Opportunistic transmission scheduling for next generation wireless communication systems with multimedia services

The explosive growth of the Internet and the continued dramatic increase for all wireless services are fueling the demand for increased capacity, data rates, and support of different quality of service (QoS) requirements for different classes of services. Since in the current and future wireless communication infrastructures, the performances of the various services are strongly correlated, as the resources are shared among them, dynamic resource allocation methods should be employed. With the demand for high data rate and support of multiple QoS, the transmission scheduling plays a key role in the efficient resource allocation process in wireless systems. The fundamental problem of scheduling the users\u27 transmissions and allocating the available resources in a realistic CDMA wireless system that supports multi-rate multimedia services, with efficiency and fairness, is investigated and analyzed in this dissertation. Our proposed approach adopts the use of dynamically assigned data rates that match the channel capacity in order to improve the system throughput and overcome the problems associated with the location-dependent and time-dependent errors and channel conditions, the variable system capacity and the transmission power limitation. We first introduce and describe two new scheduling algorithms, namely the Channel Adaptive Rate Scheduling (CARS) and Fair Channel Adaptive Rate Scheduling (FCARS). CARS exploits the channel variations to reach high throughput, by adjusting the transmission rates according to the varying channel conditions and by performing an iterative procedure to determine the power index that a user can accept by its current channel condition and transmission power. Based on the assignment of CARS and to overcome potential unfair service allocation, FCARS implements a compensation algorithm, in which the lagging users can receive compensation service when the corresponding channel conditions improve, in order to achieve asymptotic throughput fairness, while still maintaining all the constraints imposed by the system. Furthermore the problem of opportunistic fair scheduling in the uplink transmission of CDMA systems, with the objective of maximizing the uplink system throughput, while satisfying the users\u27 QoS requirements and maintaining the long-term fairness among the various users despite their different varying channel conditions, is rigorously formulated, and a throughput optimal fair scheduling policy is obtained. The corresponding problem is expressed as a weighted throughput maximization problem, under certain power and QoS constraints, where the weights are the control parameters that reflect the fairness constraints. With the introduction of the power index capacity it is shown that this optimization problem can be converted into a binary knapsack problem, where all the corresponding constraints are replaced by the users\u27 power index capacities at some certain system power index. It is then argued that the optimal solution can be obtained as a global search within a certain range, while a stochastic approximation method is presented in order to effectively identify the required control parameters. Finally, since some real-time services may demand certain amount of service within specific short span of time in order to avoid service delays, the problem of designing policies that can achieve high throughput while at the same time maintain short term fairness, is also considered and investigated. To this end a new Credit-based Short-term Fairness Scheduling (CSFS) algorithm, which achieves to provide short-term fairness to the delay-sensitive users while still schedules opportunistically the non-delay-sensitive users to obtain high system throughput, is proposed and evaluated

Real-Time Task Scheduling under Thermal Constraints

As the speed of integrated circuits increases, so does their power consumption. Most of this power is turned into heat, which must be dissipated effectively in order for the circuit to avoid thermal damage. Thermal control therefore has emerged as an important issue in design and management of circuits and systems. Dynamic speed scaling, where the input power is temporarily reduced by appropriately slowing down the circuit, is one of the major techniques to manage power so as to maintain safe temperature levels. In this study, we focus on thermally-constrained hard real-time systems, where timing guarantees must be met without exceeding safe temperature levels within the microprocessor. Speed scaling mechanisms provided in many of today’s processors provide opportunities to temporarily increase the processor speed beyond levels that would be safe over extended time periods. This dissertation addresses the problem of safely controlling the processor speed when scheduling mixed workloads with both hard-real-time periodic tasks and non-real-time, but latency-sensitive, aperiodic jobs. We first introduce the Transient Overclocking Server, which safely reduces the response time of aperiodic jobs in the presence of hard real-time periodic tasks and thermal constraints. We then propose a design-time (off-line) execution-budget allocation scheme for the application of the Transient Overclocking Server. We show that there is an optimal budget allocation which depends on the temporal character istics of the aperiodic workload. In order to provide a quantitative framework for the allocation of budget during system design, we present a queuing model and validate the model with results from a discrete-event simulator. Next, we describe an on-line thermally-aware transient overclocking method to reduce the response time of aperiodic jobs efficiently at run-time. We describe a modified Slack-Stealing algorithm to consider the thermal constraints of systems together with the deadline constraints of periodic tasks. With the thermal model and temperature data provided by embedded thermal sensors, we compute slack for aperiodic workload at run-time that satisfies both thermal and temporal constraints. We show that the proposed Thermally-Aware Slack-Stealing algorithm minimizes the response times of aperiodic jobs while guaranteeing both the thermal safety of the system and the schedulability of the real-time tasks. The two proposed speed control algorithms are examples of so-called proactive schemes, since they rely on a prediction of the thermal trajectory to control the temperature before safe levels are exceeded. In practice, the effectiveness of proactive speed control for the thermal management of a system relies on the accuracy of the thermal model that underlies the prediction of the effects of speed scaling and task execution on the temperature of the processor. Due to variances in the manufacturing of the circuit and of the environment it is to operate, an accurate thermal model can be gathered at deployment time only. The absence of power data makes a straightforward derivation of a model impossible. We, therefore, study and describe a methodology to infer efficiently the thermal model based on the monitoring of system temperatures and number of instructions used for task executions

