Search

Highlights • Approximately achieve tradeoff between capacity and Jain's fairness that cannot increase fairness while not decrease capacity. • Analog beamforming and user grouping are designed to suppress interference. • Complexity is comparable to most of the existing scheduling approaches. Abstract This paper investigates user scheduling in millimeter-wave multiple-input multiple-output systems that can achieve a tradeoff between capacity and Jain's fairness index. By decomposing the original problem into digital beamforming design, duration allocation, and analog beamforming design, each subproblem is solvable. It is proved that the duration allocation problem possesses a property such that the convex optimization algorithm can be employed for the near optimal result. Moreover, the analog beamforming vectors and the user grouping are designed to avoid interference caused by the reuse of the same subspace. Finally, the computational complexity of the proposed approach is analyzed and is compared with that of other approaches. The simulations verify that the proposed approach can achieve the highest fairness and a higher capacity than approaches of close fairness. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a hybrid method combining differential evolution (DE) and sequential quadratic programming (SQP) for solving short term hydrothermal scheduling problem with non-convex fuel cost function. In this paper, differential evolution (DE) is used as a global optimizer and sequential quadratic programming (SQP) method as a local optimizer to fine tune the solution. The proposed method has been tested on a multichain cascaded reservoir with an equivalent thermal test system and the simulation results are compared with existing methods reported in literatures. From the results, it clearly shows that the proposed method is giving better quality solutions than existing methods. [ABSTRACT FROM AUTHOR]

Abstract Non-preemptive tasks with strict periods are usually adopted in practical multi-core real-time systems when continual sampling and processing of data are required. Systems designers need to provide a proper scheduling strategy such that the tasks' deadlines will be met even under the worst-case conditions. In this paper, we study the scheduling problem of non-preemptive tasks with strict periods in multi-core real-time systems. We first derive a necessary and sufficient condition to determine whether a new task is schedulable upon a multi-core platform without changing the allocations of the existing tasks. Then, with a game theory analogy, we design a recursive method to calculate the maximum permissible execution time for a given task, and propose a new schedulability condition used when the start time and processor assignments of the existing tasks can be modified. Finally, based on the conditions derived previously, we present a task assignment algorithm, which not only provides valid allocations for all tasks, but also obtains the minimum number of processors required by the system. Simulation experiments with randomly generated task sets have been conducted to show the high efficiency and reliability of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper considers scheduling parallelizable jobs in the non-clairvoyant speed scaling setting to minimize the objective of weighted flow time plus energy. Previously, strong lower bounds were shown on this model in the unweighted setting even when the algorithm is given a constant amount of resource augmentation over the optimal solution. However, these lower bounds were given only for certain families of algorithms that do not recognize the parallelizability of alive jobs. In this work, we circumvent previous lower bounds shown and give a scalable algorithm under the natural assumption that the algorithm can know the current parallelizability of a job. When a general power function is considered, this is also the first algorithm that has a constant competitive ratio for the problem using any amount of resource augmentation. [ABSTRACT FROM AUTHOR]

Abstract: Clouds are rapidly becoming an important platform for scientific applications. In the Cloud environment with uncountable numeric nodes, resource is inevitably unreliable, which has a great effect on task execution and scheduling. In this paper, inspired by Bayesian cognitive model and referring to the trust relationship models of sociology, we first propose a novel Bayesian method based cognitive trust model, and then we proposed a trust dynamic level scheduling algorithm named Cloud-DLS by integrating the existing DLS algorithm. Moreover, a benchmark is structured to span a range of Cloud computing characteristics for evaluation of the proposed method. Theoretical analysis and simulations prove that the Cloud-DLS algorithm can efficiently meet the requirement of Cloud computing workloads in trust, sacrificing fewer time costs, and assuring the execution of tasks in a security way. [Copyright &y& Elsevier]

