The emerging paradigm of Network Function Virtualization (NFV) promises to shorten the renewal cycles of network functions and reduce the capital expenses by flexibly deploying virtualized network functions (VNFs) implementation on commodity servers. However, the required resource of each type (CPU, memory, etc.) for the running VNF should be provisioned to guarantee the performance when processing packets. This comes with different deployment cost, especially in a heterogeneous cloud consisting of a large number of network function platforms from various vendors. To optimally operate VNFs, it is necessary for the network operator to dynamically deploy VNFs in the expensive cloud infrastructures. In this article, we initiate the study of minimizing the deployment cost under multi-resource constraints in a heterogeneous cloud. We formulate multi-resource VNF deployment problem (MVDP) as an optimization program and prove its hardness. We propose an offline$(1, d+1)$-bicriteria approximation algorithm and an$(\mathcal {O}(1), \mathcal {O}(n \cdot \log n))$-competitive online algorithm to deploy VNFs in a scalable manner, where$d$is the number of resource types and$n$is the number of required VNFs. Large-scale simulations and DPDK-based OpenNetVM implementation show that our algorithms can reduce the overall cost by 34% and improve the performance in terms of multi-resource allocation.

Today's cellular core relies on a few expensive and dedicated hardware racks to connect the radio access network and the egress point to the Internet, which are geographically placed at fixed locations and use the specific routing policies. This inelastic architecture fundamentally leads to increased capital and operating expenses, poor application performance and slow evolution. The emerging paradigm of Network Function Virtualization (NFV) and Software Defined Networking (SDN) bring new opportunities for cellular networks, which makes it possible to flexibly deploy service chains on commodity servers and fine-grained control the routing policies in a centralized way. We present a two-stage optimization framework Plutus. The network-level optimization aims to minimize the service chain deployment cost, while the server-level optimization requires to determine which Virtualized Network Function (VNF) should be deployed onto which CPU core to balance the CPU processing capability. We formulate these two problems as two optimization programs and prove their hardness. Based on parallel multi-block ADMM, we propose a (δ, 2)-bicriteria approximation algorithm and a learning-based algorithm to address two cases whether the flow information and the resource consumption can be known as a priori, respectively. Large-scale simulations and DPDK-based OpenNetVM platform show that Plutus can reduce the capital cost by 84% and increase the throughput by 36% on average.

In this paper, we consider the scenario in which a mobile charger (MC) periodically travels within a sensor network to recharge the sensors wirelessly. We design joint charging and scheduling schemes to maximize the Quality of Monitoring (QoM) for stochastic events, which arrive and depart according to known probability distributions of time. Information is considered captured if it is sensed by at least one sensor. We focus on two closely related research issues, i.e., how to choose the sensors for charging and decide the charging time for each of them, and how to schedule the sensors' activation schedules according to their received energy. We formulate our problem as the maximum QoM CHArging and SchEduling problem (CHASE). We first ignore the MC's travel time and study the resulting relaxed version of the problem, which we call CHASE-R. We show that both CHASE and CHASE-R are NP-hard. For CHASE-R, we prove that it can be formulated as a submodular function maximization problem, which allows two algorithms to achieve 1/6- and 1/(4 + ε)-approximation ratios. Then, for CHASE, we propose approximation algorithms to solve it by extending the CHASE-R results. We conduct simulations to validate our algorithm design.

With the development of data center networks, traditional TCP/IP cannot support the demand in data centers. Remote Direct Memory Access (RDMA) technology could improve the performance of DCN significantly because of high throughput and low latency. However, load balancing is a key issue in RDMA which has not been solved distribute. This paper will propose an algorithm to solve the load balance problem in RDMA on application layer without hardware changing. The main idea is to divide data to chunks and data chunks on multiple reachable paths for transmission. However, it is no trivial to find the optimal chunk size and the path number, some empirical values are found by varieties of experiments and tests. Moreover, the chunk allocation scheme also needs to consider the traffic condition in DCNs to find more free paths to transmit. We evaluate the algorithm in application layer with ns3 simulator. The experiment results show that with our algorithm the completion time can decrease 81.03% at most.

Today's cellular core relies on a few expensive and dedicated hardware racks to connect the radio access network and the egress point to the Internet, which are geographically placed at fixed locations and use the specific routing policies. This inelastic architecture fundamentally leads to increased capital and operating expenses, poor application performance and slow evolution. The emerging paradigm of Network Function Virtualization (NFV) and Software Defined Networking (SDN) bring new opportunities for cellular networks, which makes it possible to flexibly deploy service chains on commodity servers and fine-grained control the routing policies in a centralized way.

State-of-the-art inter-datacenter WANs rely on software defined networking (SDN) to orchestrate their data transmission. Optimization requires frequent network update operations to switch forwarding tables. When scheduling inter-datacenter WANs, the utility of services should be respected. Yet, existing network update approaches do not respect network utility and could result in performance degradation during the network update procedure. Further, the update causes not only performance degradation, but also the degradation period is unnecessarily prolonged. In this paper we propose Hermes, a utility-aware network update system. We aim to find a rate limiting scheme for update which maximizes the sum of service utility, while ensuring the congestion-free property during the update. We propose an optimization framework for the maximum utility network update problem (MUP). MUP is NP-hard and a series of algorithms are developed to solve it. Extensive simulation and testbed experiments with a prototype demonstrate that Hermes can increase the total utility by 80% compared to state-of-the-art. At the same time, it reduces the total update time and control overhead by 40% and 55%, respectively.