Data-protection regulations such as the GDPR grant every participant in a federated system a right to be forgotten. Federated unlearning has therefore emerged as a research frontier, aiming to remove a specific party's contribution from the learned model while preserving the utility of the remaining parties. However, most unlearning techniques focus on Horizontal Federated Learning (HFL), where data are partitioned by samples. In contrast, Vertical Federated Learning (VFL) allows organizations that possess complementary feature spaces to train a joint model without sharing raw data. The resulting feature-partitioned architecture renders HFL-oriented unlearning methods ineffective. In this paper, we propose ReMisVFU, a plug-and-play representation-misdirection framework that enables fast, client-level unlearning in splitVFL systems. When a deletion request arrives, the forgetting party collapses its encoder output to a randomly sampled anchor on the unit sphere, severing the statistical link between its features and the global model. To maintain utility for the remaining parties, the server jointly optimizes a retention loss and a forgetting loss, aligning their gradients via orthogonal projection to eliminate destructive interference. Evaluations on public benchmarks show that ReMisVFU suppresses back-door attack success to the natural class-prior level and sacrifices only about 2.5% points of clean accuracy, outperforming state-of-the-art baselines.

Keeping runtime alive is critical for mitigating cold start issues in Function-as-a-Service (FaaS) platforms. State-of-the-Art (SOTA) keep-alive policies often draw an analogy to data caching, adapting classic replacement algorithms to manage runtime pools. Our analysis reveals that this analogy is fundamentally flawed: 1) they violate their own eviction priorities due to conflicts with running containers, and 2) they ignore the exorbitant memory-time resource cost of runtime replacement. To address these challenges, we present FaaShadow, a lightweight keep-alive policy that recasts the problem from simple caching to cost-aware resource allocation. FaaShadow introduces the concept of a shadow pool, a per-function data structure that enables online estimation of the marginal utility of memory adjustments. By quantifying both the potential performance gain from allocating new containers and the performance loss from removing existing ones, FaaShadow makes data-driven reallocation decisions that maximize the global warm start rate. Experimental results show that FaaShadow achieves a 95% warm start rate using only 60% of the memory required by the best-in-class baseline. When paired with our dynamic scaling mechanism, FaaShadow reduces average memory consumption by a staggering 81.71% compared to the SOTA predictive scaler, while upholding performance targets.

Mobile applications such as browsers, video, and other interactive software are tightly coupled with frame rendering, which is critical for user experience. Frame rendering requires the collaboration of CPU, GPU, and memory to ensure energy efficiency and maintain Quality of Experience (QoE). However, there are notable deficiencies in resource management for these components. Our observations reveal three critical issues: 1) the operating system fails to accurately identify rendering-related threads, 2) thread groups lack strict resource constraints, leading to insufficient resources for rendering-related threads, and 3) frequency scaling across components is not coordinated, resulting in performance degradation and power inefficiencies. To address these issues, we propose Trident, a holistic resource management framework. Trident includes a cross-layer thread tracer to identify rendering-related threads, a reinforcement learning-based governor to coordinate the frequency of multiple hardware components, and a gain scheduling-based share controller to constrain resources among thread groups dynamically. Our framework aims to minimize power consumption while maintaining QoE. We implement Trident as a system service on five distinct smartphones, from older models to recent flagships, and evaluate its effectiveness on popular applications under various workloads. The results demonstrate that Trident reduces power consumption by up to 16.8% compared to three state-of-the-art techniques while ensuring QoE on mobile platforms. Additionally, the overhead introduced by Trident is minimal, making it an efficient solution for real-world deployment.

Attribute Inference Attacks (AIAs) pose a significant threat to recommendation systems (RS) by enabling adversaries to use threat models to infer sensitive user attributes like gender or race from user embeddings, resulting in privacy breaches such as unauthorized profiling and discriminatory policies against specific groups. Existing attribute protection methods are primarily applied during training, suffering from significant limitations, such as architectural inflexibility, dependence on interaction data, and potential catastrophic degradation in recommendation performance. To overcome these challenges, we propose AttrCloak, an efficient and effective post-training attribute unlearning (AU) framework that removes sensitive information from user embeddings without altering RS training architectures. AttrCloak employs dual-objective optimization with parameter self-sharing to minimize mutual information between user embeddings and sensitive attributes while preserving recommendation quality. Furthermore, it accommodates data-free scenarios by leveraging regularization loss when interaction data is unavailable. Comprehensive evaluations on four real-world datasets demonstrate AttrCloak's good performance in privacy protection and recommendation performance.

