In this paper we design DCMA, a novel multicast membership management scheme for data center networks. Unlike traditional protocols like IGMP/MLD, DCMA leverages the characteristics of data center multicast application and the emerging software defined networking (SDN) technique to manage multicast members in an easier and better way. Multicast application master delivers the membership to the SDN controller, and the SDN controller assigns the group addresses in a coordinated way to minimize the forwarding table size in switches. In particular, by formulating the multicast group address allocation problem and capturing the relationship among forwarding entries in different switches, we design both a batch algorithm and an incremental algorithm to allocate the multicast group addresses. Evaluations based on real-world traces show that, DCMA can save almost half multicast forwarding entries in switches compared with random multicast address allocation.

KPI (Key Performance Indicator) anomaly detection is critical for Internet-based services to ensure the quality and reliability. However, existing algorithms' performance in reality is far from satisfying due to the lack of sufficient KPI anomaly data to help train and evaluate these algorithms. In this paper, we argue that labeling overhead is the main hurdle to obtain such datasets. Thus, we novelly propose a semi-automatic labelling tool called Label-Less, which minimizes the labeling overhead in order to enable an ImageNet-like large-scale KPI anomaly dataset with high-quality ground truth. One novel technique in Label-Less is robust and rapid anomaly similarity search, which saves operators from scanning and checking the long KPIs back and forth for abnormal patterns or label consistency. In our evaluations using 30 real KPIs from a large Internet company, our anomaly similarity search achieves the best F-score of 0.95 on average, and a real-time per-KPI response time (less than 0.5 second). Overall, the feedback from deployment in practice shows that Label-Less can reduce operators' labeling overhead by more than 90%.

The failures of software service directly affect user experiences and service revenue. Thus operators monitor both service-level KPIs (e.g., response time) and machine-level KPIs (e.g., CPU usage) on each machine underlying the service. When a service fails, the operators must localize the root cause machines, and mitigate the failure as quickly as possible. Existing approaches have limited application due to the difficulty to obtain the required additional measurement data. As a result, failure localization is largely manual and very time-consuming. This paper presents FluxRank, a widely-deployable framework that can automatically and accurately localize the root cause machines, so that some actions can be triggered to mitigate the service failure. Our evaluation using historical cases from five real services (with tens of thousands of machines) of a top search company shows that the root cause machines are ranked top 1 (top 3) for 55 (66) cases out of 70 cases. Comparing to existing approaches, FluxRank cuts the localization time by more than 80% on average. FluxRank has been deployed online at one Internet service and six banking services for three months, and correctly localized the root cause machines as the top 1 for 55 cases out of 59 cases.

Internet-based service companies monitor a large number of KPIs (Key Performance Indicators) to ensure their service quality and reliability. Correlating KPIs by fluctuations reveals interactions between KPIs under anomalous situations and can be extremely useful for service troubleshooting. However, such a KPI flux-correlation has been little studied so far in the domain of Internet service operations management. A major challenge is how to automatically and accurately separate fluctuations from normal variations in KPIs with different structural characteristics (such as seasonal, trend and stationary) for a large number of KPIs. In this paper, we propose CoFlux, an unsupervised approach, to automatically (without manual selection of algorithm fitting and parameter tuning) determine whether two KPIs are correlated by fluctuations, in what temporal order they fluctuate, and whether they fluctuate in the same direction. CoFlux's robust feature engineering and robust correlation score computation enable it to work well against the diverse KPI characteristics. Our extensive experiments have demonstrated that CoFlux achieves the best F1-Scores of 0.84 (0.90), 0.92 (0.95), 0.95 (0.99), in answering these three questions, in the two real datasets from a top global Internet company, respectively. Moreover, we showed that CoFlux is effective in assisting service troubleshooting through the applications of alert compression, recommending Top N causes, and constructing fluctuation propagation chains.

Key performance indicator (KPI) anomaly detection (AD) is critical to ensure service quality and reliability. Due to the effects of work days, off days, festivals, and business activities on user behavior, KPIs may exhibit different patterns within different days, which we call periodicity profiles of KPIs. However, existing KPI AD approaches have difficulties in adapting to diverse periodicity profiles due to the lack of generality. In this paper, we propose an automatic and generic framework called Period, which can accurately detect the periodicity profiles through daily subsequences clustering, and improve the performance of AD methods by robustly and automatically adapting to different periodicity profiles. In our evaluation using several real-world KPIs with different periodicity profiles from large Internet-based services, the clustering algorithm used to detect periodicity can achieve about 0.95 accuracy on average. More importantly, further evaluation on 56 KPIs shows that Period can significantly improve the best F-score of several widely used AD approaches by up to 0.66.

Failure is common instead of exception in large-scale networks. To provide high service quality to upper-layer applications, it is desired that a converged backup path can be rapidly launched when failure occurs. In this paper, we design an IP based Fast ReRouting (FRR) scheme called Metro, which can solve the traffic rerouting convergence problem after arbitrary single link/node failure with low stretch for the backup path. When failure occurs in the network, Metro first indicates all the network areas that would be affected by the failure, and then finds out a few bridge links to drain the traffic in the affected network area to the network area that is not affected by the failure. In this way, Metro does not configure tunnels, encapsulate or modify data packets, and hence it is easy to be deployed in current networks. Extensive simulations show that Metro can solve arbitrary single link/node failure with backup paths shorter than the state-of-the-art solutions, and about 98% of the backup path stretch in Metro are the same as the optimal tunnel scheme.

