Existing Scheduling Algorithms Research Articles

A distributed scheduling framework for multi-user diversity gain and quality of service in wireless mesh networks

Wireless multi-hop, mesh networks are being considered as a candidate for wireless backhaul networks which carry data traffic between access networks and the wired Internet. Although existing scheduling algorithms have been adopted for the wireless backhaul networks, they do not yield good performance. In this paper, we study the computational complexity of finding the optimal link schedule for the wireless mesh networks with time-division-duplexing (TDD) operations. We show that the problem of finding the optimal schedule for the mesh networks is #P-complete. Consequently, we propose a heuristic distributed scheduling algorithm and a link utility function for wireless mesh networks. Performance analysis shows that our proposed framework is of linear-time complexity and the proposed utility function makes the long-term throughput allocation converge to the desired level which is proportional to the requirements specified by the routing protocol in use to guarantee quality of service (QoS). Moreover, we show that our framework maintains strong temporal correlation of interference, which is required to ensure proper channel predictions for distributed scheduling and power control. Finally, we compare our scheduling algorithm with a previously proposed tree scheduling and a centralized ideal scheduling. Simulation results reveal that the proposed algorithm achieves high efficiency in terms of network objective as well as the overall network throughput.

Multiuser Diversity in a MIMO System With Opportunistic Feedback

An opportunistic feedback protocol with multiple thresholds and multiple access probabilities is proposed for multiuser diversity in multiple-input-multiple-output (MIMO) broadcast channels. Users compete to report the channel state information (CSI) via contention-based feedback channels if their scheduling metric exceeds a threshold. The users are divided into two classes according to their scheduling metric. For each class, the users have different random access probabilities and different chances to access feedback minislots. The performance of the proposed feedback protocol is evaluated in a MIMO system with zero-forcing (ZF) receivers in terms of the sum-rate capacity, the feedback load, and fairness. The simulation results show that the proposed feedback protocol significantly reduces the feedback load without sacrificing the sum-rate capacity too much. The proposed feedback protocol can be operated with existing scheduling algorithms without modification.

QoS Scheduling Based on Trust Relationship in Web Service Workflow

The influence of the uncertain or deceiving service nodes on the Web service workflow scheduling performance is generally fatal,so the problems of QoS scheduling can not be completely solved by the performance-QoS.The paper is concerned with the problem that trust mechanism is largely ignored in existing scheduling algorithms of service workflow.A novel algorithm of service workflow scheduling is proposed based on truth relationship in Web service workflow,and the trust value is computed by D-S theory.At the same time,the workflow path-level trust concept and the trust transfer are introduced,and the trust relationship is taken as an important parameter for QoS scheduling.The algorithm satisfies both the requirement of workflow performance-QoS and the requirement of trust-QoS.The experimental result shows that the new algorithm can obviously enhance the success rate of the service workflow execution,and it has better overall performance in other aspects such as executive time,stability and so on,compared to the traditional algorithm based on scheduling of performance-QoS.

Efficient Traffic Scheduling for Real Time VBR MPEG Video Transmission Over DOCSIS-Based HFC Networks

Data-over-cable service interface specifications (DOCSIS), the de facto standard in the cable industry, defines a scheduling service called real-time polling service (rtPS) to provision quality of service (QoS) transmission of real-time variable bit rate (VBR) videos. However, the rtPS service intrinsically has high latency, which makes it not applicable to real-time traffic transport. In this paper, we present a novel traffic scheduling algorithm for hybrid fiber coax (HFC) networks based on DOCSIS that aims to provide QoS for real-time VBR video transmissions. The novel characteristics of this algorithm, as compared to those described in published literatures, include 1) it predicts the bandwidth requirements for future traffic using a novel traffic predictor designed to provide simple yet accurate online prediction; and 2) it takes the attributes of physical (PHY) layer, media access control (MAC) layer and application layer into consideration. In addition, the proposed traffic scheduling algorithm is completely compatible with the DOCSIS specification and does not require any protocol changes. We analyze the performance of the proposed traffic predictor and traffic scheduling algorithm using real-life MPEG video traces. Simulation results indicate that 1) the proposed traffic predictor significantly outperforms previously published techniques with respect to the prediction error and 2) compared with several existing scheduling algorithms, the proposed traffic scheduling algorithm surpasses other mechanisms in terms of channel utilization, buffer usage, packet delay, and packet loss rate.

FEL-H Robust Control Real-Time Scheduling

The existing scheduling algorithms cannot adequately support modern embedded real-time applications. An important challenge for future research is how to model and introduce control mechanisms to real-time systems to improve real-time performance, and to allow the system to adapt to changes in the environment, the workload, or to changes in the system architecture due to failures. In this paper, we pursue this goal by formulating and simulating new real-time scheduling models that enable us to easily analyse feedback scheduling with various constraints, overload and disturbance, and by designing a robust, adaptive scheduler that responds gracefully to overload with robust H∞ and feedback error learning control.

