• Propose spatial and temporal encoders for container-level autoscaling decisions • Design a hierarchical action network adaptable to changing container numbers • Achieve higher QoS and cost savings than four state-of-the-art autoscaling methods • Validate effectiveness on real-world user request traces • Advance expert systems for complex and large-scale cloud environments Autoscaling is an important technique for cloud computing that dynamically adjusts resources allocated to cloud applications in response to fluctuating user requests to maintain Quality of Service (QoS) and adhere to a given budget. Recent advancements in Deep Reinforcement Learning (DRL) have shown promise in achieving effective autoscaling approaches. However, prior DRL-based approaches struggle to simultaneously consider the spatial dependencies within an application and the changing historical workload patterns, limiting their ability to make accurate scaling decisions. Moreover, existing approaches lack the fine-grained resource adjustment, leading to suboptimal autoscaling performance. To address these limitations, we propose a new DRL-based autoscaling approach with a novel spatial-temporal autoscaling policy, which jointly captures spatial and temporal features of cloud applications by Graph Neural Networks and Transformers. Meanwhile, this policy enables fine-grained resource adjustment. Extensive experiments on real-world user request traces show that the proposed approach significantly outperforms existing state-of-the-art methods, achieving up to a 78.23% reduction in mean response time without violating the cost budget.

Many IoT systems require deadline-constrained workflow scheduling, where missed deadlines can have serious consequences. Scheduling such IoT workflows in Fog–Cloud environments is challenging due to resource heterogeneity and the variability in workflow patterns and deadlines. Existing approaches, including heuristic and meta-heuristic algorithms, often fail to reliably satisfy deadline constraints while simultaneously minimizing the cost associated with the computational resources used for executing workflows. This paper introduces the Internet of Things Genetic Algorithm with Selective Repair under Combined Criteria (IoTGA-SRC 2 ) to effectively tackle these challenges. IoTGA-SRC 2 introduces a novel selection mechanism that prioritizes solutions based on deadline violations and execution costs. It also features an innovative repair method, which can systematically detect infeasible solutions, perform a root cause analysis to identify the key factors causing deadline violations, and reallocate critical tasks using a multi-criteria method. By properly managing delays caused by execution time, communication time, and waiting time, IoTGA-SRC 2 can consistently satisfy deadline constraints across a wide range of problem configurations. Extensive experiments demonstrate that IoTGA-SRC 2 consistently outperforms multiple state-of-the-art methods in reducing execution costs while adhering to stringent deadline constraints, making it a valuable choice for various real-world applications in heterogeneous IoT–Fog–Cloud computing environments.

The increasing complexity and variety of IoT systems require the integration of multiple services to meet a wide range of user needs. This paper addresses the challenge of multi-objective IoT service composition with replication problem by considering multiple Quality of Service (QoS) metrics such as response time and the number of selected service instances. We propose a new Memetic NSGA-II algorithm with Bottleneck-driven Local Search (MNSGA2-BLS) to effectively solve this difficult problem. By integrating genetic operations, clustering-based refinement, and a bottleneck-driven Estimation of Distribution Algorithm for local search, MNSGA2-BLS identifies and optimizes critical service instances causing QoS bottlenecks. This method leverages Pareto-optimal solutions to guide the local search refinement process, enhancing convergence and solution quality. Experimental results across various benchmark cases demonstrate that MNSGA2-BLS can outperform NSGA-II and several state-of-the-art algorithms, achieving superior results in both the hyper-volume and inverse generational distance metrics. This highlights the potential of MNSGA2-BLS to provide efficient and effective composite IoT services while addressing trade-offs between competing QoS objectives.

Community Detection (CD) in weighted social networks is a highly active research field, celebrated for its profound practical implications across a multitude of disciplines. Genetic algorithms (GAs) are frequently explored to tackle CD problems, leveraging their capability to navigate the extensive discrete search space effectively. Throughout the evolutionary process, genetic operators such as crossover and mutation assume pivotal roles in effectively exploring the vast solution space. Nonetheless, prevailing GA-based approaches often ignore crucial topology information, particularly information regarding edge weights, resulting in compromised algorithm performance. In light of this, this paper introduces Edge Information-based GA (EIGA) to effectively solve CD problems in weighted networks. This is achieved specifically through the innovative designs of edgeweight-aware crossover and mutation operators. These novel edge-weight-aware operators improve the extraction of meaningful community structures, advancing knowledge discovery from social networks. Empirical findings demonstrate the superior performance of EIGA over numerous state-of-the-art algorithms across various real-world and synthetic benchmark networks.

Containers have emerged as a prevalent mechanism for deploying software applications within cloud data centers, thereby abstracting many operational details from developers and transferring the responsibility of resource management to cloud service providers. These providers are continually motivated to reduce operational costs by optimizing both the placement of containers and the Virtual Machines (VMs) that host them, as efficient placement directly contributes to reduced energy expenditures. Furthermore, given that energy prices vary both temporally and geographically due to fluctuating power production, demand, and the increasing integration of renewable en-ergy sources, explicit consideration of dynamic, location-specific energy pricing is essential for informed resource allocation and container migration decisions. In this work, we propose a novel container-based cloud resource allocation model that integrates variable energy prices across multiple locations. To address this problem, we introduce an innovative Genetic Programming Hyper-Heuristic (GPHH) algorithm that concurrently evolves three heuristics for container allocation, VM placement, and container migration. A key technical novelty of our approach is the incorporation of newly designed terminals within the GPHH framework that are specifically engineered to capture and utilize the complex dynamic power pricing information. Experimental results demonstrate that our GPHH algorithm offers significant improvements over several state-of-the-art methods, thereby enhancing both cost efficiency and energy optimization in cloud environments.