With the rapid development of the Internet of Things (IoT), IoT devices find applications in various domains. The data generated by these devices is utilized for analysis and services, especially in the field of Artificial Intelligence (AI) applied to IoT, known as Artificial Intelligence of Things (AIoT). The enhancement of edge device computing power in the IoT has led to the emergence of research areas like edge-cloud synergy AI theories and application services. In the context of lifelong learning and real-time processes in AIoT edge-cloud synergy services, addressing unseen tasks becomes crucial. Unseen tasks arise when inference requests from edge devices involve models not present in the cloud’s model repository. Addressing these challenges involves generating data to either augment small sample problems or alter the data distribution for heterogeneous sample issues. As the application of large language models (LLMs) for data generation gains traction, challenges emerge in the context of AIoT edge-cloud synergy services. Firstly, fine-tuning LLMs with heterogeneous data exacerbates model bias issues. Secondly, the substantial data requirements for training LLMs pose a contradiction. Lastly, the involvement of manual annotation in LLM-based data generation introduces complexity and cost. This paper proposes a framework Seafarer to these challenges using Generative Adversarial Networks and Self-taught Learning. Seafarer avoids model bias, reduces data requirements, and eliminates the need for manual annotation. The design demonstrates effectiveness theoretically and is validated on the Cityscapes dataset, achieving an 80% reduction in training loss and improved validation loss stability.

In the rapidly evolving landscape of cloud computing, serverless architectures offer a paradigm shift towards fine-grained function deployment and meticulous resource auto-scaling. Despite its growing popularity, existing systems often struggle to ensure the stability of function execution due to frequent cold starts and high concurrency demands. Our observations reveal a critical issue where a few hotspot functions excessively create new containers, resulting in substantial response latency fluctuations. To address this challenge, we propose Eunomia, a SLO-aware (Service Level Objective-aware) serverless framework. Eunomia introduces an optimized Poisson model with dynamic, sliding windows to accurately capture the arrival patterns of hotspot functions. Based on the optimized Poisson model, it proposes a queuing-based delay execution approach to mitigate initialization overhead by promoting instance reuse. Additionally, Eunomia designs flexible instance orchestration, providing dedicated concurrency pools for hotspot functions and dynamically adjusting the number of active instances. Experimental results demonstrate that Eunomia ensures 97% tail latency under a 100 ms response latency SLO, and outperforms the second-best baseline by 46% when memory is limited.

Personalized federated learning (PFL) has been proposed to overcome the challenge of statistical diversity in clients’ local data distribution under federated scenarios. Some existing PFL methods encourage collaboration between clients to improve overall accuracy. However, these methods often fail to consider the individual preferences of clients; instead, they adopt a top-down decision-making approach to divide clients into disjoint groups. In contrast, we consider the rationality of self-interested clients and allow them to make their own decisions regarding which federation to join. In our proposed PFL framework pFedMGC, we formulate the autonomous decision-making process of clients where they can participate in multiple federations. We then develop a heuristic algorithm to find feasible collaboration in this process. Clients in the same federations collaborate to train a federation-wide global model. To customize these federation models into clients’ personalized models, we employ an adaptive approach to calculate the aggregation weights of federation models based on differences in loss. The experiment results show that pFedMGC can improve overall personalized accuracy up to 28.89%, compared with the state-of-the-art PFL methods.

