Dynamic Window-Constrained Scheduling for Real-Time Media Streaming

This paper describes an algorithm for scheduling packets in real-time multimedia data streams. Common to these classes of data streams are service constraints in terms of bandwidth and delay. However, it is typical for real-time multimedia streams to tolerate bounded delay variations and, in some cases, finite losses of packets. We have therefore developed a scheduling algorithm that assumes streams have window-constraints on groups of consecutive packet deadlines. A window-constraint defines the number of packet deadlines that can be missed in a window of deadlines for consecutive packets in a stream. Our algorithm, called Dynamic Window-Constrained Scheduling (DWCS), attempts to guarantee no more than x out of a window of y deadlines are missed for consecutive packets in real-time and multimedia streams. Using DWCS, the delay of service to real-time streams is bounded even when the scheduler is overloaded. Moreover, DWCS is capable of ensuring independent delay bounds on streams, while at the same time guaranteeing minimum bandwidth utilizations over tunable and finite windows of time. We show the conditions under which the total demand for link bandwidth by a set of real-time (i.e., window-constrained) streams can exceed 100% and still ensure all window-constraints are met. In fact, we show how it is possible to guarantee worst-case per-stream bandwidth and delay constraints while utilizing all available link capacity. Finally, we show how best-effort packets can be serviced with fast response time, in the presence of window-constrained traffic

Supporting Real-Time Applications in an Integrated Services Packet Network: Architecture and Mechanism

This paper considers the support of real-time applications in an Integrated Services Packet Network (ISPN). We first review the characteristics of real-time applications. We observe that, contrary to the popular view that real-time applications necessarily require a fixed delay bound, some real-time applications are more flexible and can adapt to current network conditions. We then propose an ISPN architecture that supports two distinct kinds of real-time service: guaranteed service, which is the traditional form of real-time service discussed in most of the literature and involves pre-computed worst-case delay bounds, and predicted service, which uses the measured performance of the network in computing delay bounds. We then propose a packet scheduling mechanism that can support both of these real-time services as well as accommodate datagram traffic. We also discuss two other aspects of an overall ISPN architecture: the service interface and the admission control criteria.Research at MIT was supported by DARPA through NASA Grant NAG 2-582, by NSF grant NCR-8814187, and by DARPA and NSF through Cooperative Agreement NCR-8919038 with the Corporation for National Research Initiatives

Buffer management and cell switching management in wireless packet communications

The buffer management and the cell switching (e.g., packet handoff) management using buffer management scheme are studied in Wireless Packet Communications. First, a throughput improvement method for multi-class services is proposed in Wireless Packet System. Efficient traffic management schemes should be developed to provide seamless access to the wireless network. Specially, it is proposed to regulate the buffer by the Selective- Delay Push-In (SDPI) scheme, which is applicable to scheduling delay-tolerant non-real time traffic and delay-sensitive real time traffic. Simulation results show that the performance observed by real time traffics are improved as compared to existing buffer priority scheme in term of packet loss probability. Second, the performance of the proposed SDPI scheme is analyzed in a single CBR server. The arrival process is derived from the superposition of two types of traffics, each in turn results from the superposition of homogeneous ON-OFF sources that can be approximated by means of a two-state Markov Modulated Poisson Process (MMPP). The buffer mechanism enables the ATM layer to adapt the quality of the cell transfer to the QoS requirements and to improve the utilization of network resources. This is achieved by selective-delaying and pushing-in cells according to the class they belong to. Analytical expressions for various performance parameters and numerical results are obtained. Simulation results in term of cell loss probability conform with our numerical analysis. Finally, a novel cell-switching scheme based on TDMA protocol is proposed to support QoS guarantee for the downlink. The new packets and handoff packets for each type of traffic are defined and a new cutoff prioritization scheme is devised at the buffer of the base station. A procedure to find the optimal thresholds satisfying the QoS requirements is presented. Using the ON-OFF approximation for aggregate traffic, the packet loss probability and the average packet delay are computed. The performance of the proposed scheme is evaluated by simulation and numerical analysis in terms of packet loss probability and average packet delay

Proportional switching in FIFO networks

We consider a family of discrete time multihop switched queueing networks where each packet movesalong a xed route. In this setting, BackPressure is the canonical choice of scheduling policy; this policy hasthe virtues of possessing a maximal stability region and not requiring explicit knowledge of tra c arrival rates.BackPressure has certain structural weaknesses because implementation requires information about each route,and queueing delays can grow super-linearly with route length. For large networks, where packets over manyroutes are processed by a queue, or where packets over a route are processed by many queues, these limitationscan be prohibitive.In this article, we introduce a scheduling policy for FIFO networks, the Proportional Scheduler, which isbased on the proportional fairness criterion. We show that, like BackPressure, the Proportional Scheduler hasa maximal stability region and does not require explicit knowledge of tra c arrival rates. The ProportionalScheduler has the advantage that information about the network's route structure is not required for scheduling,which substantially improves the policy's performance for large networks. For instance, packets can be routedwith only next-hop information and new nodes can be added to the network with only knowledge of thescheduling constraintsThe research of the rst author was partially supported by NSF grants DMS-1105668 and DMS-1203201. The research of the second author was partially supported by the Spanish Ministry of Economy and Competitiveness Grants MTM2013-42104-P via FEDER funds; he thanks the ICMAT (Madrid, Spain) Research Institute that kindly hosted him while developing this project

