An Efficient Priority-Driven Congestion Control Algorithm for Data Center Networks

With the emerging diverse applications in data centers,the demands on quality of service in data centers also become diverse,such as high throughput of elephant flows and low latency of deadline-sensitive flows.However,traditional TCPs are ill-suited to such situations and always result in the inefficiency(e.g.missing the flow deadline,inevitable throughput collapse)of data transfers.This further degrades the user-perceived quality of service(QoS)in data centers.To reduce the flow completion time of mice and deadline-sensitive flows along with promoting the throughput of elephant flows,an efficient and deadline-aware priority-driven congestion control(PCC)protocol,which grants mice and deadline-sensitive flows the highest priority,is proposed in this paper.Specifically,PCC computes the priority of different flows according to the size of transmitted data,the remaining data volume,and the flows’deadline.Then PCC adjusts the congestion window according to the flow priority and the degree of network congestion.Furthermore,switches in data centers control the input/output of packets based on the flow priority and the queue length.Different from existing TCPs,to speed up the data transfers of mice and deadline-sensitive flows,PCC provides an effective method to compute and encode the flow priority explicitly.According to the flow priority,switches can manage packets efficiently and ensure the data transfers of high priority flows through a weighted priority scheduling with minor modification.The experimental results prove that PCC can improve the data transfer performance of mice and deadline-sensitive flows while guaranting the throughput of elephant flows.

Resource allocation and hybrid prediction scheme for low-latency visual feedbacks to support tactile Internet multimodal perceptions

Predicting user states in future and rendering visual feedbacks accordingly can effectively reduce the visual experienced delay in the tactile Internet(TI). However, most works omit the fact that different parts in an image may have distinct prediction requirements, based on which different prediction models can be used in the predicting process, and then it can further improve predicting quality especially under resources-limited environment. In this paper, a hybrid prediction scheme is proposed for the visual feedbacks in a typical TI scenario with mixed visuo-haptic interactions, in which haptic traffic needs sufficient wireless resources to meet its stringent communication requirement, leaving less radio resources for the visual feedback. First, the minimum required number of radio resources for haptic traffic is derived based on the haptic communication requirements, and wireless resources are allocated to the haptic and visual traffics afterwards. Then, a grouping strategy is designed based on the deep neural network(DNN) to allocate different parts from an image feedback into two groups to use different prediction models, which jointly considers the prediction deviation thresholds, latency and reliability requirements, and the bit sizes of different image parts. Simulations show that, the hybrid prediction scheme can further reduce the visual experienced delay under haptic traffic requirements compared with existing strategies.

Robust multi-path routing for dynamic topology in wireless sensor networks

Wirreless sensor networks are being widely researched and are expected to be used in several scenarios. On the leading edge of treads, on-demand, high-reliability, and low-latency routing protocol is desirable for indoor environment applications. This article proposes a routing scheme called robust multi-path routing that establishes and uses multiple node-disjoint routes. Providing multiple routes helps to reduce the route recovery process and control the message overhead. The performance comparison of this protocol with dynamic source routing （DSR） by OPNET simulations shows that this protocol is able to achieve a remarkable improvement in the packet delivery ratio and average end-to-end delay.

HWM： a hybrid workload migration mechanism of metadata server cluster in data center

In data center, applications of big data analytics pose a big challenge to massive storage systems. It is signif- icant to achieve high availability, high performance and high scalability for PB-scale or EB-scale storage systems. Meta- data server （MDS） cluster architecture is one of the most effective solutions to meet the requirements of applications in data center. Workload migration can achieve load balance and energy saving of duster systems. In this paper, a hybrid workload migration mechanism of MDS cluster is proposed and named as HWM. In HWM, workload of MDS is classi- fied into two categories： metadata service and state service, and they can be migrated rapidly from a source MDS to a target MDS in different ways. Firstly, in metadata service migration, all the dirty metadata of one sub file system is flushed to a shared storage pool by the source MDS, and then is loaded by the target MDS. Secondly, in state service mi- gration, all the states of that sub file system are migrated from source MDS to target MDS through network at file granular- ity, and then all of the related structures of these states are reconstructed in target MDS. Thirdly, in the process of work- load migration, instead of blocking client requests, the source MDS can decide which MDS will respond to each request according to the operation type and the migration stage. The proposed mechanism is implemented in the Blue Whale MDS cluster. The performance measurements show that the HWM mechanism is efficient to migrate the workload of a MDS cluster system and provides low-latency access to metadata and states.

Understanding the relationship between High Reliability Theory (HRT) ofmindful organizing and Safety Management Systems (SMS) within theaerospace industry: A cross-sectional quantitative assessment

There seems to be a paucity in extant literature that assesses the relationship between Safety Management Systems (SMS) and High Reliability Theory (HRT) behavior process of mindful organizing (MO) among aerospaceorganizations. There could be benefits for organizational safety by exploring this relationship in high-reliabilityorganizations (HROs) like the aerospace industry. Using a modified Safety Organizing Scale (SOS) by Vogus andSutcliffe (2007) and a validated SMS scale, the relationship between SMS and MO was measured. The perceptionsof a cross-section of respondents from commercial airlines with SMS and commercial space licensees without SMSin the United States (U.S.) was assessed. A four-factor model of MO had acceptable fit. A model showing the relationship between SMS and MO had good fit and showed a high significant strength of relationship (r = 0.82,p = 0.000) with a big effect size. There were also significant differences in mean responses among managementpersonnel and non-management personnel on the MO factor “sensitivity to operations” and the result suggestsmanagers were better at identifying personnel with skills and knowledge to ensure safer task accomplishmentthan non-management personnel. The study results suggest that the SMS requirements for commercial airlines inthe U.S. can enrich the identification and understanding of MO factors and it may be beneficial for the commercial space industry to formally adopt SMS. Future research studies may include direct comparisons in multipleaerospace organizations using a larger sample size to determine the overall understanding of MO factors and howit affects SMS.