Dual Priority Scheduling is Not Optimal

In dual priority scheduling, periodic tasks are executed in a fixed-priority manner, but each job has two phases with different priorities. The second phase is entered after a fixed amount of time has passed since the release of the job, at which point the job changes its priority. Dual priority scheduling was introduced by Burns and Wellings in 1993 and was shown to successfully schedule many task sets that are not schedulable with ordinary (single) fixed-priority scheduling. Burns and Wellings conjectured that dual priority scheduling is an optimal scheduling algorithm for synchronous periodic tasks with implicit deadlines on preemptive uniprocessors. We demonstrate the falsity of this conjecture, as well as of some related conjectures that have since been stated. This is achieved by means of computer-verified counterexamples

Scheduling Independent Moldable Tasks on Multi-Cores with GPUs

The number of parallel systems using accelerators is growing up.The technology is now mature enough to allow sustainedpetaflop/s. However, reaching this performance scale requiresefficient scheduling algorithms to manage the heterogeneouscomputing resources.We present a new approach for scheduling independent tasks onmultiple CPUs and multiple GPUs. The tasks are assumed to beparallelizable on CPUs using the moldable model: the final numberof cores allotted to a task can be decided and set by thescheduler. More precisely, we design an algorithm aiming atminimizing the makespan---the maximum completion time of alltasks---for this scheduling problem. The proposed algorithmcombines a dual approximation scheme with a fast integer linearprogram (ILP). It determines both the partitioning of the tasks,ie whether a task should be mapped to CPUs or a GPU, and thenumber of CPUs allotted to a moldable task if mapped to the CPUs.A worst case analysis shows that the algorithm has anapproximation ratio of $\frac{3}{2} + \epsilon$. However, sincethe complexity of the ILP-based algorithm could benon-polynomial, we also present a proved polynomial-timealgorithm with an approximation ratio of $2+\epsilon$.We complement the theoretical analysis of our two novelalgorithms with an experimental study. In these experiments, wecompare our algorithms to a modified version of the classical\heft algorithm, adapted to handle moldable tasks. Theexperimental results show that our algorithm with the$\frac{3}{2} + \epsilon$ approximation ratio producessignificantly shorter schedules than the modified \heft for mostof the instances. In addition, the experiments provide evidencethat this ILP-based algorithm is also practically able to solvelarger problem instances in a reasonable amount of time

Ordonnancement de camions dans une plateforme logistique : complexité, méthodes de résolution et incertitudes

La problématique dite de crossdocking a été source de beaucoup d'attention ces dernières années dans la littérature. Un crossdock est une plateforme logistique favorisant, par une synchronisation efficace des camions entrants et sortants, une rotation rapide des produits, le volume de produits stockés devant être le plus faible possible. Le crossdocking soulève de nombreux problèmes logistiques, dont notamment celui de l'ordonnancement des camions entrants et sortants sur les quais de la plateforme. L'objectif classiquement considéré dans la littérature pour ce problème est la minimisation du makespan, critère très répandu en d'ordonnancement. Pour le crossdocking néanmoins, minimiser la date de départ du dernier camion ne garantie pas nécessairement une bonne synchronisation des camions et le makespan ne semble donc pas être l'objectif le plus pertinent. Pour répondre au besoin de synchronisation et favoriser les rotations rapides, notre travail propose alternativement de minimiser la somme des temps de séjour des palettes dans le stock. Nous étudions d'abord la version déterministe de ce problème d'ordonnancement. Sa complexité est détaillée selon différentes hypothèses pour identifier les éléments menant à sa NP-difficulté. Différentes méthodes de résolutions sont proposées. Une méthode classique de programmation linéaire en nombres entiers utilisant des variables de décision indexées par le temps. Une famille d'inégalités valides est également proposée et exploitée dans un algorithme avec ajout itératif de coupes. Des méthodes basées sur la programmation par contraintes sont enfin proposées. Une analyse comparative de ces différentes méthodes est proposée. Dans un deuxième temps, nous étudions une version non-déterministe de notre problème d'ordonnancement dans laquelle des incertitudes sur les dates d'arrivée des camions sont introduites sous la forme d'intervalles de temps équiprobables. Une méthode d'ordonnancement proactive-réactive utilisant le concept de groupes d'opérations permutables est proposée pour faire face aux incertitudes. Des groupes de camions permutables sont séquencés et affectés aux quais puis, durant l'exécution d'ordonnancement, en fonction de la réalisation des dates d'arrivée, un ordre est choisi dans chaque groupe à l'aide d'un algorithme réactif.Crossdocking has received a lot of attention in the literature in recent years. A crossdock is a logistic platform that promotes rapid product turnover through efficient synchronization of incoming and outgoing trucks, with the volume of products stored being kept as low as possible. Crossdocking raises many logistical problems, including the scheduling of incoming and outgoing trucks on the platform's docks. The classical objective considered in the literature for this problem is the minimization of the makespan, a very common criterion in scheduling. However, for crossdocking, minimizing the departure date of the last truck does not necessarily guarantee a good synchronization of the trucks and the makespan does not seem to be the most relevant objective. In order to meet the need for synchronization and to help fast rotations, our work proposes alternatively to minimize the sum of the pallets' sojourn times in the warehouse. We first study the deterministic version of this scheduling problem. Its complexity is detailed under different assumptions to identify the elements leading to its NP-hardness. Different solution methods are proposed. A classical integer linear programming method using time-indexed decision variables. A family of valid inequalities is also proposed and exploited in an algorithm with iterative addition of cuts. Finally, methods based on constraint programming are proposed. A comparative analysis of these different methods is proposed. In a second step, we study a non-deterministic version of our scheduling problem in which uncertainties on truck arrival dates are introduced in the form of equiprobable time intervals. A proactive-reactive scheduling method using the concept of permutable operation groups is proposed to cope with the uncertainties. Groups of permutable trucks are sequenced and assigned to the docks and then, during the scheduling run, based on the realization of arrival dates, an order is chosen in each group using a reactive algorithm