Cost-aware Dynamic Workflow Scheduling (CADWS) is a key challenge in cloud computing, focusing on devising an effective scheduling policy to efficiently schedule dynamically arriving workflow tasks, represented as Directed Acyclic Graphs (DAG), to suitable virtual machines (VMs). Deep reinforcement learning (DRL) has been widely employed for automated scheduling policy design. However, the performance of DRL is heavily influenced by the design of the problem-tailored policy network and is highly sensitive to hyperparameters and the design of reward feedback. Considering the above-mentioned issues, this study proposes a novel DRL method combining Graph Attention Networks-based policy network and Evolution Strategy, referred to as GATES. The contributions of GATES are summarized as follows: (1) GATES can capture the impact of current task scheduling on subsequent tasks by learning the topological relationships between tasks in a DAG. (2) GATES can assess the importance of each VM to the ready task, enabling it to adapt to dynamically changing VM resources. (3) Utilizing Evolution Strategy's robustness, exploratory nature, and tolerance for delayed rewards, GATES achieves stable policy learning in CADWS. Extensive experimental results demonstrate the superiority of the proposed GATES in CADWS, outperforming several state-of-the-art algorithms. The source code is available at: https://github.com/YaShen998/GATES.

Community detection, a vital technology for real-world applications, uncovers cohesive node groups (communities) by leveraging both topological and attribute similarities in social networks. However, existing Graph Convolutional Networks (GCNs) trained to maximize modularity often converge to suboptimal solutions. Additionally, directly using human-labeled communities for training can undermine topological cohesiveness by grouping disconnected nodes based solely on node attributes. We address these issues by proposing a novel Topological and Attributive Similarity-based Community detection (TAS-Com) method. TAS-Com introduces a novel loss function that exploits the highly effective and scalable Leiden algorithm to detect community structures with global optimal modularity. Leiden is further utilized to refine human-labeled communities to ensure connectivity within each community, enabling TAS-Com to detect community structures with desirable trade-offs between modularity and compliance with human labels. Experimental results on multiple benchmark networks confirm that TAS-Com can significantly outperform several state-of-the-art algorithms.

Integrated order batching and picker routing (IOBPR) is a complex combinatorial optimization problem in real-world intelligent manufacturing systems. Heuristics are often used for solving such complex scheduling problems. Manually designing scheduling heuristics suffer from two limitations: 1) few problem features can be taken into account and 2) the design process is time consuming. Genetic programming hyper heuristic (GPHH) approaches have been proposed on many scheduling problems to automatically evolve effective heuristics. However, existing GPHH approaches are often problem specific and requires careful design of problem specific terminal sets and evolution operators. The aim of this work is to develop a GPHH approach to evolve heuristics for the IOBPR problem. In particular, we propose a novel terminal set (NT) with three types of terminals, and a GPHH with elitist mutation (GPHH-EM) algorithm. Extensive experiments demonstrate that the heuristics evolved by GPHH-EM can significantly outperform other state-of-the-art competing algorithms designed by human experts. Further analysis indicates that the three types of terminals effectively complement to improve evolved heuristics for decision making. Furthermore, the newly developed elitist mutation operator expedites the evolutionary process for GPHH to find high-quality heuristics.

Dynamic workflow scheduling (DWS) is a challenging and important optimization problem in cloud computing, aiming to execute multiple heterogeneous workflows on dynamically leased virtual machine resources to satisfy user-defined Quality of Service requirements. For the popular deadline-constrained DWS in cloud problem, a virtual machine selection rule (VMSR) and a task selection rule (TSR) need to be designed simultaneously to minimize the rental fee and deadline violation penalty. For this purpose, Dual-Tree Genetic Programming (DTGP) has been previously developed to automatically evolve effective VMSRs and TSRs. However, existing DTGP approaches assume that VMSR and TSR, as well as terminals used by VMSRs and TSRs are equally important and evolve both VMSRs and TSRs in a black box manner, i.e., without using any knowledge about different impacts of trees and terminals. Several recent studies clearly indicate that different trees or terminals have varied performance impacts, making it critical to develop adaptive mutation mechanisms for effective DTGP. Driven by this motivation, this paper proposes two new levels of adaptive mutation mechanisms, contributing to the development of a new DTGP algorithm, which features the use of three new probability vectors for adaptive tree selection of VMSR and TSR at the first level and adaptive terminal selection at the second level while mutating any existing dual-tree individuals. Extensive experimental results demonstrate that the proposed two adaptive mechanisms can improve the effectiveness of DTGP compared to four baseline algorithms.

Effectively scheduling the execution of deadline-constrained IoT workflows in Fog-Cloud computing is an im-portant practical problem. The execution of IoT workflows must be carefully controlled, especially in applications like IoT health, where missed deadlines can be life-threatening. Existing approaches, including heuristic and meta-heuristic algorithms, have limited capabilities of handling deadline constraints while ensuring efficient execution of workflows. In this paper, we develop a Genetic Algorithm (GA) with a novel repair method to properly allocate workflow tasks to IoT devices and Fog/Cloud servers in order to meet deadlines while minimizing the resource costs. To effectively handle deadline constraints, our repair method first identifies infeasible solutions evolved by GA that can be potentially repaired. It then carries out root cause analysis to determine the primary reasons for deadline violation, and finally reallocate critical tasks to resolve violated deadlines. In this way, the risks of violating deadline constraints can be substantially reduced. On a wide range of problem instances commonly studied in literature, our experiments show that the new algorithm can clearly outperform multiple recently developed methods since it enjoys better chance of finding solutions that satisfy deadline constraints and can notably reduce the resource cost simultaneously.

In the domain of Cloud computing, Fog computing is integrated with the Cloud to offer a balanced approach that combines Cloud's scalability with Fog's low latency, enabling efficient software application deployment. However, many current studies overlook the unpredictability of future user requests, such as assuming all requests are known beforehand. User requests often arrive dynamically and may have different quality of service (QoS) preferences. Therefore we need effective methods to handle dynamic application deployment with multiple objectives. This paper tackles this gap by modeling a multi-objective application deployment problem that considers dynamically arriving users' requests on application deployment in a Cloud-Fog environment. We further introduce a multi-objective Genetic Programming Hyper-Heuristic based approach to automatically generate a set of deployment rules that can be chosen according to users' QoS preferences. These rules are generated with different trade-offs of two optimization objectives, i.e., minimizing cost and latency, which can be used for deploying applications dynamically. Our experimental evaluation using real-world data demonstrates that our GPHH approach can generate effective heuristics for deploying applications in an integrated Cloud-Fog environment.

