QoS-aware Scheduling Algorithm Research Articles

A task clustering based QoS aware scheduling algorithm for task execution in cloud-Iot model for education services

A task clustering based QoS aware scheduling algorithm for task execution in cloud-Iot model for education services

QoS Aware Multi-Path Routing using Link Scheduling Algorithm

Objective: The demand for Cellular based multimedia services is growing day by day, in order to fulfill such demand the present day cellular networks needs to be upgraded to support excessive capacity calls along with high data accessibility. Analysis of traffic and huge network size could become very challenging issue for the network operators for scheduling the available bandwidth between different users. In the proposed work a novel QoS Aware Multi Path scheduling algorithm for smooth CAC in wireless mobile networks. The performance of the proposed algorithm is assessed and compared with existing scheduling algorithms. The simulation results show that the proposed algorithm outperforms existing CAC algorithms in terms of throughput and delay. The CAC algorithm with scheduling increases end-to-end throughput and decreases end-to-end delay. Methods: The key idea to implement the proposed research work is to adopt spatial reuse concept of wireless sensor networks to mobile cellular networks. Spatial reusability enhances channel reuse when the node pairs are far away and distant. When Src and node b are communicating with each other, the other nodes in the discovered path should be idle without utilizing the channel. Instead the other nodes are able to communicate parallelly the end-to-end throughput can be improved with acceptable delay. Incorporating link scheduling algorithms to this key concept further enhances the end-to-end throughput with in the turnaround time. So, in this research work we have applied spatial reuse concept along with link scheduling algorithm to enhance end-to-end throughput with in turnaround time. The proposed algorithm not only ensures that a connection gets the required bandwidth at each mobile node on its way by scheduling required slots to meet the QoS requirements. By considering the bandwidth requirement of the mobile connections, the CAC module at the BS not only considers the bandwidth requirement but also conforming the constrains of system dealy and jitter are met. Result: To verify the feasibility and effectiveness of our proposed work, with respect to scheduling the simulation results clearly shows the throughput improvement with Call Admission Control. The number of dropped calls is significantly less and successful calls are more with CAC. The percentage of dropped calls is reduced by 9 % and successful calls are improved by 91%. The simulation is also conducted on time constraint and ratio of dropped calls are shown. The total time taken to forward the packets and the ration of dropped calls is less when compared to non CAC. On a whole the CAC with scheduling algorithms out performs existing scheduling algorithms. Conclusion: In this research work we have proposed a novel QoS aware scheduling algorithm that provides QoS in Wireless Cellular Networks using Call Admission Control (CAC). The simulation results show that the end-to-end throughput has been increased by 91% when CAC is used. The proposed algorithm is also compared with existing link scheduling algorithms. The results reveal that CAC with scheduling algorithm can be used in Mobile Cellular Networks in order to reduce packet drop ratio. The algorithm is also used to send the packets within acceptable delay.

QoS-DPSO: QoS-aware Task Scheduling for Cloud Computing System

Dynamic provisioning of computational resources in cloud computing system is a challenging problem. With many VMs being deployed in clouds, managing so much resources to stable work becomes a critical problem. Therefore, how to meet the different quality of service (QoS), we consider the service quality requirements is very important. In this paper, we address this problem. We first establish a QoS scheduling model by incorporating the cloud characteristics, and then, we develop a task scheduling objects to ensure faults can be tolerated during the task exaction. Finally, we proposed a QoS-aware scheduling algorithm, QoS-DPSO, to satisfy the QoS required in cloud computing systems. For the target requirements of QoS requirement, we take the time, reliability and cost as a single object problem. Experimental results show that QoS-DPSO can effectively improve the performance and obtain the high reliability.