Abstract: In this paper, we focus on the semi-partitioned scheduling of sporadic tasks with constrained deadlines and identical processors. We study two cases of semi-partitioning: (i) the case where the worst case execution time (WCET) of a job can be portioned, each portion being executed on a dedicated processor, according to a static pattern of migration; (ii) the case where the jobs of a task are released on a processor, 1 time out of p, where p is an integer at most equal to the number of processors, according to a round-robin migration pattern. The first approach has been investigated in the state-of-the-art by migrating a job at its local deadline, computed from the deadline of the task it belongs to. We study several local deadline assignment heuristics (fair, based on processor utilization and based on the minimum acceptable local deadline for a job on a processor). In both cases, we propose feasibility conditions for the schedulability of sporadic tasks scheduled using earliest deadline first (EDF) semi-partitioned scheduling. We show that the load function used for global scheduling to establish the feasibility of sporadic task sets exhibits interesting properties in the semi-partitioning context. We carry out simulations to study the performance of the two approaches in terms of success rate and number of migrations, for platforms composed of four and eight processors. We compare the performance of these semi-partitioned heuristics with the performance of classical partitioned scheduling algorithms and with a global scheduling heuristic which is currently considered to have good performances. [Copyright &y& Elsevier]

Abstract: The most promising solutions to increase bandwidth efficiency in IP over DWDM networks are represented by Optical Packet and Burst Switching. Due to the statistical multiplexing approach adopted, these paradigms must deal with packet/burst contentions at the node output channels and require the use of scheduling algorithms to optimize resource assignment and bandwidth utilization. However, very strict time constraints on the scheduling operations are imposed by the extremely high bit rates used, making the actual feasibility of the scheduler control logic a major implementation issue common to both technologies. This paper provides a specific formulation of the scheduling problem that can be used as the basis for the practical design of a fast and versatile scheduler, capable of implementing many algorithms found in literature. A possible implementation of the scheduler sub-functions, mainly based on the use of combinatorial operations, is also discussed and supported by the simulation of the hardware implementation time response, with results that demonstrate the feasibility of the proposed solution. [ABSTRACT FROM AUTHOR]

Abstract: The most crucial aspect of distributed real-time systems is the scheduling algorithm, which must guarantee that every job in the system will meet its deadline. In this paper, we evaluate by simulation the performance of strategies for the dynamic scheduling of composite jobs in a heterogeneous distributed real-time system. Each job that arrives in the system is a directed acyclic graph of component tasks and has an end-to-end deadline. For each scheduling policy, we provide alternative versions which allow the insertion of tasks into idle time slots, using various bin packing techniques. The comparison study, based on different workloads and system heterogeneity levels, shows that the alternative versions of the algorithms outperform their respective counterparts. [ABSTRACT FROM AUTHOR]

Abstract: This paper explores scheduling a realistic variant of open shops with parallel machines per working stage. Since real production floors seldom employ a single machine for each operation, the regular open shop problem is very often in practice extended with a set of parallel machines at each stage. The purpose of duplicating machines in parallel is to either eliminate or to reduce the impact of bottleneck stages on the overall shop efficiency. The objective is to find the sequence which minimizes total completion times of jobs. We first formulate the problem as an effective mixed integer linear programming model, and then we employ memetic algorithms to solve the problem. We employ Taguchi method to evaluate the effects of different operators and parameters on the performance of memetic algorithm. To further enhance the memetic algorithm, we hybridize it with a simple form of simulated annealing as its local search engine. To assess the performance of the model and algorithms, we establish two computational experiments. The first one is small-sized instances by which the model and general performance of the algorithms are evaluated. The second one consists of large-sized instances by which we further evaluate the algorithms. [ABSTRACT FROM AUTHOR]

Abstract: Thread-level redundancy is an efficient approach for transient fault detection and recovery in Chip Multiprocessors (CMPs), in which two adjacent cores are statically coupled to form a functional Dual Modular Redundancy (DMR). Manufacturing process variations cause core-to-core (C2C) performance asymmetry across the chip, which can be further divided into the asymmetry among core-pairs and the asymmetry within a core-pair. We call them inter- and intra-pair asymmetries, respectively, both of which should be taken into considerations in application scheduling for CMPs with static core coupling. In this paper, we first formulate the above scheduling problem as a 0–1 programming problem to maximize the system Weighted Throughput. An efficient IVF&AppSen algorithm is then proposed, which we prove to be optimal when the number of applications equals to that of core-pairs. We also adapt the Simulated Annealing technique to tackle this problem when applications are less than core-pairs on chip. Simulations on a 64-core CMP shows that the proposed algorithms achieve 2.5–9.3% improvement in Weighted Throughput when compared to prior VarF&AppIPC algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Nowadays, increasing attention has been directed towards the issue of security service for real-time applications with security requirements on clusters. However, the study of integrating security demands of real-time applications into scheduling is rare. In this paper, we propose a novel two-phase scheduling strategy TPSS which takes timing constraints and security needs into consideration for security-critical real-time applications on heterogeneous clusters. In the first-phase, a novel algorithm DSRF is proposed to schedule real-time tasks. When the system is in heavy burden, DSRF is able to degrade the security levels of new tasks and tasks waiting in local queues so as to enhance guarantee ratio. On the contrary, when the system is in light burden, DSRF is capable of employing slack time to improve the security quality of new tasks and adequately utilize the system resource. The minimal security level can guarantee the system security, and higher security level is able to make the system more secure. In the second-phase, a new algorithm FMSL is proposed to minimize the difference of security levels of accepted tasks and further improve the security levels of accepted tasks on the whole, which degrades the probability of the applications being attacked. We compare TPSS, DSRF, SAEDF and RF by extensive simulations. The experimental results indicate that TPSS significantly improves the flexibility of scheduling and outperforms other algorithms. [Copyright &y& Elsevier]

