Energy efficiency in serverless computing has remained under-explored despite its growing adoption. To fill this gap, we propose LoAPE, a load-aware and power-elastic serverless platform. Unlike existing methods limited to CPU core frequency scaling or density-based consolidation, LoAPE leverages c-states and uncore frequency scaling, which offer greater energy benefits but pose deployment challenges. Uncore changes affect the performance of all co-located functions, and cstates require sustained idle periods for effective energy savings. LoAPE’s insight is to strategically exploit function elasticity. By creating idle intervals across cores and servers, it enables aggressive c-state activation and uncore frequency reduction. To achieve this goal, LoAPE adopts topology-aware instance scheduling and eviction, which scale the number of active servers along with serverless functions. Within servers, LoAPE proactively manages active cores based on incoming loads. Our evaluations demonstrate that LoAPE delivers a 3.02× improvement in cluster energy reduction compared to frequency-scaling frameworks and extends savings by 1.17× over state-of-the-art density-aware schedulers, while meeting performance requirements.

Deep learning (DL) based Wi-Fi sensing has witnessed great development in recent years. Although decent results have been achieved in certain scenarios, Wi-Fi based activity recognition is still difficult to deploy in real smart homes due to the limited cross-environment adaptability, i.e. a well-trained Wi-Fi sensing neural network in one environment is hard to adapt to other environments. To address this challenge, we proposeAdaWiFi, a DL-based Wi-Fi sensing framework that allows multiple Internet-of-Things (IoT) devices to collaborate and adapt to various environments effectively. The key innovation ofAdaWiFiincludes a collective sensing model architecture that utilizes complementary information between distinct devices and avoids the biased perception of individual sensors and an accompanying model adaptation technique that can transfer the sensing model to new environments with limited data. We evaluate our system on a public dataset and a custom dataset collected from three complex sensing environments. The results demonstrate thatAdaWiFiis able to achieve significantly better sensing adaptation effectiveness (e.g. 30% higher accuracy with one-shot adaptation) as compared with state-of-the-art baselines.

Recent advancements in Low Earth Orbit (LEO) satellites are facilitating the provision of Deep Neural Networks (DNNs)-inherent services to achieve ubiquitous coverage via satellite computing. However, the computational demands and energy consumption of DNN models present significant challenges for satellite computing with limited power and computation resources. Based on the layered characteristics of DNN models, a satellite-ground co-inference strategy has been introduced, which executes certain layers on satellites and the remaining layers on ground servers. Determining the optimal layers for in-orbit processing, however, is non-trivial due to the under-explored energy consumption of satellite computing across different models and restricted yet varying communication conditions of satellite-ground links. In this paper, we first conduct a comprehensive measurement to uncover energy consumption of satellite computing across different layers and models. By summarizing the key observations, we develop a layer-specific energy consumption model tailored to diverse DNN architectures and kernels. We then investigate the energy-efficient satellite-ground co-inference problem and formulate it as an integer-nonlinear programming problem, which presents high computational complexity. To tackle these difficulties, we propose a satellite-ground co-inference algorithm that employs a branch-and-bound strategy, combined with the Sobol sequence and Lagrange multiplier, to reduce complexity and ensure stability across diverse DNN architectures. To evaluate the proposed algorithm, we conduct experiments based on real-world satellite parameters. The results demonstrate that our proposed algorithm can achieve an average energy savings of 96% under various data volumes compared to the existing benchmarks.

Edge computing plays a pivotal role in supporting services necessitating sub-second latency, notably in domains like Industry 4.0 and autonomous driving. However, unpredictable failure occurring at edge servers can result in prolonged response time and decreased service reliability, posing significant risks to both safety and property. Traditional reliability mechanisms, namely task re-execution and task replication, are often inadequate for edge environments. The former struggles to meet the stringent end-to-end service latency requirements, while the latter imposes a high resource consumption burden on resource-limited edge clouds. To address this issue, this paper introduces a novel Flexible Shadow mechanism, where the backup instance, referred to as the Flexible Shadow, is allocated fewer computation resources compared to its primary instance to conserve computation resources, and temporally preempts a portion of resources from neighboring shadows to accelerate when necessary. To support the implementation of this mechanism, we propose the Flexible Shadow Backup Framework, a resource-efficient reliability enhancement framework for edge services through dynamic shadow coordination. This framework integrates three key components: a deployment algorithm for resource allocation, an adjustment algorithm for migration cost-latency tradeoffs, and a reconfiguration algorithm for adaptation optimization. Comprehensive experiments conducted on a Docker-based prototype demonstrate the effectiveness of the Flexible Shadow mechanism, achieving nearly 60% reduction in computing resource consumption compared to traditional approaches while maintaining sub-second latency.

