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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.

Transverse fracture of the skull base is common both in the crushing of temporal regions of the skull and in the case of force acting on one temporal region. However, the mechanism of transverse skull base fracture caused by maxillofacial force has not been fully clarified. To provide an injury identification basis for forensic pathologists and clinicians, this paper combines accident reconstruction and finite element analysis methods to study the injury mechanism of an incomplete transverse fracture of skull base after the injured individual’s mandible was subjected to violence in a traffic accident. The results show that after the injured individual’s mandible was subjected to violence, forces in the direction of the left mandibular fossa and the right mandibular fossa were generated, creating the component forces. The combination of the two forces can produce a crushing effect toward the center of the skull base, as if the left and right temporal regions are being crushed, and the stress is concentrated at the joint of the mandible, the middle cranial fossa and the hypophyseal fossa. When the stress exceeds a certain limit, it will cause a transverse fracture of the skull base.

In recent years, deep neural networks (DNNs) have witnessed a booming of artificial intelligence Internet of Things applications with stringent demands across high accuracy and low latency. A widely adopted solution is to process such computation-intensive DNNs inference tasks with edge computing. Nevertheless, existing edge-based DNN processing methods still cannot achieve acceptable performance due to the intensive transmission data and unnecessary computation. To address the above limitations, we take the advantage of Multi-exit DNNs (ME-DNNs) that allows the tasks to exit early at different depths of the DNN during inference, based on the input complexity. However, naively deploying ME-DNNs in edge still fails to deliver fast and consistent inference in the wild environment. Specifically, 1) at the model-level, unsuitable exit settings will increase additional computational overhead and will lead to excessive queuing delay; 2) at the computation-level, it is hard to sustain high performance consistently in the dynamic edge computing environment. In this paper, we present a Low Latency Edge Intelligence Scheme based on Multi-Exit DNNs (LEIME) to tackle the aforementioned problem. At the model-level, we propose an exit setting algorithm to automatically build optimal ME-DNNs with lower time complexity; At the computation-level, we present a distributed offloading mechanism to fine-tune the task dispatching at runtime to sustain high performance in the dynamic environment, which has the property of close-to-optimal performance guarantee. Finally, we implement a prototype system and extensively evaluate it through testbed and large-scale simulation experiments. Experimental results demonstrate that LEIME significantly improves applications' performance, achieving 1.1–18.7 × speedup in different situations.

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.

With the continuous enrichment of cloud services, an increasing number of applications are being deployed in data centers. These emerging applications are often communication-intensive and data-parallel, and their performance is closely related to the underlying network. With their distributed nature, the applications consist of tasks that involve a collection of parallel flows. Traditional techniques to optimize flow-level metrics are agnostic to task-level requirements, leading to poor application-level performance. In this paper, we address the heterogeneous task-level requirements of applications and propose task-aware flow scheduling. First, we model tasks' sensitivity to their completion time by utilities. Second, on the basis of Nash bargaining theory, we establish a flow scheduling model with heterogeneous utility characteristics, and analyze it using Lagrange multiplier method and KKT condition. Third, we propose two utility-aware bandwidth allocation algorithms with different practical constraints. Finally, we present Tasch, a system that enables tasks to maintain high utilities and guarantees the fairness of utilities. To demonstrate the feasibility of our system, we conduct comprehensive evaluations with real-world traffic trace. Communication stages complete up to 1.4× faster on average, task utilities increase up to 2.26×, and the fairness of tasks improves up to 8.66× using Tasch in comparison to per-flow mechanisms.

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%.

Large data centers are usually built to support increasing computational and data storage demand of growing global business and industry, which consume an enormous amount of energy, at a huge cost to both business and the environment. However, much of that energy is wasted to maintain excess service capacity during periods of low load. In this paper, we investigate the problem of "right-sizing" data center for energy-efficiency through virtualization which allows consolidation of workloads into smaller number of servers while dynamically powering off the idle ones. In view of the dynamic nature of data centers, we propose a stochastic model based on Queueing theory to capture the main characteristics. Solving this model, we notice that there exists a tradeoff between the energy consumption and performance. We hereby develop a BFGS based algorithm to optimize the tradeoff by searching for the optimal system parameter values for the data center operators to "right-size" the data centers. We implement our Stochastic Right-sizing Model (SRM) and deploy it in the real-world cloud data center. Experiments with two real-world workload traces show that SRM can significantly reduce the energy consumption while maintaining high performance. A Stochastic Right-sizing Model(SRM) based on Queueing theory is proposed.A BFGS based algorithm is proposed to achieve energy-efficiency.SRM is implemented with open-source cloud platform OpenStack.

© 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.