QoS-aware Scheduling Research Articles

An improved downlink packet scheduling algorithm for delay sensitive devices in both H2H and M2M communications in LTE-advanced networks

The demand for increased data rate with improved QoS for real-time data traffic is ever increasing in the present day wireless environment. The scheduling schemes available in the literature incur lot of scheduling overhead at the eNodeB. Therefore, this work recommends an energy efficient, QoS-aware scheduler with reduced scheduling complexity at the eNodeB, for transmission of delay sensitive data. The scheduling problem is composed as a gain of weighted transmission rates of all possible combinations of various resources required by the channel for transmitting data. An improved greedy algorithm at the eNodeB, has been developed to allocate the resources dynamically to the user equipments (UEs) for the transmission of real-time data. The input video frames to the algorithm are compressed using discrete wavelet transform. The results of this research work show that the proposed scheduling algorithm greatly improves the coverage of the cell edge users. The performance of this greedy scheduler is compared with other two notable schedulers in the literature namely LOG rule and EXP-rule. This scheduling algorithm outperforms the other schemes in terms of QoS parameters for real-time data transmission.

Cost-Efficient and Robust On-Demand Video Transcoding Using Heterogeneous Cloud Services

Video streams usually have to be transcoded to match the characteristics of viewers' devices. Streaming providers have to store numerous transcoded versions of a given video to serve various display devices. Given the fact that viewers' access pattern to video streams follows a long tail distribution, for the video streams with low access rate, we propose to transcode them in an on-demand manner using cloud computing services. The challenge in utilizing cloud services for on-demand video transcoding is to maintain a robust QoS for viewers and cost-efficiency for streaming service providers. To address this challenge, we present the Cloud-based Video Streaming Services (CVS2) architecture. It includes a QoS-aware scheduling that maps transcoding tasks to the VMs by considering the affinity of the transcoding tasks with the allocated heterogeneous VMs. To maintain robustness in the presence of varying streaming requests, the architecture includes a cost-efficient VM Provisioner. This component provides a self- configurable cluster of heterogeneous VMs. The cluster is reconfigured dynamically to maintain the maximum affinity with the arriving workload. Results obtained under diverse workload conditions demonstrate that CVS2 architecture can maintain a robust QoS for viewers while reducing the incurred cost of the streaming service provider up to 85%

An improved efficient: Artificial Bee Colony algorithm for security and QoS aware scheduling in cloud computing environment

Cloud Computing is an emerging Technology which provides IT services. Using cloud resources, computational resource requirement and ubiquitous nature are available at ease and also to pay only for availed resources are achieved through Cloud Computing environment. Here scheduling the job to the appropriate resource is an NP hard problem. Ensuring QoS during job scheduling to the users is a most prominent need. As scheduling takes place in third party’s boundary, assuring its security is an important criterion. To provide QoS such as makespan, cost, reducing task migration during the schedule and enforcing the security are the significant objective of the proposed work. Using the proposed improved efficient—Artificial Bee Colony algorithm these objectives are achieved. The implementation results prove that the proposed system achieves the objective of secure job scheduling with assured QoS.

Distributed QoS-aware scheduling optimization for resource-intensive mobile application in hybrid cloud

In the paper, the distributed scheduling optimization model for resource-intensive mobile application is proposed. Lagrangian method is applied to achieve distributed scheduling optimization in hybrid cloud. By decomposing the Kuhn–Tucker conditions into different roles of mobile user, public cloud supplier and local cloud supplier, the scheduling optimization problem in hybrid cloud is converted into a distributed problem. The system design and example of distributed scheduling optimization for resource intensive mobile application is also given. The local or public cloud provider uses service-level agreement (SLA) in determining the share of resources to be allocated to the mobile user. The distributed scheduling optimization algorithm for resource intensive mobile application is proposed, which includes three parts: local cloud agent scheduling optimization, public cloud service scheduling and mobile application QoS optimization. The experiments study how data size, request arrival rate, number of mobile users and mobility have effect on the proposed algorithm and other related works.

