Mobile edge-cloud (MEC) aims to support low latency mobile services by bringing remote cloud services nearer to mobile users. However, in order to deal with dynamic workloads, MEC is deployed in a large number of fixed-location microclouds, leading to resource wastage during stable/low workload periods. Limiting the number of micro-clouds improves resource utilization and saves operational costs, but faces service performance degradations due to insufficient physical capacity during peak time from nearby micro-clouds. To efficiently support services with low latency requirement under varying workload conditions, we adopt the emerging Network Function Virtualization (NFV)-enabled MEC, which offers new flexibility in hosting MEC services in any virtualized network node, e.g., access points, routers, etc. This flexibility overcomes the limitations imposed by fixed-location solutions, providing new freedom in terms of MEC service-hosting locations. In this paper, we address the questions on where and when to allocate resources as well as how many resources to be allocated among NFVenabled MECs, such that both the low latency requirements of mobile services and MEC cost efficiency are achieved. We propose a dynamic resource allocation framework that consists of a fast heuristic-based incremental allocation mechanism that dynamically performs resource allocation and a reoptimization algorithm that periodically adjusts allocation to maintain a nearoptimal MEC operational cost over time. We show through extensive simulations that our flexible framework always manages to allocate sufficient resources in time to guarantee continuous satisfaction of applications' low latency requirements. At the same time, our proposal saves up to 33% of cost in comparison to existing fixed-location MEC solutions.Index Terms-Mobile edge-cloud, low latency applications, dynamic resource allocation, approximation algorithm.

Abstract-The evolution toward emerging active distribution networks (ADNs) can be realized via a real-time state estimation (RTSE) application facilitated by the use of phasor measurement units (PMUs). A critical challenge in deploying PMU-based RTSE applications at large scale is the lack of a scalable and flexible communication infrastructure for the timely (i.e., sub-second) delivery of the high volume of synchronized and continuous synchrophasor measurements. We address this challenge by introducing a communication platform called C-DAX based on the information-centric networking (ICN) concept. With a topicbased publish-subscribe engine that decouples data producers and consumers in time and space, C-DAX enables efficient synchrophasor measurement delivery, as well as flexible and scalable (re)configuration of PMU data communication for seamless full observability of power conditions in complex and dynamic scenarios. Based on the derived set of requirements for supporting PMU-based RTSE in ADNs, we design the ICN-based C-DAX communication platform, together with a joint optimized physical network resource provisioning strategy, in order to enable the agile PMU data communications in near real-time. In this paper, C-DAX is validated via a field trial implementation deployed over a sample feeder in a real-distribution network; it is also evaluated through simulation-based experiments using a large set of real medium voltage grid topologies currently operating live in The Netherlands. This is the first work that applies emerging communication paradigms, such as ICN, to smart grids while

Abstract-Emerging mobile multimedia applications, such as augmented reality, have stringent latency requirements and high computational cost. To address this, mobile edge-cloud (MEC) has been proposed as an approach to bring resources closer to users. Recently, in contrast to conventional fixed cloud locations, the advent of network function virtualization (NFV) has, with some added cost due to the necessary decentralization, enhanced MEC with new flexibility in placing MEC services to any nodes capable of virtualizing their resources. In this work, we address the question on how to optimally place resources among NFVenabled nodes to support mobile multimedia applications with low latency requirement and when to adapt the current resource placements to address workload changes. We first show that the placement optimization problem is NP-hard and propose an online dynamic resource allocation scheme that consists of an adaptive greedy heuristic algorithm and a detection mechanism to identify the time when the system will no longer be able to satisfy the applications' delay requirement. Our scheme takes into account the effect of current existing techniques (i.e., autoscaling and load balancing). We design and implement a realistic NFV-enabled MEC simulated framework and show through extensive simulations that our proposal always manages to allocate sufficient resources on time to guarantee continuous satisfaction of the application latency requirements under changing workload while incurring up to 40% less cost in comparison to existing overprovisioning approaches.

Abstract-With the introduction of distributed renewable energy resources and new loads, such as electric vehicles, the power grid is evolving to become a highly dynamic system, that necessitates continuous and fine-grained observability of its operating conditions. In the context of the medium voltage (MV) grid, this has motivated the deployment of Phasor Measurement Units (PMUs), that offer high precision synchronized grid monitoring, enabling mission-critical applications such as fault detection/location. However, PMU-based applications present stringent delay requirements, raising a significant challenge to the communication infrastructure. In contrast to the high voltage domain, there is no clear vision for the communication and network topologies for the MV grid; a full fledged optical fiber-based communication infrastructure is a costly approach due to the density of PMUs required. In this work, we focus on the support of low-latency PMU-based applications in the MV domain, identifying and addressing the trade-off between communication infrastructure deployment costs and the corresponding performance. We study a large set of real MV grid topologies to get an in-depth understanding of the various key latency factors. Building on the gained insights, we propose three algorithms for the careful placement of high capacity links, targeting a balance between deployment costs and achieved latencies. Extensive simulations demonstrate that the proposed algorithms result in low-latency network topologies while reducing deployment costs by up to 80% in comparison to a ubiquitous deployment of costly high capacity links.

With the introduction of new power sources, such as distributed renewable energy resources, and loads, such as electric vehicles, electrical distribution networks must accommodate new energy flow patterns in a considerably dynamic environment. This leads to the need for increasing the observability of the grid to enable a series of mission-critical applications such as voltage/congestion control and fault detection/location. The deployment of Phasor Measurement Units appears to be a promising approach, offering high precision grid monitoring. However, while the low delay requirements of such applications raise a significant challenge to the communication infrastructure, there is currently no clear vision on the exact communication technologies and network topologies that could support these requirements. In this paper, we address this challenge by taking a systematic approach on the design of low latency communication infrastructures. Based on a large set of real medium voltage grid topologies from a European distribution network, we first perform a detailed analysis of the communication requirements. Guided by this analysis, we then propose two algorithms, PLeC and BW-PLeC algorithms, for the design of low latency communication infrastructures that enhance the currently available power-line communication technology with newer high-speed communication links at strategic points in the grid to satisfy the delay requirements while reducing deployment costs.