Group-based Scheduling Research Articles

Evaluating the impact of a champion on implementation of the Back Skills Training (BeST) programme in Canada: a mixed methods feasibility study protocol

IntroductionThere is global recognition that low back pain (LBP) should be managed with a biopsychosocial approach. Previous implementation of this approach resulted in low uptake and highlighted the need for...

Adaptive fault-tolerant scheduling strategies for mobile cloud computing

Mobile cloud computing is a form of cloud computing that incorporates mobile devices such as smartphones and tablet PCs into the cloud infrastructure. As mobile devices are resource-constrained in nature, new scheduling strategies are required when using them as resource providers. Based on our previous group-based scheduling algorithm, we present fault-tolerant scheduling algorithms considering checkpoint and replication mechanisms to actively cope with faults. We carried out the performance evaluation with simulation to demonstrate that our algorithm is more efficient than the existing one lacking fault tolerance in terms of accuracy rate, resource consumption, and average execution time. In particular, the average execution time was reduced by about 60%, resulting in the reduction of resource consumption.

Dynamic group-based scheduling of machine-to-machine communication for uplink traffic in LTE networks

In wireless machine-to-machine communication, the signalling load and radio resource allocation are critical to successful service because the technology features a large number of connected devices that introduce significant connection- and resource-related signalling overheads to eNodeB. This paper proposes an uplink scheduling scheme to minimise signalling overhead and maximise system throughput in machine-type communication devices (MTCDs) in long-term evolution networks. The proposed scheme dynamically adjusts group members by considering channel conditions and quality of service and applies the allocation-before-request (AbR) concept to allocate the residual bandwidth and realise the above objectives. Exhaustive simulations were conducted to examine the performance of the proposed scheme. The simulation results showed that the proposed scheme achieves not only better system throughput but also decreased buffer status report (BSR) signalling when compared with the static group-based scheme.

Dynamic Group-based Scheduling of Machine-to-Machine Communication for Uplink Traffic in LTE Networks

In wireless machine-to-machine communication, the signalling load and radio resource allocation are critical to successful service because the technology features a large number of connected devices that introduce significant connection- and resource-related signalling overheads to eNodeB. This paper proposes an uplink scheduling scheme to minimise signalling overhead and maximise system throughput in machine-type communication devices (MTCDs) in long-term evolution networks. The proposed scheme dynamically adjusts group members by considering channel conditions and quality of service and applies the allocation-before-request (AbR) concept to allocate the residual bandwidth and realise the above objectives. Exhaustive simulations were conducted to examine the performance of the proposed scheme. The simulation results showed that the proposed scheme achieves not only better system throughput but also decreased buffer status report (BSR) signalling when compared with the static group-based scheme.

Grouping-based Scheduling with Load Balancing for Fine-Grained Jobs in Grid Computing

Grid computing is characterized by the existence of a collection of heterogeneous geographically distributed resources that are connected over high speed networks. Job scheduling and resource management have been a great challenge to researchers in the area of grid computing. Very often, there are applications having a large number of fine-grained jobs. Sending these fine-grained jobs individually to be executed on grid resources that have high processing power reduces resource utilization and is thus uneconomical. This paper presents efficient grouping-based scheduling models that group fine-grained jobs to form coarse-grained jobs which are sent for execution on grid resources. Our grouping strategy is based on the processing capability of resources and the processing requirements of grouped jobs. A load balancing approach is also presented to achieve efficient utilization of resources. Simulation experiments were conducted using the Gridsim toolkit. Results show that the total simulation time and the cost are improved by grouping. Furthermore, our load balancing approach enhances resource utilization and achieves load balancing among resources.

Throughput Capacity of MANETs with Group-Based Scheduling and General Transmission Range

Throughput Capacity of MANETs with Group-Based Scheduling and General Transmission Range

A Time-Minimization Dynamic Job Grouping-based Scheduling in Grid Computing

computing is the novel framework that offers a flexible, secure and high performance computing, on demand for solving high compute-intensive applications with large number of independent jobs. However, user jobs developed for grid might be small and of varying lengths according to their computational needs and other requirements. Certainly, it is a real challenge to design an efficient scheduling strategy to achieve high performance in grid computing. But there exists some grouping based job scheduling strategy that intends to minimize total processing time by reducing overhead time and computation time, and on the other hand maximizing resource utilization than without grouping based scheduling. The purpose of the study is to analyze and achieve better performance by extending the concept of grouping based job scheduling. Therefore, this paper proposes A Time-Minimization Dynamic Grouping-Based Job Scheduling in Grid Computing with the objective of minimizing overhead time and computation time, thus reducing overall processing time of jobs. The work is verified through various observations made in different simulated grid environments. The results obtained shows that the proposed grouping-based scheduling algorithm is on average, comparable to, or even better than, other grouping based scheduling algorithms. Keywords— Grid computing; Job grouping; Job scheduling

G-MST: A dynamic group-based scheduling algorithm for MEMS-based mobile storage devices

MEMS-based storage is anticipated to be used as the secondary storage of mobile consumer devices due to its attractive features such as small size, low-power consumption, and low cost. However, physical structures of MEMS-based storage are vastly different from traditional hard disks especially in the way of data placement and seeking. Hence, it is important to revise the software management techniques appropriate for MEMS-based storage. In this paper, we present a new I/O scheduling algorithm for MEMS based storage called G-MST that exploits the two-dimensional graph algorithm and group-based SCAN. The proposed algorithm aims at reducing the total seek distance in the two dimensional MEMS structure and providing fast and uniform response time simultaneously. Simulations with synthetic and real world I/O traces are performed to assess the effectiveness of G-MST.

Supporting lock-free synchronization in Pfair-scheduled real-time systems

We consider the use of lock-free techniques for implementing shared objects in real-time Pfair-scheduled multiprocessor systems. Lock-free objects are more economical than locking techniques when implementing relatively simple objects such as buffers, stacks, queues, and lists. However, the use of such objects on real-time multiprocessors is generally considered impractical due to the need for analytical real-time guarantees. In this paper, we explain how the quantum-based nature of Pfair scheduling enables the effective use of such objects on real-time multiprocessors and present analysis specific to Pfair-scheduled systems. In addition, we show that analytical improvements can be obtained by using such objects in conjunction with group-based scheduling techniques. In this approach, a group of tasks is scheduled as a single entity (called a supertask in the Pfair literature). Such grouping prevents tasks from executing simultaneously, and hence from executing in parallel. Consequently, grouping tasks can improve the worst-case scenario with respect to object contention. Grouping also enables the use of less costly uniprocessor algorithms when all tasks sharing an object reside within the same group. We illustrate these optimizations with a case study that focuses on shared queues. Finally, we present and experimentally evaluate a simple heuristic for grouping tasks in order to reduce object contention. Though the analysis presented herein focuses specifically on Pfair-scheduled systems, the observations and techniques should be applicable to other quantum-scheduled systems as well.