While most clouds use on-server rate limiters for bandwidth allocation, we propose to implement them on switches. On-switch rate limiters can simplify network management and promote the performance of control-plane rate limiting applications. We leverage the recent progress of programmable switches to implement on-switch rate limiters, named SwRL. In the design of SwRL, we make design choices according to the programmable hardware characteristics, we deeply optimize the memory usage of the algorithm so as to fit a cloud-scale (one million) rate limiters in a single switch, and we complement the missing computation primitives of the hardware using a pre-computed approximate table. We further developed three control-plane applications and integrate them with SwRL, showing the control-plane interoperability of SwRL. We prototype and evaluate SwRL in both testbed and production environments, demonstrating its good properties of precision rate control, scalability, interoperability, and manageability (execution environmental isolation).

As online service systems continue to grow in terms of complexity and volume, how service incidents are managed will significantly impact company revenue and user trust. Due to the cascading effect, cloud failures often come with an overwhelming number of incidents from dependent services and devices. To pursue efficient incident management, related incidents should be quickly aggregated to narrow down the problem scope. To this end, in this paper, we propose GRLIA, an incident aggregation framework based on graph representation learning over the cascading graph of cloud failures. A representation vector is learned for each unique type of incident in an unsupervised and unified manner, which is able to simultaneously encode the topological and temporal correlations among incidents. Thus, it can be easily employed for online incident aggregation. In particular, to learn the correlations more accurately, we try to recover the complete scope of failures’ cascading impact by leveraging fine-grained system monitoring data, i.e., Key Performance Indicators (KPIs). The proposed framework is evaluated with real-world incident data collected from a large-scale online service system of Huawei Cloud. The experimental results demonstrate that GRLIA is effective and outperforms existing methods. Furthermore, our framework has been successfully deployed in industrial practice.

Cloud computing has become a promising paradigm in the field of Services Computing for its advantage of providing infrastructures and resources as a service. However, cloud networks consume huge amount of energy consumption due to the various applications and large scale of users with high mobility. In this paper, we investigate the problem of service migration to minimize the energy consumption of cloud infrastructures while ensuring the service delivery with satisfied latency and economic cost. We propose an energy-efficient migration scheme named SMPA considering the economic aspects and the quality of service. Furthermore, the mechanism of virtual machine consolidation running different services is also designed in SMPA, when the requests arrive, change or depart. Extensive simulations conducted on random topologies show that our proposal reduces requests' SLA violation ratio and improves the utilization of provider resources by service migration. Moreover, by taking advantage of physical resources, SMPA decreases the number of running hosts and reduces the power consumption while ensuring the operational cost effectively under dynamic workload scenarios.

With the increasing popularity of cloud computing, the huge amount of energy consumed by data centers has gained much attention. Current proposals address the energy efficiency problem by two major methods: optimizing the allocation of physical servers and network elements (routers or switches). In order to improve resource utilization and minimize the energy consumption, the former method focuses on virtual machine (VM) placement regardless of the inherent traffic between VMs. The latter designs power saving routing and flow scheduling while this method neglects the resource demands in VMs. In this paper, we jointly consider the VM placement and network routing to optimize energy cost. In addition, we take advantage of the centralized and global controller in Software Defined Networking (SDN) paradigm. Inspired by the idea of Data Field, we propose a novel algorithm to evaluate the importance and relationships among multiple VMs. The potential score based on Data Field is a more accurate global ranking considering the resource demands and inter-VM traffic. Extensive simulations are conducted on different scales of typical data center topologies, such as Fat-Tree and BCube. Results show that our proposal can reduce the number of active devices including the servers and network elements, and thereby saves power consumption. Moreover, the proposed algorithm improves network performance by decreasing the average hops of per flow.

It is a challenge to improve utilization of physical resources when allocating resources for virtual data centers (VDCs). In this work we proposed two reliability-aware VDC embedding methods NMP and NMPD. To reduce embedding cost, the proposals try to split VMs into clusters based on topological potential and modularity and assign each cluster to a server. Via simulations, we show that NMP can accept more VDCs and help InPs improve the revenue. Although accepting a little less VDCs, NMPD can reduce resources occupied by VDCs and alleviate the congestion of core links.

Cloud computing is a promising paradigm for future computing platform. It enables the physical resources(computing, storage, networking, etc) to be provided on-demand. Despite the cloud computing brings many benefits to the IT network structure, it still faces new challenges for allocating heterogeneous resources automatically to services. Software Defined Networking (SDN) is a new concept of the network infrastructure as it decouples the control and data planes.With its characteristic of programmability, it's feasible to achieve network virtualization or create a slice of network in the form of virtual data centers(VDCs), which have pushed huge development of cloud computing. The VDCs embedding problem deals with allocating VDC requests to fulfill the requirements of cloud services with minimal cost and high revenue, which is an NPhard problem. In this paper, we propose an algorithm based on Entropy Weighted topological Potential considering multiple types of resources to tackle the problem in SDN-enabled data center network. Extensive simulations show that our proposed algorithm can embed more virtual requests efficiently with higher acceptance ratio and resource revenue over time.