Coordinated Chance-constrained Optimization of Multi-energy Microgrid System for Balancing Operation Efficiency and Quality-of-service

To enhance the flexible interactions among multiple energy carriers,i.e.,electricity,thermal power and gas,a coordinated programming method for multi-energy microgrid(MEMG)system is proposed.Various energy requirements for both residential and parking loads are managed simultaneously,i.e.,electric and thermal loads for residence,and charging power and gas filling requirements for parking vehicles.The proposed model is formulated as a two-stage joint chance-constrained programming,where the first stage is a day-ahead operation problem that provides the hourly generation,conversion,and storage towards the minimization of operation cost considering the forecasting error of photovoltaic output and load demand.Meanwhile,the second stage is an on-line scheduling which adjusts the energy scheme in hourly time-scale considering the uncertainty.Simulations have demonstrated the validity of the proposed method,i.e.,collecting the flexibilities of thermal system,gas system,and parking vehicles to facilitate the operation of electrical networks.Sensitivity analysis shows that the proposed scheme can achieve a compromise between the operation efficiency and service quality.

Cloud Datacenter Selection Using Service Broker Policies:A Survey

Amid the landscape of Cloud Computing(CC),the Cloud Datacenter(DC)stands as a conglomerate of physical servers,whose performance can be hindered by bottlenecks within the realm of proliferating CC services.A linchpin in CC’s performance,the Cloud Service Broker(CSB),orchestrates DC selection.Failure to adroitly route user requests with suitable DCs transforms the CSB into a bottleneck,endangering service quality.To tackle this,deploying an efficient CSB policy becomes imperative,optimizing DC selection to meet stringent Qualityof-Service(QoS)demands.Amidst numerous CSB policies,their implementation grapples with challenges like costs and availability.This article undertakes a holistic review of diverse CSB policies,concurrently surveying the predicaments confronted by current policies.The foremost objective is to pinpoint research gaps and remedies to invigorate future policy development.Additionally,it extensively clarifies various DC selection methodologies employed in CC,enriching practitioners and researchers alike.Employing synthetic analysis,the article systematically assesses and compares myriad DC selection techniques.These analytical insights equip decision-makers with a pragmatic framework to discern the apt technique for their needs.In summation,this discourse resoundingly underscores the paramount importance of adept CSB policies in DC selection,highlighting the imperative role of efficient CSB policies in optimizing CC performance.By emphasizing the significance of these policies and their modeling implications,the article contributes to both the general modeling discourse and its practical applications in the CC domain.

Quality of Service Improvement with Optimal Software-Defined Networking Controller and Control Plane Clustering

The controller is indispensable in software-defined networking(SDN).With several features,controllers monitor the network and respond promptly to dynamic changes.Their performance affects the quality-of-service(QoS)in SDN.Every controller supports a set of features.However,the support of the features may be more prominent in one controller.Moreover,a single controller leads to performance,single-point-of-failure(SPOF),and scalability problems.To overcome this,a controller with an optimum feature set must be available for SDN.Furthermore,a cluster of optimum feature set controllers will overcome an SPOF and improve the QoS in SDN.Herein,leveraging an analytical network process(ANP),we rank SDN controllers regarding their supporting features and create a hierarchical control plane based cluster(HCPC)of the highly ranked controller computed using the ANP,evaluating their performance for the OS3E topology.The results demonstrated in Mininet reveal that a HCPC environment with an optimum controller achieves an improved QoS.Moreover,the experimental results validated in Mininet show that our proposed approach surpasses the existing distributed controller clustering(DCC)schemes in terms of several performance metrics i.e.,delay,jitter,throughput,load balancing,scalability and CPU(central processing unit)utilization.

