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Recent efforts in the network function virtualization (NFV) field have targeted ameliorating end-to-end latency of service function chains (SFCs) using network function (NF) composition. NF composition breaks the NF into building blocks and decides the appropriate combination for these blocks. However, two issues remain in current NF composition methods: (1) In the sequential scope, the fine-grained function’s block-level composition eliminates redundancy, but with the drawback of flexibility restriction. (2) In the vertical scope, complete NF parallelism adds overhead in packet copying and reordering. To reconcile this, here we present ParaGraph , a subgraph-level NF composition with delay-balanced parallelism. ParaGraph has three main components: an NF subgraph-extraction module that extrapolates right-grained core function subgraphs from NFs; an orchestrator that dynamically composes subgraphs with delay-balanced parallelism; and an infrastructure performing lightweight packet copying and merging. We implement a ParaGraph prototype based on Click and the Data Plane Development Kit (DPDK); extensive evaluations show that with minimum overhead, ParaGraph reaches line-speed packet processing and reduces latency by up to 55% compared to state-of-the-art methods.

Cloud data centers, such as Amazon EC2, host myriad big data applications using Virtual Machines (VMs). As these applications are communication-intensive, optimizing network transfer between VMs is critical to the performance of these applications and network utilization of data centers. Previous studies have addressed this issue by scheduling network flows with coflow semantics or optimizing VM placement with traffic considerations. However, coflow scheduling and VM placement have been conducted orthogonally. In fact, these two mechanisms are mutually dependent, and optimizing these two complementary degrees of freedom independently turns out to be suboptimal. In this paper, we present VirtCO, a practical framework that jointly schedules coflows and places VMs ahead of VM launch to optimize the overall performance of data center applications. We model the joint coflow scheduling and VM placement optimization problem, and propose effective heuristics for solving it. We further implement VirtCO with OpenStack and deploy it in a testbed environment. Extensive evaluation of real-world traces shows that compared with state-of-the-art solutions, VirtCO greatly reduces the average coflow completion time by up to 36.5%. This new framework is also compatible with and readily deployable within existing data center architectures.

Because of the low-latency and high-throughput benefits of RDMA, an increasing number of collaborative applications in datacenters are re-designed with RDMA to boost the performance. Among various low-level hardware primitives provided by RDMA, exposed as parameters of APIs, the application designers select and hardcode them to exploit all the performance benefits of RDMA. However, with the dynamic nature of datacenter application, the hardcoded and fixed parameter selection fails to take full advantages of RDMA capabilities, which can cause up to 35% throughput performance loss. To address this issue, we present a tunable RDMA parameter selection framework, which allows parameter tuning at runtime, adaptive to the dynamic application and server status. To attain the native RDMA performance, we use a lightweight decision tree to reduce the overhead of RDMA parameter selection. Finally, we implement the tunable RDMA parameter selection framework with native RDMA API to provide a more abstract API. To demonstrate the effectiveness of our method, we implement a key-value service based on the abstract API. Experiment results show that our implementation has only a very small overhead compared with the native RDMA, while the optimized key-value service achieves 112% more throughput than Pilaf and 66% more throughput than FaRM.

Remote Direct Memory Access (RDMA) suffers from unfairness issues and performance degradation when multiple applications share RDMA network resources. Hence, an efficient resource scheduling mechanism is urged to optimally allocates RDMA resources among applications. However, traditional Network Utility Maximization (NUM) based solutions are inadequate for RDMA due to three challenges: 1) The standard NUM-oriented algorithm cannot deal with coupling variables introduced by multiple dependent RDMA operations; 2) The stringent constraint of RDMA on-board resources complicates the standard NUM by bringing extra optimization dimensions; 3) Naively applying traditional algorithms for NUM suffers from scalability and convergence issues in solving a large-scale RDMA resource scheduling problem.

Modern multi-queue data centers often use the standard Explicit Congestion Notification (ECN) scheme to achieve high network performance. However, one substantial drawback of this approach is that micro-burst traffic can cause the instantaneous queue length to exceed the ECN’s threshold, resulting in numerous mismarkings. After enduring too many mismarkings, senders may overreact, leading to severe throughput loss. As a solution to this dilemma, we propose our own adaptationthe Micro-burst ECN (MBECN) scheme-to mitigate mismarking. MBECN finds a more appropriate threshold baseline for each queue to absorb micro-bursts, based on steady-state analysis and an ideal generalized processor sharing (GPS) model. By adopting a queue-occupation-based dynamically adjusting algorithm, MBECN effectively handles packet backlog without hurting latency. Through testbed experiments, we find that MBECN improves throughput by ~20% and reduces flow completion time (FCT) by ~40%. Using large scale simulations, we find that throughput can be improved by 1.5~2.4× with DCTCP and 1.26~1.35× with ECN*. We also measure network delay and find that latency only increases by 7.36%.

