The communication traffic among servers is a principal bottleneck for data center networks (DCNs), in which redundant traffic has a significant impact on the performance of DCNs. In this paper, we propose Pache, a distributed and efficient cache scheme to achieve traffic redundancy elimination in DCNs. In Pache, nodes cache packets to reduce bandwidth consumption and gain performance revenue. Each cache node manages the local cache and improves cache efficiency with counting bloom filter, hash table and Trie. Each cache node broadcasts the local cache information by a bloom filter to other servers, and servers have cache information tables (cache-table) to record them, which achieves a cache sharing mechanism. Moreover, Pache employs false positive information table (fp-table) to reduce the false positive rate caused by bloom filters. A server determines an original packet or the corresponding fingerprint packet to send by querying cache-table and fp-table. Cache node placement mechanism is also designed to select which node to cache a packet. For evaluating the performance of Pache, we execute extensive simulations and conduct a case study on Amazon EC2 platform from different aspects. The results show that Pache can eliminate about 40 percent redundant traffic on average, and only increase 10 percent runtime, and it is also a scalable and feasible cache scheme in DCNs.

Consistent routing update based on Software-Defined Networks (SDN) is a complicated problem due to the asynchronous and distributed data plane. Existing ordered update approaches mostly focus on the consistent routing update problem for unicast other than multicast, which should guarantee two consistencies, drop-freeness and duplicate-freeness. In this paper, we propose Shifter, a novel dynamic ordered update scheme for consistent multicast routing update based on SDN to guarantee both consistencies. Shifter advocates configuring inport match field in the forwarding rules to avoid duplicate. In order to guarantee drop-freeness, Shifter employs a dependency graph to dynamically schedule update operations, and uses a greedy solution to solve a subproblem named Replace Operation Tree Migration Problem (ROTMP). We conduct simulations to evaluate Shifter and find that Shifter can give a near optimal solution of ROTMP with very few rounds and little runtime for multicast routing update scenarios. To the best of our knowledge, Shifter is the first ordered update scheme to guarantee the two consistencies simultaneously.

How to increase the utilization of data center networks (DCN) is a critical problem to ensure the quality of cloud services. Previous researches showed that the key is to increase the time-average utilization and decrease the overload ratio, and proposed many efficient virtual machine (VM) placement algorithms to achieve higher utilization. However, most of those works did not consider the quality assurance and statistical multiplexing methods, which can greatly improve the effectiveness of VM placement. In this paper, we propose a novel quality-assured VM placement scheme that dynamically places VMs to better multiplex time-varying resource demands. We firstly apply AutoRegressive Integrated Moving Average (ARIMA) and Generalized AutoRegressive Conditional Heteroskedasticity (GARCH model) to forecast the trend and volatility of the future demand, and then develop a Modern Portfolio Theory (MPT)-based method to enlarge DCN utilization and hedge the risk of server overloads. Extensive simulations and detailed analysis are conducted to validate the efficiency of our proposed scheme, which outperforms the previous works greatly.