Experimental Evaluation of a Coarse-Grained Switch Scheduler

Modern high performance routers rely on sophisticated interconnection networks to meet ever increasing demands on capacity. Regulating the flow of packets through these interconnects is critical to providing good performance, particularly in the presence of extreme traffic patterns that result in sustained overload at output ports. Previous studies have used a combination of analysis and idealized simulations to show that coarse-grained scheduling of traffic flows can be effective in preventing congestion, while ensuring high utilization. In this paper, we study the performance of a coarse-grained scheduler in a real router with a scalable architecture similar to those found in high performance commercial systems. Our results are obtained by taking fine-grained measurements of an operating router that provide a detailed picture of how the scheduling algorithm behaves under a variety of conditions, giving a more complete and realistic understanding of the short time-scale dynamics than previous studies could provide. We also examine computation and communication overheads of our scheduler implementation to assess its resource usage and to provide the basis for an analysis of how the resource usage scales with system size

Unreliable Retrial Queues in a Random Environment

This dissertation investigates stability conditions and approximate steady-state performance measures for unreliable, single-server retrial queues operating in a randomly evolving environment. In such systems, arriving customers that find the server busy or failed join a retrial queue from which they attempt to regain access to the server at random intervals. Such models are useful for the performance evaluation of communications and computer networks which are characterized by time-varying arrival, service and failure rates. To model this time-varying behavior, we study systems whose parameters are modulated by a finite Markov process. Two distinct cases are analyzed. The first considers systems with Markov-modulated arrival, service, retrial, failure and repair rates assuming all interevent and service times are exponentially distributed. The joint process of the orbit size, environment state, and server status is shown to be a tri-layered, level-dependent quasi-birth-and-death (LDQBD) process, and we provide a necessary and sufficient condition for the positive recurrence of LDQBDs using classical techniques. Moreover, we apply efficient numerical algorithms, designed to exploit the matrix-geometric structure of the model, to compute the approximate steady-state orbit size distribution and mean congestion and delay measures. The second case assumes that customers bring generally distributed service requirements while all other processes are identical to the first case. We show that the joint process of orbit size, environment state and server status is a level-dependent, M/G/1-type stochastic process. By employing regenerative theory, and exploiting the M/G/1-type structure, we derive a necessary and sufficient condition for stability of the system. Finally, for the exponential model, we illustrate how the main results may be used to simultaneously select mean time customers spend in orbit, subject to bound and stability constraints

Integration of Massive Plug-in Hybrid Electric Vehicles into Power Distribution Systems: Modeling, Optimization, and Impact Analysis

With the development of vehicle-to-grid (V2G) technology, it is highly promising to use plug-in hybrid electric vehicles (PHEVs) as a new form of distributed energy resources. However, the uncertainties in the power market and the conflicts among different stakeholders make the integration of PHEVs a highly challenging task. Moreover, the integration of PHEVs may lead to negative effects on the power grid performance if the PHEV fleets are not properly managed. This dissertation studies various aspects of the integration of PHEVs into power distribution systems, including the PHEV load demand modeling, smart charging algorithms, frequency regulation, reliability-differentiated service, charging navigation, and adequacy assessment of power distribution systems. This dissertation presents a comprehensive methodology for modeling the load demand of PHEVs. Based on this stochastic model of PHEV, a two-layer evolution strategy particle swarm optimization (ESPSO) algorithm is proposed to integrate PHEVs into a residential distribution grid. This dissertation also develops an innovative load frequency control system, and proposes a hierarchical game framework for PHEVs to optimize their charging process and participate in frequency regulation simultaneously. The potential of using PHEVs to enable reliability-differentiated service in residential distribution grids has been investigated in this dissertation. Further, an integrated electric vehicle (EV) charging navigation framework has been proposed in this dissertation which takes into consideration the impacts from both the power system and transportation system. Finally, this dissertation proposes a comprehensive framework for adequacy evaluation of power distribution networks with PHEVs penetration. This dissertation provides innovative, viable business models for enabling the integration of massive PHEVs into the power grid. It helps evolve the current power grid into a more reliable and efficient system