QoS-Aware Virtual Machine Scheduling for Video Streaming Services in Multi-Cloud

Video streaming services are trending to be deployed on cloud. Cloud computing offers better stability and lower price than traditional IT facilities. Huge storage capacity is essential for video streaming service. More and more cloud providers appear so there are increasing cloud platforms to choose. A better choice is to use more than one data center, which is called multi-cloud. In this paper a closed-loop approach is proposed for optimizing Quality of Service (QoS) and cost. Modules of monitoring and controlling data centers are required as well as the application feedback such as video streaming services. An algorithm is proposed to help choose cloud providers and data centers in a multi-cloud environment as a video service manager. Performance with different video service workloads are evaluated. Compared with using only one cloud provider, dynamically deploying services in multi-cloud is better in aspects of both cost and QoS. If cloud service costs are different among data centers, the algorithm will help make choices to lower the cost and keep a high QoS.     

An Energy-Saving Task Scheduling Strategy Based on Vacation Queuing Theory in Cloud Computing

High energy consumption is one of the key issues of cloud computing systems. Incoming jobs in cloud computing environments have the nature of randomness, and compute nodes have to be powered on all the time to await incoming tasks. This results in a great waste of energy. An energy-saving task scheduling algorithm based on the vacation queuing model for cloud computing systems is proposed in this paper. First, we use the vacation queuing model with exhaustive service to model the task schedule of a heterogeneous cloud computing system.Next, based on the busy period and busy cycle under steady state, we analyze the expectations of task sojourn time and energy consumption of compute nodes in the heterogeneous cloud computing system. Subsequently, we propose a task scheduling algorithm based on similar tasks to reduce the energy consumption. Simulation results show that the proposed algorithm can reduce the energy consumption of the cloud computing system effectively while meeting the task performance.     

Robust Disaster Recovery System Mode

Highly security-critical system should possess features of continuous service. We present a new Robust Disaster Recovery System Model （RDRSM）. Through strengthening the ability of safe communications, RDRSM guarantees the secure and reliable command on disaster recovery. Its self-supervision capability can monitor the integrality and security of disaster recovery system itself. By 2D and 3D rea-time visible platform provided by GIS, GPS and RS, the model makes the using, management and maintenance of disaster recovery system easier. RDRSM possesses predominant features of security, robustness and controllability. And it can be applied to highly security-critical environments such as E-government and bank. Conducted by RDRSM, an important E-government disaster recovery system has been constructed successfully. The feasibility of this model is verified by practice. We especially emphasize the significance of some components of the model, such as risk assessment, disaster recovery planning, system supervision and robust communication support.     

Fault Tolerant Data Aggregation Scheduling with Local Information in Wireless Sensor Networks

We study the problem of efficient data aggregation in unreliable wireless sensor networks by designing a fault tolerant data aggregation protocol.A fault tolerant data aggregation protocol consists of two parts:basic aggregation scheduling and amendment strategies.On default,data is aggregated according to the basic aggregation scheduling strategy.The amendment strategy will start automatically when a middle sensor node is out of service.We focus our attention on the amendment strategies and assume that the network adopts a connected dominating set (CDS) based aggregation scheduling as its basic aggregation scheduling strategy.The amendment scheme includes localized aggregation tree repairing algorithms and distributed rescheduling algorithms.The former are used to find a new aggregation tree for every child of the corrupted node,whereas the latter are used to achieve interference free data aggregation scheduling after the amendment.These amendment strategies impact only a very limited number of nodes near the corrupted node and the amendment process is transparent to all the other nodes.Theoretical analyses and simulations show that the scheme greatly improves the efficiency of the data aggregation operation by reducing both message and time costs compared to rebuilding the aggregation tree and rescheduling the entire network.     