Internet of Things Service Composition (SCIoT) aims to find the best composite IoT service to fulfil users' requirements. Given the NP-hard complexity of SCIoT, Evolutionary Computation methods, especially Estimation of Distribution Algorithms (EDAs), have received increasing attention to solve SCIoT problems. As one of the most popular EDA methods, Population-Based Incremental Learning (PBIL) has demonstrated its strong competency in optimising composed services in SCIoT. However, conventional PBIL does not explicitly utilize problem knowledge such as QoS and service replication, limiting its effectiveness for IoT service composition. In this paper, we propose a new PBIL based approach, named Population-Based Incremental Learning to Improve Service Composition (PBILISC), to solve the SCIoT problem. Different from traditional PBIL, PBILISC seamlessly integrates PBIL with QoS-aware local search (QLS) to effectively handle replicated services in the SCIoT problem. Specifically, PBILISC evolves a series of populations of solutions jointly through PBIL and QLS. PBIL leverages a probability distribution for population updates, while QLS focuses on improving the best evolved solution by searching promising neighboring solutions under the guidance of QoS. Experimental results show that PBILISC can outperform PBIL and several state-of-the-art methods on multiple benchmark SCIoT problems.

Containers are a popular way of deploying software in cloud data centers. Containers are allocated to Virtual machines (VMs) which are allocated to Physical machines (PMs) within the data center. Since the resources required by containers often do not match those of VMs, where to allocate them must be decided. A poor solution can result in high energy costs. Many existing methods to solve this problem use heuristics which do not consider containers leaving the data center after being allocated. Some do consider migrating containers between VMs but few do for energy efficiency reasons. These overlooked aspects may lead to increased energy usage, particularly since studies have demonstrated that many containers run for only a brief duration. In this paper, we develop a model of the container-based cloud resource allocation problem that considers the energy impact of leaving and migrating containers. We then design a new Genetic Programming Hyper-Heuristic (GPHH) algorithm to jointly evolve three heuristics for container placement, VM placement and container migration control. We utilize newly designed terminals to ensure the effectiveness of our GPHH algorithm. Experiments have been conducted with results indicating that the heuristics evolved by our GPHH algorithm can achieve better performance compared to several state-of-the-art techniques.

Location-Aware Dynamic Online Application dePloyment (LADOAP) in clouds is an NP-hard combinatorial optimisation problem. Genetic Programming Hyper-Heuristic (GPHH) has emerged as a promising approach for addressing LADOAP demands by dynamically generating Virtual Machine (VM) selection heuristics online. However, the performance of GPHH is impeded by long simulation times and low sampling efficiency. In this paper, we propose a novel hyper-heuristic framework that integrates Genetic Programming Hyper-Heuristic (GPHH) and Reinforcement Learning (RL) approaches to evolve rules for efficiently selecting location-aware Virtual Machines (VMs) capable of hosting multiple containers. The RL policy's value function acts as a surrogate model, significantly expediting the evaluation of generated VM selection rules. By applying this hybrid framework to LADOAP problems, we achieve competitive performance with a notable reduction in the number of required simulations. This innovative approach not only enhances the efficiency of VM selection but also contributes to advancing the state-of-the-art in addressing complex LADOAP challenges.

Genetic Programming Hyper-heuristics (GPHH) have been successfully used to evolve scheduling rules for Dynamic Workflow Scheduling (DWS) as well as other challenging combinatorial optimization problems. The method of sampling training instances has a significant impact on the generalization ability of GPHH, yet they are rarely addressed in existing research. This article aims to fill this gap by proposing a GPHH algorithm with a sampling strategy to thoroughly investigate the impact of six instance sampling strategies on algorithmic generalization, including one rotation strategy, three mini-batch strategies, and two hybrid strategies. Experiments across four scenarios with varying settings reveal that: (1) mini-batch with random sampling can outperform rotation in generalizing to unseen workflow scheduling problems under the same computational cost; (2) employing a hybrid strategy that combines rotation and mini-batch further enhances the generalization ability of GPHH; and (3) mini-batch and hybrid strategies can effectively enable heuristics trained on small-scale training instances generalizing well to large-scale unseen ones. These findings highlight the potential of mini-batch strategies in GPHH, offering improved generalization performance while maintaining diversity and suggesting promising avenues for further exploration in GPHH domains.

Service composition in Internet of Things (SCIoT), as an emerging topic in service computing, aims to select optimal services to complete user requests according to various user requirements such as minimizing energy consumption and response time. To solve this NP-hard problem, numerous heuristic methods, e.g., local search and population-based algorithms, have been proposed, wherein Estimation of Distribution Algorithm (EDA) gains increasing attention because of its explicit global probabilistic nature. However, existing EDAs increase solution diversity by using fixed bias, yet interfere the stability of the learned distribution in the later stage of optimization. Therefore, this paper proposes an EDA with an adaptive bias strategy (EDA-AdaBias) to solve the service composition in IoT problem. The decreasing bias value is added onto the probability values for all choices of each solution variable over generations, which improves diversity of sampled solutions and avoids dramatic change of constructed distribution. Experiments indicate that EDA-AdaBias presents promising performance compared to other competitive methods on this problem.

Web service composition composes existing Web services to accommodate users’ requests for required functionalities with the best possible quality of services (QoS). Due to the computational complexity of this problem, evolutionary computation (EC) techniques have been employed to efficiently find composite services with near-optimal functional quality (i.e., quality of semantic matchmaking, QoSM for short) or nonfunctional quality (i.e., QoS) for each composition request individually. With a rapid increase in composition requests from a growing number of users, solving one composition request at a time can hardly meet the efficiency target anymore. Driven by the idea that the solutions obtained from solving one request can be highly useful for tackling other related requests, multitasking service composition approaches have been proposed to efficiently deal with multiple composition requests concurrently. However, existing attempts have not been effective in learning and sharing knowledge among solutions for multiple requests. In this article, we model the problem of collectively handling multiple service composition requests as a new multitasking service composition problem and propose a new permutation-based multifactorial evolutionary algorithm based on an estimation of distribution algorithm (EDA), named PMFEA-EDA, to effectively and efficiently solve this problem. In particular, we introduce a novel method for effective knowledge sharing across different service composition requests. For that, we develop a new sampling mechanism to increase the chance of identifying high-quality service compositions in both the single-tasking and multitasking contexts. Our experiment shows that our proposed approach, PMFEA-EDA, takes much less time than existing approaches that process each service request separately, and also outperforms them in terms of both QoSM and QoS.