Deep Learning Research and Development Platform: Characterizing and Scheduling with QoS Guarantees on GPU Clusters

Deep learning (DL) has been widely adopted in various domains of artificial intelligence (AI), achieving dramatic developments in industry and academia. Besides giant AI companies, numerous small and medium-sized enterprises, institutes, and universities (EIUs) have focused on the research and development (R&D) of DL. Considering the high cost of datacenters and high performance computing (HPC) systems, EIUs prefer adopting off-the-shelf GPU clusters as a DL R&D platform for multiple users and developers to process diverse DL workloads. In such scenarios, the scheduling of multiple DL tasks on a shared GPU cluster is both significant and challenging in terms of efficiently utilizing limited resources. Existing schedulers cannot predict the resource requirements of diverse DL workloads, leading to the under-utilization of computing resources and a decline in user satisfaction. This paper proposes GENIE, a QoS-aware dynamic scheduling framework for a shared GPU cluster, which achieves users’ QoS guarantee and high system utilization. In accordance with an exhaustive characterization, GENIE analyzes the key factors that affect the performance of DL tasks and proposes a prediction model derived from lightweight profiling to estimate the processing rate and response latency for diverse DL workloads. Based on the prediction models, we propose a QoS-aware scheduling algorithm to identify the best placements for DL tasks and schedule them on the shared cluster. Experiments on a GPU cluster and large-scale simulations demonstrate that GENIE achieves a QoS-guarantee percentage improvement of up to 67.4 percent and a makespan reduction of up to 28.2 percent, compared to other baseline schedulers.

QoS-aware scheduling algorithm in relay-assisted LTE-A system

This paper details on the uplink scheduling algorithm for long term evolution advanced (LTE-A) system with relays. While emulating quality of service (QoS)-aware services with different bit-rate and delay budget requirements for the upstream direction, a new QoS-aware scheduling algorithm for in-band relays is proposed. In this work, an improved scheduling metric calculation method and bit-rate guarantee scheme is applied. Moreover, this algorithm proposes an efficient scheme for the backhaul link allocation which allows information of the most backlogged users to be transmitted first. Finally, this paper concludes with simulation results to demonstrate how the proposed resource allocation strategy improves the performance of the system.

A novel resource scheduling algorithm for QoS-aware services on the Internet

The popularity and availability of Internet connection has opened up the opportunity for network-centric collaborative work that was impossible a few years ago. Contending traffic flows in this collaborative scenario share different kinds of resources such as network links, buffers, and router CPU. The goal should hence be overall fairness in the allocation of multiple resources rather than a specific resource. In this paper, firstly, we present a novel QoS-aware resource scheduling algorithm called Weighted Composite Bandwidth and CPU Scheduler (WCBCS), which jointly allocates the fair share of the link bandwidth as well as processing resource to all competing flows. WCBCS also uses a simple and adaptive online prediction scheme for reliably estimating the processing times of the incoming data packets. Secondly, we present some analytical results, extensive NS-2 simulation work, and experimental results from our implementation on Intel IXP2400 network processor. The simulation and implementation results show that our low complexity scheduling algorithm can efficiently maximise the CPU and bandwidth utilisation while maintaining guaranteed Quality of Service (QoS) for each individual flow.

QoS- and revenue aware adaptive scheduling algorithm

In the near future packet networks should support applications which can not predict their traffic requirements in advance, but still have tight quality of service requirements, e.g., guaranteed bandwidth, jitter, and packet loss. These dynamic characteristics mean that the sources can be made to modify their data transfer rates according to network conditions. Depending on the customer's needs, network operator can differentiate incoming connections and handle those in the buffers and the interfaces in different ways. In this paper, dynamic QoS-aware scheduling algorithm is presented and investigated in the single node case. The purpose of the algorithm is — in addition to fair resource sharing to different types of traffic classes with different priorities — to maximize revenue of the service provider. It is derived from the linear type of revenue target function, and closed form globally optimal formula is presented. The method is computationally inexpensive, while still producing maximal revenue. Due to the simplicity of the algorithm, it can operate in the highly nonstationary environments. In addition, it is nonparametric and deterministic in the sense that it uses only the information about the number of users and their traffic classes, not about call density functions or duration distributions. Also, Call Admission Control (CAC) mechanism is used by hypothesis testing.

STF: Provide rate guarantees in input-queued switch

A QoS-aware input-queued scheduling algorithm, called Smallest Timestamp First (STF), is proposed, which is improved upon iSLIP and can allocate bandwidth among inputs sharing a common output based on their reservation by assigning suitable finishing timestamps to contending cells. STF can also provide isolation between flows that share a common output link. Misbehaving flows will be restricted to guarantee the behaving flows’ bandwidth. Simulations prove the feasibility of our algorithm.