38 research outputs found

    Efficient scheduling algorithms for quality-of-service guarantees in the Internet

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    Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (p. 167-172).The unifying theme of this thesis is the design of packet schedulers to provide quality-of- service (QoS) guarantees for various networking problem settings. There is a dual emphasis on both theoretical justification and simulation evaluation. We have worked on several widely different problem settings - optical networks, input-queued crossbar switches, and CDMA wireless networks - and we found that the same set of scheduling techniques can be applied successfully in all these cases to provide per-flow bandwidth, delay and max-min fairness guarantees. We formulated the abstract scheduling problems as a sum of two aspects. First, the particular problem setting imposes constraints which dictate what kinds of transmission patterns are allowed by the physical hardware resources, i.e., what are the feasible solutions. Second, the users require some form of QoS guarantees, which translate into optimality criteria judging the feasible solutions. The abstract problem is how to design an algorithm that finds an optimal (or near-optimal) solution among the feasible ones. Our schedulers are based on a credit scheme. Specifically, flows receive credits at their guaranteed rate, and the arrival stream is compared to the credit stream acting as a reference. From this comparison, we derive various parameters such as the amount of unspent credits of a flow and the waiting time of a packet since its corresponding credit arrived. We then design algorithms which prioritize flows based on these parameters. We demonstrate, both by rigorous theoretical proofs and by simulations, that these parameters can be bounded. By bounding these parameters, our schedulers provide various per-flow QoS guarantees on average rate, packet delay, queue length and fairness.by Anthony Chi-Kong Kam.Ph.D

    Cross-layer design of multi-hop wireless networks

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    MULTI -hop wireless networks are usually defined as a collection of nodes equipped with radio transmitters, which not only have the capability to communicate each other in a multi-hop fashion, but also to route each others’ data packets. The distributed nature of such networks makes them suitable for a variety of applications where there are no assumed reliable central entities, or controllers, and may significantly improve the scalability issues of conventional single-hop wireless networks. This Ph.D. dissertation mainly investigates two aspects of the research issues related to the efficient multi-hop wireless networks design, namely: (a) network protocols and (b) network management, both in cross-layer design paradigms to ensure the notion of service quality, such as quality of service (QoS) in wireless mesh networks (WMNs) for backhaul applications and quality of information (QoI) in wireless sensor networks (WSNs) for sensing tasks. Throughout the presentation of this Ph.D. dissertation, different network settings are used as illustrative examples, however the proposed algorithms, methodologies, protocols, and models are not restricted in the considered networks, but rather have wide applicability. First, this dissertation proposes a cross-layer design framework integrating a distributed proportional-fair scheduler and a QoS routing algorithm, while using WMNs as an illustrative example. The proposed approach has significant performance gain compared with other network protocols. Second, this dissertation proposes a generic admission control methodology for any packet network, wired and wireless, by modeling the network as a black box, and using a generic mathematical 0. Abstract 3 function and Taylor expansion to capture the admission impact. Third, this dissertation further enhances the previous designs by proposing a negotiation process, to bridge the applications’ service quality demands and the resource management, while using WSNs as an illustrative example. This approach allows the negotiation among different service classes and WSN resource allocations to reach the optimal operational status. Finally, the guarantees of the service quality are extended to the environment of multiple, disconnected, mobile subnetworks, where the question of how to maintain communications using dynamically controlled, unmanned data ferries is investigated

    Final report on the evaluation of RRM/CRRM algorithms

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    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

    Improving smart charging for electric vehicle fleets by integrating battery and prediction models

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    With increasing electrification of vehicle fleets there is a rising demand for the effective use of charging infrastructure. Existing charging infrastructures are limited by undersized connection lines and a lack of charging stations. Upgrades require significant financial investment, time and effort. Smart charging represents an approach to making the most of existing charging infrastructure while satisfying charging needs. Smart charging involves scheduling for electric vehicles (EVs). In other words, smart charging approaches decide which EV may charge at which charging station and at which current during which time periods. Planning flexibility is determined by the length of stay and the available electrical supply. First, we present an approach for smart charging combining day-ahead planning with real-time planning. For day-ahead planning, we use a mixed integer programming model to compute optimal schedules while making use of information available ahead of time. We then describe a schedule guided heuristic which adapts precomputed schedules in real-time. Second, we address uncertainty in smart charging. For example, EV departure times are an important component in prioritization but are uncertain ahead of time. We use a regression model trained on historical data to predict EV departure times. We integrate predictions directly in the smart charging heuristic used in the first approach. Experimental results show a more accurate EV departure time leads to a more accurate EV prioritization and a higher amount of delivered energy. Third, we present two approaches which allow the smart charging heuristic to take EV charging behavior into account. In practice, EVs charge using nonlinear charge profiles where power declines towards the end of each charging process. There is thus a gap between the scheduled power and the actual charging power if nonlinear charge profiles are not taken into account. The first approach uses a traditional equivalent circuit model (ECM) to model EV charging behavior but in practice is limited by the availability of battery parameters. The second approach relies on a regression model trained on historical data to directly predict EV charging profiles. In each of the two approaches, the model of the EV's charging profile is directly integrated into the smart charging heuristic which allows the heuristic to produce more accurate charge plans. Experimental results show EVs charge significantly more energy because the charging infrastructure is used more effectively. Finally, we present an open source package containing the smart charging heuristic and describe results from applying the heuristic in a one-year field test. Experimental results from the field test show EVs at six charging stations can be scheduled for charging when the grid connection only allows two EVs to charge concurrently. Runtime measurements demonstrate the heuristic is applicable in real time and scales to large fleet sizes

