807 research outputs found
Bandwidth and Power Management in Broadband Wireless Networks
Bandwidth and power are considered as two important resources in wireless networks. Therefore, how to management these resources becomes a critical issue. In this thesis, we investigate this issue majorally in IEEE 802.16 networks. We first perform performance analysis on two bandwidth request mechanisms defined in IEEE 802.16 networks. We also propose two practical performance objectives. Based on the analysis, we design two scheduling algorithm to achieve the objectives.
Due to the characteristics of popular variable bit rate (VBR) traffic, it is very difficult for subscriber stations (SSs) to make appropriate bandwidth reservation. Therefore, the bandwidth may not be utilized all the time. We propose a new protocol, named bandwidth recycling, to utilized unused bandwidth. Our simulation shows that the proposed scheme can improve system utilization averagely by 40\%.
We also propose a more aggressive solution to reduce the gap between bandwidth reservation and real usage. We first design a centralized approach by linear programming to obtain the optimal solution. Further, we design a fully distributed scheme based on game theory, named bandwidth reservation (BR) game. Due to different quality of service (QoS) requirements, we customize the utility function for each scheduling class. Our numerical and simulation show that the gap between BR game and optimal solution is limited.
Due to the advantage of dynamical fractional frequency reuse (DFFR), the base station (BS) can dynamically adjust transmission power on each frequency partition. We emphasis on power allocation issue in DFFR to achieve most ecomicical data transmission. We first formulate the problem by integer linear programming (ILP). Due to high computation complexity, we further design a greedy algorithm. Our simulation shows that the results of the greedy algorithm is very close to the ILP results
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Optimized traffic scheduling and routing in smart home networks
Home networks are evolving rapidly to include heterogeneous physical access and a large number of smart devices that generate different types of traffic with different distributions and different Quality of Service (QoS) requirements. Due to their particular architectures, which are very dense and very dynamic, the traditional one-pair-node shortest path solution is no longer efficient to handle inter-smart home networks (inter-SHNs) routing constraints such as delay, packet loss, and bandwidth in all-pair node heterogenous links. In addition, Current QoS-aware scheduling methods consider only the conventional priority metrics based on the IP Type of Service (ToS) field to make decisions for bandwidth allocation. Such priority based scheduling methods are not optimal to provide both QoS and Quality of Experience (QoE), especially for smart home applications, since higher priority traffic does not necessarily require higher stringent delay than lower-priority traffic. Moreover, current QoS-aware scheduling methods in the intra-smart home network (intra-SHN) do not consider concurrent traffic caused by the fluctuation of intra-SH network traffic distributions. Thus, the goal of this dissertation is to build an efficient heterogenous multi-constrained routing mechanism and an optimized traffic scheduling tool in order to maintain a cost-effective communication between all wired-wireless connected devices in inter-SHNs and to effectively process concurrent and non-concurrent traffic in intra-SHN. This will help Internet service providers (ISPs) and home user to enhance the overall QoS and QoE of their applications while maintaining a relevant communication in both inter-SHNs and intra-SHN.
In order to meet this goal, three key issues are required to be addressed in our framework and are summarized as follows: i) how to build a cost-effective routing mechanism in heterogonous inter-SHNs ? ii) how to efficiently schedule the multi-sourced intra-SHN traffic based on both QoS and QoE ? and iii) how to design an optimized queuing model for intra-SHN concurrent traffics while considering their QoS requirements?
As part of our contributions to solve the first problem highlighted above, we present an analytical framework for dynamically optimizing data flows in inter-SHNs using Software-defined networking (SDN). We formulate a QoS-based routing optimization problem as a constrained shortest path problem and then propose an optimized solution (QASDN) to determine minimal cost between all pairs of nodes in the network taking into account the different types of physical accesses and the network utilization patterns.
To address the second issue and to solve the gaps between QoS and QoE, we propose a new queuing model for QoS-level Pair traffic with mixed arrival distributions in Smart Home network (QP-SH) to make a dynamic QoS-aware scheduling decision meeting delay requirements of all traffic while preserving their degrees of criticality. A new metric combining the ToS field and the maximum number of packets that can be processed by the system's service during the maximum required delay, is defined.
Finally, as part of our contribution to address the third issue, we present an analytic model for a QoS-aware scheduling optimization of concurrent intra-SHN traffics with mixed arrival distributions and using probabilistic queuing disciplines. We formulate a hybrid QoS-aware scheduling problem for concurrent traffics in intra-SHN, propose an innovative queuing model (QC-SH) based on the auction economic model of game theory to provide a fair multiple access over different communication channels/ports, and design an applicable model to implement auction game on both sides; traffic sources and the home gateway, without changing the structure of the IEEE 802.11 standard. The results of our work offer SHNs more effective data transfer between all heterogenous connected devices with optimal resource utilization, a dynamic QoS/QoE-aware traffic processing in SHN as well as an innovative model for optimizing concurrent SHN traffic scheduling with enhanced fairness strategy. Numerical results show an improvement up to 90% for network resource utilization, 77% for bandwidth, 40% for scheduling with QoS and QoE and 57% for concurrent traffic scheduling delay using our proposed solutions compared with Traditional methods
Maximising microprocessor reliability through game theory and heuristics
PhD ThesisEmbedded Systems are becoming ever more pervasive in our society, with most
routine daily tasks now involving their use in some form and the market predicted
to be worth USD 220 billion, a rise of 300%, by 2018. Consumers expect
more functionality with each design iteration, but for no detriment in perceived
performance. These devices can range from simple low-cost chips to expensive
and complex systems and are a major cost driver in the equipment design
phase. For more than 35 years, designers have kept pace with Moore's Law, but
as device size approaches the atomic limit, layouts are becoming so complicated
that current scheduling techniques are also reaching their limit, meaning that
more resource must be reserved to manage and deliver reliable operation. With
the advent of many-core systems and further sources of unpredictability such as
changeable power supplies and energy harvesting, this reservation of capability
may become so large that systems will not be operating at their peak efficiency.
