Multiple Quality Of Service Metrics Research Articles

A Multiple QoS Metrics-Aware Virtual Network Embedding Algorithm

Abstract As a key issue of network virtualisation, virtual network embedding (VNE) aims to embed multiple virtual network requests (VNRs) from different applications onto the substrate network effectively. In real networks, about 90% of traffic is generated by different quality of service (QoS) sensitive applications. However, most existing VNE algorithms do not account for the difference. Although several VNE algorithms considered the delay metric of applications, they usually provide strict delay guarantees for all VNRs, leading to a low VNR acceptance ratio. In this paper, we focus on the VNE problem involving multiple QoS metrics and propose a multiple QoS metrics-aware VNE algorithm based on reinforcement learning (RLQ-VNE). We first classify VNRs according to their different requirements for multiple QoS metrics including delay, jitter and packet loss rate, and then introduce reinforcement learning to implement differentiated VNE. Specifically, RLQ-VNE provides strict QoS guarantees for the VNRs with high-level QoS requirements and provides lower QoS guarantees for the VNRs with low-level QoS requirements, thus balancing the QoS guarantee and request acceptance ratio. Simulation results from multiple experimental scenarios show that RLQ-VNE improves the request acceptance ratio and network resource utilisation by sacrificing less QoS.

An On-Line Virtual Machine Consolidation Strategy for Dual Improvement in Performance and Energy Conservation of Server Clusters in Cloud Data Centers

As data centers are consuming massive amount of energy, improving the energy efficiency of cloud computing has emerged as a focus of research. However, it is challenging to reduce energy consumption while maintaining system performance without increasing the risk of Service Level Agreement violations. Most of the existing consolidation approaches for virtual machines (VMs) consider system performance and Quality of Service (QoS) metrics as constraints, which usually results in large scheduling overhead and impossibility to achieve effective improvement in energy efficiency without sacrificing some system performance and cloud service quality. In this article, we first define the metrics of peak power efficiency and optimal utilization for heterogeneous physical machines (PMs). Then we propose Peak Efficiency Aware Scheduling (PEAS), a novel strategy of VM placement and reallocation for achieving dual improvement in performance and energy conservation from the perspective of server clusters. PEAS allocates and reallocates VMs in an on-line manner and always attempts to maintain PMs working in their peak power efficiency via VM consolidation. Extensive experiments on Cloudsim show that PEAS outperforms several energy-aware consolidation algorithms with regard to energy consumption, system performance as well as multiple QoS metrics.

SeC‐SDWSN: Secure cluster‐based SDWSN environment for QoS guaranteed routing in three‐tier architecture

SummarySoftware defined wireless sensor network (SDWSN) is a recent evolution in networking that improves network performance and scalability. However, Quality of Service (QoS) and security are major the issues in SDWSN due to inefficient route selection (traffic load minimization algorithm) and insecure cryptography scheme (homomorphic algorithm). This paper proposes novel three‐tier architecture for secure cluster‐based SDWSN (SeC‐SDWSN) environment to ensure QoS and security for WSN using SDN. In the first tier, sensor nodes are segregated into multiple clusters by secure hash tree‐based clustering (SHTC) algorithm. Within each secure cluster, data transmission is performed through optimal route selected by adaptive spider monkey optimization (ASMO) algorithm in which two new fitness factors (F1, F2) are formulated by multiple QoS metrics. For data security, parallel advanced encryption standard with cipher block chaining (PAES‐CBC) algorithm is proposed. Aggregated ciphertext is transmitted to optimal switch in the second tier by using fuzzy weighted technique for order preference by similarity to ideal solution (FW‐TOPSIS) algorithm according to selection criteria. Switches forward the data to sink node based on flow rules deployed by SDN controllers in the third tier. SDN controllers provide global view on the entire network and deploy flow rules on switches in accordance to network status and security level. Extensive simulation in ns‐3 shows that the proposed three‐tier architecture achieves 5% throughput improvement, 7.8% PDR improvement, and 16% energy consumption improvement.

