One-to-many group communication is a performance bottleneck for high-performance data center applications, due to sending massive data from one source to hundreds of receivers. The state-of-the-art solutions utilize either electrical packet switch (EPS) or optical circuit switch (OCS) multicast to accelerate massive data disseminations. However, there exist competitions between multicast and unicast flows at core EPSes in the electrical multicast. Moreover, the optical multicast suffers from a non-negligible reconfiguration delay and exclusive optical links. In this paper we present iCAST, a system for reducing multicast flow completion time (MFCT) on a generic hybrid EPS/OCS network, which has multiple EPSes and OCSes supporting multicast. iCAST constructs multicast trees by integrating the electrical and optical multicast to fully utilize network resources, and seamlessly schedules flows between the static electrical and dynamic optical networks to reduce the configuration overhead. We evaluate the performance by implementing a small-scale hybrid EPS/OCS testbed and extending the high-performance framework MPICH to support iCAST. Experiments show that iCAST outperforms one order of magnitude in reducing MFCT compared with the ring algorithm. We also develop an event-based flow level simulator to evaluate the performance of iCAST at the scale of thousands of servers. Simulation results show that iCAST reduces the average MFCT by 32% and 28% compared to OCS and EPS respectively, and significantly outperforms binomial tree and ring algorithm by up to 64% and 46% respectively.

Summary Many recent studies show that data center networks usually mix with a large amount of latency‐agnostic background flows and a large number of latency‐sensitive flows. The former, although contributing most of the traffic, is deadline‐agnostic, while the latter always confronts a soft‐real‐time constraint, namely deadline. Missing this deadline may cause performance impairment. For this reason, directly using the traditional TCP in data center networks, which is deadline‐agnostic, may suffer from performance and efficiency problems. Recently, many works improve on TCP to address this problem. But all of them focus on the latency‐sensitive flows themselves. Because latency‐sensitive flows, although large in number, only account for a small part of traffic, these methods cannot effectively ensure deadline for the latency‐sensitive flows. In this paper, we propose Make‐way, a new data center network transport protocol for satisfying the deadlines of latency‐sensitive flows. In Make‐way, just as what the name depicts, once a latency‐sensitive flow encounters congestion, the latency‐agnostic background flows will make way for it. Especially, Make‐way does not need any special hardware support of modification. For the latency‐agnostic flows in data center networks usually contribute the majority traffic, by doing so, the latency‐sensitive flows may be transported nonblockingly in data center networks and thus can meet their deadlines. Extensive simulation results show that Make‐way can meet the deadlines of latency‐sensitive flows with a probability of more than 97%. Copyright © 2015 John Wiley & Sons, Ltd.

MapReduce has become a prevalent programming model for building data processing applications in the cloud. While being widely used, existing MapReduce schedulers still suffer from an issue known as partitioning skew, where the output of map tasks is unevenly distributed among reduce tasks. Existing solutions follow a similar principle that repartitions workload among reduce tasks. However, those approaches often incur high performance overhead due to the partition size prediction and repartitioning. In this paper, we present DREAMS, a framework that provides run-time partitioning skew mitigation. Instead of repartitioning workload among reduce tasks, we cope with the partitioning skew problem by controlling the amount of resources allocated to each reduce task. Our approach completely eliminates the repartitioning overhead, yet is simple to implement. Experiments using both real and synthetic workloads running on a 21-node Hadoop cluster demonstrate that DREAMS can effectively mitigate the negative impact of partitioning skew, thereby improving the job completion time by up to a factor of$2.29$over the native Hadoop YARN. Compared to the state-of-the-art solution, DREAMS can improve the job completion time by a factor of$1.65$.

MapReduce has become a popular model for large-scale data processing in recent years. However, existing MapRe-duce schedulers still suffer from an issue known as partitioning skew, where the output of map tasks is unevenly distributed among reduce tasks. In this paper, we present DREAMS, a framework that provides run-time partitioning skew mitigation. Unlike previous approaches that try to balance the workload of reducers by repartitioning the intermediate data assigned to each reduce task, in DREAMS we cope with partitioning skew by adjusting task run-time resource allocation. We show that our approach allows DREAMS to eliminate the overhead of data repartitioning. Through experiments using both real and synthetic workloads running on a 11-node virtual virtualised Hadoop cluster, we show that DREAMS can effectively mitigate negative impact of partitioning skew, thereby improving job performance by up to 20.3%.

In this paper, we propose FlyCast, an architecture using the physical layer of free-space optics (FSO) to accelerate multicast communication. FlyCast leverages off-the-shelf devices (e.g. switchable mirror, beam splitter) to physically split the FSO beam to multi receivers on demand, which enables to build dynamical multicast trees in physical layer and accelerates multicast communications. We demonstrate the feasibility of FlyCast through our theoretical analysis and the proof-of-concept prototype.