These complex systems can be controlled through many techniques, with
jobs scheduled either online prior to execution beginning or online at each time
or event change. Increased processing power and job types means that current
online scheduling methods that employ exhaustive search techniques will not
be suitable to define schedules for such enigmatic task lists and that new techniques
using statistic-based methods must be investigated to preserve Quality
of Service.
A new paradigm of scheduling through complex heuristics is one way to
administer these next levels of processor effectively and allow the use of more
simple devices in complex systems; thus reducing unit cost while retaining reliability a key goal identified by the International Technology Roadmap for Semi-conductors for Embedded Systems in Critical Environments. These changes
would be beneficial in terms of cost reduction and system
exibility within the
next generation of device. This thesis investigates the use of heuristics and
statistical methods in the operation of real-time systems, with the feasibility of
Game Theory and Statistical Process Control for the successful supervision of
high-load and critical jobs investigated. Heuristics are identified as an effective
method of controlling complex real-time issues, with two-person non-cooperative
games delivering Nash-optimal solutions where these exist. The simplified algorithms for creating and solving Game Theory events allow for its use within
small embedded RISC devices and an increase in reliability for systems operating
at the apex of their limits. Within this Thesis, Heuristic and Game Theoretic
algorithms for a variety of real-time scenarios are postulated, investigated, refined and tested against existing schedule types; initially through MATLAB
simulation before testing on an ARM Cortex M3 architecture functioning as a
simplified automotive Electronic Control Unit.Doctoral Teaching Account from the EPSRC
epcAware: a game-based, energy, performance and cost efficient resource management technique for multi-access edge computing
The Internet of Things (IoT) is producing an extraordinary volume of data daily, and it is possible that the data may become useless while on its way to the cloud for analysis, due to longer distances and delays. Fog/edge computing is a new model for analyzing and acting on time-sensitive data (real-time applications) at the network edge, adjacent to where it is produced. The model sends only selected data to the cloud for analysis and long-term storage. Furthermore, cloud services provided by large companies such as Google, can also be localized to minimize the response time and increase service agility. This could be accomplished through deploying small-scale datacenters (reffered to by name as cloudlets) where essential, closer to customers (IoT devices) and connected to a centrealised cloud through networks - which form a multi-access edge cloud (MEC). The MEC setup involves three different parties, i.e. service providers (IaaS), application providers (SaaS), network providers (NaaS); which might have different goals, therefore, making resource management a defficult job. In the literature, various resource management techniques have been suggested in the context of what kind of services should they host and how the available resources should be allocated to customers’ applications, particularly, if mobility is involved. However, the existing literature considers the resource management problem with respect to a single party. In this paper, we assume resource management with respect to all three parties i.e. IaaS, SaaS, NaaS; and suggest a game theoritic resource management technique that minimises infrastructure energy consumption and costs while ensuring applications performance. Our empirical evaluation, using real workload traces from Google’s cluster, suggests that our approach could reduce up to 11.95% energy consumption, and approximately 17.86% user costs with negligible loss in performance. Moreover, IaaS can reduce up to 20.27% energy bills and NaaS can increase their costs savings up to 18.52% as compared to other methods
Unlocking the deployment of spectrum sharing with a policy enforcement framework
Spectrum sharing has been proposed as a promising way to increase the efficiency of spectrum usage by allowing incumbent operators (IOs) to share their allocated radio resources with licensee operators (LOs), under a set of agreed rules. The goal is to maximize a common utility, such as the sum rate throughput, while maintaining the level of service required by the IOs. However, this is only guaranteed under the assumption that all “players”respect the agreed sharing rules. In this paper, we propose a comprehensive framework for licensed shared access (LSA) networks that discourages LO misbehavior. Our framework is built around three core functions: misbehavior detection via the employment of a dedicated sensing network; a penalization function; and, a behavior-driven resource allocation. To the best of our knowledge, this is the first time that these components are combined for the monitoring/policing of the spectrum under the LSA framework. Moreover, a novel simulator for LSA is provided as an open access tool, serving the purpose of testing and validating our proposed techniques via a set of extensive system-level simulations in the context of mobile network operators, where IOs and several competing LOs are considered. The results demonstrate that violation of the agreed sharing rules can lead to a great loss of resources for the misbehaving LOs, the amount of which is controlled by the system. Finally, we promote that including a policy enforcement function as part of the spectrum sharing system can be beneficial for the LSA system, since it can guarantee compliance with the spectrum sharing rules and limit the short-term benefits arising from misbehavior
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