Experience-driven Control For Networking And Computing

Modern networking and computing systems have become very complicated and highly dynamic, which makes them hard to model, predict and control. In this thesis, we aim to study system control problems from a whole new perspective by leveraging emerging Deep Reinforcement Learning (DRL), to develop experience-driven model-free approaches, which enable a network or a device to learn the best way to control itself from its own experience (e.g., runtime statistics data) rather than from accurate mathematical models, just as a human learns a new skill (e.g., driving, swimming, etc). To demonstrate the feasibility and superiority of this experience-driven control design philosophy, we present the design, implementation, and evaluation of multiple DRL-based control frameworks on two fundamental networking problems, Traffic Engineering (TE) and Multi-Path TCP (MPTCP) congestion control, as well as one cutting-edge application, resource co-scheduling for Deep Neural Network (DNN) models on mobile and edge devices with heterogeneous hardware. We first propose DRL-TE, a DRL-based framework that enables experience-driven networking for TE. DRL-TE maximizes a widely-used utility function by jointly learning network environment and its dynamics, and making decisions under the guidance of powerful DNNs. We propose two new techniques, TE-aware exploration and actor-critic-based prioritized experience replay, to optimize the general DRL framework particularly for TE. Furthermore, we propose an Actor-Critic-based Transfer learning framework for TE, ACT-TE, which solves a practical problem in experience-driven networking: when network configurations are changed, how to train a new DRL agent to effectively and quickly adapt to the new environment. In the new network environment, ACT-TE leverages policy distillation to rapidly learn a new control policy from both old knowledge (i.e., distilled from the existing agent) and new experience (i.e., newly collected samples). In addition, we propose DRL-CC to enable experience-driven congestion control for MPTCP. DRL-CC utilizes a single (instead of multiple independent) DRL agent to dynamically and jointly perform congestion control for all active MPTCP flows on an end host with the objective of maximizing the overall utility. The novelty of our design is to utilize a flexible recurrent neural network, LSTM, under a DRL framework for learning a representation for all active flows and dealing with their dynamics. Moreover, we integrate the above LSTM-based representation network into an actor-critic framework for continuous congestion control, which applies the deterministic policy gradient method to train actor, critic, and LSTM networks in an end-to-end manner. With the emergence of more and more powerful chipsets and hardware and the rise of Artificial Intelligence of Things (AIoT), there is a growing trend for bringing DNN models to empower mobile and edge devices with intelligence such that they can support attractive AI applications on the edge in a real-time or near real-time manner. To leverage heterogeneous computational resources (such as CPU, GPU, DSP, etc) to effectively and efficiently support concurrent inference of multiple DNN models on a mobile or edge device, in the last part of this thesis, we propose a novel experience-driven control framework for resource co-scheduling, which we call COSREL. COSREL has the following desirable features: 1) it achieves significant speedup over commonly-used methods by efficiently utilizing all the computational resources on heterogeneous hardware; 2) it leverages DRL to make dynamic and wise online scheduling decisions based on system runtime state; 3) it is capable of making a good tradeoff among inference latency, throughput and energy efficiency; and 4) it makes no changes to given DNN models, thus preserves their accuracies. To validate and evaluate the proposed frameworks, we conduct extensive experiments on packet-level simulation (for TE), testbed with modified Linux kernel (for MPTCP), and off-the-shelf Android devices (for resource co-scheduling). The results well justify the effectiveness of these frameworks, as well as their superiority over several baseline methods

