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 requirement...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.展开更多
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 ...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.展开更多
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 ...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.展开更多
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 a...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.展开更多
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 servi...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.展开更多
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 O...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.展开更多
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...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.展开更多
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...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.展开更多
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...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.展开更多
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 shap...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.展开更多
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 ...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.展开更多
文摘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.
文摘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.
基金supported in part by Supported by the National Natural Science Foundation of China (Grant No. 61370227)Research Foundation of Education Bureau of Hunan Province, China (Grant No. 17A070)
文摘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.
基金Project supported by IST FP6 Integrated Project DAIDALOS (No. IST-2002-506997) and the German Research Foundation (DFG) within the AKOM Framework (No. HA2207/2-3)
文摘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.
文摘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.
基金This research was supported partially by LIG Nex1It was also supported partially by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2021-2018-0-01431)supervised by the IITP(Institute for Information&Communications Technology Planning Evaluation).
文摘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.
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2020-2018-0-01431)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation).
文摘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.
文摘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.
基金Supported by the National Natural Science Foundation of China(No.61502255)the Inner Mongolia Provincial Natural Science Foundation(No.2014BS0607)the Science Research Project for Inner Mongolia College(No.NJZY14064)
文摘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.
文摘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.
文摘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.