    The 2012 Power Trading Agent Competition

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    This is the specification for the Power Trading Agent Competition for 2012 (Power TAC 2012). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given time slot. The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we do not model location-marginal pricing. Customer models include households and a variety of commercial and industrial entities, many of which have production capacity (such as solar panels or wind turbines) as well as electric vehicles. All have “real-time” metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate utility maximizers with respect to tariff selection, although the factors making up their utility functions may include aversion to change and complexity that can retard uptake of marginally better tariff offers. The distribution utility models the regulated natural monopoly that owns the regional distribution network, and is responsible for maintenance of its infrastructure and for real-time balancing of supply and demand. The balancing process is a market-based mechanism that uses economic incentives to encourage brokers to achieve balance within their portfolios of tariff subscribers and wholesale market positions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources. The broker with the highest bank balance at the end of the simulation wins

    On the Merits of Deploying TDM-based Next-Generation PON Solutions in the Access Arena As Multiservice, All Packet-Based 4G Mobile Backhaul RAN Architecture

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    The phenomenal growth of mobile backhaul capacity required to support the emerging fourth-generation (4G) traffic including mobile WiMAX, cellular Long-Term Evolution (LTE), and LTE-Advanced (LTE-A) requires rapid migration from today\u27s legacy circuit switched T1/E1 wireline and microwave backhaul technologies to a new fiber-supported, all-packet-based mobile backhaul infrastructure. Clearly, a cost effective fiber supported all-packet-based mobile backhaul radio access network (RAN) architecture that is compatible with these inherently distributed 4G RAN architectures is needed to efficiently scale current mobile backhaul networks. However, deploying a green fiber-based mobile backhaul infrastructure is a costly proposition mainly due to the significant cost associated with digging the trenches in which the fiber is to be laid. These, along with the inevitable trend towards all-IP/Ethernet transport protocols and packet switched networks, have prompted many carriers around the world to consider the potential of utilizing the existing fiber-based Passive Optical Network (PON) access infrastructure as an all-packet-based converged fixed-mobile optical access networking transport architecture to backhaul both mobile and typical wireline traffic. Passive Optical Network (PON)-based fiber-to-the-curb/home (FTTC/FTTH) access networks are being deployed around the globe based on two Time-Division Multiplexed (TDM) standards: ITU G.984 Gigabit PON (GPON) and IEEE 802.ah Ethernet PON (EPON). A PON connects a group of Optical Network Units (ONUs) located at the subscriber premises to an Optical Line Terminal (OLT) located at the service provider\u27s facility. It is the purpose of this thesis to examine the technological requirements and assess the performance analysis and feasibility for deploying TDM-based next-generation (NG) PON solutions in the access arena as multiservice, all packet-based 4G mobile backhaul RAN and/or converged fixed-mobile optical networking architecture. Specifically, this work proposes and devises a simple and cost-effective 10G-EPON-based 4G mobile backhaul RAN architecture that efficiently transports and supports a wide range of existing and emerging fixed-mobile advanced multimedia applications and services along with the diverse quality of service (QoS), rate, and reliability requirements set by these services. The techno-economics merits of utilizing PON-based 4G RAN architecture versus that of traditional 4G (mobile WiMAX and LTE) RAN will be thoroughly examine and quantified. To achieve our objective, we utilize the existing fiber-based PON access infrastructure with novel ring-based distribution access network and wireless-enabled OLT and ONUs as the multiservice packet-based 4G mobile backhaul RAN infrastructure. Specifically, to simplify the implementation of such a complex undertaking, this work is divided into two sequential phases. In the first phase, we examine and quantify the overall performance of the standalone ring-based 10G-EPON architecture (just the wireline part without overlaying/incorporating the wireless part (4G RAN)) via modeling and simulations. We then assemble the basic building blocks, components, and sub-systems required to build up a proof-of-concept prototype testbed for the standalone ring-based EPON architecture. The testbed will be used to verify and demonstrate the performance of the standalone architecture, specifically, in terms of power budget, scalability, and reach. In the second phase, we develop an integrated framework for the efficient interworking between the two wireline PON and 4G mobile access technologies, particularly, in terms of unified network control and management (NCM) operations. Specifically, we address the key technical challenges associated with tailoring a typically centralized PON-based access architecture to interwork with and support a distributed 4G RAN architecture and associated radio NCM operations. This is achieved via introducing and developing several salient-networking innovations that collectively enable the standalone EPON architecture to support a fully distributed 4G mobile backhaul RAN and/or a truly unified NG-PON-4G access networking architecture. These include a fully distributed control plane that enables intercommunication among the access nodes (ONUs/BSs) as well as signaling, scheduling algorithms, and handoff procedures that operate in a distributed manner. Overall, the proposed NG-PON architecture constitutes a complete networking paradigm shift from the typically centralized PON\u27s architecture and OLT-based NCM operations to a new disruptive fully distributed PON\u27s architecture and NCM operations in which all the typically centralized OLT-based PON\u27s NCM operations are migrated to and independently implemented by the access nodes (ONUs) in a distributed manner. This requires migrating most of the typically centralized wireline and radio control and user-plane functionalities such as dynamic bandwidth allocation (DBA), queue management and packet scheduling, handover control, radio resource management, admission control, etc., typically implemented in today\u27s OLT/RNC, to the access nodes (ONUs/4G BSs). It is shown that the overall performance of the proposed EPON-based 4G backhaul including both the RAN and Mobile Packet Core (MPC) {Evolved Packet Core (EPC) per 3GPP LTE\u27s standard} is significantly augmented compared to that of the typical 4G RAN, specifically, in terms of handoff capability, signaling overhead, overall network throughput and latency, and QoS support. Furthermore, the proposed architecture enables redistributing some of the intelligence and NCM operations currently centralized in the MPC platform out into the access nodes of the mobile RAN. Specifically, as this work will show, it enables offloading sizable fraction of the mobile signaling as well as actual local upstream traffic transport and processing (LTE bearers switch/set-up, retain, and tear-down and associated signaling commands from the BSs to the EPC and vice-versa) from the EPC to the access nodes (ONUs/BSs). This has a significant impact on the performance of the EPC. First, it frees up a sizable fraction of the badly needed network resources as well as processing on the overloaded centralized serving nodes (AGW) in the MPC. Second, it frees up capacity and sessions on the typically congested mobile backhaul from the BSs to the EPC and vice-versa

