As computing shifts toward the edge, edge datacenters are becoming essential for supporting diverse real-time applications. Unlike traditional cloud datacenters, edge datacenters face unique cooling challenges due to their requirements forproximity to end users, high density, and hardware heterogeneity. While warm water cooling is a promising technique for this infrastructure, current one-size-fits-all cooling strategies significantly compromise efficiency due to severe inter- and intra-component hotspots. In this work, we present CoolEdge+, a cost-effective component–level water cooling system for enhancing the cooling efficiency of edge datacenters. Specifically, CoolEdge+dynamically adjusts the inlet water temperature for each component through a carefully designed water circulation architecture to mitigate inter-component hotspots. To address intra-component hotspots, it employs vapor chamber–based cold plates that rapidly dissipate heat without manual intervention or additional energy consumption. We further design a fine-grained cooling control framework that leverages a well-managed power capping approach to decide on customized inlet water temperatures and hardware power limits. Based on a hardware prototype and a real-world trace from Alibaba PAI, evaluation results show that CoolEdge+reduces cooling energy consumption by up to 27.19% compared to existing coarse-grained systems, while maintaining performance guarantees. Compared to the state-of-the-art CoolEdge, CoolEdge+saves 35.24% more cooling costs with comparable energy consumption and no latency violations.

Thanks to their high maneuverability, high flexibility, and low cost, unmanned aerial vehicles (UAVs) have been widely used for data collection in the Internet of Things (IoT). To deal with UAV's onboard battery limitation, UAV-to-UAV (U2U) wireless charging mechanism emerges as a promising solution for extending flight distance and reducing mission completion time. However, U2U charging mechanisms encounter key challenges of limited wireless charging distance and energy loss. In this paper, we propose eU2U, a novel energy-efficient wireless charging and trajectory design approach for IoT data collection. We develop eU2U based on the distributed laser charging (DLC) system for its compact size and meter-level wireless power transmission. To minimize the total energy consumption, we build holistic power and energy consumption models for U2U-enabled data collection. With joint considerations on the wireless charging energy loss and delay constraint, we propose a heuristic algorithm to derive the most energy-efficient locations of the U2U charging points and the UAV trajectories, where the charging points are determined from the Fermat point and the battery drain point. Extensive evaluations show that eU2U can reduce the total energy consumption by 44.52% as compared to state-of-the-art schemes.

Vehicular Edge Computing (VEC) emerges as a rem-edy to achieve flexible and fine-grained air pollution monitoring, where vehicles equipped with onboard sensors can sense, process, calibrate and store air pollutants on the drive, and roadside units (RSUs) can be deployed for vehicles to offload data via low-cost vehicle-to-RSU (V2R) communication. However, existing VEC-based air pollution monitoring solution either suffers from high deployment cost, limited V2R communication distance, or degraded data collection latency. To address these challenges, we propose a novel cost-efficient VEC deployment solution for mobile air pollution monitoring, where a set of buses are used to monitor the air pollutants, and selected bus stations are equipped with RSU s for offloading the collected data, considering the effective communication distance and power consumption of V2R. To jointly minimize the VEC deployment cost and data collection latency, we build a multi-objective problem formulation under the constraints of resource, latency, etc. Then we propose a Two-stage Cost-efficient VEC Deployment (TCVD) algorithm based on two heuristic strategies, i.e., the near-equivalence point deployment strategy and the conditioned RSU deployment strategy, with a theoretically-proved worst-case bound. Through extensive evaluations on an open data set of Dublin bus, we verify that TCVD not only reduces the data collection latency by 25.04%, but also reduces the total VEC deployment cost by 30.81 % as compared with existing schemes.

As an enabling technology for vehicle-to-vehicle (V2V) networks, multi-access edge computing (MEC) provides a feasible platform for sharing power and resources, and offloading some of the computation-intensive tasks between vehicles. This, however, is challenging with the unpredictable variations in road traffic conditions and vehicle mobility in MEC-enabled V2V networks. Consequently, such computation task offloading can be easily disrupted, which may require frequent switching of task offloading between vehicles and degrade the Quality of Service (QoS). In this paper, we focus on the computation offloading problem under unstable connections in MEC-enabled V2V networks. We first model this as a distributed online service optimization problem, which is proved to be NP-hard. In order to minimize the out-of-service time (i.e., the service mismatching, switching and compromise time), we propose a distributed Online Instability-aware Computation Offloading (OICO) heuristic algorithm to improve the service efficiency and quality. Specifically, in order to minimize the service mismatching rate, we design an efficient Service Path Matching (SPM) algorithm for matching pairs of customer vehicles (which require offload computing services) and server vehicles (which provide edge computing services) that share the longest matching path. We evaluate OICO through real-world traces, i.e., GAIA open dataset from DiDi. Extensive simulation results demonstrate that OICO can increase the service matching rate by 25% and reduce the power consumption by about 54% per customer vehicle compared with the existing schemes.