As a lightweight and flexible infrastructure solution, containers have increasingly been used for application deployment on a global scale. By rapidly scaling containers at different locations, the deployed applications can handle dynamic workloads from the worldwide user community. Existing studies usually focus on the (dynamic) container scaling within a single data center or the (static) container deployment across geo-distributed data centers. This article studies an increasingly important container scaling problem for application deployment in geo-distributed clouds. Reinforcement learning (RL) has been widely used in container scaling due to its high adaptability and robustness. To handle high-dimensional state spaces in geo-distributed clouds, we propose a deep RL algorithm, namedDeepScale, to auto-scale containerized applications.DeepScaleinnovatively utilizes multi-step predicted future workloads to train a holistic scaling policy. It features several newly designed algorithmic components, including a domain-tailored state constructor and a heuristic-based action executor. These new algorithmic components are essential to meet the requirements of low deployment costs and achieve desirable application performance. We conduct extensive simulation studies using real-world datasets. The results show thatDeepScalecan significantly outperform an industry-leading scaling strategy and two state-of-the-art baselines in terms of both cost-effectiveness and constraint satisfaction.

With the popularity of cloud computing, many organizations process their workflow tasks in cloud resources based on the Pay-As-Per-Use model. Dynamic Workflow Scheduling (DWS) aims to allocate dynamically arriving workflow tasks to cloud resources with optimal makespan, cost, load-balancing, etc. To timely allocate arriving tasks, heuristics have been used to solve the DWS problem in cloud environment. However, most of them are manually designed, considering a single objective, and use simple features to allocate resources to workflow tasks. In practice, multiple objectives should be considered to provide trade-off heuristics for users to choose from. In this paper, we propose a genetic programming hyper-heuristic (GPHH) approach to automatically generate multiple heuristics for multi-objective DWS. Our experimental evaluation using benchmark datasets demonstrates the effectiveness of our proposed GPHH approach.

Containers are lightweight and provide the potential to reduce more energy consumption of data centers than Virtual Machines (VMs) in container-based clouds. On-line resource allocation is the most common operation in clouds. However, the on-lineResource Allocation in Container-based clouds (RAC)is new and challenging because of its two-level architecture, i.e., the allocations of containers to VMs and the allocation of VMs to physical machines. These two allocations interact with each other, and hence cannot be made separately. Since on-line container allocation requires a real-time response, most current allocation techniques rely on heuristics (e.g., First Fit and Best Fit), which do not consider the comprehensive information such as workload patterns and VM types. As a result, resources are not used efficiently and the energy consumption is not sufficiently optimized. We first propose a novel model of the on-lineRACproblem with the consideration of VM overheads, VM types and an affinity constraint. Then, we design a Cooperative Coevolution Genetic Programming (CCGP) hyper-heuristic approach to solve theRACproblem, namedCCGP-RAC.CCGP-RACcan learn the workload patterns and VM types from historical workload traces and generate allocation rules. The experiments show significant improvement in energy consumption compared to the state-of-the-art algorithms.

Quality-of-service (QoS)-aware automated semantic Web service composition aims to find a composite service with optimized or near-optimized QoS and quality of semantic matchmaking within polynomial time. To cope with this NP-hard problem with high complexity, a variety of evolutionary computation (EC) techniques has been developed. To improve the effectiveness and efficiency of these techniques, in this article, we proposed a novel memetic estimation of the distribution algorithm-based approach, namely, MEEDA, to tackle this problem. In particular, MEEDA explores four different domain-dependent local search methods that search for effective composite services by utilizing several neighborhood structures. Apart from that, to significantly reduce the computational time of MEEDA, an efficient local search strategy is introduced by combining a uniform fitness distribution scheme for selecting suitable solutions and stochastic local search operators for effectively and efficiently exploiting neighbors. To better demonstrate MEEDA’s effectiveness and scalability, we create a more challenging, augmented version of the service composition benchmark dataset. Experimental results on this benchmark show that MEEDA with newly developed domain-dependent local search operator, i.e., layer-based constrained one-point swaps, significantly outperforms existing state-of-the-art algorithms in finding high-quality composite services.

Multi-cloud is becoming a popular cloud ecosystem because it allows enterprise users to share the workload across multiple cloud service providers to achieve high-quality services with lower operation cost and higher application resilience. In multi-cloud, cloud services are widely distributed at different locations with differentiated prices. Therefore, Web application providers face the challenge to select proper cloud services for application replication and deployment with the aim of minimizing the deployment cost. Meanwhile, the deployed application replicas must satisfy the constraint on request response time to maintain the quality of user experience. To meet the two major requirements, this article studies a new problem of Web application replication and deployment in multi-cloud (WARDMC) that jointly considers both the cost minimization and constraints on average response time, including particularly request processing time and network latency. To address the problem, we develop a new approach named MCApp. MCApp combines iterative mixed integer linear programming with domain-tailored large neighborhood search to optimize both application replicas deployment and user requests dispatching. Extensive experiments using the real-world datasets demonstrate that MCApp significantly outperforms several recently proposed approaches.

In distributed computing,Web Service Composition (WSC)leads to the effective reuse of existing services and produces added value. WSC must fulfil functional requirements and optimiseQuality of Service (QoS)attributes, simultaneously.Memetic Algorithms (MAs)are promising for automatically composing numerous Web services to satisfy the above requirements.Data-intensive Web servicesfocus on providing and updating data with a significant volume of data operation and exchange. However, current composition approaches have ignored the impact of data communication and the distribution of services, which significantly affect the performance when applied to the challengingDistributed Data-intensive Web Service Composition (DDWSC)problem. Although recent approaches have revealed the usefulness of local search, they have completely overlooked the question of preferring appropriate composition solutions for local search. To address this research issue, we propose a priority-based selection method for the local search that can be consistently integrated with any MA for DDWSC. This enables us to develop state-of-the-art algorithms for DDWSC by explicitly considering the problem-specific, population and solution-related information for choosing a solution. Extensive experimental evaluation using benchmark datasets shows that our proposed method significantly outperforms several recently proposed methods.

