Edge computing provides computing capability at close-user proximity to reduce service latency for end users. To improve the efficiency of edge computing infrastructures, geographically-distributed edge datacenters can co-work with each other and with cloud datacenters, forming a new paradigm referred to as cooperative edge-cloud computing. In this context, applications typically run on a virtual machine (VM) that can be replicated at multiple sites, and thus user traffic can be served at all the sites where corresponding VMs reside. For the performance of many applications, latency is a critical parameter. In this work, taking applications’ latencies as the primary constraint, we model the problem of “VM placement and workload assignment” as a mixed integer linear program and develop heuristic algorithms accordingly. The goal is to minimize the consumption of information technology (IT) infrastructures for placing VMs in cooperative edge-cloud computing, while meeting the heterogeneous latency demands of different applications. Some preliminary results indicate that edge datacenter's resource efficiency can be optimized by proper cross-site VM placement and workload re-direction.

Network function virtualization (NFV) aims to simplify service deployment using virtual network functions (VNFs). Service deployment involves the placement of VNFs and in-sequence routing of traffic flows through VNFs comprising a service chain (SC). The joint VNF placement and traffic routing is called SC mapping. In a wide-area network (WAN), where several traffic flows, generated by many distributed node pairs, require the same SC; a single instance (or occurrence) of that SC might not be enough. SC mapping with multiple SC instances for same SC is a very complex problem, since sequential traversal of VNFs has to be maintained while accounting for traffic flows in various directions. This paper is the first to deal with the problem of SC mapping with multiple SC instances to minimize network resource consumption. We propose an integer linear program (ILP), a column-generation-based ILP (CG-ILP), and a two-phase column-generation-based model (2PhMod) to solve this problem. ILP does not scale to large networks and CG-ILP scalability is limited by quadratic constraints. So, to get results over large network topologies within reasonable computational times, we propose 2PhMod. Using such an approach, we observe that an appropriate choice of only a small set of SC instances leads to a solution very close to minimum bandwidth consumption. Furthermore, this approach also helps us to analyze effects of number of VNF replicas and number of NFV nodes on bandwidth consumption when deploying these minimum number of SC instances.

Network Function Virtualization (NFV) aims to simplify deployment of network services by running Virtual Network Functions (VNFs) on commercial off-the-shelf servers. Service deployment involves placement of VNFs and in-sequence routing of traffic flows through VNFs comprising a Service Chain (SC). The joint VNF placement and traffic routing is usually referred as SC mapping. In a Wide Area Network (WAN), a situation may arise where several traffic flows, generated by many distributed node pairs, require the same SC, one single instance (or occurrence) of that SC might not be enough. SC mapping with multiple SC instances for the same SC turns out to be a very complex problem, since the sequential traversal of VNFs has to be maintained while accounting for traffic flows in various directions. Our study is the first to deal with SC mapping with multiple SC instances to minimize network resource consumption. Exact mathematical modeling of this problem results in a quadratic formulation. We propose a two-phase column-generation-based model and solution in order to get results over large network topologies within reasonable computational times. Using such an approach, we observe that an appropriate choice of only a small set of SC instances can lead to solution very close to the minimum bandwidth consumption.

As more organizations rapidly adopt cloud services, energy consumption in data centers (DCs) is increasing such that today Information and Communication Technology (ICT) has become a major consumer of energy. A large portion of ICT energy consumption is used to power servers running in DCs and the network they use to communicate. In this study, we consider that, often, energy cost at a particular DC is related to the electricity price regulated by Independent System Operators / Regional Transmission Organizations (ISOs/RTOs). As these prices vary in time and depend on the geographical locations of the DCs, recent studies have shown that the spatio-temporal variations of electricity price can be exploited to reduce electricity cost. While most prior works consider a quasi-static scenario with known workload patterns, our study proposes a dynamic workload-aware algorithm that exploits the spatio-temporal variations of electricity costs with the goal to minimize the energy cost in ICT. Our algorithm uses dynamic request rerouting and live virtual machine (VM) migration to move workloads to DCs with lower electricity cost. We consider VM migration cost (including electricity cost at optical backbone network nodes), bandwidth constraints for migration, VM consolidation, constraints from Service Level Agreement (SLA), and administrative overhead of VM migration. Our simulation studies show that the proposed algorithm reduces operational cost and improves energy efficiency of data centers significantly.