Prototype-based personalized federated learning methods have emerged as a promising strategy due to their ability to represent client-specific class characteristics effectively through learned class prototypes. These prototypes capture salient features of client-local data, facilitating personalized model adaptation. However, existing prototype-based aggregation strategies predominantly rely on weighted averaging, implicitly assuming prototype consistency across clients. This assumption neglects the intrinsic heterogeneity and non-independent and identically distributed (non-IID) nature of client data, compelling diverse local prototypes to align toward a singular global prototype and consequently causing significant aggregation bias. Motivated by observations from intra-class feature saliency analysis, we identify that clients inherently emphasize distinct feature regions even for the same class. To leverage this intra-class diversity, we introduce FedIC, a novel prototype clustering and collaborative classifier optimization approach. Specifically, FedIC first clusters prototypes based on intra-class similarity to form intra-class prototype subspaces, ensuring that aggregation occurs exclusively within each cluster, thus eliminating the bias stemming from forced global unification. To further exploit the benefits of intra-cluster collaboration, we quantify the combined predictive gains of classifiers from clients within the same cluster as a function of classifier combination weights. This targeted aggregation and collaborative optimization strategy effectively circumvents the bias introduced by global alignment. Extensive experiments under various non-IID settings show that FedIC significantly outperforms existing Prototype-based and Clustered PFL Methods.

The limited availability of high-quality training data poses a persistent challenge for synthetic aperture radar (SAR) target classification. Existing data augmentation methods mainly adopt a simplistic application of GAN-based style transfer techniques to directly synthesize pseudo-SAR images from optical images. However, our in-depth analysis of this cross-modal conversion reveals that such straightforward strategies primarily focus on transferring high-level semantic information (e.g., target shapes), thus failing to adequately capture the essential low-level features unique to SAR imagery (e.g., scattering textures). To address this inherent trade-off between high-level semantic preservation and low-level feature authenticity, we propose a Hierarchical Feature-Constrained GAN (HiFC-GAN) tailored for optical-to-SAR style transfer. Specifically, HiFC-GAN enhances the representation of low-level SAR features by introducing local texture contrast constraints at shallow layers, while introducing explicit feature mapping constraints at deeper layers to maintain high-level semantic consistency throughout the reconstruction process. Experimental results demonstrate that HiFC-GAN significantly outperforms existing GAN-based techniques in image generation quality, particularly improving the low-level feature authenticity of pseudo-SAR images. Moreover, the generated pseudo-SAR images further improve the performance of downstream target classification tasks, yielding accuracy gains ranging from 3.56% to 5.90% on average with mainstream CNN-based models.

Negative transfer (NF) is a critical challenge in personalized federated learning (pFL). Existing methods primarily focus on adapting local data distribution on the client side, which can only resist NF, rather than avoid NF itself. To tackle NF at its root, we investigate its mechanism through the lens of the global model, and argue that it is caused by update conflicts among clients during server aggregation. In light of this, we propose a conflict-free client update aggregation strategy (ConFREE), which enables us to avoid NF in pFL. Specifically, ConFREE guides the global update direction by constructing a conflict-free guidance vector through projection and utilizes the optimal local improvements of the worst-performing clients near the guidance vector to regularize server aggregation. This prevents the conflicting components of updates from transferring, achieving balanced updates across different clients. Notably, ConFREE is model-agnostic and can be straightforwardly adopted as a complement to enhance various existing NF-resistance methods implemented on the client side. Extensive experiments demonstrate substantial improvements to existing pFL algorithms by leveraging ConFREE.