Workflow with different patterns and sizes arrive at a cloud data center dynamically to be processed at virtual machines in the data center, with the aim to minimize overall cost and makespan while satisfying Service Level Agreement (SLA) requirement. To efficiently schedule workflows, manually designed heuristics are proposed in the literature. However, it is time consuming to manually design heuristics. The designed heuristics may not work effectively for heterogeneous workflow since only simple problem related factors are considered in the heuristics. Further, most of the existing approaches ignore the deadline constraints set in SLAs. Genetic Programming Hyper Heuristic (GPHH) can be used to automatically design heuristics for scheduling problems. In this paper, we propose a GPHH approach to automatically generate heuristics for the dynamic workflow scheduling problem, with the goal of minimizing the VM rental fees and SLA penalties. Experiments have been conducted to evaluate the performance of the proposed approach. Compared with several existing heuristics and conventional Genetic Programming (GP) approaches, the proposed Dynamic Workflow Scheduling Genetic Programming (DWSGP) has better performance and is highly adaptable to variations in cloud environment.

Traditional human-designed heuristics-based algorithms are commonly employed to address workflow scheduling problems. Such as Heterogeneous Earliest Finish Time (HEFT) and CriticalPath are two best-known list based heuristics. Generally, heuristics-based approaches can generate a single heuristic by making decisions based on the current status of the tasks and available resources and map the unscheduled tasks to the available resources. However, traditional heuristics can easily cause unbalancing load problem. For example, with the objective of minimizing the makespan, traditional heuristics prefer to use computation resources with high computation capacity. Usually, such resources are expensive, which will lead to a high cost as a result. Therefore it is hard for single traditional heuristics to do a trade-off; thus, traditional heuristics are hard to apply for multi-objective workflow scheduling. In this paper, we develop a novel algorithm for workflow scheduling by considering both minimizing cost and makespan. Experiments show that the MOSGP approach can effectively minimize makespan and cost simultaneously.

With the ever increasing number of functionally similar web services being available on the Internet, the market competition is becoming intense. Web service providers (WSPs) realize that good Quality of Service (QoS) is a key of business success and low network latency is a critical measurement of good QoS. Because network latency is related to location, a straightforward way to reduce network latency is to allocate services to proper locations. However, Web Service Location Allocation Problem (WSLAP) is a challenging task since there are multiple objectives potentially conflicting with each other and the solution search space has a combinatorial nature. In this paper, we consider minimizing the network latency and total cost simultaneously and model the WSLAP as a multi-objective optimization problem. We develop a new PSO-based algorithm to provide a set of trade-off solutions. The results show that the new algorithm can provide a more diverse range of solutions than the compared three well known multi-objective optimization algorithms. Moreover, the new algorithm performs better especially on large problems.

Micro-services is a widely adopted architecture to develop large scale web applications. To provide a scalable and low-overhead resource service to micro-service applications, the new container-based clouds are proposed. The new clouds use both containers and VMs to manage resources to achieve a low-overhead, high-utilization data center. However, existing resource allocation approaches either do not consider the dependencies between containers or can only be applied in OS-level container clouds which allocate containers directly to physical machines. To address the multi-objective optimization problem, this work proposes a multi-objective NSGA-II to optimize the availability of applications and the energy consumption requirement of container-based clouds. Our goal is to provide solutions with different tradeoffs between two objectives for cloud providers to choose from. We evaluate the algorithm with a wide range of scenarios by simulation and compare with state-of-the-art algorithms. The results show that our approach significantly outperforms other approaches.

To gain technical and economic benefits, enterprise application providers are increasingly moving their workloads to the cloud. With the increasing number of cloud resources from multiple cloud providers at different locations with differentiated prices, application providers face the challenge to select proper cloud resources to replicate and deploy applications to maintain low response time and high quality of user experience without running into the risk of over-spending. In this paper, we study the global-wide cloud application replication and deployment problem considering the application average response time, including particularly application execution time and network latency, subject to the budgetary control. To address the problem, we propose a GA-based approach with domain-tailored solution representation, fitness measurement, and population initialization. Extensive experiments using the real-world datasets demonstrate that our proposed GA-based approach significantly outperforms common application placement strategies, i.e., NearData and NearUsers, and our recently proposed hybrid GA approach.

Enterprise application providers are increasingly moving their workloads to the cloud for technical and economic benefits. Multi-cloud environment makes it possible to orchestrate multiple cloud resources. With the increasing number of available cloud resources provided by multiple cloud providers at different locations with different prices, application providers face the challenge to select proper cloud resources to deploy their applications in the form of a workflow of component service units. Existing studies usually consider minimizing execution time or/and deployment cost. From the perspective of application providers, however, they also pay huge attention to application response time, including particularly network latency between deployed services and users. Meanwhile, application deployment is often subject to stringent budgetary control to ensure financial viability. This article studies a new type of composite application deployment problem that jointly considers both the performance optimization and budget control in multi-cloud at the global scale. To find solutions with minimal response time without running into the risk of over-spending, we propose a hybrid GA-based approach, featuring new design of domain-tailored service clustering, repair algorithm, solution representation, population initialization, and genetic operators. Extensive experiments using the real-world dataset demonstrate that our proposed hybrid GA approach outperforms some recently proposed approaches.

Containerization does not only support fast development and deployment of web applications but also provides the potential to improve the energy efficiency in cloud data centers. In container-based clouds, containers are allocated to virtual machines (VMs) and VMs are allocated to physical machines (PMs). This new architecture requires consolidation algorithms to select heterogeneous VMs to host containers and consolidate VMs to PMs simultaneously. Existing server consolidation techniques in VM-based clouds can hardly be applied because of the two-level architecture of the container-based clouds. This paper proposes a novel genetic algorithm (GA) with dual chromosome representation to solve the problem. The experiments show that the proposed GA achieves significantly higher energy efficiency than the compared state-of-the-art algorithms on a wide range of test problems.

