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To overcome the unbalanced flow distribution problem caused by private routing on service overlay network, network service providers usually use load balancing strategy, but traditional methods do not consider making the load balance adapt to the status of the network, neither consider that load balancing may damage performance of some application links. In order to solve the two problems, based on Wardrop equilibrium theory, the paper proposes a flexible load-balancing traffic grooming strategy, which aims to achieve system optimization. According to the principle of stochastic user equilibrium, a network traffic optimization control model is proposed. The path impedance calculation formula is given based on the Logit stochastic add-in term, in which the value of the equilibrium parameter determines the level and scope of load balancing. Finally, we designed the elastic traffic grooming algorithm ElaGo. The simulation results verified the effectiveness of the proposed algorithm.

Due to the dynamicity of resources and owners' behaviors, the grid resource state changes continuously. As a result, scheduling strategies only based on current static information can no longer meet the needs of more effective application. Prediction of future resource state which combines current state and historical records can improve the efficiency and reliability of scheduling. In this paper, we present a Markov chain based prediction method, which comprehensively considers the rate of CPU usage, level of network load, and resource failure rate to forecast resource future state for getting better job scheduling results. An evaluation measurement is designed to quantify the prediction result for scheduling. Experiments show that this method has a better performance on resource idle rate and prediction accuracy.

Internet video on demand is a new application provided by peer to peer network. Thousands of users are using real multimedia streaming systems like PPlive and PPstream which are the killer applications in the Internet. But jitters are always happening during rush time especially when users are watching less popular videos with low bandwidth network. To improve this, a cache scheduling scheme based on layered coding is proposed in this paper. Our scheme aims at optimizing cache scheduling scheme in peers' local cache while other researches often focus on the proxy server. The available bandwidth considered as energy is allocated by Fermi distribution function for different layer data of FGS, and the prefix buffer is extended dynamically. When the available bandwidth is insufficient, peers are forced to allocate more bandwidth for base layer data, and their prefix buffer is extended so that more base layer data is downloaded for users who watch a video continuously and with interactive operations. Simulation shows that our approach reduces cache missing rate effectively when available bandwidth is insufficient. In local cache, peers replace segments in the cache by formulas of popularity which is proposed to reduce cache missing rate more successful. In general, our scheme can effectively reduce starting delay and times of jitters in a multimedia system with low bandwidth.

As dynamic-level scheduling algorithm does not consider the risk taken by resource nodes and users' demand, it proposes an analytic hierarchy process approach (AHP) based improved DLS algorithm. By such AHP approach, it manages the task provided by users, and obtains the weight value which reflects users' demand. Because the weight value contains the information about deadline, reparation duty and profit of task, it integrates DLS and the weight value to get an improved DLS, called R-DLS which contains the degree of risk taken by resource nodes. Experiments show that R-DLS receives higher average success rate of task execution and profit in one unit of time with task number increasing than DLS.

For the autonomous and selfish features of peers, there are many issues existed in P2P network, such as dynamics of networks, heterogeneity of peers, free-riders, tragedy of commons and malicious behaviors. The topology optimization solutions based on trust are effective methods to solve these problems. However, the trust evaluations in related works only focus on the service trust, or consider both the service trust and the routing trust, but the routing trust evaluation algorithm can not stimulate peers to forward query messages. On this basis, a routing trust evaluation algorithm based on profit sharing is proposed. In the algorithm, according to the contributions of routing recommendation peers, the service demanders share the transaction profit with them in order that the routing recommendation peers can get the routing trust of their neighbors by forwarding query messages. Then, a routing trust aware topology optimization protocol is put forward, which considers both service trust and routing trust. Simulations and analysis show that, the routing trust aware topology optimization protocol can not only promote honest peers to the center of the network, exclude malicious peers and free-riders to the edge of the network, but also improve the query efficiency for honest peers.

The problem of file pollution is prevalent in P2P file sharing system nowadays. The pollution reduces the availability of system dramatically. As the same as immune system, P2P file sharing system is also highly distributive, self-adaptive and self-organized. Artificial immunology based anti-pollution object reputation mechanism is established to select neighbor peer set and improve the system availability. Vector space similarity is computed to weight the votes collected from other peers. Horting graph-theoretic approach is employed to deal with the data sparsity problem. Self-adaptive reputation threshold scheme is adopted to estimate the file authenticity. Simulation results show that the system can distinguish between good and polluted files accurately and thwart the proliferation of pollution in the network with low communication cost.

The problem of file pollution is prevalent in p2p file sharing system nowadays. The pollution reduces the availability of system sharply. Compared with peer reputation, object reputation is more efficient in face of pollution. Pollution proliferation model based on file object is presented. Vector space similarity is computed to weight the votes collected from other peers. Horting graph-theoretic approach is employed to deal with data sparsity problem. Self-adaptive reputation threshold scheme is adopted to estimate the file authenticity. An anti-pollution p2p file sharing system based on object reputation is established to improve the availability. Simulation results show that the system can distinguish between good and polluted files accurately and thwart the proliferation of pollution in the network with low communication cost. The system can subject freeriders to a penalty and do well in the presence of collusion and bad-mouthing attacks.

To distinguish the reliabilities of different resources is difficult in the grid environment. For the first time the signaling game theory is applied to the research on grid resource reliability, and a grid resource reliability model based on promise is proposed in this paper. The analyses and the emulation results indicate that the resource provider should abandon the cheating motivation voluntarily. The resource demander could make right decision without other nodes' recommendations, the calculation and communication is reduced remarkably. This is a new solution to the problem of distinguishing resources reliabilities in the grid environment.

One of the key steps in economy based grid resource allocation is to make reasonable prices for the grid resources. For resources' prices decide the resource flow in the grid, further influence grid resources' trading efficiency. This article, instead of discussing the pricing algorithm, argues an autonomous pricing mechanism that leaves the prices to be decided by the grid traders within their trading process. We firstly point out the factors that influence the resources' prices; then make out the trading flow for resource consumer agents and provider agents. By doing these, the two trading agents can decide their price due to the dynamic changes of the grid environment without any manmade interferences. Since price is the core of market economy, the autonomous pricing strategy provides us with a foundation to use the invisible hand of market to allocate the grid resource.

Traditional RBAC has trouble in prohibiting cheat and changing roles of users as the amount of users and resources increase, while the trust management can establish trust relationships among entities in grid environment. So the dynamic-role based access control framework assembles both of their merits and simplifies the administration of RBAC. An implementation is deployed in HanHai grid environment.

Based on Utility and Mechanism Design theory, this paper describes grid resources trade and allocation mechanism in Decentralized Computational Market, focusing on the design of double auction and aggregate market mechanism. Double auction mechanism employs decentralized architecture in decision making. The price fluctuates according to the wave of supply & demand of the market and the participants' bid strategy. Comparatively, there has little participants' influence in aggregate market mechanism; the price is determined by the central price setup system. Although presenting higher performance, aggregate market mechanism sacrifices interest of the bargainers. The simulations of single type commodity (CPU slices) market illustrate validity of the two kinds of trade mechanism. Comparing analysis is explicated from the view of transaction price, deal amount and the utility increment.