QoS Stochastic Traffic Engineering for the wireless support of real-time streaming applications

In this work, the Stochastic Traffic Engineering (STE) problem arising from the support of QoS-demanding real-time (e.g., delay and delay-jitter sensitive) media-streaming applications over unreliable IP-over-wireless pipes is addressed. Two main contributions are presented. First, we develop an optimal resource-management policy that allows a joint scheduling of the source rate, transmit energy and playout rate. Salient features of the proposed scheduling policy are that: (i) it is self-adaptive; and, (ii) it is able to provide hard (i.e., deterministic) QoS guarantees, in terms of hard limited playout delay, playout rate-jitter and pre-roll delay. Second, by referring to power and bandwidth limited access scenarios, we develop a traffic analysis of the underlying IP-over-wireless pipes that allows us to analyze the effects of both fading-induced errors and congestion-induced packet’s losses on the end-to-end performance of the proposed scheduler.     

Joint power-playout control for media streaming over wireless links

Media streaming applications over wireless links face various challenges, due to both the nature of the wireless channel and the stringent delivery requirements of media traffic. In this paper, we seek to improve the performance of media streaming over an interference-limited wireless link, by using appropriate transmission and playout control. In particular, we choose both the power at the transmitter and the playout scheduling at the receiver, so as to minimize the power consumption and maximize the media playout quality. We formulate the problem using a dynamic programming approach, and study the structural properties of the optimal solution. We further develop a justified, low-complexity heuristic that achieves significant performance gain over benchmark systems. In particular, our joint power-playout heuristic outperforms: 1) the optimal power control policy in the regime where power is most important and 2) the optimal playout control policy in the regime where media (playout) quality is most important; furthermore, this heuristic has only a slight performance loss as compared to the optimal joint power-playout control policy over the entire range of the investigation.     

Content-Aware Playout and Packet Scheduling for Video Streaming Over Wireless Links

Media streaming over wireless links is a challenging problem due to both the unreliable, time-varying nature of the wireless channel and the stringent delivery requirements of media traffic. In this paper, we use joint control of packet scheduling at the transmitter and content-aware playout at the receiver, so as to maximize the quality of media streaming over a wireless link. Our contributions are twofold. First, we formulate and study the problem of joint scheduling and playout control in the framework of Markov decision processes. Second, we propose a novel content-aware adaptive playout control, that takes into account the content of a video sequence, and in particular the motion characteristics of different scenes. We find that the joint scheduling and playout control can significantly improve the quality of the received video, at the expense of only a small amount of playout slowdown. Furthermore, the content-aware adaptive playout places the slowdown preferentially in the low-motion scenes, where its perceived effect is lower.      

Distributed scheduling algorithms for channel access in TDMA wireless mesh networks

In this paper, we have considered the distributed scheduling problem for channel access in TDMA wireless mesh networks. The problem is to assign time-slot(s) for nodes to access the channels, and it is guaranteed that nodes can communicate with all their one-hop neighbors in the assigned time-slot(s). And the objective is to minimize the cycle length, i.e., the total number of different time-slots in one scheduling cycle. In single-channel ad hoc networks, the best known result for this problem is proved to be K ² in arbitrary graphs (IEEE Trans Comput C-36(6):729–737, 1987) and 25K in unit disk graphs (IEEE/ACM Trans Netw pp 166–177, 1993) with K as the maximum node degree. There are multiple channels in wireless mesh networks, and different nodes can use different control channels to reduce congestion on the control channels. In this paper, we have considered two scheduling models for wireless mesh networks. The first model is that each node has two radios, and the scheduling is simultaneously done on the two radios. We have proved that the upper bound of the cycle length in arbitrary graphs can be 2K. The second model is that the time-slots are scheduled for the nodes regardless of the number of radios on them. In this case, we have proved that the upper bound can be (4K?2). We also have proposed greedy algorithms with different criterion. The basic idea of these algorithms is to organize the conflicting nodes by special criterion, such as node identification, node degree, the number of conflicting neighbors, etc. And a node cannot be assigned to a time-slot(s) until all neighbor nodes, which have higher criterion and might conflict with the current node, are assigned time-slot(s) already. All these algorithms are fully distributed and easy to realize. Simulations are also done to verify the performance of these algorithms.     