A Novel Genetic Algorithm for Stable Multicast Routing in Mobile Ad Hoc Networks

Data transmission among multicast trees is an efficient routing method in mobile ad hoc networks(MANETs). Genetic algorithms(GAs) have found widespread applications in designing multicast trees. This paper proposes a stable quality-of-service(QoS) multicast model for MANETs. The new model ensures the duration time of a link in a multicast tree is always longer than the delay time from the source node. A novel GA is designed to solve our QoS multicast model by introducing a new crossover mechanism called leaf crossover(LC), which outperforms the existing crossover mechanisms in requiring neither global network link information, additional encoding/decoding nor repair procedures. Experimental results confirm the effectiveness of the proposed model and the efficiency of the involved GA. Specifically, the simulation study indicates that our algorithm can obtain a better QoS route with a considerable reduction of execution time as compared with existing GAs.

Distributed QoS multicast routing in networks with imprecise state information

The goal of quality-of-service （QoS） multicast routing is to establish a multicast tree which satisfies certain constraints on bandwidth, delay and other metrics. The network state information maintained at every node is often im- precise in a dynamic environment because of non-negligible propagation delay of state messages, periodic updates due to overhead concern, and hierarchical state aggregation. The existing QoS multicast routing algorithms do not provide satisfactory performance with imprecise state information. We propose a distributed QoS multicast routing scheme based on traffic lights, called QMRI algorithm, which can probe multiple feasible tree branches, and select the optimal or near-optimal branch through the UR or TL mode for constructing a multicast tree with QoS guarantees if it exists. The scheme is designed to work with imprecise state information. The proposed algorithm considers not only the QoS requirements but also the cost optimality of the multicast tree. The correctness proof and the complexity analysis about the QMRI algorithm are also given. In addition, we develop NS2 so that it is able to simulate the imprecise network state information. Extensive simulations show that our algorithm achieves high call-admission ratio and low-cost multicast trees with modest message overhead.

QoS-Aware Energy-Efficient Task Scheduling on HPC Cloud Infrastructures Using Swarm-Intelligence Meta-Heuristics

Cloud computing infrastructure has been evolving as a cost-effective platform for providing computational resources in the form of high-performance computing as a service(HPCaaS)to users for executing HPC applications.However,the broader use of the Cloud services,the rapid increase in the size,and the capacity of Cloud data centers bring a remarkable rise in energy consumption leading to a significant rise in the system provider expenses and carbon emissions in the environment.Besides this,users have become more demanding in terms of Quality-of-service(QoS)expectations in terms of execution time,budget cost,utilization,and makespan.This situation calls for the design of task scheduling policy,which ensures efficient task sequencing and allocation of computing resources to tasks to meet the trade-off between QoS promises and service provider requirements.Moreover,the task scheduling in the Cloud is a prevalent NP-Hard problem.Motivated by these concerns,this paper introduces and implements a QoS-aware Energy-Efficient Scheduling policy called as CSPSO,for scheduling tasks in Cloud systems to reduce the energy consumption of cloud resources and minimize the makespan of workload.The proposed multi-objective CSPSO policy hybridizes the search qualities of two robust metaheuristics viz.cuckoo search(CS)and particle swarm optimization(PSO)to overcome the slow convergence and lack of diversity of standard CS algorithm.A fitness-aware resource allocation(FARA)heuristic was developed and used by the proposed policy to allocate resources to tasks efficiently.A velocity update mechanism for cuckoo individuals is designed and incorporated in the proposed CSPSO policy.Further,the proposed scheduling policy has been implemented in the CloudSim simulator and tested with real supercomputing workload traces.The comparative analysis validated that the proposed scheduling policy can produce efficient schedules with better performance over other well-known heuristics and meta-heuristics scheduling policies.