With the rapid evolution of the Internet, Internet of Things (IoT), and geographic information systems (GIS), spatio-temporal Big Data (STBD) is experiencing exponential growth, marking the onset of the STBD era. Recent studies have concentrated on developing algorithms and techniques for the collection, management, storage, processing, analysis, and visualization of STBD. Researchers have made significant advancements by enhancing STBD handling techniques, creating novel systems, and integrating spatio-temporal support into existing systems. However, these studies often neglect resource management and system optimization, crucial factors for enhancing the efficiency of STBD processing and applications. Additionally, the transition of STBD to the innovative Cloud-Edge-End unified computing system needs to be noticed. In this survey, we comprehensively explore the entire ecosystem of STBD analytics systems. We delineate the STBD analytics ecosystem and categorize the technologies used to process GIS data into five modules: STBD, computation resources, processing platform, resource management, and applications. Specifically, we subdivide STBD and its applications into geoscience-oriented and human-social activity-oriented. Within the processing platform module, we further categorize it into the data management layer (DBMS-GIS), data processing layer (BigData-GIS), data analysis layer (AI-GIS), and cloud native layer (Cloud-GIS). The resource management module and each layer in the processing platform are classified into three categories: task-oriented, resource-oriented, and cloud-based. Finally, we propose research agendas for potential future developments.

In serverless computing, cold start results in long response latency. Existing approaches strive to alleviate the issue by reducing the number of cold starts. However, our measurement based on real-world production traces shows that the minimum number of cold starts does not equate to the minimum response latency, and solely focusing on optimizing the number of cold starts will lead to sub-optimal performance. The root cause is that functions have different priorities in terms of latency benefits by transferring a cold start to a warm start. In this paper, we proposeIncendio, a serverless computing framework exploiting priority-based scheduling to minimize the overall response latency from the perspective of cloud providers. We reveal the priority of a function is correlated to multiple factors and design a priority model based on Spearman’s rank correlation coefficient. We integrate a hybrid Prophet-LightGBM prediction model to dynamically manage runtime pools, which enables the system to prewarm containers in advance and terminate containers at the appropriate time. Furthermore, to satisfy the low-cost and high-accuracy requirements in serverless computing, we propose a Clustered Reinforcement Learning-based function scheduling strategy. The evaluations show that Incendio speeds up the native system by 1.4×, and achieves 23% and 14.8% latency reductions compared to two state-of-the-art approaches.

Generative Adversarial Networks (GANs) have become predominant in mobile computing for their ability to generate data. The concern for data privacy has made it arduous to collect large-scale datasets for GAN training on centralized servers. Federated Learning (FL) has emerged as a promising solution to address data privacy concerns. In this paper, we propose Oasis, a multiplayer-oriented federated GAN training system. We present a motivation, highlighting the Nash Equilibrium (NE) shift in vanilla federated GANs, exacerbated by data heterogeneity, leading to poor training performance with issues of vanishing gradient and mode collapse. To address mode collapse, Oasis extracts privacy-preserving data representations and generates a similarity table for clustering clients. Each group independently trains a GAN model and conducts distribution and fusion. By introducing a coordinator, Oasis generalizes intra-group games intoSeparable Zero-sum Multiplayer Gamesto tackle vanishing gradient. Thus, Oasis considers the overall federated GAN training asGroup-wise Separable Zero-sum Multiplayer Games. Practically, we evaluate our theoretical results both on a hardware prototype and in a simulated environment. Evaluation results demonstrate the effectiveness of Oasis, with an average improvement of 23.13% and 26.33% in terms of FID and NDB/K respectively, compared to threestate-of-the-artFL approaches over three datasets.

Function-as-a-Service (FaaS) is a promising cloud computing model known for its scalability and elasticity. In various application domains, FaaS workflows have been widely adopted to manage user requests and complete computational tasks efficiently. Motivated by the fact that function containers collaboratively use the image layer's memory, co-placing functions would leverage memory sharing to reduce cluster memory footprint, this paper studies layer- wise memory sharing for serverless functions. We find that overwhelming memory sharing by placing containers in the same cluster machine may lead to performance deterioration and Service Level Objective (SLO) violations due to the increased CPU pressure. We investigate how to maximally reduce cluster memory footprint via layer- wise memory sharing for serverless workflows while guaranteeing their SLO. First, we study the container memory sharing problem under serverless workflows with a static Directed Acyclic Graph (DAG) structure. We prove it is NP-Hard and propose a 2-approximation algorithm, namely MDP. Then we consider workflows with dynamic DAG structure scenarios, where the memory sharing problem is also NP-Hard. We design a Greedy-based algorithm called GSP to address this issue. We implement a carefully designed prototype on the OpenWhisk platform, and our evaluation results demonstrate that both MDP and GSP achieve a balanced and satisfying state, effectively reducing up to 63% of cache memory usage while guaranteeing serverless workflow SLO.