Tight end-to-end per-flow delay bounds in FIFO multiplexing sink-tree networks

Aggregate scheduling has been proposed as a solution for achieving scalability in large-size networks. However, in order to enable the provisioning of real-time services, such as video delivery or voice conversations, in aggregate scheduling networks, end-to-end delay bounds for single flows are required. In this paper, we derive per-flow end-to-end delay bounds in aggregate scheduling networks in which per-egress (or sink-tree) aggregation is in place, and flows traffic is aggregated according to a FIFO policy. The derivation process is based on Network Calculus, which is suitably extended to this purpose. We show that the bound is tight by deriving the scenario in which it is attained. A tight delay bound can be employed for a variety of purposes: for example, devising optimal aggregation criteria and rate provisioning policies based on pre-specified flow delay bounds.     

Path-vector contracting: Profit maximization and risk management

We consider an Internet Service Provider’s (ISP’s) problem of providing end-to-end (e2e) services with bandwidth guarantees, using a path-vector based approach. In this approach, an ISP uses its edge-to-edge (g2g) single-domain contracts and vector of contracts purchased from neighboring ISPs as the building blocks to construct, or participate in constructing, an end-to-end “contract path”. We develop a spot-pricing framework for the e2e bandwidth guaranteed services utilizing this path contracting strategy, by formulating it as a stochastic optimization problem with the objective of maximizing expected profit subject to risk constraints. In particular, we present time-invariant path contracting strategies that offer high expected profit at low risks, and can be implemented in a fully distributed manner. Simulation analysis is employed to evaluate the contracting and pricing framework under different network and market conditions. An admission control policy based on the path contracting strategy is developed and its performance is analyzed using simulations.     

Backpressure scheduling in IEEE 802.11 wireless mesh networks: Gap between theory and practice

Achieving efficient bandwidth utilization in wireless networks requires solving two important problems: (1) which packets to send (i.e., packet scheduling) and (2) which links to concurrently activate (i.e., link scheduling). To address these scheduling problems, many algorithms have been proposed and their throughput optimality and stability are proven in theory. One of the most well-known scheduling algorithms is backpressure scheduling which performs both link and packet scheduling assuming a TDMA (Time Division Multiple Access) MAC (Medium Access Control) layer. However, there has been limited work on realizing backpressure scheduling with a CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) MAC layer (e.g., IEEE 802.11). In IEEE 802.11 networks, it is expected that the throughput optimality will not be achieved. In this paper, we investigate the extent of this throughput gap between theoretical TDMA-based backpressure scheduling and an approximation of it for IEEE 802.11 WMNs (Wireless Mesh Networks). Through extensive testbed measurements, we verify that there is indeed a non-negligible throughput gap. We present two main reasons behind this gap: Control inaccuracy that results from approximation of link scheduling and information inaccuracy that results from late or incorrect information, for instance, about queue lengths or network topology. Our results show that losses by MAC-layer collisions and backoff, which mainly occur due to control inaccuracy plays a major role for the throughput gap. On the other hand, while losses by queue drops, typically due to information inaccuracy, do occur, their effect can be tolerated. Nevertheless, both types of inaccuracies need to be mitigated in order to improve throughput.     

Distributed scheduling algorithms for channel access in TDMA wireless mesh networks

In this paper, we have considered the distributed scheduling problem for channel access in TDMA wireless mesh networks. The problem is to assign time-slot(s) for nodes to access the channels, and it is guaranteed that nodes can communicate with all their one-hop neighbors in the assigned time-slot(s). And, the objective is to minimize the cycle length, i.e., the total number of different time-slots in one scheduling cycle. In single-channel ad hoc networks, the best known result for this problem is proved to be K ² in arbitrary graphs (Chlamtac and Pinter in IEEE Trans. Comput. C-36(6):729–737, 1987) and 25K in unit disk graphs () with K as the maximum node degree. There are multiple channels in wireless mesh networks, and different nodes can use different control channels to reduce congestion on the control channels. In this paper, we have considered two scheduling models for wireless mesh networks. The first model is that each node has two radios, and the scheduling is simultaneously done on the two radios. We have proved that the upper bound of the cycle length in arbitrary graphs can be 2K. The second model is that the time-slots are scheduled for the nodes regardless of the number of radios on them. In this case, we have proved that the upper bound can be (4K−2). We also have proposed greedy algorithms with different criterion. The basic idea of these algorithms is to organize the conflicting nodes by special criterion, such as node identification, node degree, the number of conflicting neighbors, etc. And, a node cannot be assigned to a time-slot(s) until all neighbor nodes, which have higher criterion and might conflict with the current node, are assigned time-slot(s) already. All these algorithms are fully distributed and easy to realize. Simulations are also done to verify the performance of these algorithms.     