Design of Joint Cooperative Routing, MAC and Physical Layer with QoS-aware Traffic-based Scheduling for Wireless Sensor Networks

Compare the WSN with other conventional network system, the changing or charging the existing battery of sensor node within WSN is a tough job. Therefore, since last decade the effort has been made to design and introduce a large number of communication protocol for WSN with given concern on the performance parameter of energy efficiency and still the key requirements within WSN domain, that how to incrementally expands the energy minimization consuming techniques of sensor battery. The other parameters include latency, fairness, throughput and delivery ratio. In this work, we propose a novel joint cooperative routing, medium access control (MAC) and physical layer protocol with traffic differentiation based QoS- aware for wireless sensor network (WSNs). This is referred to as a Joint Routing, MAC and Physical layer protocol (J-RMP). By blending the classical layered approach and combining routing, MAC and physical layer functions, the proposed J-RMP protocol achieves a solution for energy efficiency in WSNs. Convention ally, the problem of energy efficient protocol design is considered independently at respective layer of protocol stack. This paper has taken a holistic approach of finding solution by addressing the possible energy gain in Routing, MAC and Physical layer together. Firstly, it is seen that communication path with the large number of short hop substantially minimizes energy consumption. This phenomenon is used while instantaneous network information is collected with minimum overhead through the control packets for selecting next hop. Based on the updated network knowledge the next hop neighbor is chosen with reduced control overhead. Further, we describe how J-RMP protocol uses the approach for finding the constrained shortest path for forwarding packets [1], which results in load balancing in WSNs and provide mathematical analysis of node forwarding path determination within network. Finally, we compare our proposed method with existing protocols by ns-2 simulator and confirm that J-RMP outperform in some cases in term of sensitive traffics. The experimental results have proved that the traffic load has been balanced to a certain traffic, and energy efficiency has been achieved by properly adjusting the applied theory. The outcome supports the theory, and proves the effectiveness of our proposed scheme. This kind of protocol should have significant implications for various WSNs application where energy constraint is the main point of concerns.

A new Channel- and QoS-Aware scheduling Scheme for Real-time Services in LTE Network

This paper proposes a new scheduling scheme which is based on Channel-and QoS-Aware for real-time traffics in LTE down-link direction. The real-time services (VoIP, Video, etc.) are very sensitive to network impairments such as delay, packet loss, jit-ter, etc. The proposed scheduling scheme considers Maximum Queue Size (MQS) as an essential factor for the priority metric and the combination of well-known M-LWDF-based algorithms such as M-LWDF, VT-MLWDF and Queue-HOL-MLWDF. The simulation results show that the proposed scheme has the performance which not only satisfies QoS requirements of real-time services but also outperforms the M-LWDF, VT-MLWDF and Queue-HOL-MLWDF schedulers in terms of Packet Loss Rate (PLR), cell throughput, Fairness Index (FI) and Spectral Efficiency (SE), especially for Video flow. The proposed sched-uler also significantly improves the delay in comparison with the VT-MLWDF and Queue-HOL-MLWDF schedulers ones for both VoIP and Video flows. The performance evaluation is compared in terms of Delay, PLR, Throughput, FI and SE for M-LWDF, VT-MLWDF, Queue-HOL-MLWDF and the proposed schedulers.

Kani: a QoS-aware hypervisor-level scheduler for cloud computing environments

Cloud computing environments (CCEs) are expected to deliver their services with qualities in service level agreements. On the other hand, they typically employ virtualization technology to consolidate multiple workloads on the same physical machine, thereby enhancing the overall utilization of physical resources. Most existing virtualization technologies are, however, unaware of their delivered quality of services (QoS). For example, the Xen hypervisor merely focuses on fair sharing of processor resources. We believe that CCEs have got married with traditional virtualization technologies without many traits in common. To bridge the gap between these two technologies, we have designed and implemented Kani, a QoS-aware hypervisor-level scheduler. Kani dynamically monitors the quality of delivered services to quantify the deviation between desired and delivered levels of QoS. Using this information, Kani determines how to allocate processor resources among running VMs so as to meet the expected QoS. Our evaluations of Kani scheduler prototype in Xen show that Kani outperforms the default Xen scheduler namely the Credit scheduler. For example, Kani reduces the average response time to requests to an Apache web server by up to $$93.6\,\%$$93.6%; improves its throughput by up to $$97.9\,\%$$97.9%; and mitigates the call setup time of an Asterisk media server by up to $$96.6\,\%$$96.6%.

Data classification and scheduling for sensor virtualisation scheme in public healthcare system

It is really surprising that most of the sensor nodes in wireless sensor network WSN remain idle for maximum period of its lifetime resulting underutilisation of resources. There are many ongoing researches to utilise WSN resources in an efficient way. Virtualisation of sensor network VSN is one of the novel approaches to utilise physical infrastructure of WSN. VSN can be simply defined as the virtual version of WSN over the physical sensor infrastructure. By allowing sensor nodes to coexist on a shared physical substrate, VSN may provide flexibility, cost effectiveness and manageability. This paper proposes quality of service QoS aware data classification and scheduling framework for VSN in the healthcare sector. We develop a tiny virtual machine called VSNware for healthcare application which facilitates QoS aware data classification and scheduling ensuring reliability, delay guarantee and speed. Simulation results also show that proposed scheme over performs conventional WSN approaches.