Optimal Power Control and Scheduling under Hard Deadline Constraints for Continuous Fading Channels

We consider a joint scheduling-and-power-allocation problem of a downlink cellular system. The system consists of two groups of users: real-time (RT) and non-real-time (NRT) users. Given an average power constraint on the base station, the problem is to find an algorithm that satisfies the RT hard deadline constraint and NRT queue stability constraint. We propose a sum-rate-maximizing algorithm that satisfies these constraints. We also show, through simulations, that the proposed algorithm has an average complexity that is close-to-linear in the number of RT users. The power allocation policy in the proposed algorithm has a closed-form expression for the two groups of users. However, interestingly, the power policy of the RT users differ in structure from that of the NRT users. We also show the superiority of the proposed algorithms over existing approaches using extensive simulations.Comment: Submitted to Asilomar 2017. arXiv admin note: text overlap with arXiv:1612.0832

Reliability of Dynamic Load Scheduling with Solar Forecast Scenarios

This paper presents and evaluates the performance of an optimal scheduling algorithm that selects the on/off combinations and timing of a finite set of dynamic electric loads on the basis of short term predictions of the power delivery from a photovoltaic source. In the algorithm for optimal scheduling, each load is modeled with a dynamic power profile that may be different for on and off switching. Optimal scheduling is achieved by the evaluation of a user-specified criterion function with possible power constraints. The scheduling algorithm exploits the use of a moving finite time horizon and the resulting finite number of scheduling combinations to achieve real-time computation of the optimal timing and switching of loads. The moving time horizon in the proposed optimal scheduling algorithm provides an opportunity to use short term (time moving) predictions of solar power based on advection of clouds detected in sky images. Advection, persistence, and perfect forecast scenarios are used as input to the load scheduling algorithm to elucidate the effect of forecast errors on mis-scheduling. The advection forecast creates less events where the load demand is greater than the available solar energy, as compared to persistence. Increasing the decision horizon leads to increasing error and decreased efficiency of the system, measured as the amount of power consumed by the aggregate loads normalized by total solar power. For a standalone system with a real forecast, energy reserves are necessary to provide the excess energy required by mis-scheduled loads. A method for battery sizing is proposed for future work.Comment: 6 pager, 4 figures, Syscon 201

Towards An Efficient Cloud Computing System: Data Management, Resource Allocation and Job Scheduling

