Aiming at the conflict between the makespan and execution cost of cloud workflows with deadlines， a Hybrid Adaptive Particle Swarm Optimization algorithm for workflow scheduling （HAPSO） was proposed. Firstly， a Directed Acyclic Graph （DAG） cloud workflow scheduling model was established based on deadlines. Secondly， through the combination of norm ideal points and adaptive weights， the DAG scheduling model was transformed into a multi-objective optimization problem that weighs DAG makespan and execution cost. Finally， based on Particle Swarm Optimization （PSO） algorithm， the adaptive inertia weight， the adaptive learning factors， the probability switching mechanism of flower pollination algorithm， Firefly Algorithm （FA） and the particle out-of-bound processing method were added to balance the global search ability and the local search ability of the particle swarm， and then to solve the objective optimization problem of DAG makespan and execution cost. The optimization results of PSO， Weight Particle Swarm Optimization （WPSO）， Ant Colony Optimization （ACO） and HAPSO were compared and analyzed in the experiment. Experimental results show that HAPSO reduces the multi-objective function value by 40.9% to 81.1% that weighs the makespan and execution cost of workflow （30~300 tasks）， and HAPSO effectively weighs the makespan and execution cost with the constraints of workflow deadlines. In addition， HAPSO also has a good effect on the single objective of reducing the makespan or execution cost， which verifies the universality of HAPSO.

Probabilistic Packet Marking (PPM) algorithm is an important method to prevent the Distributed Denial of Service (DDoS) attacks. But it has the weakest chain and the weak convergence of issues because of the repeated marking, as well as large amount of computation because of the fragmentation problem when reconstructing the path. A new marking algorithm — IDCPPM was proposed which was based on router interface (ID number) with a consistency probability. The algorithm enabled the marking information to reach the victims with a consistency probability .For its non-fragmentation, it effectively reduced the number of packets needed to reconstruct the path and reduce the complexity of the algorithm. Also it can be applied to IPv6. The theoretical analysis and experimental results prove the effectiveness of this method.

To solve the problem of Probabilistic Packet Marking (PPM) method for IPv6 that the computational complexity of reconstruction and false alarm rate are too large, a new traceback method for IPv6 based on deterministic linear network coding was proposed. The method chose the hop-by-hop option extension header of IPv6 for the marked region and applied the deterministic linear network coding to the probabilistic packet marking. Moreover, the 64bit sampling was employed to check the attack paths. The theoretical analysis and simulation in NS2 environment show that the method decreases the wasted network bandwidth and the amount of packets needed to reconstruct the path, reduces the computational complexity of reconstruction and false alarm rate, and improves the mark efficiency.