    An Ex-Ante Rational Distributed Resource Allocation System using Transfer of Control Strategies for Preemption with Applications to Emergency Medicine

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    Within the artificial intelligence subïŹeld of multiagent systems, one challenge that arises is determining how to efficiently allocate resources to all agents in a way that maximizes the overall expected utility. In this thesis, we explore a distributed solution to this problem, one in which the agents work together to coordinate their requests for resources and which is considered to be ex-ante rational: in other words, requiring agents to be willing to give up their current resources to those with greater need by reasoning about what is for the common good. Central to our solution is allowing for preemption of tasks that are currently occupying resources; this is achieved by introducing a concept from adjustable autonomy multiagent systems known as a transfer of control (TOC) strategy. In essence a TOC strategy is a plan of an agent to acquire resources at future times, and can be used as a contingency plan that an agent will execute if it loses its current resource. The inclusion of TOC strategies ultimately provides for a greater optimism among agents about their future resource acquisitions, allowing for more generous behaviours, and for agents to more frequently agree to relinquish current resources, resulting in more eïŹ€ective preemption policies. Three central contributions arise. The ïŹrst is an improved methodology for generating transfer of control strategies efficiently, using a dynamic programming approach, which enables a more eïŹ€ective employment of TOCs in our resource allocation solution. The second is an important clarification of the value of integrating learning techniques in order for agents to acquire improved estimates of the costs of preemption. The last is a validation of the overall multiagent resource allocation (MARA) solution, using simulations which show quantiïŹable benefits of our novel approach. In particular, we consider in detail the emergency medical application of mass casualty incidents and are able to demonstrate that our approach of integrating transfer of control strategies results in eïŹ€ective allocation of patients to doctors: ones which in simulations re- sult in dramatically fewer patients in a critical healthstate than are produced by competing MARA algorithms. In short, we oïŹ€er a principled solution to the problem of preemption, allowing the elimination of a source of inefficiencies in fully distributed multiagent resource allocation systems; a faster method for generation of transfer of control strategies; and a convincing application of the system to a real world problem where human lives are at stake