Freight Vehicle Routing Optimization for Sporadic Orders Using Floating Car Data

The increasing popularity of e-commerce brings large volumes of sporadic orders from different customers,which have to be handled by freight trucks and distribution centers. To improve the level of service and reduce the total shipping cost as well as traffic congestions in urban area, flexible methods and optimal vehicle routing strategies should be adopted to improve the efficiency of distribution effort. An optimization solution for vehicle routing and scheduling problem with time window for sporadic orders （VRPTW- S） was provided based on time-dependent travel time extracted from floating car data （FCD） with ArcGIS platform. A VRPTW-S model derived from the traditional vehicle routing problem was proposed, in which uncertainty of customer orders and travel time were considered. Based on this model, an advanced vehicle routing algorithm was designed to solve the problem. A case study of Shenzhen, Guangdong province, China, was conducted to demonstrate the vehicle operation flow,in which process of FCD and efficiency of delivery systems under different situations were discussed. The final results demonstrated a good performance of application of time-dependent travel time information using FCD in solving vehicle routing problems.     

Dependent-Chance Programming Models for Capital Budgeting in Fuzzy Environments

Capital budgeting is concerned with maximizing the total net profit subject to budget constraints by selecting an appropriate combination of projects. This paper presents chance maximizing models for capital budgeting with fuzzy input data and multiple conflicting objectives. When the decision maker sets a prospective profit level and wants to maximize the chances of the total profit achieving the prospective profit level, a fuzzy dependent-chance programming model, a fuzzy multi-objective dependent-chance programming model, and a fuzzy goal dependent-chance programming model are used to formulate the fuzzy capital budgeting problem. A fuzzy simulation based genetic algorithm is used to solve these models. Numerical examples are provided to illustrate the effectiveness of the simulation-based genetic algorithm and the potential applications of these models.     

Organizing of Virtual Enterprise for Customized Artificial Joint and Scheduling of Manufacturing Resources Based on Manufacturing Grid

In order to improve efficiency of developing customized artificial joint, a virtual enterprise is organized based on the manufacturing grid. The application-oriented framework of the virtual enterprise is created for organization of the virtual enterprise. The manufacturing system of the virtual enterprise is built up based on the exterior grid and manufacturing subsystems of member enterprises are set up based on the interior grid. Furthermore, according to characteristic matching of manufacturing resources, the scheduling flow is studied in detail and the related scheduling strategies are established. The scheduling procedure is divided into distributed scheduling stage and local scheduling stage. Based on the manufacturing grid, the organization of the virtual enterprise for customized artificial joint and the corresponding scheduling strategies can be utilized to shorten developing cycle and reduce produce cost.     

A Phase Space Reconstruction Based Approach to Throughput Prediction in Semiconductor Wafer Fabrication System

In order to manage and control semiconductor wafer fabrication system (SWFS) more effectively,the daily throughput prediction data of wafer fab are often used in the planning and scheduling of SWFS.In this paper,an artificial neural network (ANN) prediction method based on phase space reconstruction (PSR) and ant colony optimization (ACO) is presented,in which the phase space reconstruction theory is used to reconstruct the daily throughput time series,the ANN is used to construct the daily throughput prediction model,and the ACO is used to train the connection weight and bias values of the neural network prediction model.Testing with factory operation data and comparing with the traditional method show that the proposed methodology is effective.     

On the Edge of Web-Based Multiple Sequence Alignment Services

There are many web-based multiple sequence alignment services accessible around the world. However, many researchers working on biological sequence analysis still struggle with inefficient, unfriendly user interface, and limited capability multiple sequence alignment software. In this study, we provide a comprehensive survey of regional and continental facilities that provide web-based alignment services. We also analyze and identify much needed services that are not available through these existing service providers. We then implement a web-based model to address these needs. From that perspective, our web-based multiple sequence alignment server, SeqAna, provides a unique set of services that none of these studied facilities have. For example, SeqAna provides a multiple sequence alignment scoring and ranking service. This service, the only of its kind, allows SeqAna’s users to perform multiple sequence alignment with several alignment tools and rank the results of these alignments in the order of quality. With this service, SeqAna’s users will be able to identify which alignment tools are more appropriate for their specific set of sequences. In addition, SeqAna’s users can customize a small alignment sample as a reference for SeqAna to automatically identify the best tool to align their large set of sequences.     