Efficient link scheduling for online admission control of real-time traffic in wireless mesh networks

Link scheduling is used in wireless mesh networks (WMNs) to guarantee interference-free transmission on the shared wireless medium in a time division multiple access approach. Several papers in the literature address the problem of link scheduling guaranteeing a minimum throughput to the flows traversing the WMN. However, none of the existing works address the problem of computing a schedule that guarantees that pre-specified end-to-end delay constraints are met. In this paper, we make a first step forward in this direction by defining a link scheduling algorithm that works in sink-tree WMNs, i.e. those whose traffic is routed towards a common sink (i.e. the Internet gateway). Our iterative algorithm exploits a delay-based admission control procedure, devised through Network Calculus, which solves an optimization problem and tests the feasibility of a schedule from the point of view of delay guarantees. Thanks to a clever solution approach for the optimization problem, the iterative algorithm computes feasible solutions in affordable times for networks of several tens of nodes, and is thus amenable to online admission control of real-time traffic.     

Perceiving who and when to leverage data delivery for maritime networks: An optimal stopping view

The advances in the integration of wireless communication and sensor technologies have stimulated an innovative paradigm named Crowd Sensing Networks, which caters to the exponential growth of service demands on the sea and drives the development of potential maritime wideband networks. This paper investigates the issue of sensed traffic data scheduling between vessels, combining Time Division Long Term Evolution (TD-LTE) and delay-tolerant networks (DTNs) on the sea. Specially, we propose a unique network topology which combines maritime crowd sensing network and delay tolerant networks, i.e., a store-carry-and-forward routing topology is explored to address the intermittent network connectivity in maritime context. Notably, the alternative eco-friendly green energy in maritime environment will make the scheduling issue more challenging. To the best of our knowledge, this is the first work to do such investigation with the goal of minimizing the costs associated with end-to-end delay of data delivery and energy consumption of DTN throw box. On this basis, we design the scheme of data cooperation transmission between vessels that the hosting vessel decides which DTN throw box to store the data, and when a vessel arrives, the DTN throw box determines whether to stop, i.e., let the arriving vessel carry the data, or skip it and continue to wait for other vessels. A Two-step Time and Energy Oriented Optimal-stopping (TTEOO) algorithm leveraging backward induction method is proposed, based on the optimal stopping rules. Simulation results are presented to show the effectiveness of the proposed method, in terms of consumption cost and data delivery ratio.     

Efficient fault-tolerant collision-free data aggregation scheduling for wireless sensor networks

This paper investigates the design of fault-tolerant TDMA-based data aggregation scheduling (DAS) protocols for wireless sensor networks (WSNs). DAS is a fundamental pattern of communication in wireless sensor networks where sensor nodes aggregate and relay data to a sink node. However, any such DAS protocol needs to be cognisant of the fact that crash failures can occur. We make the following contributions: (i) we identify a necessary condition to solve the DAS problem, (ii) we introduce a strong and weak version of the DAS problem, (iii) we show several impossibility results due to the crash failures, (iv) we develop a modular local algorithm that solves stabilising weak DAS and (v) we show, through simulations and an actual deployment on a small testbed, how specific instantiations of parameters can lead to the algorithm achieving very efficient stabilisation.     

Cross-layer optimization for wireless multihop multicast networks

Cyber-Physical System (CPS) is envisioned to tightly integrate the cyber-world of computation, communication, and control with the physical world. CPS is typically designed as a networked system of interacting sensors, actuators, and embedded computing devices to monitor and control the physical world. Thus, one of the essential building blocks of such a system is a highly efficient networking infrastructure. In this paper, we aims to develop an efficient wireless networking technology which can be utilized in CPS. More specifically, we develop a cross-layer optimization model based on the Network Utility Maximization (NUM) framework and its distributed solution for wireless multihop multicast networks exploiting multi-user diversity. It is known that the capacity of a wireless network can be increased by exploiting different channel conditions at different users, i.e., multi-user diversity; however, it is yet to be determined how much performance gain can be achieved by exploiting multi-user diversity in wireless multihop multicast networks. To address this problem, we extend the NUM framework and derive a new optimization problem including the benefits of multi-user diversity for multicasting scenarios in wireless multihop networks under a probabilistic media access control (MAC). In our problem, multi-user diversity is achieved via opportunistic scheduling. Then, we propose a distributed approximation algorithm for the problem. Our numerical results confirm that the benefit of multi-user diversity is prominent in a wireless multihop network with multicast flows.     