    Embedding Games

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    Large scale distributed computing infrastructures pose challenging resource management problems, which could be addressed by adopting one of two perspectives. On the one hand, the problem could be framed as a global optimization that aims to minimize some notion of system-wide (social) cost. On the other hand, the problem could be framed in a game-theoretic setting whereby rational, selfish users compete for a share of the resources so as to maximize their private utilities with little or no regard for system-wide objectives. This game-theoretic setting is particularly applicable to emerging cloud and grid environments, testbed platforms, and many networking applications. By adopting the first, global optimization perspective, this thesis presents NetEmbed: a framework, associated mechanisms, and implementations that enable the mapping of requested configurations to available infrastructure resources. By adopting the second, game-theoretic perspective, this thesis defines and establishes the premises of two resource acquisition mechanisms: Colocation Games and Trade and Cap. Colocation Games enable the modeling and analysis of the dynamics that result when rational, selfish parties interact in an attempt to minimize the individual costs they incur to secure shared resources necessary to support their application QoS or SLA requirements. Trade and Cap is a market-based scheduling and load-balancing mechanism that facilitates the trading of resources when users have a mixture of rigid and fluid jobs, and incentivizes users to behave in ways that result in better load-balancing of shared resources. In addition to developing their analytical underpinnings, this thesis establishes the viability of NetEmbed, Colocation Games, and Trade and Cap by presenting implementation blueprints and experimental results for many variants of these mechanisms. The results presented in this thesis pave the way for the development of economically-sound resource acquisition and management solutions in two emerging, and increasingly important settings. In pay-as-you-go settings, where pricing is based on usage, this thesis anticipates new service offerings that enable efficient marketplaces in the presence of non-cooperative, selfish agents. In settings where pricing is not a function of usage, this thesis anticipates the development of service offerings that enable trading of usage rights to maximize the utility of a shared infrastructure to its tenants

    Link Scheduling in UAV-Aided Networks

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    Unmanned Aerial Vehicles (UAVs) or drones are a type of low altitude aerial mobile vehicles. They can be integrated into existing networks; e.g., cellular, Internet of Things (IoT) and satellite networks. Moreover, they can leverage existing cellular or Wi-Fi infrastructures to communicate with one another. A popular application of UAVs is to deploy them as mobile base stations and/or relays to assist terrestrial wireless communications. Another application is data collection, whereby they act as mobile sinks for wireless sensor networks or sensor devices operating in IoT networks. Advantageously, UAVs are cost-effective and they are able to establish line-of-sight links, which help improve data rate. A key concern, however, is that the uplink communications to a UAV may be limited, where it is only able to receive from one device at a time. Further, ground devices, such as those in IoT networks, may have limited energy, which limit their transmit power. To this end, there are three promising approaches to address these concerns, including (i) trajectory optimization, (ii) link scheduling, and (iii) equipping UAVs with a Successive Interference Cancellation (SIC) radio. Henceforth, this thesis considers data collection in UAV-aided, TDMA and SICequipped wireless networks. Its main aim is to develop novel link schedulers to schedule uplink communications to a SIC-capable UAV. In particular, it considers two types of networks: (i) one-tier UAV communications networks, where a SIC-enabled rotary-wing UAV collects data from multiple ground devices, and (ii) Space-Air-Ground Integrated Networks (SAGINs), where a SIC-enabled rotary-wing UAV offloads collected data from ground devices to a swarm of CubeSats. A CubeSat then downloads its data to a terrestrial gateway. Compared to one-tier UAV communications networks, SAGINs are able to provide wide coverage and seamless connectivity to ground devices in remote and/or sparsely populated areas

    Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees

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    Many problems, such as online ad display, can be formulated as online bipartite matching. The crucial challenge lies in the nature of sequentially-revealed online item information, based on which we make irreversible matching decisions at each step. While numerous expert online algorithms have been proposed with bounded worst-case competitive ratios, they may not offer satisfactory performance in average cases. On the other hand, reinforcement learning (RL) has been applied to improve the average performance, but it lacks robustness and can perform arbitrarily poorly. In this paper, we propose a novel RL-based approach to edge-weighted online bipartite matching with robustness guarantees (LOMAR), achieving both good average-case and worst-case performance. The key novelty of LOMAR is a new online switching operation which, based on a judicious condition to hedge against future uncertainties, decides whether to follow the expert's decision or the RL decision for each online item. We prove that for any ρ∈[0,1]\rho\in[0,1], LOMAR is ρ\rho-competitive against any given expert online algorithm. To improve the average performance, we train the RL policy by explicitly considering the online switching operation. Finally, we run empirical experiments to demonstrate the advantages of LOMAR compared to existing baselines. Our code is available at: https://github.com/Ren-Research/LOMARComment: Accepted by ICML 202
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