With the tremendous growth of cloud computing, it is increasingly critical to provide quantifiable performance to tenants and to improve resource utilization for the cloud provider. Though many recent proposals focus on guaranteeing job performance (with a particular note on network bandwidth) in the cloud, they usually lack efficient utilization of cloud resource, or vice versa. In this paper we present DCloud, which leverages the (soft) deadlines of cloud computing jobs to enable flexible and efficient resource utilization in data centers. With the deadline requirement of a job guaranteed, DCloud employs both time sliding (postponing the launching time of a job) and bandwidth scaling (adjusting the bandwidth associated with VMs) in resource allocation, so as to better match the resource allocated to the job with the cloud's residual resource. Extensive simulations and testbed experiments show that DCloud can accept much more jobs than existing solutions, and significantly increase the cloud provider's revenue with less cost for individual tenants.

Enterprises deploy their middlebox services in cloud seeking for easy management, flexible scalability and economic savings. However, existing elastic virtual network function(VNF) placement strategy often leads to an unpredictable placing location due to the ever-changing workload, which may waste much precious bandwidth resource and bring a lot of VM operation overhead(e.g. VM launch, termination and migration). A key problem for cloud providers is how to conduct an effective service placement and provide resource provision according to various workload, satisfying the bandwidth requirement of each service while saving as much cloud resource as possible. In this paper we solve both the virtual network function(VNF) placement and scaling problem based on preplanned allocation with bandwidth guarantee. We first propose a concept of VNF instance communication graph to describe the bandwidth demand of each VNF instance and explore the placement requirement for bandwidth savings. Then we design an on-line heuristic algorithm to achieve approximate optimal allocation. At last, we also provide an off-line optimal solution for comparison. Our simulation shows that our heuristic solution saves 20% more bandwidth resource and reduce more VM migration overhead than existing elastic placement solution. Its performance is also very close to the optimal solution.

Cloud computing not only requires high-capacity data center networks to accelerate bandwidth-hungry computations, but also causes considerable power expenses to cloud providers. In recent years many advanced data center network architectures have been proposed to increase the network throughput, such as Fat-Tree [1] and BCube [2], but little attention has been paid to the power efficiency of these network architectures. This paper makes the first comprehensive comparison study for typical data center networks with regard to their Network Power Effectiveness(NPE), which indicates the end-to-end bps per watt in data transmission and reflects the tradeoff between power consumption and network throughput. We take switches, server NICs and server CPU cores into account when evaluating the network power consumption. We measure NPE under both regular routing and power-aware routing, and investigate the impacts of topology size, traffic load, throughput threshold in power-aware routing, network power parameter as well as traffic pattern. The results show that in most cases Flattened Butterfly possesses the highest NPE among the architectures under study, and server-centric architectures usually have higher NPEs than Fat-Tree and VL2 architectures. In addition, the sleep-on-idle technique and power-aware routing can significantly improve the NPEs for all the data center architectures, especially when the traffic load is low. We believe that the results are useful for cloud providers, when they design/upgrade data center networks or employ network power management.

The ability to provide guaranteed network bandwidth for tenants is essential to the prosperity of cloud computing platforms, as it is a critical step for offering predictable performance to applications. Despite its importance, it is still an open problem for efficient network bandwidth sharing in a multitenant environment, especially when applications have diverse bandwidth requirements. More precisely, it is not only that different tenants have distinct demands, but also that one tenant may want to assign bandwidth differently across her virtual machines (VMs), i.e., the heterogeneous bandwidth requirements. In this paper, we tackle the problem of VM allocation with bandwidth guarantee in multitenant data center networks. We first propose an online VM allocation algorithm that improves on the accuracy of the existing work. Next, we develop a VM allocation algorithm under heterogeneous bandwidth demands. We conduct extensive simulations to demonstrate the efficiency of our method.

Cloud computing has experienced explosive growth in last ten years. Since cloud applications usually come up with large amounts of data transfer, the network capability becomes one of the dominant performance factors. Judging from common sense, it seems obvious that the higher quantity of bandwidth guaranteed for a job, the higher performance we will achieve. Rather, in this paper, we found that the effect of providing higher bandwidth guarantee turns to be uncertain. Without efficient work and flow scheduling mechanism, merely increasing the bandwidth allocated for the job may actually be as harmful as reducing the bandwidth.

To efficiently utilize their infrastructure and thus increase their revenue, cloud providers need mechanisms to provide resource allocation and performance isolation for different tenants in the shared platform. In particular, network bandwidth sharing is a critical yet still an open problem to most cloud providers. In this paper, we study the problem of virtual machine (VM) allocation under the consideration of providing bandwidth guarantees. We first propose an online allocation algorithm for tenants with homogeneous bandwidth demand, which improves on the accuracy of existing algorithms. Subsequently, we extend it to handle heterogeneous bandwidth demand. Extensive simulations show that our algorithm makes much more efficient utilization of the network resource than existing algorithms, and performs close to the optimal offline allocation.