ActMesh- A Cognitive Resource Management paradigm for dynamic mobile Internet Access with Reliability Guarantees

Wireless Mesh Networks (WMNs) are going increasing attention as a flexible low-cost networking architecture to provide media Internet access over metropolitan areas to mobile clients requiring multimedia services. In WMNs, Mesh Routers (MRs) from the mesh backbone and accomplish the twofold task of traffic forwarding, as well as providing multimedia access to mobile Mesh Clients (MCs). Due to the intensive bandwidth-resource requested for supporting QoS-demanding multimedia services, performance of the current WMNs is mainly limited by spectrum-crowding and traffic-congestion, as only scarce spectrum-resources is currently licensed for the MCs' access. In principle, this problem could be mitigated by exploiting in a media-friendly (e.g., content-aware) way the context-aware capabilities offered by the Cognitive Radio (CR) paradigm. As integrated exploitation of both content and context-aware system's capabilities is at the basis of our proposed Active Mesh (ActMesh) networking paradigm. This last aims at defining a network-wide architecture for realizing media-friendly Cognitive Mesh nets (e.g., context aware Cognitive Mesh nets). Hence, main contribution of this work is four fold: 1. After introducing main functional blocks of our ActMesh architecture, suitable self-adaptive Belief Propagation and Soft Data Fusion algorithms are designed to provide context-awareness. This is done under both cooperative and noncooperative sensing frameworks. 2. The resulting network-wide resource management problem is modelled as a constrained stochastic Network Utility Maximization (NUM) problem, with the dual (contrasting) objective to maximize spectrum efficiency at the network level, while accounting for the perceived quality of the delivered media flows at the client level. 3. A fully distributed, scalable and self-adaptive implementation of the resulting Active Resource Manager (ARM) is deployed, that explicitly accounts for the energy limits of the battery powered MCs and the effects induced by both fading and client mobility. Due to informationally decentralized architecture of the ActMesh net, the complexity of (possibly, optimal) centralized solutions for resource management becomes prohibitive when number of MCs accessing ActMesh net grow. Furthermore, centralized resource management solutions could required large amounts of time to collect and process the required network information, which, in turn, induce delay that can be unacceptable for delay sensitive media applications, e.g., multimedia streaming. Hence, it is important to develop network-wide ARM policies that are both distributed and scalable by exploiting the radio MCs capabilities to sense, adapt and coordinate themselves. We validate our analytical models via simulation based numerical tests, that support actual effectiveness of the overall ActMesh paradigm, both in terms of objective and subjective performance metrics. In particular, the basic tradeoff among backbone traffic-vs-access traffic arising in the ActMesh net from the bandwidth-efficient opportunistic resource allocation policy pursued by the deployed ARM is numerically characterized. The standardization framework we inspire to is the emerging IEEE 802.16h one

ActMesh- A Cognitive Resource Management paradigm for dynamic mobile Internet Access with Reliability Guarantees

Wireless Mesh Networks (WMNs) are going increasing attention as a flexible low-cost networking architecture to provide media Internet access over metropolitan areas to mobile clients requiring multimedia services. In WMNs, Mesh Routers (MRs) from the mesh backbone and accomplish the twofold task of traffic forwarding, as well as providing multimedia access to mobile Mesh Clients (MCs). Due to the intensive bandwidth-resource requested for supporting QoS-demanding multimedia services, performance of the current WMNs is mainly limited by spectrum-crowding and traffic-congestion, as only scarce spectrum-resources is currently licensed for the MCs' access. In principle, this problem could be mitigated by exploiting in a media-friendly (e.g., content-aware) way the context-aware capabilities offered by the Cognitive Radio (CR) paradigm. As integrated exploitation of both content and context-aware system's capabilities is at the basis of our proposed Active Mesh (ActMesh) networking paradigm. This last aims at defining a network-wide architecture for realizing media-friendly Cognitive Mesh nets (e.g., context aware Cognitive Mesh nets). Hence, main contribution of this work is four fold: 1. After introducing main functional blocks of our ActMesh architecture, suitable self-adaptive Belief Propagation and Soft Data Fusion algorithms are designed to provide context-awareness. This is done under both cooperative and noncooperative sensing frameworks. 2. The resulting network-wide resource management problem is modelled as a constrained stochastic Network Utility Maximization (NUM) problem, with the dual (contrasting) objective to maximize spectrum efficiency at the network level, while accounting for the perceived quality of the delivered media flows at the client level. 3. A fully distributed, scalable and self-adaptive implementation of the resulting Active Resource Manager (ARM) is deployed, that explicitly accounts for the energy limits of the battery powered MCs and the effects induced by both fading and client mobility. Due to informationally decentralized architecture of the ActMesh net, the complexity of (possibly, optimal) centralized solutions for resource management becomes prohibitive when number of MCs accessing ActMesh net grow. Furthermore, centralized resource management solutions could required large amounts of time to collect and process the required network information, which, in turn, induce delay that can be unacceptable for delay sensitive media applications, e.g., multimedia streaming. Hence, it is important to develop network-wide ARM policies that are both distributed and scalable by exploiting the radio MCs capabilities to sense, adapt and coordinate themselves. We validate our analytical models via simulation based numerical tests, that support actual effectiveness of the overall ActMesh paradigm, both in terms of objective and subjective performance metrics. In particular, the basic tradeoff among backbone traffic-vs-access traffic arising in the ActMesh net from the bandwidth-efficient opportunistic resource allocation policy pursued by the deployed ARM is numerically characterized. The standardization framework we inspire to is the emerging IEEE 802.16h one