Server aggregation conflict is a key challenge in personalized federated learning (PFL). While existing PFL methods have achieved significant progress with shallow base models (e.g., four-layer CNNs), they often overlook the negative impacts of deeper base models on personalization mechanisms. In this paper, we identify the phenomenon of deep model degradation in PFL, where as base model depth increases, the model becomes more sensitive to local client data distributions, thereby exacerbating server aggregation conflicts and ultimately reducing overall model performance. Moreover, we show that these conflicts manifest in insufficient global average updates and mutual constraints between clients. Motivated by our analysis, we proposed a two-stage conflict-aware layer-wise mitigation algorithm (FedCALM), which first constructs a conflict-free global update to alleviate negative conflicts, and then maximizes the benefits of all clients through a conflict-aware strategy. Notably, our method naturally leads to a selective mechanism that balances the tradeoff between clients involved in aggregation and the tolerance for conflicts. Consequently, it can boost the positive contribution to the clients even with the greatest conflicts with the global update. Extensive experiments across multiple datasets and deeper base models demonstrate that FedCALM outperforms four state-of-the-art (SOTA) methods by up to 9.88% and seamlessly integrates into existing PFL methods with performance improvements of up to 9.01%.

Many existing personalized federated learning (PFL) methods utilize clustering-based aggregation to group clients with similar data characteristics, improving model performance by promoting collaboration among clients with shared features. While this method effectively mitigates some challenges posed by data heterogeneity, it predominantly relies on static data features, making it challenging to capture the dynamic changes in client models during iterative training. This limitation impedes accurate clustering based on evolving model updates. To address this issue, we propose a Gradient-Driven Adaptive Clustering method in PFL (GradPFL), which more effectively captures the personalized deviations in locally updated models. Our approach also introduces an adaptive historical gradient mechanism that refines the clustering process by incorporating both current and past update characteristics. This enables more accurate model aggregation that adapts to ongoing changes in client models during training. Experimental results demonstrate that GradPFL outperforms existing clustering-based PFL methods, especially in more complex non-IID environments.

Existing SAR image classification methods based on Contrastive Learning often rely on sample generation strategies designed for optical images, failing to capture the distinct semantic and physical characteristics of SAR data. To address this, we propose Physics-Driven Contrastive Mutual Learning for SAR Classification (PCM-SAR), which incorporates domain-specific physical insights to improve sample generation and feature extraction. PCM-SAR utilizes the gray-level co-occurrence matrix (GLCM) to simulate realistic noise patterns and applies semantic detection for unsupervised local sampling, ensuring generated samples accurately reflect SAR imaging properties. Additionally, a multi-level feature fusion mechanism based on mutual learning enables collaborative refinement of feature representations. Notably, PCM-SAR significantly enhances smaller models by refining SAR feature representations, compensating for their limited capacity. Experimental results show that PCM-SAR consistently outperforms SOTA methods across diverse datasets and SAR classification tasks.

The integration of mobile edge computing (MEC) and heterogeneous networks enables network operators to provide task offloading services to a large number of user devices (UDs) for low-latency task processing by equipping macro base stations and densely deployed small base stations with edge servers. Federated deep reinforcement learning allows each UD to collaboratively learn useful knowledge from the interaction with the environment in a privacy-preserving and high-efficiency way and thus has been applied to solve the task offloading problem in recent studies. However, very few of these studies have considered the energy and time costs incurred by the federated learning process. In this article, the goal is to minimize the total UDs’ energy consumption while guaranteeing deadline constraints considering both the task offloading process and the federated learning process in MEC-enabled heterogeneous networks. Toward this end, we propose a federated deep Q-network (DQN) method where each UD optimizes the offloading decision for the offloading process and the participation decision and training volume for the learning process based on its local DQN model. The simulation results demonstrate the proposed method is superior to several existing methods in terms of energy efficiency and Quality of Service (QoS).

User requests in edge computing environments are inherently decentralized and dynamic, posing significant challenges for efficient and adaptive service replica placement. To address this, we formulate the service replica placement problem in an edge-cloud collaborative environment, explicitly incorporating the spatio-temporal distribution of user requests. By capturing spatial and temporal correlations, we predict future request patterns to enable forward-looking replica placement. Given the NP-hard nature of the optimization problem, we design a DRL algorithm that optimizes replica placement decisions based on predictive modeling. To validate our approach, we conduct extensive experiments on real-world datasets across two typical application scenarios―grid-based and graph-based request distributions. Experimental results show our method reduces average response latency by up to 59.6% and boosts service provider profitability by 4.85% compared to reactive and temporal-only baselines. The proposed framework provides a novel and effective solution for proactive service provisioning in edge computing environments.