Dynamic multimedia stream adaptation and rate control for heterogeneous networks

Dynamic adaptation of multimedia content is seen as an important feature of next generation networks and pervasive systems enabling terminals and applications to adapt to changes in e.g. context, access network, and available Quality-of-Service(QoS) due to mobility of users, devices or sessions. We present the architecture of a multimedia stream adaptation service which enables communication between terminals having heterogeneous hardware and software capabilities and served by heterogeneous networks. The service runs on special content adaptation nodes which can be placed at any location within the network. The flexible structure of our architecture allows using a variety of different adaptation engines. A generic transcoding engine is used to change the codec of streams. An MPEG-21 Digital Item Adaptation (DIA) based transformation engine allows adjusting the data rate of scalable media streams. An intelligent decision-taking engine implements adaptive flow control which takes into account current network QoS parameters and congestion information. Measurements demonstrate the quality gains achieved through adaptive congestion control mechanisms under conditions typical for a heterogeneous network.

Proportional rate scheduling based on QoS in mobile WiMAX networks

A new scheduling scheme based on users＇ quality of service（QoS） in mobile WiMAX networks is presented.The proposed scheme tracks each user＇s average rate and adjusts the corresponding scheduling weight adaptively to result in：（a） each user＇s average rate is proportional to the corresponding QoS level;（b） the constraints of the minimal and/or maximal rates required by QoS can be satisfied;（c） the utility function of system is maximal under the constraints（a） and（b）.Theoretical analysis based on utility function and simulation results indicates the system throughput can be improved dramatically in the proposed scheme.

Network Traffic Clustering with QoS-Awareness

Network traffic classification is essential in supporting network measurement and management.Many existing traffic classification approaches provide application-level results regardless of the network quality of service(QoS)requirements.In practice,traffic flows from the same application may have irregular network behaviors that should be identified to various QoS classes for best network resource management.To address the issues,we propose to conduct traffic classification with two newly defined QoSaware features,i.e.,inter-APP similarity and intraAPP diversity.The inter-APP similarity represents the close QoS association between the traffic flows that originate from the different Internet applications.The intra-APP diversity describes the QoS variety of the traffic even among those originated from the same Internet application.The core of performing the QoS-aware feature extraction is a Long-Short Term Memory neural network based Autoencoder(LSTMAE).The QoS-aware features extracted by the encoder part of the LSTM-AE are then clustered into the corresponding QoS classes.Real-life data from multiple applications are collected to evaluate the proposed QoS-aware network traffic classification approach.The evaluation results demonstrate the efficacy of the extracted QoS-aware features in supporting the traffic classification,which can further contribute to future network measurement and management.

A Novel Features Prioritization Mechanism for Controllers in Software-Defined Networking

The controller in software-defined networking(SDN)acts as strategic point of control for the underlying network.Multiple controllers are available,and every single controller retains a number of features such as the OpenFlow version,clustering,modularity,platform,and partnership support,etc.They are regarded as vital when making a selection among a set of controllers.As such,the selection of the controller becomes a multi-criteria decision making(MCDM)problem with several features.Hence,an increase in this number will increase the computational complexity of the controller selection process.Previously,the selection of controllers based on features has been studied by the researchers.However,the prioritization of features has gotten less attention.Moreover,several features increase the computational complexity of the selection process.In this paper,we propose a mathematical modeling for feature prioritization with analytical network process(ANP)bridge model for SDN controllers.The results indicate that a prioritized features model lead to a reduction in the computational complexity of the selection of SDN controller.In addition,our model generates prioritized features for SDN controllers.

Power cost minimization in data centers Via Lyapunov optimization

With the advent of big data,the demand for computing has been increasing in a very large scale for the past decade,so geographically distributed data centers are erected in the direction of cloud computing development. A Lyapunov optimization approach is considered for the problem of minimizing energy cost for distributed Internet data centers( IDCs). By capturing the power cost of servers and cooling systems,the Lyapunov optimization technique is formulated to design a decisive strategy that offers provable power cost minimization and Qo S guarantees. The algorithm performance and effectiveness are validated via simulations driven by real world traces.

