TCP was designed to provide stream-oriented communication service for bulk data transfer applications (e.g., FTP and Email). With four-decade development, Internet applications have undergone significant changes, which now involve highly dynamic traffic pattern and message-oriented communication paradigm. However, the impact of this substantial evolution on congestion control (CC) has not been fully studied. Most of the network transports today still make the long-held assumption about application traffic, i.e., a byte stream with an unlimited data arrival rate.

In recent years, enterprises are increasingly moving from private WANs to hybrid WANs, for the purpose of cost saving, better scalability and improved user experience. To utilize network resource of both private and public networks, existing traffic engineering (TE) solutions for hybrid WANs perform traffic classification at the application level, i.e., sending traffic of high-priority applications to private WANs while letting other traffic go to the Internet. Unfortunately, such a strategy falls short of achieving optimal performance and has inherent limitation in making desired tradeoff between cost and performance. In this paper, we present comboTE, a mixed-link based TE framework to optimize both performance and cost over hybrid WAN. Specifically, comboTE designs a fine-grained TE strategy that considers link(s) from both types of networks at every hop when deciding the routing path for each traffic demand. To address the scalability issues, we leverage Lagrangian relaxation, a decomposition technique for solving large scale integer linear programs, combined with a novel augmented-graph-based approach to derive the most cost-efficient link compositions within a segment. Our comboTE achieves the near-optimal solution with a theoretically proven gap that is better than linear programming relaxation. The experimental results on realistic topologies and traffic matrices show that comboTE scales well and achieves solutions of high quality. Compared to prior TE algorithms, comboTE achieves a solution with tighter gap in limited time, and takes 2.2× less execution time without a time budget.

With the increasing demand of low-latency network services, mobile edge computing (MEC) emerges as a new paradigm, which provides server resources and processing capacities in close proximity to end users. Based on network function virtualization (NFV), network services can be flexibly provisioned as virtual network function (VNF) chains deployed at edge servers. However, due to the resource shortage at the network edge, how to efficiently deploy VNF chains with latency guarantees and resource efficiency remains as a challenging problem. In this work, we focus on jointly optimizing the resource utilization of both edge servers and physical links under the latency limitations. Specifically, we formulate the VNF chain deployment problem as a mixed integer linear programming (MILP) to minimize the total resource consumption. We design a novel two-stage latency-aware VNF deployment scheme: highlighted by a constrained depth-first search algorithm (CDFSA) for selecting paths, and a path-based greedy algorithm (PGA) for assigning VNFs by reusing as many VNFs as possible. We demonstrate that our proposed algorithm can efficiently achieve a near-optimal solution with a theoretically-proved worstcase performance bound. Extensive simulation results show that the proposed algorithm outperforms three previous heuristic algorithms.

With the evolution of Network Function Virtual-izaiton (NFV), enterprises are increasingly outsourcing their network functions to the cloud. However, using virtualized network functions (VNFs) to provide flexible services in today's cloud is challenging due to the inherent difficulty in intelligently scaling VNFs to cope with traffic fluctuations. To best utilize cloud resources, NFV providers need to dynamically scale the VNF deployments and reroute traffic demands for their customers. Since most existing work is reactive in nature, we seek a proactive approach to provision new instances for overloaded VNFs ahead of time based on the estimated flow rates. We formulate the VNF provisioning problem in order that the cost incurred by inaccurate prediction and VNF deployment is minimized. In the proposed online algorithm, we first employ an efficient online learning method which aims at minimizing the error in predicting the service chain demands. We then derive the requested instances with adaptive processing capacities and call two other algorithms for new instance assignment and service chain rerouting, respectively, while achieving good competitive ratios. The joint online algorithm is proven to provide good performance guarantees by both theoretical analysis and trace-driven simulation.

Flash deal applications, which offer significant benefits (e.g., discount) to subscribers within a short period of time, are becoming increasingly prevalent. Motivated by such transient profit, flash crowds of subscribers request services simultaneously. Considering the unique business logic, a hybrid cloud with soft guarantee, i.e., bounding the response time of delay-tolerant requests, has great potential to handle flash crowds. In this paper, to cost-effectively withstand flash crowds with soft guarantee, we propose a solution that makes smart decisions on scheduling requests in the hybrid cloud and adjusting the capacity of the public cloud. In respect of scheduling requests, we apply Sequential Quadratic Programming (SQP) to achieve soft guarantee. Furthermore, for adjusting capacity, we design an online algorithm to tune the scale of the public cloud towards jointly minimizing cost and response time, yet without a priori knowledge of request arrival rate. We prove that the online algorithm can obtain a competitive ratio of 1-6ε against the optimal solution, where ε can be tuned close to 0. By conducting extensive trace-driven experiments in a website prototype deployed on OpenStack Mitaka and Amazon Web Service, our solution reduces response time by 15% compared with previous work under given budget.

Network functions virtualization (NFV) is increasingly adopted by telecommunications (telecos) service providers for cost savings and flexible management. However, deploying virtual network functions (VNFs) in geo-distributed central offices (COs) is not straightforward. Unlike most existing centralized schemes in clouds, VNFs of a service chain usually need to be deployed in multiple COs due to limited resource capacity and uneven setup cost at various locations. To ensure the Quality of Service of service chains, a key problem for service providers is to determine where a VNF should go, in order to achieve cost-efficiency and load balancing of both computing and bandwidth resources, across all selected COs. To this end, we present a framework of CO Selection (CS) and VNF Assignment (VA) for distributed deployment of NFV. Specifically, we first select a set of COs that minimizes the communication cost among the selected COs. Then, we employ a shadow-routing based approach, which minimizes the maximum of appropriately defined CO utilizations, to jointly solve the VNF-CO and VNF-server assignment problem. Simulations demonstrate the effectiveness of CS algorithm, and asymptotic optimality, scalability and high adaptivity of the VNF assignment approach.