The increasing demand for artificial intelligence and cloud computing has led to skyrocketing energy consumption of data centers (DCs). This paper focuses on tackling this energy challenge through cooling control system optimization, which aims to ensure thermal safety with minimal cooling energy consumption. Current industry practice involves human operators, while many data-driven methods have also been proposed. However, human intervention often results in unnecessary energy consumption, particularly in the face of fluctuating server loads, whereas existing data-driven methods struggle to maintain thermal safety in practice. To overcome these issues, we propose TESLA, a thermally safe, load-aware, and energy-efficient cooling control system for data centers. TESLA employs a novel data-driven framework that integrates domain knowledge to predict DC temperature and cooling energy under dynamic server load. Based on these predictions, a Bayesian optimizer (BO) finds the energy-optimal settings for the cooling system at every control step. Besides cooling energy, BO’s optimization objective also includes minimizing cooling interruption that causes rapid temperature rise within the data center and leads to thermal safety violations. We deploy TESLA on a real data-center testbed and show that it achieves on average <?TeX $10.1\%$?> Math 1 cooling energy saving relative to a fixed cooling system parameter setting and no thermal safety violation relative to previous data-driven methods.

Edge computing plays a pivotal role in supporting services necessitating sub-second latency, notably in domains like Industry 4.0 and autonomous driving. However, unpredictable failure occurring at edge servers can result in prolonged response time and decreased service reliability, posing significant risks to both safety and property. Traditional reliability mechanisms, namely re-execution and replication, are often inadequate for edge environments, with the former often struggle to meet latency requirements and the latter imposing a high resource consumption burden on resource-limited edge clouds. To address this issue, this paper introduces a novel "Flexible Shadow" mechanism, where the backup instance, referred to as the "Flexible Shadow", is allocated fewer computation resources compared to its primary instance to conserve computation resources, and temporally preempts a portion of resources from neighboring shadows to accelerate when necessary. To tackle the implementation challenges of this framework arising from diverse service requirements, dynamic nature of edge environments and potential deadlock in the reconfiguration process, we devised the Flexible Shadow Deployment Algorithm for accurate shadow deployment and the Flexible Shadow Reconfiguration Algorithm for dynamic strategy adjustment. We have implemented our Flexible Shadow framework on Docker and evaluated it via comprehensive experiments. The experiment results demonstrate a nearly 60% reduction in computing resource consumption while ensuring sub-second latency.

Recent advancements in Low Earth Orbit (LEO) satellites are facilitating the provision of Deep Neural Networks (DNNs)-inherent services to achieve ubiquitous coverage via satellite computing. However, the computational demands and energy consumption of DNN models pose significant challenges for satellite computing with limited power and computation resources. Based on the hierarchical characteristics of DNN models, we propose a satellite-ground co-inference strategy that executing certain layers on satellites and the remaining layers on ground servers. However, identifying the optimal layers for in-orbit processing with latency constraints is challenging due to the uncertain energy consumption across diverse models. To explore the correlation between energy consumption and layer types, we conduct comprehensive measurements on a hardware device commonly found in commercial LEO satellites and develop a layer-based energy consumption prediction model. Then, we formulate an optimization problem of minimizing the energy consumption on the satellite within the latency constraint as an integer nonlinear programming problem. Solving this problem is difficult due to combinatorial explosion in the discrete solution space. To address this, we propose an improved algorithm based on genetic algorithms. Using configurations from a real satellite, we conduct simulation experiments, concluding that our algorithm significantly improves energy savings by an average of 27 ×.

Due to the popularity of deep neural networks (DNNs) and considerations over network overhead, data privacy, and inference latency, there is a growing interest in deploying DNNs to edge devices in recent years. However, the limited memory becomes a major bottleneck for on-device DNN deployment, making it crucial to reduce the memory footprint of DNN. The mainstream model customization solutions require intensive deployment efforts and may lead to severe accuracy degradation, and existing deep learning (DL) frameworks don't take memory as a priority. Besides, recent works to enhance the memory management scheme cannot be directly applied because of several challenges, including the unbalanced memory footprint across layers, the inevitable overhead of memory management, and the memory budget dynamicity. To tackle these challenges, we introduce FlexNN, an efficient and adaptive memory management framework for DNN inference on memory-constrained devices. FlexNN uses a slicing-loading-computing joint planning approach, to achieve optimal memory utilization and minimal memory management overhead. We implemented FlexNN atop NCNN, and conducted comprehensive evaluations with common model architectures on various devices. The results have shown that our approach is able to adapt to different memory constraints with optimal latency-memory trade-offs. For example, FlexNN can reduce the memory consumption by 93.81% with only a 3.64% increase in latency, as compared with the original NCNN on smartphones.

