. In this thesis an adaptive packet aggregation algorithm called the Adaptive Aggregation Mechanism (AAM) is proposed which achieves an aggregation trade-off in terms of realizing the largest average throughput with the smallest average delay compared to a number of other popular aggregation algorithms under saturation conditions in wireless networks. The AAM algorithm is the first packet aggregation algorithm that employs an adaptive selection window mechanism where the selection window size is adaptively adjusted in order to respond to the varying nature of both the packet size and packet rate. This algorithm is essentially a feedback control system incorporating a hybrid selection strategy for selecting the packets. Simulation results demonstrate that the proposed algorithm can (a) achieve a large number of sub-packets per aggregate packet for a given delay and (b) significantly improve the performance in terms of the aggregation trade-off for different traffic loads. Furthermore, the AAM algorithm is a robust algorithm as it can significantly improve the performance in terms of the average throughput in error-prone wireless networks

Managing server energy and reducing operational cost for online service providers

The past decade has seen the energy consumption in servers and Internet Data Centers (IDCs) skyrocket. A recent survey estimated that the worldwide spending on servers and cooling have risen to above $30 billion and is likely to exceed spending on the new server hardware . The rapid rise in energy consumption has posted a serious threat to both energy resources and the environment, which makes green computing not only worthwhile but also necessary. This dissertation intends to tackle the challenges of both reducing the energy consumption of server systems and by reducing the cost for Online Service Providers (OSPs). Two distinct subsystems account for most of IDC’s power: the server system, which accounts for 56% of the total power consumption of an IDC, and the cooling and humidifcation systems, which accounts for about 30% of the total power consumption. The server system dominates the energy consumption of an IDC, and its power draw can vary drastically with data center utilization. In this dissertation, we propose three models to achieve energy effciency in web server clusters: an energy proportional model, an optimal server allocation and frequency adjustment strategy, and a constrained Markov model. The proposed models have combined Dynamic Voltage/Frequency Scaling (DV/FS) and Vary-On, Vary-off (VOVF) mechanisms that work together for more energy savings. Meanwhile, corresponding strategies are proposed to deal with the transition overheads. We further extend server energy management to the IDC’s costs management, helping the OSPs to conserve, manage their own electricity cost, and lower the carbon emissions. We have developed an optimal energy-aware load dispatching strategy that periodically maps more requests to the locations with lower electricity prices. A carbon emission limit is placed, and the volatility of the carbon offset market is also considered. Two energy effcient strategies are applied to the server system and the cooling system respectively. With the rapid development of cloud services, we also carry out research to reduce the server energy in cloud computing environments. In this work, we propose a new live virtual machine (VM) placement scheme that can effectively map VMs to Physical Machines (PMs) with substantial energy savings in a heterogeneous server cluster. A VM/PM mapping probability matrix is constructed, in which each VM request is assigned with a probability running on PMs. The VM/PM mapping probability matrix takes into account resource limitations, VM operation overheads, server reliability as well as energy effciency. The evolution of Internet Data Centers and the increasing demands of web services raise great challenges to improve the energy effciency of IDCs. We also express several potential areas for future research in each chapter

Wireless industrial monitoring and control networks: the journey so far and the road ahead

While traditional wired communication technologies have played a crucial role in industrial monitoring and control networks over the past few decades, they are increasingly proving to be inadequate to meet the highly dynamic and stringent demands of today’s industrial applications, primarily due to the very rigid nature of wired infrastructures. Wireless technology, however, through its increased pervasiveness, has the potential to revolutionize the industry, not only by mitigating the problems faced by wired solutions, but also by introducing a completely new class of applications. While present day wireless technologies made some preliminary inroads in the monitoring domain, they still have severe limitations especially when real-time, reliable distributed control operations are concerned. This article provides the reader with an overview of existing wireless technologies commonly used in the monitoring and control industry. It highlights the pros and cons of each technology and assesses the degree to which each technology is able to meet the stringent demands of industrial monitoring and control networks. Additionally, it summarizes mechanisms proposed by academia, especially serving critical applications by addressing the real-time and reliability requirements of industrial process automation. The article also describes certain key research problems from the physical layer communication for sensor networks and the wireless networking perspective that have yet to be addressed to allow the successful use of wireless technologies in industrial monitoring and control networks