Most existing synthetic aperture radar (SAR) ship classification technologies heavily rely on correctly labeled data, ignoring the discriminate features of unlabeled SAR ship images. Even though researchers try to enrich CNN-based features by introducing traditional handcrafted features, existing methods easily cause information redundancy and fail to capture the interaction between them. To address these issues, we propose a novel dual-stream contrastive predictive network (DCPNet), which consists of two asymmetric tasks and a false negative sample elimination module. The first task is to construct positive sample pairs, guiding the core encoder to learn more general representations. The second task is to encourage adaptive capture of the correspondence between deep features and handcrafted features, achieving knowledge transfer within the model, and effectively improving the redundancy caused by the feature fusion. To increase the separability between clusters, we also design a cluster-level task. The experimental results on OpenSARShip and FUSAR-Ship datasets demonstrate the improvement in classification accuracy of supervised models and confirm the capability of learning effective representations of DCPNet.

In current synthetic aperture radar (SAR) object classification, one of the major challenges is the severe overfitting issue due to the limited dataset (few-shot) and noisy data. Considering the advantages of knowledge distillation as a learned label smoothing regularization, this paper proposes a novel Double Reverse Regularization Network based on Self-Knowledge Distillation (DRRNet-SKD). Specifically, through exploring the effect of distillation weight on the process of distillation, we are inspired to adopt the double reverse thought to implement an effective regularization network by combining offline and online distillation in a complementary way. Then, the Adaptive Weight Assignment (AWA) module is designed to adaptively assign two reverse-changing weights based on the network performance, allowing the student network to better benefit from both teachers. The experimental results on OpenSARShip and FUSAR-Ship demonstrate that DRRNet-SKD exhibits remarkable performance improvement on classical CNNs, outperforming state-of-the-art self-knowledge distillation methods.

In recent years, since edge computing has improved the performance of transportation systems, research on edge-computing-enabled transportation systems has received widespread attention. However, most previous studies overlooked that task requests in transportation systems are unevenly distributed in time and space, which easily causes the overloading of edge servers, resulting in high response latency. To this end, we present a novel task offloading scheme based on graph neural network (GNN) and deep reinforcement learning (DRL) in edge-computing-enabled transportation systems (TransEdge). Specifically, we first propose an adaptive node placement algorithm to assign Internet of Things sensors to appropriate edge servers, thereby minimizing transmission latency. Then, an improved DRL scheme based on GNN is designed to capture the spatial features between sensors, aiming to improve the accuracy of task offloading decisions. Finally, we introduce a task forwarding strategy based on the greedy algorithm to achieve collaborative task offloading between different edge servers and overcome the system instability caused by a sudden surge in task requests. We conduct extensive experiments on two real-world traffic data sets. The results show that TransEdge reduces the response latency by at least 3.7% compared to four baselines while achieving a success rate of 99%.

Recently, the Industrial Internet of Things (IIoT) has shown great application value in environmental monitoring. However, it suffers from serious bottlenecks in energy and computing capability. To address them, researchers have made lots of effort. Nevertheless, they neglect either the edge–end collaboration or the impact of task queue backlog, resulting in low system revenue. To this end, we design a queue-aware computation offloading method based on DRL (QDRL). Specifically, we represent the long-term system operation as a multistage stochastic mixed-integer optimization problem (M-SMIP), which is further converted into a deterministic problem using Lyapunov optimization. Given that the resource allocation and computation offloading in this deterministic problem are strongly coupled and difficult to solve, we decompose this problem into two subproblems. Subsequently, a reinforcement learning scheme with actor–critic architecture is designed to solve these subproblems. The Actor module is designed based on a deep learning model and quantization strategy for generating computation offloading actions. The mathematical reasoning and learning-based methods are integrated as the Critic module for achieving resource allocation. Extensive simulation results show that the performance of QDRL surpasses four baselines and approaches the approximate optimal algorithm in terms of average task queue length, normalized real computation rate, and computation time.