Cloud computing allows software application or service providers to purchase computational resources for hosting software applications or services. There is an increasing number of cloud service vendors, each offering different types of computational resources, such as virtual machines (VMs), in distinct physical locations, meaning that the chosen computational resources directly affect the cost and performance of the deployed applications. Given the increasing number of cloud providers and types of computational resources, the process of selecting the most suitable resources to meet multiple quality of service (QoS) requirements becomes quite challenging. Thus, the aim of this paper is to propose an efficient and effective approach to the problem of application deployment in multi-cloud with the objective of minimizing overall deployment costs and response time. This is accomplished by employing a hybrid NSGA-II approach with a local search method. Experimental evaluations with benchmark datasets demonstrate that our proposed hybrid approach outperforms NSGA-II and SPEA2.

Web Service Composition (WSC) is a particularly promising application of Web services, where multiple individual services with specific functionalities are composed to accomplish a more complex task, which must fulfil functional requirements and optimise Quality of Service (QoS) attributes, simultaneously. Additionally, large quantities of data, produced by technological advances, need to be exchanged between services. Data-intensive Web services, which manipulate and deal with those data, are of great interest to implement data-intensive processes, such as distributed Data-intensive Web Service Composition (DWSC). Researchers have proposed Evolutionary Computing (EC) fully-automated WSC techniques that meet all the above factors. Some of these works employed Memetic Algorithms (MAs) to enhance the performance of EC through increasing its exploitation ability of searching neighbourhood area of a solution. However, those works are not efficient or effective. This paper proposes an MA-based approach to solving the problem of distributed DWSC in an effective and efficient manner. In particular, we develop an MA that hybridises EC with a flexible local search technique incorporating distance of services. An evaluation using benchmark datasets is carried out, comparing existing state-of-the-art methods. Results show that our proposed method has the highest quality with an acceptable execution time.

To gain technical and economic benefits, various enterprises are increasingly moving their workloads to the cloud. Multi-cloud environment makes it possible to coordinate access and utilize multiple cloud resources. When business application developers host their business process in the cloud, they face the issue of choosing which cloud to deploy the instance-intensive business workflows. However, the existing studies rarely consider the optimization techniques for organizing cloud services with respect to various criteria, such as cost and performance. In this paper, we propose a seeding-based GA approach to address the multi-cloud workflow deployment problem, i.e. selecting and leasing virtual machines (VMs) to minimize deployment cost and response time. Experimental results show that the proposed GA approach with seeding strategy outperforms the existing approach proposed in the literature and standard GA algorithm.

Container technology has become a new trend in both the software industry and cloud computing. Containers support the fast development of web applications and they have the potential to reduce energy consumption in data centers. Containers are usually first allocated to virtual machines (VMs) and VMs are allocated to physical machines. The container allocation is a challenging task which involves a two-level allocation problem. Current research overly simplifies the container allocation into a one-level allocation problem and uses simple rule-based approaches to solve the problem. As a result, the resource is not allocated efficiently which leads to high energy consumption. This paper provides a novel definition of the two-level container allocation problem. Then, we develop a hybrid approach using genetic programming hyper-heuristics combined with human-designed rules to solve the problem. The experiments show that our hybrid approach is able to significantly reduce energy consumption than solely using human-designed rules.

Web services are basic functions of a software system to support the concept of service-oriented architecture. They are often composed together to provide added values, known as web service composition. Researchers often employ Evolutionary Computation techniques to efficiently construct composite services with near-optimized functional quality (i.e., Quality of Semantic Matchmaking) or non-functional quality (i.e., Quality of Service) or both due to the complexity of this problem. With a significant increase in service composition requests, many composition requests have similar input and output requirements but may vary due to different preferences from different user segments. This problem is often treated as a multi-objective service composition so as to cope with different preferences from different user segments simultaneously. Without taking a multi-objective approach that gives rise to a solution selection challenge, we perceive multiple similar service composition requests as jointly forming an evolutionary multi-tasking problem in this work. We propose an effective permutation-based evolutionary multi-tasking approach that can simultaneously generate a set of solutions, with one for each service request. We also introduce a neighborhood structure over multiple tasks to allow newly evolved solutions to be evaluated on related tasks. Our proposed method can perform better at the cost of only a fraction of time, compared to one state-of-art single-tasking EC-based method. We also found that the use of the proper neighborhood structure can enhance the effectiveness of our approach.

Cloud computing enables enormous computational resources to be scheduled as parallel workflow applications. Most traditional heuristics can only solve one particular scheduling problem. For example, Heterogeneous Earliest Finish Time (HEFT) and Greedy algorithms allocate resources to given ordered list of tasks using a specific single heuristic, which only caters for a specific scheduling problem, e.g. the fixed number of tasks in a workflow and available resources. Many researchers considered the heterogeneous workflows and cloud resources in scheduling in order to minimize the cost and makespan, but the solutions provided are only for specific workflow pattern. In this paper, we demonstrate a workflow scheduling problem which considers the combination of heterogeneous workflows as well as heterogeneous computing resources. We proposed Flexible Scheduling using Genetic Programming (FSGP) approach to min-imize the total cost and makespan of heterogeneous workflows in the cloud. The performance of our proposed FSGP is regardless of the number of tasks in the workflow, available resources and workflow patterns. We evaluated our proposed approach using a benchmark dataset. Performance evaluation of some well-known algorithms such as HEFT and greedy algorithms exhibit that our FSGP approach perform better than other competing algorithms.

Automatic Web service composition has received much interest in the last decades. Data-intensive concepts have provided a promising computing paradigm for data-intensive Web service composition. Due to the complexity of the problem, metaheuristics in particular Evolutionary Computing (EC) techniques have been used for solving this composition problem. However, most of the current works neglected the distributed nature of data-intensive Web services. In this paper, we study the problem of distributed data-intensive service composition and propose a model which integrates attributes of constituent data-intensive Web services and attributes of the network. The core idea is to propose a communication cost and time model of a composed Web service considering communication delay and cost. We therefore propose a novel method based on Genetic Algorithm (GA) which uses a variation of K-means clustering algorithm.