Statistical Multiplexing of Homogeneous Streams results in Linear Bandwidth Gains

Statistical multiplexing of traffic streams results in reduced network bandwidth requirement. The resulting gain increases with the increase in the number of streams being multiplexed together. However, the exact shape of the gain curve, as more and more streams are multiplexed together, is not known. In this paper, we first present the generalized result that the statistical gain of combining homogeneous traffic streams, of any traffic type, is a linear function of the number of streams being multiplexed. That is, given a fixed Quality of Service (QoS) constraint, like percentile delay, D, the bandwidth requirement of n streams to satisfy the delay constraint D is n x R x c where R is the bandwidth requirement of a single stream that satisfies the constraint D and c e (0,1]. We present the linear bandwidth gain result, using an extensive simulation study for video traces, specifically, streaming video (IPTV traces) and interactive video (CISCO Telepresence traces). The linear bandwidth gain result is then verified using analytical tools from two different domains. First, we validate the linearity using Queueing Theory Analysis, specifically using Interrupted Poisson Process (IPP) and Markov Modulated Poisson Process (MMPP) modeling. Second, we formally prove the linear behavior using the Asymptotic Analysis of Algorithms, specifically, the Big-O analysis.

SHA: QoS-Aware Software and Hardware Auto-Tuning for Database Systems

While databases are widely-used in commercial user-facing services that have stringent quality-of-service(QoS)requirement,it is crucial to ensure their good performance and minimize the hardware usage at the same time.Our investigation shows that the optimal DBMS(database management system)software configuration varies for different user request patterns(i.e.,workloads)and hardware configurations.It is challenging to identify the optimal software and hardware configurations for a database workload,because DBMSs have hundreds of tunable knobs,the effect of tuning a knob depends on other knobs,and the dependency relationship changes under different hardware configurations.In this paper,we propose SHA,a software and hardware auto-tuning system for DBMSs.SHA is comprised of a scaling-based performance predictor,a reinforcement learning(RL)based software tuner,and a QoS-aware resource reallocator.The performance predictor predicts its optimal performance with different hardware configurations and identifies the minimum amount of resources for satisfying its performance requirement.The software tuner fine-tunes the DBMS software knobs to optimize the performance of the workload.The resource reallocator assigns the saved resources to other applications to improve resource utilization without incurring QoS violation of the database workload.Experimental results show that SHA improves the performance of database workloads by 9.9%on average compared with a state-of-the-art solution when the hardware configuration is fixed,and improves 43.2%of resource utilization while ensuring the QoS.

Service-Oriented Wireless Multimedia Multicasting with Partial Frequency Reuse

Service-Oriented Architecture（SOA） is employed in a wide range of applications because it promises to expose computation-intensive tasks as services and combine them with new applications to accelerate their applications development process. For service-oriented multimedia applications, the performance of multicasting transmission services under multimedia traffic must be evaluated. Multiview Video（MVV） is a promising and emerging type of multimedia traffic that consists of multiple video streams, allows stream switching, and requires strict Quality-of-Service（Qo S）. In this study, we investigated multicast transmission of MVV in wireless cellular networks with Partial Frequency Reuse（PFR） and focused on two challenging problems：（1） multicast group formation and（2） subchannel and power allocation. Initially, we propose a novel Content-based Partial frequency reuse Diversity Grouping（CPDG） scheme to allocate users to multicast groups on the basis of Partial frequency reuse Diversity（PD） in wireless networks with PFR. After group formation, an optimization problem for subchannel and power allocation is formulated to minimize network power consumption under Qo S constraint. Thereafter,the optimization problem is divided into two subproblems and solved using simplex method and Karush-KuhnTucker（KKT） condition. Finally, to solve the optimization problem, a suboptimal scheme referred to as Partial frequency reuse Diversity Based Energy-efficient Multicasting（PDEM） is proposed by dynamically allocating wireless resources under Qo S constraint. The simulation results show that the proposed PDEM scheme can achieve close-to-optimal performance. Moreover, mathematical analysis of the effect of PD on network performance can provide guidelines for optimization of multimedia traffic in multicasting-enabled wireless cellular networks with PFR.