Federated Learning (FL) constructs a distributed machine learning framework that involves multiple remote clients collaboratively training models. However in real-world situations, the emergence of non-Independent and Identically Distributed (non-IID) data makes the global model generated by traditional FL algorithms no longer meet the needs of all clients, and the accuracy is greatly reduced. In this paper, we propose a personalized federated multi-task learning method via similarity awareness (PFedSA), which captures the similarity between client data through model parameters uploaded by clients, thus facilitating collaborative training of similar clients and providing personalized models based on each client’s data distribution. Specifically, it generates the intrinsic cluster structure among clients and introduces personalized patch layers into the cluster to personalize the cluster model. PFedSA also maintains the generalization ability of models, which allows each client to benefit from nodes with similar data distributions when training data, and the greater the similarity, the more benefit. We evaluate the performance of the PFedSA method using MNIST, EMNIST and CIFAR10 datasets, and investigate the impact of different data setting schemes on the performance of PFedSA. The results show that in all data setting scenarios, the PFedSA method proposed in this paper can achieve the best personalization performance, having more clients with higher accuracy, and it is especially effective when the client’s data is non-IID.

Multi-feature SAR ship classification aims to build models that can process, correlate, and fuse information from both handcrafted and deep features. Although handcrafted features provide rich expert knowledge, current fusion methods inadequately explore the relatively significant role of handcrafted features in conjunction with deep features, the imbalances in feature contributions, and the cooperative ways in which features learn. In this paper, we propose a novel multi-feature collaborative fusion network with deep supervision (MFCFNet) to effectively fuse handcrafted features and deep features for SAR ship classification tasks. Specifically, our framework mainly includes two types of feature extraction branches, a knowledge supervision and collaboration module, and a feature fusion and contribution assignment module. The former module improves the quality of the feature maps learned by each branch through auxiliary feature supervision and introduces a synergy loss to facilitate the interaction of information between deep features and handcrafted features. The latter module utilizes an attention mechanism to adaptively balance the importance among various features and assign the corresponding feature contributions to the total loss function based on the generated feature weights. We conducted extensive experimental and ablation studies on two public datasets, OpenSARShip-1.0 and FUSAR-Ship, and the results show that MFCFNet is effective and outperforms single deep feature and multi-feature models based on previous internal FC layer and terminal FC layer fusion. Furthermore, our proposed MFCFNet exhibits better performance than the current state-of-the-art methods.

Collaborative cloud-edge-end computing is a promising solution to support computation-intensive and latency-sensitive tasks by utilizing rich computing resources of cloud datacenters and low access delay of mobile edge computing (MEC) servers. Compared with traditional cloud computing and MEC, the cloud-edge environment has a stronger heterogeneity of servers and networks, resulting in significant differences between servers in the computation speed and access delay. However, few studies on cloud-edge-end task offloading focused on the characteristic of 5G heterogeneous networks in the cloud-edge environment. In this paper, we study the task offloading problem for collaborative cloud-edge-end computing in MEC-enabled small cell networks with low-cost distributed wireless backhaul. We aim to minimize the energy consumption of all user devices (UDs) via jointly optimizing the offloading decision, UDs’ transmission power, and the allocation of spectrum and computation resources. To solve the non-convex problem, we decouple the original problem into three subproblems, and design an efficient method with solving these three subproblems iteratively to obtain a high-quality solution. The simulation results indicate that our proposed method can lead to significant reduction in the energy consumption of all UDs compared with other conventional methods.