Packet loss is a common problem in data center networks. The factors causing packet loss are various. Among them, micro-burst is the most important reason. Some previous works have studied the causes and influence o f micro-burst in single queue data center. However, through simulations and experiments, we find that micro-burst could bring m ore serious performance degradation in multi-queue data centers. The micro-burst traffic could cause E CN marking ratio rising from 4% to 22%, and cause throughput loss by up to 40%. Through observing queue length, we find that the standard E CN, which adopts immutable threshold, is not suitable for micro-burst traffic because micro-burst could trigger spurious congestion signals frequently, especially in DCTCP. In this paper, we not only show how much influence the micro-burst brings, but also propose Queue-length Aware ECN (QA-ECN) scheme to mitigate micro-burst. Finally, the simulations and experiments show that QAECN could reduce ECN marking ratio to 2.5%. In addition, the throughput and flow completion time could be improved by up to 22.9% and 34.1%, respectively.

With the fast development of cloud technologies, cloud-based operating systems are becoming increasingly popular, while several enterprise products, such as Chrome OS and SUNDE, are released continuously. According to the way of usage, the existing cloud-based operating systems can be divided into two categories: browser-driven operating system and virtualization-based operating system. However, traditional cloud-based operating systems have some disadvantages: As the functionality of browser is limited, it is difficult to transplant traditional desktop software to browser based cloud operating system; on the other hand, as for systems which are based on remote virtual machines (VMs) and Virtual Network Console (VNC), the system performance is constrained by the limited network bandwidth, which will also lead to performance degradation. To solve these problems, this paper designs AntOS, a full-featured cloud operating system. AntOS takes advantage of Docker and X11 technique, to reduce the load of network over 50% while increasing CPU utilization by 45%, which can effectively support the private cloud scenario in enterprises.

Diagnosing faults in virtual networks is always a popular research area. Existing researches primarily focus on diagnosing faults in physical networks, while they could not identify the faults introduced by virtual networks. Besides, the high complexity of algorithms and the requirement for modifying hardware may limit their scope of use. To address these drawbacks, in this paper, we propose a novel approach to diagnose faults in virtual networks. The rational of our approach is that the faults can be identified when located in the packet traces, with the knowledge that the possible known faults that can happen in that location. To achieve this goal, we apply packet marking, fault injection and machine learning techniques to provide precise fault diagnosis. Experimental results show that our approach can efficiently identify 73% of the faults while for virtual network-specific faults, our approach can diagnose 86% of them. Our system can also support real-time or near real-time fault analysis.

© 2008-2012 IEEE. Bandwidth allocation to virtual machines (VMs) has a significant impact on the performance of communication-intensive big data applications hosted in VMs. It is crucial to accurately determine how much bandwidth to be reserved for VMs and when to adjust it. Past approaches typically resort to predicting the long-term network demands of applications for bandwidth allocation. However, lacking of prediction accuracy, these methods lead to the unpredictable application performance. Recently, it is conceded that the network demands of applications can only be accurately derived right before each of their execution phases. Hence, it is challenging to timely allocate the bandwidth to VMs with limited information. In this paper, we design and implement AppBag, an Application-aware Bandwidth guarantee framework, which allocates the accurate bandwidth to VMs with one-step-ahead traffic information. We propose an algorithm to allocate the bandwidth to VMs and map them onto feasible hosts. To reduce the overhead when adjusting the allocation, an efficient Lazy Migration (LM) algorithm is proposed with bounded performance. We conduct extensive evaluations using real-world applications, showing that AppBag can handle the bandwidth requests at run-time, while reducing the execution time of applications by 47.3 percent and the global traffic by 36.7 percent, compared to the state-of-the-art methods.

Summary With the rapid development of information technology, enormous volumes of data are being generated by enterprises at all times. The management and storage of these large-scale data have always been challenging enterprises. As these data are usually shared among users in a collaborative manner, secure data access and access performance are 2 key concerns for data storage of enterprises. However, current solutions fail to meet the requirements of enterprises since they suffer from the following drawbacks: (1) they do not support fine-grained access control and cannot meet the strict secure data access requirements of enterprises, and (2) they suffer from the unpredictable access latency. Thus in this paper, we propose Frostor, an enterprise-oriented cloud storage system, which addresses the secure data access issue through a user account and IP-based fine-grained access control mechanism, and guarantees the access performance via a two-level performance optimization mechanism. We further implement Frostor and deploy it on the testbed environment in a real data center. Extensive evaluations have shown that Frostor implements fine-grained access control, while achieving a significant reduction (≥60%) on access latency.