Data centers are widely used for big data analytics, which often involve data-parallel jobs, including query and web service. Meanwhile, cluster frameworks are rapidly developed for application management in data center networks (DCNs). To promote the performance of these frameworks, many efforts have been paid to improve scheduling strategies and resource allocation algorithms. With the deployment of geo-distributed data centers and data-intensive applications, the optimization in DCNs regains pervasive attention both in industry and academia. Many solutions, such as the coflow-aware scheduling and speculative execution, have been proposed to meet various requirements. Therefore, we present a solid starting ground and comprehensive overview in this area to help readers quickly understand state-of-the-art technologies and research progress. We observe that algorithms in cluster frameworks are implemented with different guidelines and can be classified according to scheduling granularity, controller management and prior-knowledge requirement. In addition, mechanisms for conquering crucial challenges in DCNs are discussed, including providing low latency and minimizing job completion time. Moreover, we analyse desirable properties of fault tolerance and scalability to illuminate the design principles of distributed systems. We hope that this paper will shed light on this promising land and serve as a guide for further researches.

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.