Current synthetic aperture radar (SAR) ship classification research mainly focuses on modifying deep convolutional neural networks (DCNNs) and injecting manual features on DCNNs. Yet, the weak robustness of individual models in high-risk scenarios makes it difficult to gain the trust of SAR experts. In this letter, an automated method of heterogeneous DCNNs model ensemble based on two-stage filtration (MetaBoost) is proposed, effectively achieving robustness and high accuracy recognition on SAR ship classification. The principle of MetaBoost is generating a pool of diverse heterogeneous classifiers, selecting a subset of the most diverse and accurate classifiers, and finally fusing meta-features from the optimal subset. MetaBoost is a self-configuring algorithm that automatically determines the optimal type and number of base classifiers to be combined. Extensive experiments on the OpenSARShip and FUSAR-Ship datasets show that MetaBoost significantly outperforms individual classifiers, traditional ensemble models, and feature injection techniques.

As the number of cloud services (CSs) offering similar functionality is growing, more attention has been payed on the quality of service (QoS) of CSs. However, in a dynamic cloud environment, the explicit and inherent variation of QoS causes the single CS selection via collaborative filtering techniques (CSS-CFT) to be challenging. A variation-aware approach via collaborative QoS prediction is proposed to select an optimal CS according to users’ non-functional requirements. Based on time series QoS data, this approach utilizes a set of specific cloud models to quantify the variation characteristics of QoS from the four aspects including central tendency, variation range, frequency of variation and period. To exactly identify the neighboring users for a current user, this paper employs the double Mahalanobis distances to measure the similarity of QoS cloud models. The variation-aware CSS-CFT is formulated as a multi-criteria decision-making problem, and an improved TOPSIS method is exploited to solve it, by considering both the objective QoS variation and subjective user preferences during different time periods. The experiments based on a real-world dataset demonstrate that the proposed approach can enhance the accuracy of CSS-CFT in a high-variance environment without noticeable increase of selection time, in comparison to the existing approaches.

The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing. UAVs can greatly support data collecting and processing for Internet of Things devices (IoTDs) in mobile edge computing (MEC) systems due to their advantages of high environmental flexibility. This paper focuses on the scenario where multiple heterogeneous rotary-wing UAVs complete data collection and processing missions cooperatively. This paper introduces an energy minimization problem for UAV-assisted MEC system which attempts to optimize route planning and task allocation of UAVs. The energy consumption of a UAV includes hovering energy and flight energy depending on its configuration. By jointly choosing optimal UAVs for tasks and routes, we aim to obtain a sub-optimal solution of allocating IoTD tasks to UAVs and UAV flying route design while minimizing energy consumption. The Ant Colony System (ACS) algorithm is employed to obtain a high-quality near-optimal solution to solve this optimization problem. Finally, the simulation results show the effectiveness and efficiency of our proposed solution.

Graph-clustering algorithms are designed to split large-scale Resource Description Framework (RDF) graphs into subgraphs to improve RDF query performance. However, triple semantics and graph structures embedded in RDF graphs are often ignored during the RDF graph partition. Accordingly, this study utilizes the Leiden algorithm for uncovering community structure to cluster RDF graphs. We propose an optimized WLeidenRDF algorithm to uncover RDF communities and cluster vertex with strong semantics. Different weights are set to predicates in RDF triples to identify semantic-relevance degree and ensure semantic connections after RDF clustering and segmentation. The experiments on WatDiv data sets demonstrate that our WLeidenRDF algorithm obtains better partitions in accordance with modularity than those of the Leiden algorithm. We implement our algorithm on Presto distributed SQL query engine. Experimental results indicate that our algorithm can substantially reduce query time by clustering RDF graphs than with other RDF query methods.