Cloud computing is an emerging technology in distributed computing, and it has proved to be an effective infrastructure to provide services to users. Cloud is developing day by day and faces many challenges. One of challenges is to build cost-effective data management system that can ensure high data availability while maintaining consistency. Another challenge in cloud is efficient resource allocation which ensures high resource utilization and high SLO availability. Scheduling, referring to a set of policies to control the order of the work to be performed by a computer system, for high throughput is another challenge. In this dissertation, we study how to manage data and improve data availability while reducing cost (i.e., consistency maintenance cost and storage cost); how to efficiently manage the resource for processing jobs and increase the resource utilization with high SLO availability; how to design an efficient scheduling algorithm which provides high throughput, low overhead while satisfying the demands on completion time of jobs. Replication is a common approach to enhance data availability in cloud storage systems. Previously proposed replication schemes cannot effectively handle both correlated and non-correlated machine failures while increasing the data availability with the limited resource. The schemes for correlated machine failures must create a constant number of replicas for each data object, which neglects diverse data popularities and cannot utilize the resource to maximize the expected data availability. Also, the previous schemes neglect the consistency maintenance cost and the storage cost caused by replication. It is critical for cloud providers to maximize data availability hence minimize SLA (Service Level Agreement) violations while minimize cost caused by replication in order to maximize the revenue. In this dissertation, we build a nonlinear programming model to maximize data availability in both types of failures and minimize the cost caused by replication. Based on the model\u27s solution for the replication degree of each data object, we propose a low-cost multi-failure resilient replication scheme (MRR). MRR can effectively handle both correlated and non-correlated machine failures, considers data popularities to enhance data availability, and also tries to minimize consistency maintenance and storage cost. In current cloud, providers still need to reserve resources to allow users to scale on demand. The capacity offered by cloud offerings is in the form of pre-defined virtual machine (VM) configurations. This incurs resource wastage and results in low resource utilization when the users actually consume much less resource than the VM capacity. Existing works either reallocate the unused resources with no Service Level Objectives (SLOs) for availability\footnote{Availability refers to the probability of an allocated resource being remain operational and accessible during the validity of the contract~\cite{CarvalhoCirne14}.} or consider SLOs to reallocate the unused resources for long-running service jobs. This approach increases the allocated resource whenever it detects that SLO is violated in order to achieve SLO in the long term, neglecting the frequent fluctuations of jobs\u27 resource requirements in real-time application especially for short-term jobs that require fast responses and decision making for resource allocation. Thus, this approach cannot fully utilize the resources to process data because they cannot quickly adjust the resource allocation strategy dealing with the fluctuations of jobs\u27 resource requirements. What\u27s more, the previous opportunistic based resource allocation approach aims at providing long-term availability SLOs with good QoS for long-running jobs, which ensures that the jobs can be finished within weeks or months by providing slighted degraded resources with moderate availability guarantees, but it ignores deadline constraints in defining Quality of Service (QoS) for short-lived jobs requiring online responses in real-time application, thus it cannot truly guarantee the QoS and long-term availability SLOs. To overcome the drawbacks of previous works, we adequately consider the fluctuations of unused resource caused by bursts of jobs\u27 resource demands, and present a cooperative opportunistic resource provisioning (CORP) scheme to dynamically allocate the resource to jobs. CORP leverages complementarity of jobs\u27 requirements on different resource types and utilizes the job packing to reduce the resource wastage and increase the resource utilization. An increasing number of large-scale data analytics frameworks move towards larger degrees of parallelism aiming at high throughput. Scheduling that assigns tasks to workers and preemption that suspends low-priority tasks and runs high-priority tasks are two important functions in such frameworks. There are many existing works on scheduling and preemption in literature to provide high throughput. However, previous works do not substantially consider dependency in increasing throughput in scheduling or preemption. Considering dependency is crucial to increase the overall throughput. Besides, extensive task evictions for preemption increase context switches, which may decrease the throughput. To address the above problems, we propose an efficient scheduling system Dependency-aware Scheduling and Preemption (DSP) to achieve high throughput in scheduling and preemption. First, we build a mathematical model to minimize the makespan with the consideration of task dependency, and derive the target workers for tasks which can minimize the makespan; second, we utilize task dependency information to determine tasks\u27 priorities for preemption; finally, we present a probabilistic based preemption to reduce the numerous preemptions, while satisfying the demands on completion time of jobs. We conduct trace driven simulations on a real-cluster and real-world experiments on Amazon S3/EC2 to demonstrate the efficiency and effectiveness of our proposed system in comparison with other systems. The experimental results show the superior performance of our proposed system. In the future, we will further consider data update frequency to reduce consistency maintenance cost, and we will consider the effects of node joining and node leaving. Also we will consider energy consumption of machines and design an optimal replication scheme to improve data availability while saving power. For resource allocation, we will consider using the greedy approach for deep learning to reduce the computation overhead caused by the deep neural network. Also, we will additionally consider the heterogeneity of jobs (i.e., short jobs and long jobs), and use a hybrid resource allocation strategy to provide SLO availability customization for different job types while increasing the resource utilization. For scheduling, we will aim to handle scheduling tasks with partial dependency, worker failures in scheduling and make our DSP fully distributed to increase its scalability. Finally, we plan to use different workloads and real-world experiment to fully test the performance of our methods and make our preliminary system design more mature