Computational Time-Division and Code-Division Channel Access Scheduling in Ad Hoc Networks

Using two-hop neighborhood information, we present the hybrid activation multiple access (HAMA) protocol for time-division channel access scheduling in ad hoc networks with omni-directional antennas. Different from other approaches, HAMA is a node-activation channel access protocol that also maximizes the chance of link activations using time- and code-division schemes. The throughput and delay characteristics of HAMA in randomly-generated multihop wireless networks are studied by analyses and simulations. The results of the analyses show that HAMA achieves higher channel utilization in ad hoc networks than previous similar works, namely, the node activation multiple access (NAMA), the link activation multiple access (LAMA) and pair-wise link activation multiple access (PAMA). In addition, HAMA achieves better throughout than an existing scheduling algorithm based on complete topology information, and much higher throughout than the ideal CSMA and CSMA/CA protocols. The main contribution of this work is to computationally derive channel access schedules according to local network topology information instead of on-demand negotiations or static global coordinations.

A throughput maximization strategy for scheduling transaction‐intensive workflows on SwinDeW‐G

AbstractWith the rapid development of e‐business, workflow systems now have to deal with transaction‐intensive workflows whose main characteristic is the huge number of concurrent workflow instances. For such workflows, it is important to maximize the overall throughput to provide good quality of service. However, most of the existing scheduling algorithms are designed for scheduling of a single complex scientific workflow instance and are not efficient enough for scheduling transaction‐intensive workflows. To address this problem, we propose a throughput maximization strategy (TMS), which contains two specific algorithms for scheduling transaction‐intensive workflows at the instance and task levels, respectively. The first algorithm called Opposite Average Load tries to maximize the overall throughput by pursuing the overall load balance at the instance level, whereas the second algorithm called Extended Min–Min tries to further maximize the overall throughput at the task level by increasing the utilization rate of resources within each local autonomous group. The comparison and simulation performed on Swinburne Decentralized Workflow for Grid (SwinDeW‐G), a peer‐to‐peer‐based grid workflow environment, demonstrate that our strategy can improve the overall throughput significantly over existing scheduling algorithms when scheduling transaction‐intensive workflows. Copyright © 2008 John Wiley & Sons, Ltd.

A low complexity user scheduling algorithm for uplink multiuser MIMO systems

A low complexity user scheduling algorithm based on a novel adaptive Markov chain Monte Carlo (AMCMC) method is proposed to achieve the maximal sum capacity in an uplink multiple-input multiple-output (MIMO) multiuser system. Compared with the existing scheduling algorithms, our algorithm is not only more efficient but also converges to within 99% of the optimal capacity obtained by exhaustive search. We demonstrate the convergence of the proposed scheduling algorithm and study the tradeoff between its complexity and performance.

Dynamic Scheduling Real-Time Task Using Primary-Backup Overloading Strategy for Multiprocessor Systems

The scheduling of real-time tasks with fault-tolerant requirements has been an important problem in multiprocessor systems. The primary-backup (PB) approach is often used as a fault-tolerant technique to guarantee the deadlines of tasks despite the presence of faults. In this paper we propose a dynamic PB-based task scheduling approach, wherein an allocation parameter is used to search the available time slots for a newly arriving task, and the previously scheduled tasks can be re-scheduled when there is no available time slot for the newly arriving task. In order to improve the schedulability we also propose an overloading strategy for PB-overloading and Backup-backup (BB) overloading. Our proposed task scheduling algorithm is compared with some existing scheduling algorithms in the literature through simulation studies. The results have shown that the task rejection ratio of our real-time task scheduling algorithm is almost 50% lower than the compared algorithms.

An Availability-Aware Task Scheduling Strategy for Heterogeneous Systems

High availability is a key requirement in the design and development of heterogeneous systems where processors operate at different speeds and are not continuously available for computation. Most existing scheduling algorithms designed for heterogeneous systems do not factor in availability requirements imposed by multiclass applications. To remedy this shortcoming, we investigate in this paper the scheduling problem for multiclass applications running in heterogeneous systems with availability constraints. In an effort to explore this issue, we model each node in a heterogeneous system using the node's computing capability and availability. Multiple classes of tasks are characterized by their execution times and availability requirements. To incorporate availability and heterogeneity into scheduling, we define new metrics to quantify system availability and heterogeneity for multiclass tasks. We then propose a scheduling algorithm to improve the availability of heterogeneous systems while maintaining good performance in the response time of tasks. Experimental results show that our algorithm achieves a good trade-off between availability and responsiveness.