Seamless cross-edge migration for real-time rendering applications is challenging. The strong interactive nature of real-time rendering applications demands a downtime lower than 15ms to achieve an imperceptible migration. Existing methods based on virtual machine migration and container migration suffer from unpleasant downtime brought by dirty page retransmission-induced repeated memory data copy and the shared storage failure-induced extensive disk data copy. In this paper, we propose Cloud-assisted Service Migration (CSM) which leverages cloud-edge collaboration to achieve seamless service migration for real-time rendering applications. CSM improves service migration user experience in three folds: First, it introduces a dual rendering mechanism to bypass the peer-to-peer data copy and compresses the freezing stage. Second, a user equipment-centric session switch mechanism is proposed to save time by well coordinating application session switches and 5G user plane session switches. Third, a smooth switching mechanism is leveraged to prevent unpleasant frame flickers during session switching. We implement CSM in edge-rendering multiplayer games and deploy it on a 5G test bed with a full-stack user plane protocol stack. The evaluation results show that CSM can reduce downtime to < 14ms and the service migration process is user imperceptible.

WiFi-based human activity recognition is a promising sensing application in smart homes due to the low cost, wide availability, and privacy preservation of WiFi devices. However, pushing WiFi sensing technology to industry-scale deployment is difficult due to its poor robustness against environment differences. How to systematically test such sensing system is crucial to improve its practicality, and is also challenging because the sensing performance is significantly influenced by the underlying physical environments. In this paper, we introduce the problem of testing environment-dependent sensing systems, including how to measure test coverage and how to effectively generate data to improve the coverage. We describe our initial attempts on examining test sufficiency with environment-neuron joint coverage and improving the coverage through targeted environment variations and signal transformations. Our experiments have demonstrated the higher effectiveness of using environment-neuron coverage to represent test sufficiency, as compared with using the conventional neuron coverage. Meanwhile, the coverage-guided sensing data generation can lead to higher accuracy of the sensing system under changing environments.

In a 5G network, mobile-edge computing (MEC) plays a key role in providing low access delay services. The placement of edge servers not only determines the quality of services on the user side but also affects the profit of running a MEC system. In this article, we study how to properly place edge servers so as to guarantee the access delay and maximize the profit of edge providers. We first propose a profit model which involves both access delay and energy consumption. In this model, we take the 5G user plane function (UPF) into consideration to calculate access delay for the first time. Then, we devise a particle swarm optimization-based algorithm to optimize the profit. In the algorithm, we introduce a weight value$q$to guarantee the access delay and assign base stations properly. Moreover, a service-level agreement is adopted to balance the tradeoff between access delay and energy consumption. We take advantage of our 5G network emulator called mini5Gedge and data set from Shanghai Telecom to conduct massive experiments. The results show that our algorithm stands out in terms of achieving the highest profit.

Edge servers can cache some delay-sensitive and resource-intensive applications to reduce the delay of user tasks. However, due to the limited resources of edge servers, they cannot cache all services and process all user requests like remote clouds. In order to take advantage of the low latency, we need to make full use of the limited resources of the edge servers and reasonably allocate user requests to edge servers for processing. Meanwhile, in order to maintain the efficient and long-term operation of the server cluster, we should also consider the load balancing of the cluster. How to balance the server load while making users have the best experience is an urgent problem to be solved. Solving the problem faces the challenge of the interaction between service placement and request scheduling, the tradeoff between communication and computation, and the consideration of response time and edge server load. We propose the service placement algorithm based on user visits and our request scheduling algorithm based on the simulated annealing algorithm. We verify the superiority of our algorithms in response time and server load balancing. The experimental results based on the real data sets prove our algorithm can cope with the actual situation and quickly converge to a favorable value.

The emerging 6G network will make it possible for cybertwin, which relies deeply on the low latency and powerful computation provided by the edge network. To this end, the convergence of computing and network has been attached great importance. Most existing work study either placing edge servers or deploying user plane functions (UPFs), seldom considers the two processes jointly. In this article, we study how to minimize the latency with cost limitation by means of jointly deploying edge servers and UPFs in 6G scenario. We have shown that the problem is NP-hard. Then, we simplify the problem by analyzing the placement relationship between edge servers and UPFs and prune the solution space of the problem. To solve the problem effectively, a UPF and edge server placement algorithm is proposed. Massive experiments are conducted based on real-world data set and an edge core network emulator. The evaluation results show that our algorithm outperforms the benchmark algorithms.

5G communication is making much progress in achieving the Internet of Things and improving the quality of user experience in large bandwidth scenarios. By introducing a variety of new technologies, the performance of 5G has been greatly improved. However, emerging applications put forward more stringent requirements in terms of latency, reliability, peak data rate, service continuity, etc. Communication technology still needs to be further developed. In this article, the next generation of core networks is conceptualized. Inspired by the nervous system of the octopus, we propose a new cognitive service architecture. Cognitive service architecture is a new architecture designed for the 6G core network. It is proposed to enhance the core network so that it is qualified for the increasingly high requirement for quality of service and complicated scenarios. We first give a short vision of the 6G core network. Then cognitive service architecture is demonstrated in detail. A case study is demonstrated to show how cognitive service architecture enhances the performance of the system. Enabling technologies for 6G cognitive service architecture are discussed at last.