Browsing by Subject "Resource management"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Efficient Methods for Distributed Machine Learning and Resource Management in the Internet-of-Things(2019-06) Chen, TianyiUndoubtedly, this century evolves in a world of interconnected entities, where the notion of Internet-of-Things (IoT) plays a central role in the proliferation of linked devices and objects. In this context, the present dissertation deals with large-scale networked systems including IoT that consist of heterogeneous components, and can operate in unknown environments. The focus is on the theoretical and algorithmic issues at the intersection of optimization, machine learning, and networked systems. Specifically, the research objectives and innovative claims include: (T1) Scalable distributed machine learning approaches for efficient IoT implementation; and, (T2) Enhanced resource management policies for IoT by leveraging machine learning advances. Conventional machine learning approaches require centralizing the users' data on one machine or in a data center. Considering the massive amount of IoT devices, centralized learning becomes computationally intractable, and rises serious privacy concerns. The widespread consensus today is that besides data centers at the cloud, future machine learning tasks have to be performed starting from the network edge, namely mobile devices. The first contribution offers innovative distributed learning methods tailored for heterogeneous IoT setups, and with reduced communication overhead. The resultant distributed algorithm can afford provably reduced communication complexity in distributed machine learning. From learning to control, reinforcement learning will play a critical role in many complex IoT tasks such as autonomous vehicles. In this context, the thesis introduces a distributed reinforcement learning approach featured with its high communication efficiency. Optimally allocating computing and communication resources is a crucial task in IoT. The second novelty pertains to learning-aided optimization tools tailored for resource management tasks. To date, most resource management schemes are based on a pure optimization viewpoint (e.g., the dual (sub)gradient method), which incurs suboptimal performance. From the vantage point of IoT, the idea is to leverage the abundant historical data collected by devices, and formulate the resource management problem as an empirical risk minimization task --- a central topic in machine learning research. By cross-fertilizing advances of optimization and learning theory, a learn-and-adapt resource management framework is developed. An upshot of the second part is its ability to account for the feedback-limited nature of tasks in IoT. Typically, solving resource allocation problems necessitates knowledge of the models that map a resource variable to its cost or utility. Targeting scenarios where models are not available, a model-free learning scheme is developed in this thesis, along with its bandit version. These algorithms come with provable performance guarantees, even when knowledge about the underlying systems is obtained only through repeated interactions with the environment. The overarching objective of this dissertation is to wed state-of-the-art optimization and machine learning tools with the emerging IoT paradigm, in a way that they can inspire and reinforce the development of each other, with the ultimate goal of benefiting daily life.Item Resource management in wireless heterogeneous networks: an optimization perspective(2014-12) Sanjabi Boroujeni, MaziarIn this dissertation we consider the central task of resource management in wireless Heterogeneous Networks (HetNets). Resource management plays an important role in satisfying the increasing need for wireless data in HetNets. Our emphasis is mainly on cross layer strategies. Various aspects of cross layer resource management can be formulated as optimization problems. Throughout this dissertation, we use advanced optimization techniques to develop algorithms that are capable of efficiently solving these optimization problems. First, we consider the joint base station assignment and linear {transceiver} design problem. In order to gain a better understanding of resource management problems, we analyze the complexity of solving the resulting optimization problem. We establish the NP-hardness of this problem for a wide range of system-wide utility functions.Due to the fundamental difficulty of globally solving these problems, our emphasis in the rest of this dissertation is on devising efficient algorithms that can approximately solve these problems under different practical limitations. One major practical limitation of current resource management strategies is the need for the channel state information at the transmitter side. In this thesis we consider transceiver design in wireless HetNet when the channel state information is incomplete/inexact. We propose a general stochastic successive upper-bound minimization approach to optimize the average/ergodic utility of the system. We specialize our method to obtain an efficient stochastic sum-rate maximization algorithm. The proposed algorithm can use the statistical knowledge instead of actual channel values and is guaranteed to converge to the set of stationary points of the stochastic sum-rate maximization problem. We further generalize our stochastic method to a cross layer framework for jointly optimizing the base station clustering and the downlink beamformers in a partial coordinated transmission scenario. The partial coordination is crucial in improving the overall system performance by reducing backhaul overhead. We validate the effectiveness of our methods via numerical experiments.Item Resource management in wireless networks and the smart power grid.(2012-06) Gatsis, NikolaosOptimal resource management is a crucial task in a plethora of scientific fields, including wireless communication and electric power networks, where it ensures efficient operation and user satisfaction. The pressing need to modernize the aging power grid has culminated to a vision encouraging interaction of the end users with the grid through demand response, which amounts to electricity end users adapting their power consumption in response to pricing schemes varying over time (e.g., every hour or day). By the same token, delivering data, voice, and video seamlessly over wireless networks with the quality-of-service demanded by today's multimedia applications requires optimal link-adaptive allocation of the available resources, e.g., power, to the different network nodes and layers. This thesis develops algorithms for (a) scheduling of demand response in the smart power grid, and (b) cross-layer wireless network design. First, demand response is considered in a multiple-residence setup. The utility company adopts a cost function representing the cost of providing energy to end users. Each residential end user has a base load, two types of adjustable loads, and possibly a storage device. The first load type must consume a specified amount of energy over the scheduling horizon, but the consumption can be adjusted across different slots. Charging a plug-in hybrid electric vehicle is an example. The second type does not entail a total energy requirement, but operation away from a user-specified level results in user dissatisfaction. The research issue amounts to minimizing the electricity provider cost plus the total user dissatisfaction, subject to the individual constraints of the loads. The problem can be solved by a distributed subgradient method. The utility company and the end users exchange information through the Advanced Metering Infrastructure (AMI)---a two-way communication network---in order to converge to the optimal amount of electricity production and the optimal power consumption schedule. The algorithm finds near-optimal schedules even when AMI messages are lost, which can happen in the presence of malfunctions or noise in the communications network. The algorithm amounts to a subgradient iteration with outdated Lagrange multipliers, for which convergence results of wide scope are established. Next, attention is turned to an energy consumption scheduling problem for a single residential end user, but with an added complexity. Each adjustable load is interruptible in the sense that the load can be either operated (resulting in nonzero power consump- tion), or not operated (resulting in zero power consumption). The task amounts to minimizing the cost of electricity plus user dissatisfaction, subject to individual load consumption constraints. The resulting problem is nonconvex, but it is shown to have zero duality gap if a continuous-time horizon is considered. This opens up the possibility of using Lagrangian dual algorithms without loss of optimality in order to come up with efficient demand response scheduling schemes. As regards wireless networking, the challenge is to jointly optimize application-level rates, routes, link capacities, power consumption, and power allocation across frequency tones, neighboring terminals, and fading states. The physical layer is interference-limited, whereby network terminals treat interference as noise. Provably convergent algorithms yield (near-)optimal end-to-end rates, multicommodity flows, link capacities, and average powers. These design variables are obtained offline, and are subsequently used for control during network operation. Moreover, physical layer power allocation algorithms that are seamlessly integrated into layered architectures are developed using successive convex approximations.