Schedulers for next generation wireless networks : realizing QoE trade-offs for heterogeneous traffic mixes

In this thesis we will focus on the design of schedulers for next generation wireless networks which support application mixes, characterized by different, possibly complex, application/user Quality of Experience (QoE) metrics. The central problem underlying resource allocation for such systems is realizing QoE trade-offs among various applications/users given the dynamic loads and capacity variability they would typically see. In the first part of the thesis our focus is on applications where QoE depends on flow-level delay-based metrics. We consider system-wide metrics which directly capture both users' QoE metrics and appropriate QoE trade-offs among various applications for a wide range of system loads. This approach is different from the traditional wireless scheduler designs which have been driven by rate-based criteria, e.g., utility maximizing/proportionally fair, and/or queue-based packet schedulers which do not directly reflect the link between flow-level delays and users' QoE. In the second part of this thesis we address the key design challenges in networks supporting Ultra Reliable Low Latency Communications (URLLC) traffic which requires extremely high reliability (99.999%) and very low delays (1 msec). We will explore three different types flow delay-based metrics in this proposal, based on 1) overall mean delay; 2) functions of mean delays; and, 3) mean of functions of delays. We begin by considering minimization of mean flow delay for an M/GI/1 queuing model for a wireless Base Station (BS) where the flow size distributions are of the New Better than Used in Expectation + Decreasing Hazard Rate (NBUE +DHZ) type. Such a flow size distribution have been observed in real systems and we too validate this model based on collected data. Using a combination of analysis and simulation we show that our scheduler achieves good performance for users that might correspond to interactive applications like web browsing and/or stored video streaming and is robust to variations in system loads. Next we consider a generalization of this approach where we minimize a metric based on cost functions of the mean flow delays in a multi-class system where users/flows are classified based on their respective QoE requirements and each class's QoE requirement is modeled by its respective cost function. This approach helps us model QoE more accurately and gives us more flexibility in considering QoE trade-offs among heterogeneous user classes. We optimize two different metrics based on how we average the cost functions of delays, namely, functions of mean delays; and mean of functions of delays. The former can be used when users' experiences are sensitive to mean delays and while the latter can be used when user's experience is also sensitive to higher moments of delays, e.g., variance or soft thresholds on delay. Extensive simulations confirm the effectiveness of our proposed approaches at realizing various QoE trade-offs and performance. In 5G wireless networks URLLC traffic is expected to support many applications like industrial automation, mission critical traffic, virtual traffic etc, where the wireless network has to reliability transport small packets with very high reliability and low delays. We address the following aspects related to the system design for URLLC traffic, 1) quantifying the impact of various system parameters like system bandwidth, link SINR, delay and latency constraints on URLLC 'capacity'; 2) provisioning wireless system appropriately to meet URLLC Quality of Service (QoS) requirements; and, 3) designing efficient Hybrid Automatic Repeat Request (HARQ) schemes for transmitting small packets. Further, due the heterogeneity in delay requirements between URLLC and other types of traffic, sharing radio resources between them creates its own unique challenges. We develop efficient multiplexing schemes between URLLC traffic and other mobile broadband traffic based on preemptive puncturing/superposition of the mobile broadband transmissions by URLLC transmissions.Electrical and Computer Engineerin