With the development of the Internet, the demand for data centers is growing dramatically. The cost of running a data center mainly comes from the huge electricity bill. Actually, IT (Information Technology) equipment and the HVAC (Heating, Ventilation, and Air Conditioning) system of the data center consume the majority of electricity. The existing energy--saving researches usually consider IT equipment or the HVAC system separately. But the energy consumption of HVAC is partially correlated with the running status of IT equipment. Taking methods to optimize the energy consumption of them jointly will generate more benefits. Therefore, we proposed JESO (Joint Energy Saving Optimization), a MPC (Model Predictive Control) -based method, to realize the joint energy-saving optimization of IT equipment and the HVAC system. We conducted extensive experiments based on generated transmission data and the HVAC system data from two real data centers. The experimental results demonstrated that JESO based on the joint energy-saving algorithm of IT equipment and the HVAC system can further reduce energy consumption as compared to baselines and will not negatively affect the data transmission latency of the IT equipment.

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BitTorrent is the dominating protocol in nearly all regions. Various reputation models and incentive mechanisms are proposed in recent years. However, many of them are designed for general peer-to-peer networks, only a few are designed for BitTorrent even though some models and mechanisms assert that they can be applied to BitTorrent-like systems. In this paper, we propose an incentive framework based on reputation history instead of tit-for-tat in BitTorrent. It is derived from the coalition games concept of Shapley value that will encourage selfish peers who seek to maximize their own profits to converge to Nash equilibrium. We show that this profit sharing framework exhibits several fairness properties that support the argument that this distribution of profit is desirable. Moreover, we improve SepRep reputation model and tailor it to the needs of BitTorrent protocol. In particular, we utilize the tracker of BitTorrent to process global reputation and trust values assisting the local values maintained by each peer.

Segment routing has been a novel architecture for traffic engineering in recent years. However, segment routing brings control overheads, i.e., additional packets headers should be inserted. The overheads can greatly reduce the forwarding efficiency for a large network, when segment headers become too long. To achieve the best of two targets, we propose the intelligent routing scheme for traffic engineering (IRTE), which can achieve load balancing with limited control overheads. To achieve optimal performance, we first formulate the problem as a mapping problem that maps different flows to key diversion points. Second, we prove the problem is nondeterministic polynomial (NP)-hard by reducing it to a k-dense subgraph problem. To solve this problem, we develop an ant colony optimization algorithm as improved ant colony optimization (IACO), which is widely used in network optimization problems. We also design the load balancing algorithm with diversion routing (LBA-DR), and analyze its theoretical performance. Finally, we evaluate the IRTE in different real-world topologies, and the results show that the IRTE outperforms traditional algorithms, e.g., the maximum bandwidth is 24.6% lower than that of traditional algorithms when evaluating on BellCanada topology.