This paper introduces an embedded fuzzy expert system for Adaptive Weighted Fair Queueing (AWFQ) located in the network traffic router to update weights for output queues. WFQ algorithm allows differentiated service for traffic classes according to Quality of Service (QoS) requirements. Link sharing and packet scheduling methods are the most critical factors when guaranteeing QoS. There are many different scheduling mechanisms but adequate and adaptive QoS aware scheduling solutions are still in a phase of development due to the rapid growth of multimedia in the Internet. The proposed AWFQ model in this work simplifies the link sharing to two service classes: one for UDP and another for TCP. The implementation of the model is based on adaptive change of weight coefficients that determine the amount of allowed bandwidth for the service class. New weight coefficients are calculated periodically on routers according to developed embedded fuzzy expert system. It is shown through simulations that the AWFQ model is more stable and reacts faster to different traffic states than the traditional WFQ scheduler. The embedded expert system adjusts the weights of AWFQ with two parameters that are based on the share of the UDP and TCP input traffic data rate and the change of the share of the UDP and TCP input data rate. [Copyright &y& Elsevier]

Abstract: Lagrangean decomposition has been recognized as a promising approach for solving large-scale optimization problems. However, Lagrangean decomposition is critically dependent on the method of updating the Lagrangean multipliers used to decompose the original model. This paper presents a Lagrangean decomposition approach based on Nelder–Mead optimization algorithm to update the Lagrangean multipliers. The main advantage of the proposed approach is that it results in improved objective function values for the majority of iterations. The efficiency of the proposed approach is illustrated with examples from the literature and the solution of scheduling problems. [Copyright &y& Elsevier]

Abstract: With rapid development of wireless technology, more and more applications can be served by wireless networks. To provide integrated service over wireless networks has become a very important issue in the design of wireless networks. In this paper, we propose a new MAC protocol with scheduling algorithms to provide integrated service for CDMA based wireless networks. This protocol evolves from the distributed queuing random access protocol for CDMA wireless network (DQRAP/CDMA). The proposed protocol has been designed to have an ability to accommodate integrated traffic in the network with effective scheduling schemes. A series of simulation experiments have been carried out to evaluate the performance of the proposed protocol and scheduling algorithms. The results reveal that the proposed protocol with the scheduling performs effectively to the integrated traffic composed of messages with or without time constraints and achieves fairness among different types of traffic. [Copyright &y& Elsevier]

Abstract: In this paper, we propose an adaptive multiuser frequency-time domain radio resource allocation model for the downlink OFDM systems, which efficiently exploits the time diversity, frequency diversity as well as multiuser diversity in the time, frequency and user domain, respectively. Next we give an in-depth capacity analysis of the proposed frequency-time domain radio resource allocation which achieves much higher capacity (spectral efficiency) than the conventional frequency domain resource allocation. Then according to the allocation algorithm’s computational complexity, two different-complexity adaptive resource allocation algorithms—algorithm A and algorithm B—are proposed which adopt a two-step allocation method to reduce the scheduling complexity and meanwhile improve the scheduling performance. Simulation results show that though the performance of algorithm A and B is slightly different, both of the proposed algorithms yield much higher spectral efficiency and much lower outage probability than conventional OFDM-TDMA scheme or random allocation without any scheduling policy. [Copyright &y& Elsevier]