A new TDD scheme and interference-aware precoding for device-to-device underlay massive MIMO

Cognitive radio sensor network is applied to facilitate network monitoring and management, and achieves high spectrum efficiencies in smart grid. However, the conventional traffic scheduling mechanisms are hard to provide guaranteed quality of service for the secondary users. It is because that they ignore the influence of diverse transition requirements in heterogeneous traffic. Therefore, a novel QoS-aware packet scheduling mechanism is proposed to improve transmission quality for secondary users. In this mechanism, a QoS-based prioritization model is established to address data classification firstly. And then, channel quality and the effect of channel switch are integrated into priority-based packet scheduling mechanism. At last, the simulation is implemented with MATLAB and OPNET. The results show that the proposed scheduling mechanism improves the transmission quality of high-priority secondary users and increase the whole system utilization by 10%.

Energy-Efficient, QoS-Aware Packet Scheduling in High-Speed Networks

In current commercial routers, faster network processor units (NPUs) in line cards (LCs) can significantly improve network QoS performance. However, this improvement may come at a high cost of energy consumption. In this paper, we propose and investigate two classes of QoS-aware DVFS-based packet scheduling schemes. The first class uses queue length (QL) to control execution rates in line cards, whereas the second uses link utilization to achieve the same purpose. Excessive reduction in execution rate to save energy, however, may result in a sharp increase in network delay. To address this challenge, different frequency scaling strategies are proposed, and the performance of their associated schedulers is investigated. The simulation results show that both QL-aware and Load-aware DVFS have potential for significant energy saving in high-speed networks, with acceptable delay performance. Furthermore, a simulation study is carried out to compare the performance of the proposed schemes to similar schemes described in the literature for different network environments and traffic loads. The results show that the $\text{m}\bar{\text{Q}}\text{LA}$ scheme achieves the best results, with performance gains of up to 9.5% energy saving, while meeting the QoS performance of the supported applications.

QoS-aware resource management for multimedia traffic report systems over LTE-A

The multimedia traffic report system is a new type of applications in wireless vehicular networks equipped with sensors. This system can provide various kinds of traffic information, which are directly obtained from on-car sensors and devices via LTE-A. By leveraging LTE-A technologies, the system can support wide-range transmissions and better mobility. Despite of these improvements, the resource management problem is still a challenging issue due to a large number of on-car sensors and devices. Existing resource management studies focus on channel quality or Quality of Service (QoS) requirements for non-vehicular applications. To get the most useful and complete traffic information, including location coverage, an efficient resource management mechanism for multimedia vehicular applications is indispensable. In this paper, we propose a QoS-aware resource management mechanism for multimedia traffic report systems over LTE-A (QoS-MTRS). The main idea is to schedule resources according to the importance degrees of various traffic data. Furthermore, we take channel availability into consideration to make sure that the transmission is feasible. The objective of QoS-MTRS is to improve the diversity, completeness and overall traffic data value under radio resource limitations. We have conducted a system-level simulation that includes an LTE-A network environment to evaluate our design. Simulation results show that the proposed QoS-MTRS is better than BQA (bandwidth and QoS-aware scheduler), Greedy, and FCFS in terms of appearance probability of receiving more than one kind of traffic data types per location block, duplication ratio, overall traffic data value, end-to-end delay, system throughput, location coverage, and flow rejection rate. The significance of this work is the proposed QoS-MTRS can be applied to LTE-A for supporting high-bandwidth demands Internet of Things (IoTs).