Analytic evaluation of the expectation and variance of different performance measures of a schedule on a single machine under processing time variability

In this paper, we present closed-form expressions, wherever possible, or devise algorithms otherwise, to determine the expectation and variance of a given schedule on a single machine. We consider a variety of completion time and due date-based objectives. The randomness in the scheduling process is due to variable processing times with known means and variances of jobs and, in some cases, a known underlying processing time distribution. The results that we present in this paper can enable evaluation of a schedule in terms of both the expectation and variance of a performance measure considered, and thereby, aid in obtaining a stable schedule. Additionally, the expressions and algorithms that are presented, can be incorporated in existing scheduling algorithms in order to determine expectation-variance efficient schedules.

Performance comparison of scheduling algorithms in network mobility environment

Network mobility (NEMO) supports a network moving as a whole, and this may cause the bandwidth on its wireless link varying with time and locations. The quick and frequent bandwidth fluctuation makes the resource reservation and admission control lack of scalability but with heavy overhead. A feasible solution for this problem is using scheduling algorithms to optimise the resource distribution based on the varying available bandwidth. In this paper, the performance comparison of several well-known priority queue (PQ) and fair queue (FQ) scheduling algorithms are given and their advantages and disadvantages in the NEMO environment are analysed. Moreover, a novel scheduling algorithm, named adaptive rotating priority queue (ARPQ), is proposed to avoid the problems of the existing algorithms. ARPQ operates with a “priority first, fairness second” policy and guarantees the delay bounds for the flows with higher priorities and maintain the reasonable throughput for the flows with lower priorities. The simulation results show that ARPQ outperforms all the existing scheduling algorithms in mobile networks, whose capacities are time-varying and location-dependent.

Stochastic scheduling for multiclass applications with availability requirements in heterogeneous clusters

High availability plays an important role in heterogeneous clusters, where processors operate at different speeds and are not continuously available for processing. Existing scheduling algorithms designed for heterogeneous clusters do not factor in availability. We address in this paper the stochastic scheduling problem for heterogeneous clusters with availability constraints. Each node in a heterogeneous cluster is modeled by its speed and availability, and different classes of tasks submitted to the cluster are characterized by their execution times and availability requirements. To incorporate availability and heterogeneity into stochastic scheduling, we introduce metrics to quantify availability and heterogeneity in the context of multiclass tasks. A stochastic scheduling algorithm SSAC (stochastic scheduling with availability constraints) is then proposed to improve availability of heterogeneous clusters while reducing average response time of tasks. Experimental results show that our algorithm achieves a good trade-off between availability and responsiveness.

Performance analysis of a novel traffic scheduling algorithm in slotted optical networks

This paper considers the scheduling problem in a new slotted optical network called Time-Domain Wavelength Interleaved network (TWIN). The TWIN architecture possesses interesting properties, which may offer solutions for next-generation optical networks. Besides, better Quality of Service (QoS) could be achieved in TWIN by minimizing two parameters: queueing delay and delay variance. However, to the best of our knowledge, most of the existing scheduling algorithms in TWIN ignored consideration of QoS and focused mainly on maximizing the throughput. In this paper, we formulate the scheduling problem into an Integer Linear Programming (ILP) problem and propose a novel heuristic – Destination Slot Set (DSS) algorithm to solve it fast and efficiently. Besides, we derive an analytical model for TWIN and investigate the performance of DSS in it. By means of simulations, we demonstrate that our analytical model approximates the TWIN network very well; moreover, DSS incurs smaller queueing delay and delay variance, which ensures better QoS.

A high performance algorithm for static task scheduling in heterogeneous distributed computing systems

Effective task scheduling is essential for obtaining high performance in heterogeneous distributed computing systems (HeDCSs). However, finding an effective task schedule in HeDCSs requires the consideration of both the heterogeneity of processors and high interprocessor communication overhead, which results from non-trivial data movement between tasks scheduled on different processors. In this paper, we present a new high-performance scheduling algorithm, called the longest dynamic critical path (LDCP) algorithm, for HeDCSs with a bounded number of processors. The LDCP algorithm is a list-based scheduling algorithm that uses a new attribute to efficiently select tasks for scheduling in HeDCSs. The efficient selection of tasks enables the LDCP algorithm to generate high-quality task schedules in a heterogeneous computing environment. The performance of the LDCP algorithm is compared to two of the best existing scheduling algorithms for HeDCSs: the HEFT and DLS algorithms. The comparison study shows that the LDCP algorithm outperforms the HEFT and DLS algorithms in terms of schedule length and speedup. Moreover, the improvement in performance obtained by the LDCP algorithm over the HEFT and DLS algorithms increases as the inter-task communication cost increases. Therefore, the LDCP algorithm provides a practical solution for scheduling parallel applications with high communication costs in HeDCSs.