Internet of Things (IoT) gateways integrate various sensors and compute initial decisions before transmitting data to the cloud for further processing. As the functions they need to support become increasingly complex, gateways must upgrade their hardware. Network functions (NF) and video analytics (VAs) are two typical examples of hardware requirements: NFs need specialized hardware accelerators, while VAs need parallel processing power. However, gateways are typically constrained by factors, such as power, size, and cost, leading to a need to multiplex functions and minimize hardware overprovisioning. This article proposes a novel accelerator, the transmission-analytic processing unit (TAPU), which uses multi-image FPGA to accelerate VAs and NFs for IoT gateways. We preconfigure one image for VAs and one image for NFs, then multiplex the FPGA resources in the time dimension. The TAPU system design requires both hardware and software revisions. In the hardware design, we discuss our considerations on hardware choice and present a new abstraction of hardware functions to overcome the challenge of application development on different multi-image FPGAs. For the software, we develop a fully functional TAPU system to adapt to dynamic network and VAs workloads. Our evaluation shows that TAPU utilization can reach 92%, considerably increasing VAs and network processing throughput over the current approach. We further evaluate TAPU through two case studies that support a campus traffic monitoring system and an office surveillance system, demonstrating excellent performance improvement and low overhead.

As widely used indices in key-value stores, the Log-Structured Merge-tree (LSM-tree) and its variants suffer from severe write amplification due to frequent merges in compactions for write-intensive applications. To address the problem, we first propose the Log-Structured Append-tree (LSA-tree), which tries to compact data with appends instead of merges, significantly reduces the write amplification and solves the issues existed in current append trees. However LSA increases read and space amplifications. Furthermore based on LSA, we design the Integrated Append/Merge-tree (IAM-tree). IAM selects appends or merges in compaction operations according to the size of memory-cached data. Theoretical analysis shows that IAM reduces the write amplification of LSM while keep the same read and space amplification.

In this paper, we present an optimized framework that can efficiently perform massive spatial queries on the current GPUs. To benefit the widely adopted filter-and-verify paradigm from GPUs, the skewed workloads are first associated with certain cells in a scaled spatial grid, such that the following range verification cost against the massive spatial objects can be significantly reduced. Particularly on the Kepler architecture, we highlight a two-level scheduling method to exploit good data localities by developing a novel dynamic scheduling method. Based on this virtual warp-based scheduling method, groups of threads can compete for the unbalanced tasks to ensure good load balance. We conduct various of skewed workloads with different object positions and query distributions, to evaluate our optimized methods. Experimental results show that, as compared to the existing fixed-size allocation methods, the proposed adaptive scheduling strategies improve the query throughput by one order of magnitude.

Data replication is widely used in geo-distributed storage systems, and strong consistency is preferred for correctness and programming simplicity at the application layer. To address the inefficiency and insufficiency of the causal consistency model, a strong consistency model named distributed context consistency is defined. It explicitly defines the necessary dependencies among distributed clients to effectively reduce falsepositive dependencies among operations. A consensus algorithm named CC-Paxos is proposed to implement this distributed context consistency model. It exploits timestamps for operation sequencing in distributed contexts and adopts fine-granularity dependency checking to effectively reduce the number of potential conflicts. Experimental results show that, compared with implementations using causal+ consistency model in the upper layer and Egalitarian Paxos in system layer, CC-Paxos can significantly decreases latency and increases throughput with no sacrifice on scalability.

Cloud-based file systems are widely accepted and adopted for personal and business purposes in recent years. Statistics shows that ∼25% of file operations from a typical user are random writes. Inherited from traditional disk-based file systems, most distributed file systems are also based on objects or chunks of fixed sizes, which work well for sequential writes but poorly for random writes. This paper investigates the design paradigm of variable-sized objects for a distributed file system, where a new file write interface is proposed to provide rich write semantics. A novel distributed file system named VarFS, is presented to incorporate variable object indexing, support the random write interface and remain POSIX compatible. VarFS reduces the amount of unnecessary data being read and the number of objects modified in face of updates and consequently alleviates the total amount of data transferred. VarFS is implemented based on Ceph and the performance measurements show that it can achieve 1–2 orders of magnitude less latency than Ceph on random writes. At the same time, the overhead for initial writes and re-writes is acceptable.