QoS-aware composite scheduling using fuzzy proactive and reactive controllers

We consider in this paper downlink scheduling for different traffic classes at the MAC layer of wireless systems based on orthogonal frequency division multiple access (OFDMA), such as the recent 3rd Generation Partnership Project (3GPP) long-term evolution (LTE)/LTE-A wireless standard. Our goal is to provide via the scheduling decisions quality of service (QoS), but also to guarantee fairness among the different users and traffic classes (including delay-sensitive and best-effort traffic). QoS-aware scheduling strategies, such as modified largest weighted delay first (M-LWDF), exponential (EXP), exponential proportional fair (EXP-PF), and the log-based scheduling rules, prioritize delay-sensitive traffic by considering rules based on the head-of-line (HoL) packet delay and the tolerated packet loss rate, whereas they serve best-effort traffic by considering the classical proportional fair (PF) rule. These scheduling rules do not prevent resource starvation for best-effort traffic. On the other side, if both traffic types are scheduled according to the PF rule, then delay-sensitive flows suffer from delay bound violations. In order to fairly distribute the resources among different service classes according to their QoS requirements and channel conditions, we employ the concept of fuzzy logic in our scheduling framework. By employing the fuzzy logic concept, we serve all the traffic classes with one priority rule. Simulation results show better intra-class and inter-class fairness than state-of-the-art scheduling rules. The proposed scheduling framework enables to appropriately balance delay requirements of traffic, system throughput, and fairness.

QoS-Aware Scheduling of Services-Oriented Internet of Things

The Internet of Things (IoT) contains a large number of different devices and heterogeneous networks, which make it difficult to satisfy different quality of service (QoS) requirements and achieve rapid services composition and deployment. In addition, some services in service-oriented IoT are required to be reconfigurable and composable for QoS-aware services. This paper proposed a three-layer QoS scheduling model for service-oriented IoT. At application layer, the QoS schedule scheme explores optimal QoS-aware services composition by using the knowledge of each component service. At network layer, the model aims at dealing with scheduling of heterogeneous networks environment; at sensing layer, it deals with the information acquisition and resource allocation scheduling for different services. The proposed QoS-aware scheduling for service-oriented IoT architecture is able to optimize the scheduling performance of IoT network and minimize the resource costs.

Low Complexity in Exaggerated Earliest Deadline First Approach for Channel and QoS-aware Scheduler

Long Term Evolution (LTE) is a Quality of Service (QoS) provisioning wireless network for today’s technology and as well as for future demands. There is a high demand for better network performance over LTE network, either for real-time or non-real-time traffic. Specifically, the existing scheduling algorithms for real-time application, Exponential/Proportional Fair (EXP/PF), Proportional Fair (PF), and Modified-Largest Weighted Delay First (M-LWDF) have not fully optimized in LTE network. Hence, this paper aims to deliver new scheduling algorithm in the LTE network which overcomes several QoS and channel concerns. Several algorithms were studied, tested and compared which includes EXP/PF, PF, and M-LWDF, which are the popular scheduling algorithms for real-time application in today’s deployment. A typical LTE network is simulated and several experiments were conducted. Extensive simulation results showed that our proposed scheduling algorithm, Exaggerated Earliest Deadline First (E2DF), has outperformed the three existing scheduling algorithms. The proposed algorithm is a LTE compliance module and it able to provide great performance improvement as compared to the other algorithms for real-time application. 

From volunteer to trustable computing: Providing QoS-aware scheduling mechanisms for multi-grid computing environments

The exploitation of service oriented technologies, such as Grid computing, is being boosted by the current service oriented economy trend, leading to a growing need of Quality of Service (QoS) mechanisms. However, Grid computing was created to provide vast amounts of computational power but in a best effort way. Providing QoS guarantees is therefore a very difficult and complex task due to the distributed and heterogeneous nature of their resources, specially the volunteer computing resources (e.g., desktop resources).The scope of this paper is to empower an integrated multi QoS support suitable for Grid Computing environments made of either dedicated and volunteer resources, even taking advantage of that fact. The QoS is provided through SLAs by exploiting different available scheduling mechanisms in a coordinated way, and applying appropriate resource usage optimization techniques. It is based on the differentiated use of reservations and scheduling in advance techniques, enhanced with the integration of rescheduling techniques that improve the allocation decisions already made, achieving a higher resource utilization and still ensuring the agreed QoS. As a result, our proposal enhances best-effort Grid environments by providing QoS aware scheduling capabilities.This proposal has been validated by means of a set of experiments performed in a real Grid testbed. Results show how the proposed framework effectively harnesses the specific capabilities of the underlying resources to provide every user with the desired QoS level, while, at the same time, optimizing the resources’ usage.