As the computing frontier drifts to the edge, edge datacenters play a crucial role in supporting various real-time applications. Different from cloud datacenters, the requirements of proximity to end-users, high density, and heterogeneity, present new challenges to cool the edge datacenters efficiently. Although warm water cooling has become a promising cooling technique for this infrastructure, the one-size-fits-all cooling control would lower the cooling efficiency considerably because of the severe thermal imbalance across servers, hardware, and even inside one hardware component in an edge datacenter. In this work, we propose CoolEdge, a hotspot-relievable warm water cooling system for improving the cooling efficiency and saving costs of edge datacenters. Specifically, through the elaborate design of water circulations, CoolEdge can dynamically adjust the water temperature and flow rate for each heterogeneous hardware component to eliminate the hardware-level hotspots. By redesigning cold plates, CoolEdge can quickly disperse the chip-level hotspots without manual intervention. We further quantify the power saving achieved by the warm water cooling theoretically, and propose a custom-designed cooling solution to decide an appropriate water temperature and flow rate periodically. Based on a hardware prototype and real-world traces from SURFsara, the evaluation results show that CoolEdge reduces the cooling energy by 81.81% and 71.92%, respectively, compared with conventional and state-of-the-art water cooling systems.

Based on network function virtualization (NFV) and software defined network (SDN),network slicingis proposed as a new paradigm for building service-customized 5G network. In each network slice, service-required virtual network functions (VNFs) can be flexibly deployed in an on-demand manner, which will support a variety of 5G use cases. However, due to the real-time network variations and diverse performance requirements among different 5G scenarios, online adaptive VNF deployment and migration are needed to dynamically accommodate to service-specific requirements. In this paper, we first propose a time-slot based 5G network slice model, which jointly includes both edge cloud servers and core cloud servers. Since VNF consolidation may cause severe performance degradation, we adopt a demand-supply model to quantify the VNF interference. To achieve our objective—maximizing the total reward of accepted requests (i.e., the total throughput minus the weighted total VNF migration cost), we propose an Online Lazy-migration Adaptive Interference-aware Algorithm (OLAIA) for real-time VNF deployment and cost-efficient VNF migration in a 5G network slice, where an Adaptive Interference-aware Algorithm (AIA) is proposed as OLAIA’s core function for placing a given set of requests’ VNFs with maximized total throughput. Through trace-driven evaluations on two typical 5G network slices, we demonstrate that OLAIA can efficiently handle the real-time network variations and the VNF interference when deploying VNFs for real-time arriving requests. In particular, OLAIA improves the total reward by 22.18% in the autonomous driving scenario and by 51.10% in the 4K/8K HD video scenario, as compared with other state-of-the-art solutions.

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.

Warm water cooling has been regarded as a promising method to improve the energy efficiency of water-cooled datacenters. In warm water-cooling systems, hot spots occur as a common problem where the hybrid cooling architecture integrating thermoelectric coolers (TECs) emerges as a new remedy. Equipped with this architecture, the inlet water temperature can be raised higher, which provides more opportunities for heat recycling. However, currently, the heat absorbed from the server components is ejected directly into the water without being recycled, which leads to energy wasting. In order to further improve the energy efficiency, we propose Heat to Power (H2P), an economical and energy-recycling warm water cooling architecture, where thermoelectric generators (TEGs) harvest thermal energy from the “used” warm water and generate electricity for reusing in datacenters. Specifically, we propose some efficient optimization methods, including an economical water circulation design, fine-grained adjustments of the cooling setting and dynamic workload scheduling for increasing the power generated by TEGs. We evaluate H2P based on a real hardware prototype and cluster traces from Google and Alibaba. Experiment results show that TEGs equipped with our optimization methods can averagely generate 4.349 W, 4.203 W, and 3.979 W (4.177 W averagely) electricity on one CPU under the drastic, irregular and common workload traces, respectively. The power reusing efficiency (PRE) can reach 12.8%~16.2% (14.23% averagely) and the total cost of ownership (TCO) of datacenters can be reduced by up to 0.57%.

