Software defined networking is increasingly prevalent in data center networks for it enables centralized network configuration and management. However, since switches are statically assigned to controllers and controllers are statically provisioned, traffic dynamics may cause long response time and incur high maintenance cost. To address these issues, we formulate the dynamic controller assignment problem (DCAP) as an online optimization to minimize the total cost caused by response time and maintenance on the cluster of controllers. By applying the randomized fixed horizon control framework, we decompose DCAP into a series of stable matching problems with transfers, guaranteeing a small loss in competitive ratio. Since the matching problem is NP-hard, we propose a hierarchical two-phase algorithm that integrates key concepts from both matching theory and coalitional games to solve it efficiently. Theoretical analysis proves that our algorithm converges to a near-optimal Nash stable solution within tens of iterations. Extensive simulations show that our online approach reduces total cost by about 46%, and achieves better load balancing among controllers compared with static assignment.

There has been a recent trend that video-on-demand (VoD) providers such as Netflix are leveraging resources from cloud services for multimedia streaming. In this paper, we consider the scenario that a VoD provider can make reservations for bandwidth guarantees from cloud service providers to guarantee the streaming performance in each video channel. We propose a predictive resource auto-scaling system that dynamically books the minimum bandwidth resources from multiple data centers for the VoD provider to match its short-term demand projections. We exploit the anti-correlation between the demands of video channels for statistical multiplexing and for hedging the risk of under-provision. The optimal load direction from channels to data centers is derived with provable performance. We further provide suboptimal solutions that balance bandwidth and storage costs. The system is backed up by a demand predictor that forecasts the demand expectation, volatility and correlations based on learning. Extensive simulations are conducted driven by the workload traces from a commercial VoD system.

Many cloud services are running on geographically distributed datacenters for better reliability and performance. We consider the emerging problem of joint request mapping and response routing with distributed datacenters in this paper. We formulate the problem as a general workload management optimization. A utility function is used to capture various performance goals, and the location diversity of electricity and bandwidth costs are realistically modeled. To solve the large-scale optimization, we develop a distributed algorithm based on the alternating direction method of multipliers (ADMM). Following a decomposition-coordination approach, our algorithm allows for a parallel implementation in a datacenter where each server solves a small sub-problem. The solutions are coordinated to find an optimal solution to the global problem. Our algorithm converges to near optimum within tens of iterations, and is insensitive to step sizes. We empirically evaluate our algorithm based on real-world workload traces and latency measurements, and demonstrate its effectiveness compared to conventional methods.

Short TCP flows that are critical for many interactive applications in data centers are plagued by long flows and head-of-line blocking in switches. Hash-based load balancing schemes such as ECMP aggravate the matter and result in long-tailed flow completion times (FCT). Previous work on reducing FCT usually requires custom switch hardware and/or protocol changes. We propose RepFlow, a simple yet practically effective approach that replicates each short flow to reduce the completion times, without any change to switches or host kernels. With ECMP the original and replicated flows traverse distinct paths with different congestion levels, thereby reducing the probability of having long queueing delay. We develop a simple analytical model to demonstrate the potential improvement. Further, we conduct NS-3 simulations and Mininet implementation and show that RepFlow provides 50%-70% speedup in both mean and 99-th percentile FCT for all loads, and offers near-optimal FCT when used with DCTCP.

Recently, data center carbon emission has become an emerging concern for the cloud service providers. Previous works are limited on cutting down the power consumption of the data centers to defuse such a concern. In this paper, we show how the spatial and temporal variabilities of the electricity carbon footprint can be fully exploited to further green the cloud running on top of geographically distributed data centers. We jointly consider the electricity cost, service level agreement (SLA) requirement, and emission reduction budget. To navigate such a three-way tradeoff, we take advantage of Lyapunov optimization techniques to design and analyze a carbon-aware control framework, which makes online decisions on geographical load balancing, capacity right-sizing, and server speed scaling. Results from rigorous mathematical analyses and real-world trace-driven empirical evaluation demonstrate its effectiveness in both minimizing electricity cost and reducing carbon emission.

We present Anchor, a general resource management architecture that uses the stable matching framework to decouple policies from mechanisms when mapping virtual machines to physical servers. In Anchor, clients and operators are able to express a variety of distinct resource management policies as they deem fit, and these policies are captured as preferences in the stable matching framework. The highlight of Anchor is a new many-to-one stable matching theory that efficiently matches VMs with heterogeneous resource needs to servers, using both offline and online algorithms. Our theoretical analyses show the convergence and optimality of the algorithm. Our experiments with a prototype implementation on a 20-node server cluster, as well as large-scale simulations based on real-world workload traces, demonstrate that the architecture is able to realize a diverse set of policy objectives with good performance and practicality.