QoS-Aware scheduling in heterogeneous datacenters with paragon

Large-scale datacenters (DCs) host tens of thousands of diverse applications each day. However, interference between colocated workloads and the difficulty of matching applications to one of the many hardware platforms available can degrade performance, violating the quality of service (QoS) guarantees that many cloud workloads require. While previous work has identified the impact of heterogeneity and interference, existing solutions are computationally intensive, cannot be applied online, and do not scale beyond a few applications. We present Paragon, an online and scalable DC scheduler that is heterogeneity- and interference-aware. Paragon is derived from robust analytical methods, and instead of profiling each application in detail, it leverages information the system already has about applications it has previously seen. It uses collaborative filtering techniques to quickly and accurately classify an unknown incoming workload with respect to heterogeneity and interference in multiple shared resources. It does so by identifying similarities to previously scheduled applications. The classification allows Paragon to greedily schedule applications in a manner that minimizes interference and maximizes server utilization. After the initial application placement, Paragon monitors application behavior and adjusts the scheduling decisions at runtime to avoid performance degradations. Additionally, we design ARQ, a multiclass admission control protocol that constrains application waiting time. ARQ queues applications in separate classes based on the type of resources they need and avoids long queueing delays for easy-to-satisfy workloads in highly-loaded scenarios. Paragon scales to tens of thousands of servers and applications with marginal scheduling overheads in terms of time or state. We evaluate Paragon with a wide range of workload scenarios, on both small and large-scale systems, including 1,000 servers on EC2. For a 2,500-workload scenario, Paragon enforces performance guarantees for 91% of applications, while significantly improving utilization. In comparison, heterogeneity-oblivious, interference-oblivious, and least-loaded schedulers only provide similar guarantees for 14%, 11%, and 3% of workloads. The differences are more striking in oversubscribed scenarios where resource efficiency is more critical.

Delay-Constrained Video Transmission: Quality-Driven Resource Allocation and Scheduling

Real-time video demands quality-of-service (QoS) guarantees such as delay bounds for end-user satisfaction. Furthermore, the tolerable delay varies depending on the use case such as live streaming or two-way video conferencing. Due to the inherently stochastic nature of wireless fading channels, deterministic delay bounds are difficult to guarantee. Instead, we propose providing statistical delay guarantees using the concept of effective capacity. We consider a multiuser setup whereby different users have (possibly different) delay QoS constraints. We derive the resource allocation policy that maximizes the sum video quality and applies to any quality metric with concave rate-quality mapping. We show that the optimal operating point per user is such that the rate-distortion slope is the inverse of the supported video source rate per unit bandwidth, a key metric we refer to as the source spectral efficiency. We extend the resource allocation policy to capture video quality-driven adaptive user-subcarrier assignment in wideband channels as well as capture the impact of adaptive modulation and coding. We also solve the alternative problem of fairness-based resource allocation whereby the objective is to maximize the minimum video quality across users. Finally, we derive user admission and scheduling policies that enable selecting a maximal user subset such that all selected users can meet their statistical delay requirement. Results show that video users with differentiated QoS requirements can achieve similar video quality with vastly different resource requirements. Thus, QoS-aware scheduling and resource allocation enable supporting significantly more users under the same resource constraints.

QoS-aware scheduling with optimization of base-station power allocation in downlink cooperative OFDMA systems

Quality-of-Service (QoS) aware scheduling in cooperative orthogonal frequency division multiple access (OFDMA) systems has long been a critical yet challenging research topic for achieving full system utilization. The optimal performance of such systems can only be achieved by joint optimization of various resources (power, bandwidth and \emph{etc}). In this paper, we investigate the optimization of base station power allocation with QoS requirements in cooperative OFDMA systems. We formulate the QoS aware resource allocation problem and exploit its structure to derive an efficient solution. By employing Lagrangian relaxation, the problem decouples on each subcarrier into a hierarchy of subproblems. Combined with an iterative procedure, some separable and parallel structures could be exploited to obtain an efficient solution. Simulation results demonstrate that our proposed method outperforms previous works in terms of spectrum efficiency and QoS satisfaction.

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 Data Classification and Scheduling Scheme in the Virtualization of Wireless Sensor Networks

Most of the nodes in a wireless sensor network (WSN) remain idle for the maximum period of their lifetime resulting in underutilization of their resources. There are many ongoing research studies to utilize the resources of sensor nodes in an efficient way. Virtualization of sensor network (VSN) is one of the novel approaches to utilize the physical infrastructure of a WSN. VSN can be simply defined as the virtual version of a WSN over the physical sensor infrastructure. By allowing sensor nodes to coexist on a shared physical substrate, VSN may provide flexibility, cost effectiveness, and manageability. This paper proposes a QoS-aware data classification and scheduling framework for VSN in the health care sector. We develop a tiny virtual machine called VSNware for health care applications, which facilitates QoS-aware forwarding of data packets, maintaining the reliability, delay guarantee, and speed. The simulation results also show that the proposed scheme outperforms the conventional WSN approaches.