Solid State Devices (SSD) are supplementing and gradually replacing traditional mechanical hard drives to become the mainstream of storage devices with better performance and lower power consumption. However, the disk I/O schedulers designed for traditional disks do not consider the characteristics of SSDs. Additionally in a virtualized environment the virtual machines I/O requests would be scheduled twice, in guest and host OSes. The request latency characteristics observed at both places are quite different. Based on the characteristics of SSD, we design an adaptive I/O scheduler which gives read requests higher priority and further analyze the best possible combination of I/O schedulers for the guest and host OSes. The experimental results show that the average delay of read requests with our adaptive scheduler is reduced by about 11.55 percent compared to the I/O scheduler with fixed dispatch ratio of read and writes requests. Meanwhile, when the guest and the host both adopt our scheduler, its average delay outperforms others by about 11.13 percent.

P2P content distribution has been an important Internet application in recent years. Its popularity draws attentions of Internet Service Providers (ISPs) for traffic management even throttling. This paper presents a new P2P content distribution protocol named ShareStorm that is efficient and ISP friendly. The key is our receiver-driven distance centric heuristics with probabilistic randomization. Compared with BitTorrent, ShareStorm accelerates the content distribution greatly and reduces the operating cost of the ISPs. In addition, it allows the ISPs to adjust the traffic distribution of specified areas dynamically. We conducted both emulation and Internet experiments to demonstrate ShareStorm's performance. Particularly, Share Storm outperforms BitTorrent by more than 60% in our Internet experiments in terms of download time.

Internet applications require high-performance network server architecture. This paper proposes a kind of software architecture for network server, MEANS, which aims at supporting Internet applications. By introducing a new thread abstraction, micro-thread, MEANS upwardly provides the micro-thread environment to programmers, and downwardly accesses the OS services concurrently through traditional thread primitives, and uses the event-driven mechanism to manage and schedule micro-threads, which takes advantage of both multithread and event-driven architecture. Moreover, MEANS is general, scalable, robust and adaptable. By preliminary evaluation, in terms of the concurrence policy, MEANS has features similar to event-driven architecture; and it outperforms multithread architecture in I/O accessing.

Anycast routing is very useful for many applications such as resource discovery in delay tolerant networks (DTNs). In this paper, based on a new DTN model, we first analyze the any-cast semantics for DTNs. Then we present a novel metric named EMDDA (expected multi-destination delay for anycast) and a corresponding routing algorithm for anycast routing in DTNs. Extensive simulation results show that the proposed EMDDA routing scheme can effectively improve the efficiency of anycast routing in DTNs. It outperforms another algorithm, minimum expected delay (MED) algorithm, by 11.3% on average in term of routing delays and by 19.2% in term of average max queue length.

Grid computing is a new paradigm for distributed computing, and service has become building block of grid applications. However, current approaches can not free developers from low-level laborious work when building grid applications. We propose a service-oriented virtual machine called Abacus Virtual Machine to simplify the task of grid application development. As a language level virtual machine, it provides a service-oriented instruction set to abstract the operations on the services of a grid application. It also virtualizes services and creates a virtual global system image for grid applications, thus services can be transparently distributed and shared. In this way, Abacus Virtual Machine hides the cumbersome underlying details from programmers and reduces the complexity greatly in grid application development

Resource discovery is crucial to efficient deployment of a grid system whose dynamic, heterogeneous characteristics make it difficult. In this paper, Vega Infrastructure for Resource Discovery (VIRD) is developed, then augmented with new features (i.e., some new algorithms) to build a C/S (client/server) and P2P (peer-to-peer) hybrid resource discovery framework. The three layered architecture of the VIRD is developed to make advantage of the physical and logical topologies of the Internet to facilitate resource discovery. With our simulations and theoretical analysis, it is proved that VIRD is of good scalability with respect to the sizes of the underlying backbone. Even when the resource density is low and the max TTL (time-to-live) is small, VIRD still achieves high search success rates in a small amount of hops. Compared with flooding and random walk algorithms via the same search success rates, VIRD outperforms them in both network traffic and response time.