60,371 research outputs found
A QoS-Aware Scheduling Algorithm for High-Speed Railway Communication System
With the rapid development of high-speed railway (HSR), how to provide the
passengers with multimedia services has attracted increasing attention. A key
issue is to develop an effective scheduling algorithm for multiple services
with different quality of service (QoS) requirements. In this paper, we
investigate the downlink service scheduling problem in HSR network taking
account of end-to-end deadline constraints and successfully packet delivery
ratio requirements. Firstly, by exploiting the deterministic high-speed train
trajectory, we present a time-distance mapping in order to obtain the highly
dynamic link capacity effectively. Next, a novel service model is developed for
deadline constrained services with delivery ratio requirements, which enables
us to turn the delivery ratio requirement into a single queue stability
problem. Based on the Lyapunov drift, the optimal scheduling problem is
formulated and the corresponding scheduling service algorithm is proposed by
stochastic network optimization approach. Simulation results show that the
proposed algorithm outperforms the conventional schemes in terms of QoS
requirements.Comment: 6 pages, 3 figures, accepted by IEEE ICC 2014 conferenc
Scheduling for Optimal Rate Allocation in Ad Hoc Networks With Heterogeneous Delay Constraints
This paper studies the problem of scheduling in single-hop wireless networks
with real-time traffic, where every packet arrival has an associated deadline
and a minimum fraction of packets must be transmitted before the end of the
deadline. Using optimization and stochastic network theory we propose a
framework to model the quality of service (QoS) requirements under delay
constraints. The model allows for fairly general arrival models with
heterogeneous constraints. The framework results in an optimal scheduling
algorithm which fairly allocates data rates to all flows while meeting
long-term delay demands. We also prove that under a simplified scenario our
solution translates into a greedy strategy that makes optimal decisions with
low complexity
Minimal-Variance Distributed Deadline Scheduling in a Stationary Environment
Many modern schedulers can dynamically adjust their service capacity to match the incoming workload. At the same time, however, variability in service capacity often incurs operational and infrastructure costs. In this paper, we propose distributed algorithms that minimize service capacity variability when scheduling jobs with deadlines. Specifically, we show that Exact Scheduling minimizes service capacity variance subject to strict demand and deadline requirements under stationary Poisson arrivals. We also characterize the optimal distributed policies for more general settings with soft demand requirements, soft deadline requirements, or both. Additionally, we show how close the performance of the optimal distributed policy is to that of the optimal centralized policy by deriving a competitive-ratio-like bound
Proactive and Dynamic Task Scheduling in Fog-cloud Environment
Fog computing was introduced for the first time by Cisco in 2012. Since then, there has been a great number of studies on fog computing, in which vacant and free-of-charge computing resources in local networks provide low-latency services to end devices.
Even though traditional architecture with scalable and powerful central servers in cloud can accommodate those tasks, it is costly to allocate resources in cloud to execute all those tasks. In addition, it falls short of satisfying Quality of Service (QoS) requirements in terms of waiting time because of long distance communication between servers and user end devices.
In this thesis, we discuss dynamic scheduling problem in fog-cloud collaboration environment for real-time applications when QoS is strict and when an answer is useless if the corresponding application finishes its execution after a pre-defined deadline. By taking into account an admission control procedure to grant only requests whose deadline requirements are feasible with respect to the available resources in the network, we study a proactive scenario using different strategies to calculate schedules and to assign resources, within the admission control procedure to accommodate an incoming request.
Then, we propose our heuristic with four variants corresponding to four different strategies, with the adjustment of a trade-off cost-makespan factor in an utility function. When evaluating performance with some baseline methods in such proactive scenario, the numerical results show that our variants can meet deadline requirements for more applications while exploiting more efficiently the resources in the fog layer and being charged less for using cloud.
Keywords: fog computing, cloud computing, dynamic scheduling, real-time scheduling, task scheduling, workflow applications, DAG, QoS requirements, heterogeneous systems
A Fast-CSMA Algorithm for Deadline-Constrained Scheduling over Wireless Fading Channels
Recently, low-complexity and distributed Carrier Sense Multiple Access
(CSMA)-based scheduling algorithms have attracted extensive interest due to
their throughput-optimal characteristics in general network topologies.
However, these algorithms are not well-suited for serving real-time traffic
under time-varying channel conditions for two reasons: (1) the mixing time of
the underlying CSMA Markov Chain grows with the size of the network, which, for
large networks, generates unacceptable delay for deadline-constrained traffic;
(2) since the dynamic CSMA parameters are influenced by the arrival and channel
state processes, the underlying CSMA Markov Chain may not converge to a
steady-state under strict deadline constraints and fading channel conditions.
In this paper, we attack the problem of distributed scheduling for serving
real-time traffic over time-varying channels. Specifically, we consider
fully-connected topologies with independently fading channels (which can model
cellular networks) in which flows with short-term deadline constraints and
long-term drop rate requirements are served. To that end, we first characterize
the maximal set of satisfiable arrival processes for this system and, then,
propose a Fast-CSMA (FCSMA) policy that is shown to be optimal in supporting
any real-time traffic that is within the maximal satisfiable set. These
theoretical results are further validated through simulations to demonstrate
the relative efficiency of the FCSMA policy compared to some of the existing
CSMA-based algorithms.Comment: This work appears in workshop on Resource Allocation and Cooperation
in Wireless Networks (RAWNET), Princeton, NJ, May, 201
An Empirical Study on Multicriteria Scheduling
This paper presents an empirical study of non-preemptive Multicriteria-Based, called MCB for short, scheduling policy. MCB scheduling policy uses multiple criteria of each request: arrival time, deadline, and processing time, to balance the requirements on both client and server sites. Weighted aggregation method is applied in this study to conduct the different measurements to a single figure of merit. For the empirical study, an M/G/1 queuing simulation system is implemented with MATLAB to represent a general server's incoming request scheduling system. Comparative simulation results of MCB with best effort scheduling policy on an overload situation show that MCB is an optimal scheduling policy
Generalized Exact Scheduling: a Minimal-Variance Distributed Deadline Scheduler
Many modern schedulers can dynamically adjust their service capacity to match the incoming workload. At the same time, however, unpredictability and instability in service capacity often incur operational and infrastructure costs. In this paper, we seek to characterize optimal distributed algorithms that maximize the predictability, stability, or both when scheduling jobs with deadlines. Specifically, we show that Exact Scheduling minimizes both the stationary mean and variance of the service capacity subject to strict demand and deadline requirements. For more general settings, we characterize the minimal-variance distributed policies with soft demand requirements, soft deadline requirements, or both. The performance of the optimal distributed policies is compared to that of the optimal centralized policy by deriving closed-form bounds and by testing centralized and distributed algorithms using real data from the Caltech electrical vehicle charging facility and many pieces of synthetic data from different arrival distribution. Moreover, we derive the Pareto-optimality condition for distributed policies that balance the variance and mean square of the service capacity. Finally, we discuss a scalable partially-centralized algorithm that uses centralized information to boost performance and a method to deal with missing information on service requirements
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