Fuzzy Logic-Based Secure and Fault Tolerant Job Scheduling in Grid

The uncertainties of grid sites security are main hurdle to make the job scheduling secure, reliable and fault-tolerant. Most existing scheduling algorithms use fixed-number job replications to provide fault tolerant ability and high scheduling success rate, which consume excessive resources or can not provide sufficient fault tolerant functions when grid security conditions change. In this paper a fuzzy-logic-based self-adaptive replication scheduling (FSARS) algorithm is proposed to handle the fuzziness or uncertainties of job replication number which is highly related to trust factors behind grid sites and user jobs. Remote sensing-based soil moisture extraction (RSBSME) workload experiments in real grid environment are performed to evaluate the proposed approach and the results show that high scheduling success rate of up to 95% and less grid resource utilization can be achieved through FSARS. Extensive experiments show that FSARS scales well when user jobs and grid sites increase.

Performance evaluation of a new scheduling algorithm for distributed systems with security heterogeneity

High quality of security service is increasingly critical for applications running on heterogeneous distributed systems. However, existing scheduling algorithms for heterogeneous distributed systems disregard security requirements of applications. To address this issue, in this paper, we introduce security heterogeneity concept for our scheduling model in the context of distributed systems. Based on the concept, we propose a novel heuristic scheduling algorithm, or SATS, which strives to maximize the probability that all tasks are executed without any risk of being attacked. Extensive experimental studies using real-world traces indicate that the scheduling performance is affected by heterogeneities of security and computational power. Additionally, empirical results demonstrate that with respect to security and performance, the proposed scheduling algorithm outperforms existing approaches under a wide spectrum of workload conditions.

Security-driven scheduling for data-intensive applications on grids

Security-sensitive applications that access and generate large data sets are emerging in various areas including bioinformatics and high energy physics. Data grids provide such data-intensive applications with a large virtual storage framework with unlimited power. However, conventional scheduling algorithms for data grids are unable to meet the security needs of data-intensive applications. In this paper we address the problem of scheduling data-intensive jobs on data grids subject to security constraints. Using a security- and data-aware technique, a dynamic scheduling strategy is proposed to improve quality of security for data-intensive applications running on data grids. To incorporate security into job scheduling, we introduce a new performance metric, degree of security deficiency, to quantitatively measure quality of security provided by a data grid. Results based on a real-world trace confirm that the proposed scheduling strategy significantly improves security and performance over four existing scheduling algorithms by up to 810% and 1478%, respectively.

Low Complexity Performance Effective Task Scheduling Algorithm for Heterogeneous Computing Environments

A heterogeneous computing environment is a suite of heterogeneous processors interconnected by high-speed networks, thereby promising high speed processing of computationally intensive applications with diverse computing needs. Scheduling of an application modeled by Directed Acyclic Graph (DAG) is a key issue when aiming at high performance in this kind of environment. The problem is generally addressed in terms of task scheduling, where tasks are the schedulable units of a program. The task scheduling problems have been shown to be NP-complete in general as well as several restricted cases. In this study we present a simple scheduling algorithm based on list scheduling, namely, low complexity Performance Effective Task Scheduling (PETS) algorithm for heterogeneous computing systems with complexity O (e) (p+ log v), which provides effective results for applications represented by DAGs. The analysis and experiments based on both randomly generated graphs and graphs of some real applications show that the PETS algorithm substantially outperforms the existing scheduling algorithms such as Heterogeneous Earliest Finish Time (HEFT), Critical-Path-On a Processor (CPOP) and Levelized Min Time (LMT), in terms of schedule length ratio, speedup, efficiency, running time and frequency of best results.

Virtual topology-based traffic scheduling algorithm for slotted optical networks

We consider the scheduling problem in a new slotted optical network called TWIN. The TWIN architecture possesses interesting properties, which may offer solutions for next-generation optical networks. Besides, TWIN has the ability to support quality of service (QoS) by controlling two important parameters: queueing delay and delay variance. However, to the best of our knowledge, the existing scheduling algorithms in TWIN focused mainly on maximizing the throughput and ignored the consideration of QoS. We formulate the scheduling problem into an integer linear programming problem and propose a heuristic--virtual topology-based dynamic scheduling (VTBDS) algorithm to solve it fast and efficiently. Besides, we derive an analytical model for TWIN and investigate the performance of VTBDS in it. By means of simulations, we demonstrate that our model approximates the TWIN network very well, and VTBDS incurs smaller queueing delay and delay variance, which are advantageous for guaranteeing better QoS.