FaaS execution models for edge applications

In this paper, we address the problem of supporting stateful workflows following a Function-as-a-Service (FaaS) model in edge networks. In particular we focus on the problem of data transfer, which can be a performance bottleneck due to the limited speed of communication links in some edge scenarios and we propose three different schemes: a pure FaaS implementation, StateProp, i.e., propagation of the application state throughout the entire chain of functions, and StateLocal, i.e., a solution where the state is kept local to the workers that run functions and retrieved only as needed. We then extend the proposed schemes to the more general case of applications modeled as Directed Acyclic Graphs (DAGs), which cover a broad range of practical applications, e.g., in the Internet of Things (IoT) area. Our contribution is validated via a prototype implementation. Experiments in emulated conditions show that applying the data locality principle reduces significantly the volume of network traffic required and improves the end-to-end delay performance, especially with local caching on edge nodes and low link speeds.     

An improved security-aware packet scheduling algorithm in real-time wireless networks

Developing security-aware packet scheduling algorithms can efficiently enhance the security while delivering packets through wireless links. Existing scheduling algorithms developed for real-time wireless networks provide security at the cost of other important performance, e.g., schedulability. The performance problem becomes especially apparent when wireless networks are heavily loaded. To address this issue, we propose in this paper an improved security-aware packet scheduling algorithm (or ISAPS in short) in wireless networks. ISAPS first gives high priority to deal with schedulability when the real-time system is heavily loaded. When the system is under light workload, ISAPS strives to improve the security levels while achieving high schedulability for real-time packets. Compared with the existing packet scheduling algorithm SPSS, ISAPS shows excellent scheduling quality under a wide range of workload characteristics.     

TCP-aware network coding with opportunistic scheduling in wireless mobile ad hoc networks

This paper presents a scheme that employs TCP-aware network coding with opportunistic scheduling to enhance TCP throughput in wireless mobile ad hoc networks. Specifically, it considers a TCP parameter, congestion window size, and wireless channel conditions simultaneously to improve TCP throughput performance. Evaluation of this scheme is carried out by using ns2 simulations in different scenarios. The results show that the proposed scheme gives approximately 35% throughput improvement in a high mobility environment and about 33% throughput increase in no or low mobility environment as compared to traditional network coding with opportunistic scheduling. This paper also proposes a new adaptive-W (i.e., adaptive Waiting time) scheme whose objective is to adaptively control waiting time of overheard packets that are stored in a buffer to achieve tradeoff between throughput and overhead.     

A hybrid genetic algorithm with multiple decoding methods for energy-aware remanufacturing system scheduling problem

Remanufacturing system scheduling is an essential and effective approach to realize the digitization and greening of the remanufacturing industry. However, previous researches on the remanufacturing system scheduling problem mainly consider a single or two production stages and economic objectives. In this paper, by integrating the three core production stages, i.e., disassembly, reprocessing and reassembly together, we study the energy-aware remanufacturing system scheduling problem in which the well-accepted Turn Off and On strategy is also considered. First, a mathematical model aiming at minimizing the total energy consumption (TEC) of the remanufacturing system is established. Then, a hybrid genetic algorithm based on variable neighborhood search (GAVNS) solution method is proposed, given the NP-hard nature of the problem. In GAVNS, each chromosome is encoded by a job sequence and three different decoding methods are specially designed according to the formation of optimization objective TEC. To enhance the algorithm's local search capability, the variable neighborhood search technique is introduced. The feasibility and effectiveness of GAVNS in addressing the energy-aware remanufacturing system scheduling problem is verified through simulation experiments on a set of designed test instances. Experimental results also demonstrate that: (1) the Turn Off and On strategy can effectively reduce TEC of the remanufacturing system, which can reach an energy saving rate of 6.68%; (2) the performance of those decoding methods varies with respect to the problem size; (3) the decoding method based on minimizing the energy consumption of the remanufacturing system (namely DM3) has the best performance among the three decoding methods in most cases; (4) GAVNS is more effective than its four peers, i.e., a variant GAVNS_R, iterated greedy algorithm (IG), extended artificial bee colony algorithm (EABC), discrete invasive weed optimization algorithm (DIWO) in seeking the optimal schedule.     

Arrival process-controlled adaptive media playout with multiple thresholds for video streaming

To improve the playout quality of video streaming services, an arrival process-controlled adaptive media playout (AMP) mechanism is designed in this study. The proposed AMP scheme sets three threshold values, denoted by P _n , L and H, for the playout controller to start playback and dynamically adjust the playout rate based on the buffer fullness. In the preroll period, the playout can start only when the buffer fullness n is not less than the dynamic playback threshold P _n ,?which is determined by the jitters of incoming video frames. In the playback period, if the buffer fullness is below L or over H,?the playout rate will slow down or speed up in a quadratic manner. Otherwise, the playback speed depends on the instantaneous frame arrival rate, which is estimated by the proposed arrival process tracking algorithm. We employ computer simulations to demonstrate the performance of the proposed AMP scheme, and compare it with several conventional AMP mechanisms. Numerical results show that our AMP design can shorten the playout delay and reduce both buffer underflow and overflow probabilities. In addition, our proposed AMP also outperforms traditional AMP schemes in terms of the variance of distortion of playout and the playout curve. Hence, the proposed arrival process-controlled AMP is really an outstanding design.     