The tags of open-source software (OSS) are important for managing and retrieving a massive amount of OSS in the OSS community, untagged OSS makes managing and retrieving OSS on GitHub difficult. However, developers sometimes neglect to write tag for repositories. For example, in our collected dataset with over 43K GitHub repositories, more than 32 % of the repository are unlabeled. To alleviate this problem, we propose an approach to automatically generate repository tag based on a neural network and LDA by utilizing package dependencies and readme among OSS in communities. We design an algorithm for extracting the tag features of dependent OSS packages and build dependent feature vectors for OSS. We then combine the vectors with topic of OSS readme file as input to train the neural network and obtain the tag distribution probability of OSS, and subsequently, recommend tags for OSS. Experiments are performed on the OSS dataset that we collected from GitHub, over 43K repositories and evaluate our approach on this dataset. Experiment results show that DepTagRec performs better than other methods in terms of precision and recall, particularly on recall when recommending the top 10 tags for OSS.

High power consumption of cloud data centres is a crucial challenge in modern cloud computing. To comply with the conceptions of green computing, power consumption prediction of the computing cluster has a major role to play in these energy conservation efforts. However, due to complexity and heterogeneity in cloud computing scenarios, it is difficult to accurately predict the power consumption using conventional approaches. To this end, this study presents a power consumption model based on feature selection and deep learning to powerfully cope with low energy efficiency. Different from other methods focusing on only a few performance attributes, the proposed method takes into account up to 12 energy‐related features and introduces deep neural network architecture, aiming at making full use of massive data to train model completely. In particular, this approach is composed of three main phases including (i) performance monitoring and energy‐related feature acquisition, (ii) essential feature selection, and (iii) model establishment and optimisation. Representative results of comprehensive experiments, in terms of the relative error, reveal that the proposed power consumption model can undoubtedly achieve state‐of‐the‐art predictive capability when compared with other models in most cases.

Summary With the increasing scale of tasks in cloud computing, the problem of high energy consumption becomes increasingly serious. To deal with the problem, we propose a cloud computing energy consumption model, which takes into account the execution and transmission cost of the processor. Then, based on this model, we put forward a task scheduling optimization algorithm named modified particle swarm optimization (M‐PSO) to handle the local optimum and slow convergence problem. Different from the PSO, M‐PSO can dynamically adjust the inertia weight coefficient to improve the speed of convergence according to the number of iterations. Finally, the performance of the proposed algorithm is evaluated through the CloudSim toolkit, and the experimental results show that the M‐PSO can efficiently reduce total cost compared with other algorithms.

As a preliminary step of many applications, skin detection serves as an irreplaceable role in image processing applications, such as face recognition, gesture recognition, web image filtering, and image retrieval systems. Combining information from multiple color spaces improves the recognition rate and reduces the error rate because the same color is represented differently in other color spaces. Consequently, a hybrid skin detection model from multiple color spaces based on a dual-threshold Bayesian algorithm (DTBA) has been proposed. In each color space, the pixels of images are divided into three categories, namely, skin, nonskin, and undetermined, when using the DTBA. Then, nearly all skin pixels are obtained by using a specific rule that combines the recognition results from multiple color spaces. Furthermore, skin texture filtering and morphological filtering are applied to the results by effectively reducing false identified pixels. In addition, the proposed skin model can overcome interference from a complex background. The method has been validated in a series of experiments using the Compaq and the high-resolution image datasets (HRIDs). The findings have demonstrated the proposed approach produced an improvement, the true positive rate (TPR) improves more than 6% and the false positive rate (FPR) reduces more than 11%, compared with the Bayesian classifier. We confirm that the method is competitive. Meanwhile, this model is robust against skin distribution, scaling, partial occlusions, and illumination variations.

Owing to the deficiency of usage experiences and the information overload of QoE (quality of experience) evaluations from consumers, how to discover the trustworthy cloud services is a challenge for potential users. This paper proposed a cloud service recommendation approach based on trust measurement using ternary interval numbers for potential user. The concept of ternary interval number is introduced. The user feature maybe affecting the QoE evaluations are analyzed and the client-side feature similarity between consumers and potential user is calculated. The transform mechanism from trust evaluations to ternary interval number is presented by employing the K-means clustering algorithm. On the basis of multi-attributes trust aggregation based On FAHP (fuzzy analytic hierarchy process) method, a new possibility degree formula is designed for ranking ternary interval numbers and selecting trustworthy service. Finally, the experiments and results show that this approach is effective to improve the accuracy of the trustworthy service recommendation.