In the last few years, the container-based Cloud computing paradigm has gradually emerged as a flexible approach for energy efficient resource utilization. Cloud providers aim to optimize resource utilization and energy consumption while performing container consolidation, which involves virtual machine (VM) selection and VM placement. Most of the related works focus either on VM selection or VM placement, neglecting the interplay of the two optimization problems. This paper proposes a Two-stage Multi-type Particle Swarm Optimization approach, named TMPSO, to energy-aware container consolidation in Cloud data centers. Our experimental evaluation based on benchmark datasets demonstrates that our proposed algorithm shows further energy saving comparing with some existing approaches.

Service-oriented architecture (SOA) has been widely employed in the field of software engineering, since it encourages attributes such as modularity and code reusability across different applications. Web service composition, where atomic services that accomplish simple tasks are combined into an application that fulfils a more complex function, is one popular application of SOA. Existing composition works focus on building functional and quality-optimised applications. A subset of these works use multi-objective evolutionary computing techniques to produce a Pareto front of compositions, though they assume that the basic structure of the composition workflow is already known. In our recent work, we removed this assumption by combining NSGA-II with a solution representation that allows for different workflow structures to be investigated. However, the multi-objective nature of the problem made it difficult to apply local search to further improve the results. In this paper we hybridise NSGA-II with MOEA/D, which allows the problem to be decomposed into multiple single-objective subproblems where a simple form of local search can be applied. Experiment results show that the use of local search improves the quality of the fronts produced by the hybrid approach for a number of composition tasks.

Web service and Cloud computing have significantly reformed the software industry. The need for web service allocation in the cloud environment is increasing dramatically. In order to reduce the cost for service providers as well as improve the utilization of cloud resource for cloud providers, this paper formulates the web service resource allocation in cloud environment problem as a two-level multi-objective bin packing problem. It proposes a NSGA-II-based algorithm with specifically designed genetic operators. We are compared with two varieties of the algorithm. The results show that the proposed algorithm can provide reasonably good results with low violation rate.

Web services have become increasingly popular in recent years, given their modular nature and reusability potential. A particularly promising application is in Web service composition, where multiple individual services with specific functionalities are composed to accomplish a more complex task. Researchers have proposed evolutionary computing techniques for creating compositions that are not only feasible, but also have the best possible Quality of Service (QoS). Some of these works employed multi-objective techniques to tackle the optimisation of compositions with conflicting QoS attributes, but they are not fully automated, i.e. they assume the composition workflow structure is already known. This assumption is often not satisfied, as the workflow is often unknown. This paper proposes a genetic programming-based method to automatically generate service compositions in a multi-objective context, based on a novel fragmented tree representation. An evaluation using benchmark datasets is carried out, comparing existing methods adapted to the multi-objective composition problem. Results show that the fragmented method has the lowest execution time overall. In terms of quality, its Pareto fronts are equivalent to those of one of the approaches but inferior to those of the other. More importantly, this work provides a foundation for future investigation of multi-objective fully automated service composition.

Evolutionary scheduling and combinatorial optimisation is an active research area and attracts the attentions of many researchers from computer science and operations research. Many advances have been made in this field and its scope in terms of techniques and applications has been continuously extended. In this position paper, we provide an overall picture of some key challenges in the field, discuss potential future research directions, and give our position in the field. We focus on three major issues that are encountered in practice, namely dynamic changes, multiple interdependent decisions, and multiple objectives. Our view is that the researchers should step out of our comfort zone to deal with messy and complicated issues in real-world applications.

Web service composition is a popular area of investigation, since it encourages code reuse as opposed to re-implementing already existing functionality modules. Performing such compositions manually can be quite time-consuming, since the functionality of each service included in a solution must be fulfilled, while at the same time selecting candidates with the best possible quality of service attributes. This work proposes a memetic algorithm that can perform Web service composition in a fully automated manner, optimising a sequence of services that is then decoded into the final solution. The key advantage of this representation is that it separates the quality optimisation technique from the enforcement of correctness constraints, thus simplifying the overall composition process and preventing solutions from being overly constrained. While this indirect composition approach has been investigated earlier, the previous representation relied on weights for establishing a service sequence and used PSO as the optimisation technique. In this work, on the other hand, sequences are directly represented using a vector of services, which reduces the overall search space. Additionally, the memetic algorithm employed in this work is better at exploring solutions within promising areas of the search space. Experiments were conducted comparing the memetic approach with the previously proposed PSO, with two key findings. Firstly, the new representation reduces the overall execution time while maintaining the original solution quality. Secondly, the use of the memetic local search improves the overall quality of solutions, though it may incur longer execution times.

Web Service Composition (WSC) is a prominent way of actualizing service-oriented architecture by integrating network-accessible Web services into a new invokable application. Evolutionary computation techniques have provided rewarding approaches in automatic Web service composition over the last decade. However, the studies on considering both functionality and non-functionality (i.e. Quality-of-Service, QoS) properties are still limited. In this paper, we propose a novel Graph-Based Memetic Algorithm (GBMA) for solving the QoS-aware WSC problems. GBMA adopts the graph representation proposed by GraphEvol, which is one of the state-of-the-art algorithms. More importantly, GBMA designs and uses a local search based on two newly designed move operators to overcome the drawbacks of the mutation operator in GraphEvol. The experimental results show that the proposed GBMA outperformed GraphEvol, which is the counterpart without local search, in terms of both solution quality and convergence speed. This demonstrates the efficacy and efficiency of combining local search with global search in solving QoS-aware WSC problems.