With the evolution of network function virtualization (NFV) and edge computing, software-based network functions (NFs) can be deployed on closer-to-end-user edge servers to support a broad range of new services with high bandwidth and low latency. However, due to the resource limitation, strict QoS requirements and real-time flow fluctuations in edge network, existing cloud-based resource management strategy in NFV platforms is inefficient to be applied to the edge. Thus, we propose Finedge, a dynamic, fine-grained and cost-efficient edge resource management platform for NFV network. First, we conduct empirical experiments to find out the effect of NFs' resource allocation and their flow-level characteristics on performance. Then, by jointly considering these factors and QoS requirements (e.g., latency and packet loss rate), Finedge can automatically assign the most suitable CPU core and tune the most cost-efficient CPU quota to each NF. Finedge is also implemented with some key strategies including real-time flow monitoring, elastic resource scaling up and down, and also flexible NF migration among cores. Through extensive evaluations, we validate that Finedge can efficiently handle heterogeneous flows with the lowest CPU quota and the highest SLA satisfaction rate as compared with the default OS scheduler and other state-of-the-art resource management schemes.

Based on network function virtualization (NFV) and software defined network (SDN), network slicing is proposed as a new paradigm for building service-customized 5G network. In each network slice, service-required virtual network functions (VNFs) can be flexibly deployed in an on-demand manner, which will support a variety of 5G use cases. However, due to the diverse performance requirements among different 5G scenarios, an adaptive VNF placement approach is needed to automatically accommodate to service-specific requirements. In this paper, we tackle the VNF placement problem by first proposing a general 5G network slice framework, which jointly contains both edge cloud and core cloud servers. Specially, based on the fact that VNF consolidation may cause severe performance degradation, we adopt a demand-supply model to quantity the VNF interference. With an aim to maximize the total throughput of accepted requests, we propose an Adaptive Interference-Aware (AIA) heuristic approach to automatically place VNFs in 5G service-customized network slices. Through simulations on two typical 5G scenarios, we demonstrate that AIA can efficiently handle traffic variation especially caused by VNF interference and improve the total throughput by 20.11% and 24.21% in autonomous driving and 4K/8K HD video network slices as compared with the state-of-the-art methods.

With the evolution of network function virtualization (NFV), diverse network services can be flexibly offered as service function chains (SFCs) consisted of different virtual network functions (VNFs). However, network state and traffic typically exhibit unpredictable variations due to stochastically arriving requests with different quality of service (QoS) requirements. Thus, an adaptive online SFC deployment approach is needed to handle the real-time network variations and various service requests. In this paper, we firstly introduce a Markov decision process (MDP) model to capture the dynamic network state transitions. In order to jointly minimize the operation cost of NFV providers and maximize the total throughput of requests, we propose NFVdeep, an adaptive, online, deep reinforcement learning approach to automatically deploy SFCs for requests with different QoS requirements. Specifically, we use a serialization-and-backtracking method to effectively deal with large discrete action space. We also adopt a policy gradient based method to improve the training efficiency and convergence to optimality. Extensive experimental results demonstrate that NFVdeep converges fast in the training process and responds rapidly to arriving requests especially in large, frequently transferred network state space. Consequently, NFVdeep surpasses the state-of-the-art methods by 32.59% higher accepted throughput and 33.29% lower operation cost on average.

Compared with executing Network Functions (NFs) on dedicated hardwares, the recent trend of Network Function Virtualization (NFV) holds the promise for operators to flexibly deploy software-based NFs on commodity servers. However, virtual NFs (VNFs) are normally "chained" together to provide a specific network service. Thus, an efficient scheme is needed to place the VNF chains across the network and effectively schedule requests to service instances, which can maximize the average resource utilization of each node in service and simultaneously minimize the average response latency of each request. To this end, we formulate first VNF chains placement problem as a variant of bin-packing problem, which is NP-hard, and we model request scheduling problem based on the key concepts from open Jackson network. To jointly optimize the performance of NFV, we propose a priority-driven weighted algorithm to improve resource utilization and a heuristic algorithm to reduce response latency. Through extensive trace-driven simulations, we show that our methods can indeed enhance performance in diverse scenarios. In particular, we can improve the average resource utilization by 33.4% and can reduce the average total latency by 19.9% as compared with the state-of-the-art methods.

In recent years, Dropbox, Google, and Microsoft have been competing in the market of consumer cloud storage (CCS) services. While once the key comparative metric, storage capacity per user has outgrown the needs of most users. Today, third-party applications based on CCS's RESTful Web APIs are becoming a primary way for users to utilize their expanded storage resources. Unfortunately, there is very little visibility into the performance of these Web APIs, even though they are primary determinants of the end user experience on these storage applications. In this paper, we report results from a comprehensive measurement study of the Web APIs of five popular CCS providers. Our results reveal significant differences and limitations in API performance, which result in performance bottlenecks visible to the user through the storage application. We analyze the underlying system designs of the five providers' Web APIs, and present the performance implications of their different design choices. Our research provides practical guidance for service providers to optimize their API performance, for developers to improve the experience of third-party applications, and for users to pick appropriate services that best match their requirements.