Big data analytics in datacenters often involves scheduling of data-parallel jobs. Traditional scheduling techniques based on improving network resource utilization are subject to limited bandwidth in datacenter networks. To alleviate the shortage of bandwidth, some cluster frameworks employ techniques of traffic compression to reduce transmission consumption. However, they tackle scheduling in a coarse-grained manner at task level and do not perform well in terms of flow-level metrics due to high complexity. Fortunately, the abstraction of coflow pioneers a new perspective for scheduling majorization. In this paper, we introduce a coflow compression mechanism to minimize the completion time in data-intensive applications. Due to the NP-hardness, we propose a heuristic algorithm called Fastest-Volume-Disposal-First (FVDV) to solve this problem. We build Swallow, an efficient scheduling system that implements our proposed algorithms. It minimizes coflow completion time (CCT) while guaranteeing resource conservation and starvation freedom. The results of both trace-driven simulations and real experiments show the superiority of our system, over existing algorithms. Specifically, Swallow speeds up CCT and job completion time (JCT) by up to 1.47 times and 1.66 times on average, respectively, over the SEBF in Varys, one of the most efficient coflow scheduling algorithms so far. Moreover, with coflow compression, Swallow reduces traffic amount by up to 48.41% on average.
Device-to-device (D2D) communication is a promising technology for expanding the next generation wireless cellular network. To deal with the security challenges and optimize the system communication quality, this paper investigates the security and efficiency problem in D2D underlay communication with the presence of malicious eavesdroppers. Fairness and strategy space of both D2D user equipment (DUE) and cellular user equipment (CUE) are taken into consideration under the control of proposed efficiency functions. Problems are formulated as a series of utility functions built on the unit price of jamming power and the amount of jamming service. Extracting system model into a price negotiation under Bargaining Game (PNBG) that a buyer and a seller both desiring maximum its profits, we solve the problems by reaching an agreement of the two sides. The step number of bargain process is also a restriction under consideration. For the Non-Step scheme, an Evaluation Function (EF) and a Comprehensive Utility Function (CUF) are demonstrated to analyze the negotiation process. For Step-Contained scheme, the step number of iteration is involved and an Attenuation Function (AF) is introduced to modify the Bargaining Game. Algorithms of two schemes are designed to derive the equilibrium point for reaching an agreement. Finally, simulations are illustrated for verifying proposed approach.