An improvement on algorithm of grid-workflow based on QoS

With the emergencc of grid computing. new challenges have arisen in workflow tasks scheduling. The goal of grid-workflow task scheduling is to achieve high system throughput and to match the application needs with the available computing resources. This matching of resources in a non-deterministically share heterogeneous environment leads to concerns on quality of service (QoS). Grid concept is presented in this paper, coupled with the QoS requirement of workflow task and an improved algorithm-ILGSS algorithm. has been brought out. The complexity of the improved sched uling algorithm has been analyzed. The experiment results show that the improved algorithm can lead to significant performance gain in various applicatkms. An important research domain adaptive workflow transaction in grid computing environment, has been explored and a new solution for the scheduling of distribute workflow has been hring forward in grid environment.     

EP-based Optimization of Strategic Safety Stocks in Reverse Logistics Systems

A new heuristic strategic safety stock optimization is proposed based on evolutionary programming(EP) algorithm for reverse logistics supply chain systems. The supply chain is described with a network and the modeling complexity of external as well as internal product returns and reuses of supply chains is considered with. It is assumed that customer demands for final products are uncertain. Products are randomly returned from external customers to stock points. The optimization model is established and three different cases with different structures are used to show the strength of the algorithm.     

A Novel Testability-Oriented Data Path Scheduling Scheme in High-Level Synthesis

Scheduling is an important step in high-level synthesis and can greatly influence the testability of the synthesized circuits. This paper presents an efficient testability-improved data path scheduling scheme based on mobility scheduling, in which the scheduling begins from the operation with least mobility. In our data path scheduling scheme, the lifetimes of the I/O variables are made as short as possible to enlarge the possibility of the intermediate variables being allocated to the I/O registers. In this way, the controllability/observability of the intermediate variables can be improved. Combined with a weighted graph-based register allocation method, this scheme can obtain better testability. Experimental results on some benchmarks and example circuits show that the proposed scheme can get higher fault coverage compared with other scheduling schemes at little area overhead and even less time delay.     

Collaborative Network Security in Multi-Tenant Data Center for Cloud Computing简

A data center is an infrastructure that supports Internet service. Cloud comput the face of the Internet service infrastructure, enabling even small organizations to quickly ng is rapidly changing build Web and mobile applications for millions of users by taking advantage of the scale and flexibility of shared physical infrastructures provided by cloud computing. In this scenario, multiple tenants save their data and applications in shared data centers, blurring the network boundaries between each tenant in the cloud. In addition, different tenants have different security requirements, while different security policies are necessary for different tenants. Network virtualization is used to meet a diverse set of tenant-specific requirements with the underlying physical network enabling multi-tenant datacenters to automatically address a large and diverse set of tenants requirements. In this paper, we propose the system implementation of vCNSMS, a collaborative network security prototype system used n a multi-tenant data center. We demonstrate vCNSMS with a centralized collaborative scheme and deep packet nspection with an open source UTM system. A security level based protection policy is proposed for simplifying the security rule management for vCNSMS. Different security levels have different packet inspection schemes and are enforced with different security plugins. A smart packet verdict scheme is also integrated into vCNSMS for ntelligence flow processing to protect from possible network attacks inside a data center network     

A Steady-State Errors Correction Method for Eliminating Measurement Errors

This paper proposes a steady-state errors correction （SSEC） method for eliminating measurement errors.This method is based on the detections of error signal E（s） and output C（s） which generate an expected output R （s）.In comparison with the conventional solutions which are based on detecting the expected output R （s） and output C （s） to obtain error signal E （s）,the measurement errors are eliminated even the error might be at a significant level.Moreover,it is possible that the individual debugging by regulating the coefficient K for every member of the multiple objectives achieves the optimization of the open loop gain.Therefore,this simple method can be applied to the weak coupling and multiple objectives system,which is usually controlled by complex controller.The principle of eliminating measurement errors is derived analytically,and the advantages comparing with the conventional solutions are depicted.Based on the SSEC method analysis,an application of this method for an active power filter （APF） is investigated and the effectiveness and viability of the scheme are demonstrated through the simulation and experimental verifications.     

