Throughput optimization for admitting NFV-enabled requests in cloud networks

Network softwarization is emerging as a techno-economic transformation trend that impacts the way that network service providers deliver their network services significantly. As a key ingredient of such a trend, network function virtualization (NFV) is shown to enable elastic and inexpensive network services for next-generation networks, through deploying flexible virtualized network functions (VNFs) running in virtual computing platforms. Different VNFs can be chained together to form different service chains for different network services, to meet various user data routing demands. From the service provider point of view, such services are usually implemented by VNF instances in a cloudlet network consisting of a set of data centers and switches. In this paper we consider provisioning network services in a cloud network for implementing VNF instances of service chains, where the VNF instances in each data center are partitioned into K types with each hosting one type of service chain. We investigate the throughput maximization problem with the aim to admit as many user requests as possible while minimizing the implementation cost of the requests, assuming that limited numbers of instances of each service chain have been instantiated in data centers. We first show the problem is NP-Complete, and propose an optimal algorithm for a special case of the problem when all requests have identical packet rates; otherwise, we devise two approximation algorithms with approximation ratios, depending on whether the packet traffic of each request is splittable. If arrivals of future requests are not known in advance, we study the online throughput maximization problem by proposing an online algorithm with a competitive ratio. We finally conduct experiments to evaluate the performance of the proposed algorithms by simulations. Simulation results show that the performance of the proposed algorithms are promising

Minimizing the operational cost of data centers via geographical electricity price diversity

Data centers, serving as infrastructures for cloud services, are growing in both number and scale. However, they usually consume enormous amounts of electric power, which lead to high operational costs of cloud service providers. Reducing the operationa

Minimizing the Operational Cost of Data Centers via Geographical Electricity Price Diversity

Abstract-Data centers, serving as infrastructures for cloud services, are growing in both number and scale. However, they usually consume enormous amounts of electric power, which lead to high operational costs of cloud service providers. Reducing the operational cost of data centers thus has been recognized as a main challenge in cloud computing. In this paper we study the minimum operational cost problem of fair request rate allocations in a distributed cloud environment by incorporating the diversity of time-varying electricity prices in different regions, with an objective to fairly allocate requests to different data centers for processing while keeping the negotiated Service Level Agreements (SLAs) between request users and the cloud service provider to be met, where the data centers and web portals of a cloud service provider are geographically located in different regions. To this end, we first propose an optimization framework for the problem. We then devise a fast approximation algorithm with a provable approximation ratio by exploiting combinatorial properties of the problem. We finally evaluate the performance of the proposed algorithm through experimental simulation on reallife electricity price data sets. Experimental results demonstrate that the proposed algorithm is very promising, which not only outperforms other existing heuristics but also is highly scalable