Intelligent Scheduling Controller Design for Networked Control Systems Based on Estimation of Distribution Algorithm

The use of communication networks in control loops has gained increasing attention in recent years due to its advantages and flexible applications. The network quality-of-service （QoS） in those socalled networked control systems always fluctuates due to changes of the traffic load and available network resources, This paper presents an intelligent scheduling controller design approach for a class of NCSs to handle network QoS variations, The sampling period and control parameters in the controller are simultaneously scheduled to compensate for the network QoS variations. The estimation of distribution algorithm is used to optimize the sampling period and control parameters for better performance. Compared with existing networked control methods, the controller has better ability to compensate for the network QoS variations and to balance network loads. Simulation results show that the plant setting time with the intelligent scheduling controller is reduced by about 64.0% for the medium network load and 49.1% for high network load and demonstrate the effectiveness of the proposed approaches.

Design and Analysis of Prioritized Medium Access Control Protocol for Backbone Routers in Wireless Mesh Networks

A Prioritized Medium Access Control （P-MAC） protocol is proposed for wireless routers of mesh networks with quality-of-service provisioning. The simple yet effective design of P-MAC offers strict service differentiation for prioritized packets. A Markov model is developed to yield important performance matrices including the packet blocking probability due to queue overflow and the packet reneging probability due to delay bound. It is further proved that the service time of P-MAC approximates exponential distribution, and can be effectively estimated. The analytic models with preemptive and non-preemptive schemes, validated via simulations, show that P-MAC can effectively support traffic differentiation and achieve very low packet dropping （both reneging and blocking） probabilities when the traffic load is below the channel capacity. When the network is overloaded, P-MAC can still maintain extremely stable and high channel throughput. Moreover, it is demonstrated that P-MAC performs superior in multihop networks, further proving the advantages of the proposed protocol.

Effective capacity of cognitive radio systems in asymmetric fading channels

As known that the effective capacity theory offers a methodology for exploring the performance limits in delay constrained wireless networks, this article considered a spectrum sharing cognitive radio （CR） system in which CR users may access the spectrum allocated to primary users （PUs）. Particularly, the channel between the CR transmitter （CR-T） and the primary receiver and the channel between the CR-T and the CR receiver （CR-R） may undergo different fading types and arbitrary link power gains. This is referred to as asymmetric fading. The authors investigated the capacity gains achievable under a given delay quality-of-service （QoS） constraint in asymmetric fading channels. The closed-form expression for the effective capacity under an average received interference power constraint is obtained. The main results indicate that the effective capacity is sensitive to the fading types and link power gains. The fading parameters of the interference channel play a vital role in effective capacity for the looser delay constraints. However, the fading parameters of the CR channel play a decisive role in effective capacity for the more stringent delay constraints. Also, the impact of multiple PUs on the capacity gains under delay constraints has also been explored.

QoS-aware dynamic bandwidth allocation algorithm for Gigabit-capable PONS

The Gigabit-capable passive optical network(GPON)technology is being considered as a promising solution for the next-generation broadband access network.Since the network topology of the GPON is point-to-multipoint,a media access control called dynamic bandwidth allocation(DBA)algorithm is an important factor for determining the performance of the GPON.In this paper,we propose a new DBA algorithm to effectively and fairly allocate bandwidths among end users.This DBA algorithm supports differentiated services-a crucial requirement for a converged broadband access network with heterogeneous traffic.In this article we first reviewed the signaling and configuration of the DBA,and then proposed a new DBA scheme that implemented QoS-based priority for this need to maximally satisfy the requirements of all optical network units(ONUs)and provide differentiated services.Analyses and simulation results show that the new algorithm can improve the bandwidth utilization and realize the fairness for both different ONUs and services.