Meeting users' deadline constraint is usually the most important goal of workflow scheduling in Grid environment. In order to consider the dynamism of Grid resource, we adopted a stochastic model to describe dynamic workloads of Grid resources. A concept called Deadline Satisfaction Degree of Workflow (DSDW) was defined to represent the probability that a workflow could be completed before its deadline. We calculated task execution priorities based on their precedence relations in the workflow, then determined the candidate resource for each task so as to maximize DSDW, finally converted distribution problem of overall workflow deadline into a nonlinear programming problem with constraints and resolved it with known solutions. A Deadline Satisfaction Enhanced Scheduling Algorithm for Workflow (DSESAW) involving deadline distribution and resource selection was presented. The extensive simulation experiments using a practical medical image analysis application was conducted to verify our algorithm. Experimental results indicated that our algorithm could adapt to dynamic Grid environment and provide a good guarantee for user's deadline requirements.

Selecting professional and authoritative experts to evaluate projects is an essential process in order to assure the quality of projects. In this paper, we present a semantic-based expert information system, which search for expert information based on semantic and knowledge reasoning, and rank the search results according to the scientific capability of experts. We design software architecture for semantic-based information service system (Esoogle). Expert information ontology is defined to store the information of experts, and reasoning rules are defined for semantic-based reasoning. Assessment model based on TOPSIS is built to estimate the scientific capability for ranking. Applications show Esoogle improves the recall and precision of semantic-based searching service compared to the traditional systems and ranking service is helpful for the users to select suitable experts quickly.

In grid computing, deadline-guarantee is one of the most mentioned QoS requirements for applications. However, the resource heterogeneity and the unpredictable workloads make it difficult for grid system to provide deadline-guarantee. In this paper, a novel approach is proposed to evaluate the deadline- guarantee of various co-allocation policies. By this approach, a hybrid-policy co-allocation model is also proposed to address the issue of deadline-constrained resource co-allocation in grid environments. The proposed model integrates multiple co- allocation policies to generate different co-allocation schemes, and selects the optimal deadline-guarantee scheme for grid applications. By this way, the hybrid-policy model combines the merits of different co-allocation policies, and overcomes the shortcomings of those policies. Extensive simulations are conducted to verify the effectiveness and the performance of the proposed model in terms of deadline-miss rate. Experimental results show that it can provide co-allocation scheme with enhanced deadline-guarantee as well as lower deadline-miss rate.

In grid systems, co-allocation is a key infrastructure to schedule heterogeneous and distributed resources for high-level applications. Although it has been widely studied, QoS-based resource co-allocation still remains an unsolved issue. In this paper, we propose a novel QoS-based co-allocation model for the grid applications with constraints to budget and deadline. A new concept, called virtual resource agent, is introduced into the co- allocation policy. Theoretical analysis indicates that virtual resource agent can provide quantitative QoS guarantee for applications in terms of budget and deadline. Experimental results show that the proposed model can significantly reduce deadline violation rate and increase the benefits of resource providers compared with other three co-allocation policies.

In dynamic grid environment, prediction and evaluation of resource availability are the prerequisite for reasonable resource selection and good QoS guarantee. Based on some related resource and task historical traces, probability theory is applied to resource availability prediction and evaluation. The availability metrics including resource off-line time, local task execution time, waiting queue length and waiting time are presented and the distribution functions of these metrics are given and proven. The experiment results show that the prediction is effective, and the amount of candidate resources determined by resource availability evaluation is decreased significantly, therefore lowering time complexity of task scheduling.