Studies on the resource workload demand in cloud computing environment aim at reducing resource wastage by optimizing the resource utilization in a cloud data center. Based on this goal, most of the existing approaches rely on resource management mechanisms such as resource allocation and Virtual Machine (VM) consolidation to reach an ideal solution for reducing resource wastage. Because of instability and high variability of the cloud resource usage and workloads, there is a demand for cloud providers to apply the prediction methods for forecasting the future cloud resource utilization. This paper employs a supervised statistical learning method, i.e., Support Vector Regression Technique (SVRT), to forecast the future usage of multi-attribute host resource. The method is particularly suitable to handle a non-linear cloud resource workload. To improve the prediction accuracy of SVRT, we decide Radial Basis Function as the kernel function of SVRT and apply Sequential Minimal Optimization Algorithm (SMOA) for the training and regression estimation of the prediction method. Besides, compared with the existing work, we consider the multi-attribute cloud resources other than the single resource. The method is employed under eight sets of real-world workloads, which are collected from BitBrain (BB), PlanetLab (PL) and Google Cluster Workload Traces (GCWT). Series of experiments conducted on the workload dataset show the effectiveness of our approach. Based on evaluation metrics, the final results show that the accuracy was enhanced by approximately 4%-16% and the error percentage was reduced by approximately 8%-60% compared with the state-of-the-art methods.

To achieve low latency and high link utilization, ECN-based transport protocols (i.e., DCTCP) are widely deployed in data center networks (DCN). In multi-tenant environment, however, the newly introduced ECN-enabled TCP greatly impairs the performance of applications with out-dated and misconfigured TCP stacks. The reason is that the ECN-enabled switch fails to treat the mixed TCP traffic fairly, resulting in the distinguished performance gap between the ECN-enabled and ECN-disabled TCPs. This paper proposes DDT (Dual Dynamic Thresholds), an active queue management algorithm (AQM) to achieve the flow-level fairness for coexisting heterogeneous TCP traffic. DDT monitors the switch queue in real time, and dynamically tunes the distance between ECN-marking and packet-dropping thresholds to mitigate the aggressiveness difference between the ECN-enabled and ECN-disabled TCPs. The results of real implementations and large-scaled simulations show that DDT elegantly fills the aggressiveness gap of heterogeneous TCP traffic without disturbing their own control loops, while only introducing acceptable deployment overhead at switch.

Nowadays, the deluge of heterogeneous data generated by various cloud-based industrial applications often has to be delivered to the data center for analysis and storage. To speed up data processing thus facilitating application performance, the modern data center network offers rich parallel paths and super high bisection bandwidth for data communications between servers, expecting to provide good transmission performance for the heterogeneous data traffic caused by cloud-based industrial applications. Due to high path diversities, however, balancing the heterogeneous traffic load across multiple parallel paths for fully utilizing the offered super high bisection bandwidth is full of challenges (i.e., how to achieve high path utilization without incurring adverse impact). Although prior studies demonstrate that the flowlet-based solutions are promising to fill the bill, we argue that their rerouting operations are still inappropriate in timing and manner. This article presents HG, a load balancing scheme adopting hybrid switching granularity to make traffic rerouting. HG embeds the flow-fragment-based and flowcell-based path switching into the flowlet-based path switching, and employs state-weighted path measurement to choose paths for newly appeared flow fragments, flowcells, and flowlets. The results of numerous NS2 tests show that, compared with the state-of-the-art data center load balancing schemes, HG significantly reduces the average and tail-flow completion times for delay-sensitive flows, and the throughput of throughput-oriented flows is always maintained at high level.

With the ever-increasing concern in network security and privacy, a major portion of Internet traffic is encrypted now. Recent research shows that more than 70% of Internet content is transmitted using HyperText Transfer Protocol Secure (HTTPS). However, HTTPS encryption eliminates the advantages of many intermediate services like the caching proxy, which can significantly degrade the performance of web content delivery. We argue that these restrictions lead to the need for other mechanisms to access sites quickly and safely. In this paper, we introduce QoS3, which is a protocol that can overcome such limitations by allowing clients to explicitly and securely re-introduce in-network caching proxies using fine-grained trust delegation without compromising the integrity of the HTTPS content and modifying the format of Transport Layer Security (TLS). In QoS3, we classify web page contents into two types: (1) public contents that are common for all users, which can be stored in the caching proxies, and (2) private contents that are specific for each user. Correspondingly, QoS3 establishes two separate TLS connections between the client and the web server for them. Specifically, for private contents, QoS3 just leverages the original HTTPS protocol to deliver them, without involving any middlebox. For public contents, QoS3 allows clients to delegate trust to specific caching proxy along the path, thereby allowing the clients to use the cached contents in the caching proxy via a delegated HTTPS connection. Meanwhile, to prevent Man-in-the-Middle (MitM) attacks on public contents, QoS3 validates the public contents by employing Document object Model (DoM) object-level checksums, which are delivered through the original HTTPS connection. We implement a prototype of QoS3 and evaluate its performance in our testbed. Experimental results show that QoS3 provides acceleration on page load time ranging between 30% and 64% over traditional HTTPS with negligible overhead. Moreover, QoS3 is deployable since it requires just minor software modifications to the server, client, and the middlebox.

Due to the highly non-linear nature of traffic data and the complex structure of road networks, traffic forecasting faces significant challenges. In this paper, we propose an improved model that combines outlook attention and graph embedding (MOAGE) for traffic forecasting, focusing on the construction of reasonable and effective spatio-temporal dependencies. Inspired by the idea of symmetry, MOAGE adopts a symmetrical encoder and decoder structure. Outlook attention blocks are important components of the encoder and decoder, consisting of spatial outlook attention and temporal outlook attention, used to model spatio-temporal dependencies in the road network. Cross attention are added to the model to reduce propagation errors. In addition, we learned the vertex representation of the graph via the node2vec algorithm and integrated the graph information into our model for a better prediction performance. Extensive experiments on two real datasets further demonstrate that the RMSE errors of the MOAGE on PEMS_BAY and METR_LA are reduced by approximately 14.6% and 12.2% for 60 min compared with the latest baseline models. Finally, the methodology used in this study will provide guidance to relevant ministries to better allocate transport resources and improve the efficiency and safety of traffic operations.