Application-aware routing with QoS support in SDN networks①

Software defined networking ( SDN ) offers programmable interface to effectively control their networks by decoupling control and data plane .The network operators utilize a centralized controller to deploy advanced network management strategies .An architecture for application-aware routing which can support dynamic quality of service ( QoS) in SDN networks is proposed .The application-aware routing as a multi-constrained optimal path ( MCOP) problem is proposed , where applications are treated as QoS flow and best-effort flows.With the SDN controller applications , it is able to dy-namically lead routing decisions based on application characteristics and requirements , leading to a better overall user experience and higher utilization of network resources .The simulation results show that the improvement of application-aware routing framework on discovering appropriate routes , which can provide QoS guarantees for a specific application in SDN networks .     

Event-driven Dynamic and Intelligent Scheduling for Agile Manufacturing Based on Immune Mechanism and Expert System

Based on the biological immune concept, immune response mechanism and expert system, a dynamic and intelligent scheduling model toward the disturbance of the production such as machine fault, task insert and cancel etc. is proposed. The antibody generation method based on the sequence constraints and the coding rule of antibody for the machining procedure is also presented. Using the heuristic antibody generation method based on the physiology immune mechanism, the validity of the scheduling optimization is improved, and based on the immune and expert system under the event-driven constraints, not only Job-shop scheduling problem with multi-objective can be solved, but also the disturbance of the production be handled rapidly. A case of the job-shop scheduling is studied and dynamic optimal solutions with multi-objective function for agile manufacturing are obtained in this paper. And the event-driven dynamic rescheduling result is compared with right-shift rescheduling and total rescheduling.     

Impact of Web Site Functions on E-Business Success in Chinese Wholesale and Retail Industries

Web sites are very important and companies can attract customers by promoting various features of the web site. This paper presents an analysis of the relationship between web site functions and firm performance. A research model based on the DeLone and McLean （D＆M） model and customer service life cycle （CSLC） theory was used to investigate the impact of web site functions on e-business success. The research model considers web site functions, web site use, customer satisfaction, and firm performance. According to the CSLC theory, there are three stages in a customer service life cycle. Therefore, the web site functions are divided into three stages： requirements, acquisition, and ownership. The functions in each stage serve to encourage usage and thus, enhance customer satisfaction and firm performance. The theoretical model and hypotheses were tested using data collected from 72 wholesale and retail firms in China using the partial least squares （PLS） method. The results suggest that web site functions in the acquisition stage have the strongest impact on web site use and that the improvement of customer satisfaction can significantly increase firm performance.     

Using multi-class queuing network to solve performance models of e-business sites

Due to e-business‘ s variety of customers with different navigational patterns and demands, multiclass queuing network is a natural performance model for it. The open multi-class queuing network(QN) models are based on the assumption that no service center is saturated as a result of the combined loads of all the classes. Several formulas are used to calculate performance measures, including throughput, residence time, queue length, response time and the average number of requests. The solution technique of closed multi-class QN models is an approximate mean value analysis algorithm (MVA) based on three key equations, because the exact algorithm needs huge time and space requirement. As mixed multi-class QN models, include some open and some closed classes, the open classes should be eliminated to create a closed multi-class QN so that the closed model algorithm can be applied. Some corresponding examples are given to show how to apply the algorithms mentioned in this article. These examples indicate that multi-class QN is a reasonably accurate model of e-business and can be solved efficiently.     

An Optimal Content Caching Framework for Utility Maximization

For desirable quality of service, content providers aim at covering content requests by large network caches. Content caching has been considered as a fundamental module in network architecture. There exist few studies on the optimization of content caching. Most existing works focus on the design of content measurement,and the cached content is replaced by a new one based on the given metric. Therefore, the performance for service provision with multiple levels is decreased. This paper investigates the problem of finding optimal timer for each content. According to the given timer, the caching policies determine whether to cache a content and which existing content should be replaced, when a content miss occurs. Aiming to maximize the aggregate utility with capacity constraint, this problem is formalized as an integer optimization problem. A linear programming based approximation algorithm is proposed, and the approximation ratio is proved. Furthermore, the problem of content caching with relaxed constraints is given. A Lagrange multiplier based approximation algorithm with polynomial time complexity is proposed. Experimental results show that the proposed algorithms have better performance.