An optimal boundary fair scheduling algorithm for multiprocessor real-time systems

With the emergence of multicore processors, the research on multiprocessor real-time scheduling has caught more researchers’ attention recently. Although the topic has been studied for decades, it is still an evolving research field with many open problems. In this work, focusing on periodic real-time tasks with quantum-based computation requirements and implicit deadlines, we propose a novel optimal scheduling algorithm, namely boundary fair (Bfair), which can achieve full system utilization as the well-known Pfair scheduling algorithms. However, different from Pfair algorithms that make scheduling decisions and enforce proportional progress (i.e., fairness) for all tasks at each and every time unit, Bfair makes scheduling decisions and enforces fairness to tasks only at tasks’ period boundaries (i.e., deadlines of periodic tasks). The correctness of the Bfair algorithm to meet the deadlines of all tasks’ instances is formally proved and its performance is evaluated through extensive simulations. The results show that, compared to that of Pfair algorithms, Bfair can significantly reduce the number of scheduling points (by up to 94%) and the overhead of Bfair at each scheduling point is comparable to that of the most efficient Pfair algorithm (i.e., PD²). Moreover, by aggregating the time allocation of tasks for the time interval between consecutive period boundaries, the resulting Bfair schedule can dramatically reduce the number of required context switches and task migrations (as much as 82% and 85%, respectively) when compared to those of Pfair schedules, which in turn reduces the run-time overhead of the system.     

Optimal admission control algorithms for scheduling burst data in CDMA multimedia systems

The third generation mobile communication systems are widely envisioned to be based on wideband code division multiple access (CDMA) technologies to support high data rate (HDR) packet data services. To effectively harness the precious bandwidth while satisfying the HDR requests from users, it is crucial to use a judicious burst admission control algorithm. In this paper, we propose and evaluate the performance of a novel jointly adaptive burst admission algorithm, called the synergistic burst admission control algorithm to allocate valuable resources (i.e., channels) in wideband CDMA systems to burst HDR requests. We consider the spatial dimension only, and by that we mean the algorithm performs scheduling and admission control, for the current frame only, based solely on the selection diversity in the geographical and mobility aspects. The scheduler does not exploit the temporal dimension in that it does not make allocation decisions about future frames (i.e., requests that do not get allocation are simply ignored and such requests will be treated as new request in future frames). In the physical layer, we use a variable rate channel-adaptive modulation and coding system which offers variable throughput depending on the instantaneous channel condition. In the MAC layer, we use the proposed optimal multiple-burst admission algorithm, induced by our novel integer programming formulation of the admission control and scheduling problem. We demonstrate that synergy could be attained by interactions between the adaptive physical layer and the burst admission layer. Both the forward link and the reverse link burst requests are considered and the system is evaluated by dynamic simulations which takes into account of the user mobility, power control and soft handoff. We found that significant performance improvement, in terms of average packet delay, data user capacity and coverage, could be achieved by our scheme compared to the existing burst assignment algorithms.     

LP rounding and combinatorial algorithms for minimizing active and busy time

We consider fundamental scheduling problems motivated by energy issues. In this framework, we are given a set of jobs, each with a release time, deadline, and required processing length. The jobs need to be scheduled on a machine so that at most g jobs are active at any given time. The duration for which a machine is active (i.e., “on”) is referred to as its active time. The goal is to find a feasible schedule for all jobs, minimizing the total active time. When preemption is allowed at integer time points, we show that a minimal feasible schedule already yields a 3-approximation (and this bound is tight) and we further improve this to a 2-approximation via LP rounding techniques. Our second contribution is for the non-preemptive version of this problem. However, since even asking if a feasible schedule on one machine exists is NP-hard, we allow for an unbounded number of virtual machines, each having capacity of g. This problem is known as the busy time problem in the literature and a 4-approximation is known for this problem. We develop a new combinatorial algorithm that gives a 3-approximation. Furthermore, we consider the preemptive busy time problem, giving a simple and exact greedy algorithm when unbounded parallelism is allowed, i.e., g is unbounded. For arbitrary g, this yields an algorithm that is 2-approximate.