Energy efficient multiconstrained optimization using hybrid ACO and GA in MANET routing

Nodes in mobile ad hoc networks (MANET) suffer from limited battery power and bandwidth. Particularly for real time multimedia communications through MANET, metrics like residual node energy, bandwidth, and end-to-end delay have major impacts. In MANET, designing a dynamic routing algorithm to satisfy quality of service (QoS) requirements is a challenging task. Additionally, multiconstrained QoS routing aims to optimize multiple QoS metrics while providing required network resources and is an admittedly complex problem. It has been proved to be NP-complete when a combination of additive, concave, and multiplicative metrics are considered. Hence, this problem can be solved using metaheuristic methods like ant colony optimization (ACO) and the genetic algorithm (GA). The proposed energy-efficient ACO GA hybrid metaheuristic approach aims to utilize the benefits of both as a combined approach in order to reduce the routing complexities in the dynamic environment. After due investigation, it has been shown that the proposed hybrid approach improves the performance of MANET routing with satisfied QoS requirements.

Managing Crowds in Hazards With Dynamic Grouping

Emergency navigation algorithms for evacuees in confined spaces typically treat all evacuees in a homogeneous manner, using a common metric to select the best exit paths. In this paper, we present a quality of service (QoS) driven routing algorithm to cater to the needs of different types of evacuees based on age, mobility, and level of resistance to fatigue and hazard. Spatial information regarding the location and the spread of hazards is also integrated into the routing metrics to avoid situations where evacuees may be directed toward hazardous zones. Furthermore, rather than persisting with a single decision algorithm during an entire evacuation process, we suggest that evacuees may adapt their course of action with regard to their ongoing physical condition and environment. A widely tested routing protocol known as the cognitive packet network with random neural networks and reinforcement learning are employed to collect information and provide advice to evacuees, and is beneficial in emergency navigation owing to its low computational complexity and its ability to handle multiple QoS metrics in its search for safe exit paths. The simulation results indicate that the proposed algorithm, which is sensitive to the needs of evacuees, produces better results than the use of a single metric. Simulations also show that the use of dynamic grouping to adjust the evacuees' category, and routing algorithms that have regard for their on-going health conditions and mobility, can achieve higher survival rates.

Quality of service-based multi-domain routing under multiple quality of service metrics

Applications such as voice and video require network paths that satisfy several different quality of service (QoS) metrics, such as delay, jitter, packet loss rate and availability. The calculation of paths under multiple QoS metrics, such as the above four metrics, is a difficult problem since these metrics are in general incompatible. The authors propose a simple method for combining the above four QoS metrics into a single composite QoS metric which can be used as a link cost in Dijkstra's algorithm in order to calculate a path. The authors evaluated the proposed method in a multi-domain routing environment where domain reachability information is available through a service oriented architecture paradigm, and they show that it outperforms two commonly used methods. The results are also applicable to routing within a single domain.

A Simulation Model Supporting Time and Non-time Metrics for Web Service Composition

A next generation network (NGN) exploits advanced multiple broadband, quality of service (QoS)-enabled service technologies. The NGN is aimed at users and service-related functions, including web services. Recently, web service composition (WSC) has become a new approach for overcoming many difficult problems confronted by B2B e-commerce, inter-organization workflow management, enterprise application integration etc. Due to the uncertainty of the NGNs and various web services, the performance of the WSC cannot be ensured. How to model it and predict its performance becomes a difficult problem for WSC. In this paper, we propose a novel performance simulation model for WSC, called STPM+. The STPM+ model can support modeling and simulating the time and non-time QoS metrics. Based on the stochastic timed colored Petri net, the STPM+ model can simulate and predict multiple QoS metrics, such as cost, reliability, reputation and so on. To examine the validation of our STPM+ model, we have designed and realized a visual performance simulation tool, called VisualWSCPE. Based on VisualWSCPE, we have fulfilled some simulation experiments. The experiment results demonstrate the feasibility and efficiency of the STPM+ model.