A modern data center may host tens of thousands of machines, mixing with hundreds of thousands flows. Large number of flows concurrently traverse in a data center network may frequently cause traffic burst and unbalance, which may further induce congestion, packet losing, and therefore low efficiency. Generally speaking, there are three large families of scheduling algorithms to address this problem: (i) stochastic load balancing, (ii) optimizing traffic distribution by VM migration, (iii) scheduling flows to spread in different paths. But all of them suffer from some shortages. Stochastic load balancing is load-agnostic, it may encounter local congestion, and VM migration is time-consuming, making it unable to fit burst quickly. Meanwhile, because the number of concurrent flows is large, the third category may also fall into inefficiency. An alternative approach is only scheduling those large flows, but usually we don't know how large it is until a flow has finished. We propose SFTB, a flow scheduling framework to quickly fit the traffic burst in data center networks. In fact, SFTB belongs to the third family, but by intelligently leveraging the Explicit Congestion Notification (ECN), SFTB can quickly respond to the burst and congestion. Especially, STFB performs in a fully distributed manner and require no traffic matrix information, making it suitable for any traffic patterns. We evaluate SFTB via large scale simulation. The results show that our method outperform single path TCP and ECMP from more than 10% to more than 70% in average throughput.

In recent years, with the rapid development of satellite technology including On Board Processing (OBP) and Inter Satellite Link (ISL), satellite network devices such as space IP routers have been experimentally carried in space. However, there are many difficulties to build a future satellite network with current terrestrial Internet technologies due to the distinguished space features, such as the severely limited resources, remote hardware/software upgrade in space. In this paper, we propose OpenSAN, a novel architecture of software-defined satellite network. By decoupling the data plane and control plane, OpenSAN provides satellite network with high efficiency, fine-grained control, as well as flexibility to support future advanced network technology. Moreover, we also discuss some practical challenges in the deployment of OpenSAN.

Network coding has emerged as a promising technology to increase network capacity and robustness of ubiquitous networks. However, network coding systems are vulnerable to pollution attacks. In this paper, we propose adapkeys, an adaptive security scheme in network coding allowing participating nodes to rapidly detect corrupted packets generated from pollution attackers. Our work is an innovative adaptive security scheme which use time and space properties of network coding to dynamically adjust the authentication strategy of participating nodes according to the security situation. In addition, adapkeys scheme provides an efficient packet authentication without requiring the existence of any extra secure channels. We also present security analysis and simulation of our scheme, and results demonstrate the practicality and efficiency of adapkeys.

Mobility Model has drawn more and more attentions since its important role in Delay/Disruption Tolerant Networks (DTNs) routing protocol performance evaluation. In this paper, we first present a survey of various mobility models, and then analyze the movement characteristics in the disaster rescue scenario in detail. After that, we introduce a novel mobility model, the Random Way Point with Attraction Direction (RWPAD) Model to represent the rescue and transport groups in this special scenario. Then we investigate the impact of the mobility model on the performance evaluation of specific DTN protocols. To this end, we compare our models to the existing ones under five classic DTN routing protocols. The results indicate that different mobility patterns significantly affect the various protocols in different ways. So before a new routing protocol is deployed in a real situation, it should be tested under the scenario based mobility models. The model we proposed in this paper provides a relatively more realistic environment basis for DTN routing protocol research in the disaster rescue scenario than the existing ones.

The current routing system has been facing serious scalability problem, mobility supporting issue, multi-homing and traffic engineering supporting issue, and provider lock-in problem. Locator/identifier split idea is considered as a promising way to solve the mentioned issue. However, how to design a proper framework for identifier-based routing in the locator/identifier split context is still an unresolved problem. In this paper, we present a scalable and secure framework to tackle the above problem. There are three main contributions of our work. First, we analyze the design challenges in detail. Second, we propose a novel routing framework for future Internet, including the design schemes of main components. Third, we adopt some approaches to enhance the security and improve the lookup efficiency.

The stringent requirement for the high efficiency of routing protocol on Internet will be satisfied by exploiting the threaded border gateway protocol (TBGP) on multi-cores. Since the TBGP performance is restricted by a mass of contentions when racing to access the routing table, a highly-efficient parallel access approach is originally proposed to achieve the ultra-high route processing speed. In this paper, a novel routing table structure consisting of two-level tries is presented for fast parallel access, and a heuristic-based divide-and-recombine algorithm is devised to balance the table accesses and release the contentions, thereby accelerating the parallel route update of multi-threading. By modifying the typical table operations such as lookup, insert, etc., the correctness of two-level tries table is validated according to the operation behaviors of traditional routing table. Experimental results on dual quad-core Xeon server show that the parallel access contentions decrease sharply by 92.5% versus traditional routing table, and the maximal update time per thread is obviously reduced by 56.8% on average with little overhead. Then, the throughput of BGP update message is measured to be improved by about 169%, delivering significant performance improvement of BGP.

BitTorrent is a popular, open-source content distribution system that is conducive for distribution of large-volume files. In this paper, we first model the parallel downloading problem as directed graph and discuss possible improving directions. Then a smart connection management algorithm is proposed, which introduces the notion of content group and sub-tracker, and decentralizes the decision process performed at the tracker for transfer coordination. Simulations show that by increasing content availability to the clients from their immediate neighbors, it can significantly improve the efficiency of distribution.

With fast increasing of Internet's bandwidth, higher performance routers are needed, and their speed depends a lot on the IP routing lookup process. This paper proposes a high performance IP routing lookup algorithm based on CAM array and TCAM. Using smaller memory it does faster searching. For real route tables with 128K prefixes this scheme needs only 17 CAMs with total volume of 1.55 MB and a TCAM with 2K entries. It needs only 1 memory access time for each lookup and few memory accesses for each update in average. Using 10 ns CAM and TCAM, it can fully match the link speed of OC-768. For its good scalability and parallelism, it can be extended to adapt larger forwarding tables and faster forwarding requirements.