Automated Web service composition is one of the holy grails of service-oriented computing, since it allows users to create an application simply by specifying the inputs the resulting application should require, the outputs it should produce, and any constraints it should respect. The composition problem has been handled using a variety of techniques, from AI planning to optimisation algorithms, however no approach so far has focused on handling three composition dimensions simultaneously, producing solutions that are: (1) fully functional (i.e. fully executable), (2) respect conditional constraints (e.g. user can specify logical branching), and (3) are optimised according to nonfunctional Quality of Service (QoS) measurements. This paper presents a genetic programming approach that addresses these three dimensions simultaneously through the fitness function, as well as through the enforcement of constraints to candidate trees during initialisation, mutation, and crossover. The approach is tested using an extended version of the WSC2008 datasets, and results show that fully functional and quality-optimised solutions can be created for all associated tasks, with an execution time that is roughly equivalent to that of a non-conditional approach.

QoS-aware web service composition has attracted increasing attention recently. Meanwhile, due to the explosion in the volume of data, providing efficient methods for composing data intensive services poses new challenges to the service composition problem. In this paper, a new search and optimisation approach based on a reduced space searching strategy, named F-MOGP, is presented to address the problem of QoS-aware data intensive service composition driven by four conflicting quality objectives. The experimental results show that the proposed approach is computationally efficient by search space reduction. Compared with the existing single-objective and multi-objective optimization methods, a set of higher-quality data intensive service compositions can be successfully generated to support decision makers by providing them with potential trade-offs among different objectives.

Web services are network-accessible modules that perform specific tasks and can be integrated into Web service compositions to accomplish more complex objectives. Due to the fast-growing number of Web services and the well-defined nature of their interfaces, the field of automated Web service composition is quickly expanding. The use of Particle Swarm Optimisation composition techniques that take Quality of Service (QoS) properties into account is well-established in the field. However, the commonly utilised approach is to optimise a preselected Web service composition workflow, which requires domain expertise and prior knowledge and thus may lead to the loss of better solutions that require different workflow configurations. This paper presents a graph-based PSO technique which simultaneously determines an optimal workflow and near-optimal Web services to be included in the composition based on their QoS properties, as well as a greedy-based PSO technique which follows the commonly utilised approach. The comparison of the two techniques shows that despite requiring more execution time, the graph-based approach provides equivalent or better solutions than the greedy-based approach, depending on the workflow preselected by the greedy-based PSO. These results demonstrate that under certain circumstances, the graph-based approach is capable of producing solutions whose fitness surpasses that of the solutions obtained by employing the greedy-based approach.

Web service composition has emerged as a promising technique for building complex web applications, thus supporting business-to-business and enterprise application integration. Nowadays there are increasing numbers of web services are distributed across the internet. For a given service request there are many ways of service composition that can meet the service functional requirements (inputs and outputs) but have different qualities of Services (QoS), like response time or execution cost. QoS-aware web service composition seeks to find a service composition with optimized QoS properties. Genetic Programming is an efficient tool for tacking such optimization problems efficiently. This paper proposes a novel GP-based approach for distributed web service composition where multiple QoS constraints are considered simultaneously. A series of experiments have been conducted to evaluate the proposed approach with test data. The results show that our approach is efficient and effective to find a near-optimal service composition solution in the context of distributed service environment.

Web services are software entities that can be deployed, discovered and invoked in the distributed environment of the Internet through a set of standards such as Simple Object Access Protocol (SOAP), Web Services Description Language (WSDL) and Universal Description, Discovery and Integration (UDDI). However, atomic web service can only provide simple functionality. A range of web services are required to be incorporated into one composite service in order to offer value-added and complicated functionality when no existing web service can be found to satisfy users' request. In service-oriented architecture (SOA), web services composition has become an efficient solution to support business-to-business and enterprise application integration (EAI). In addition to functional properties (i.e., inputs and outputs), web services have non-functional properties called quality of service (QoS) that encompasses a number of parameters such as execution cost, response time and availability. Nowadays with the rapid increase in the number of available web services, a great number of services provide overlapping or identical functionality but vary in QoS attribute values. Due to the huge search space of the composition problem, a genetic programming (GP) approach is proposed in this paper, which aims to produce the desired outputs based on available inputs, as well as ensure that the composite service has the optimal QoS value. Furthermore, an adaptive method is applied to the standard form of GP in order to avoid low rate of convergence and premature convergence. A series of experiments have been conducted to evaluate the proposed approach, and the results show that the adaptive genetic programming approach (AGP) has a good performance in finding a valid solution within low search time and is superior to the traditional approaches

The geometrically enhanced ER model (GERM) addresses conceptual geometric modelling on two levels. On the surface level GERM is an extended ER model, in which attributes are associated with types with sets of values as domains. On the internal level some of the types, the geometric types, are further associated with geometric domains, which define point sets by means of algebraic curves. For query handling the operations on geometric types give rise to operations on point sets, which can be realized by a small set of operations on algebraic curves. The core problem is then to obtain a surface representation as the result. We show that symbolic computation is essential for solving this problem.

A schema algebra comprises operations on database schemata for a given data model. Such algebras are useful in database design as well as in schema integration. In this article we address the necessary theoretical underpinnings by introducing a novel notion of conceptual schema morphism that captures at the same time the conceptual schema and its semantics by means of the set of valid instances. This leads to a category of schemata that is finitely complete and co-complete. This is the basis for a notion of completeness of schema algebras, if it captures all universal constructions in the category of schemata. We exemplify this notion of completeness for a recently introduced particular schema algebra.

The emergence of service-oriented computing and cloud computing advocate the development of new software applications on the basis of existing services. To make effectively and efficiently use of existing services, it is essential to describe services so that developers can easily discover and correctly exploit them for their own software development endeavours. In this paper, we present a framework in which existing services available through a cloud are modelled by Abstract State Services. For specification, we use TBox and ABox statements bound to XML Schema and XML. Given such a representation, XQuery can be used for service discovery.

Service-oriented computing, cloud computing, and web services composition mark cornerstones of a paradigm shift in software systems engineering. The general new idea is to use as much as possible services that are made available by others, mostly disseminated via the web. In this paper, we present an abstract model for clouds as federations of services together with a specification of semantics and quality characteristics. For the services as such we adopt the abstract model of abstract state services, which is based on views on some hidden database layer that are equipped with service operations. For the semantics we adopt types for in- and output, pre- and post-conditions, and a description of functionality within an operations ontology. In addition, quality characteristics capture performance, costs, availability, etc. On the basis of this model of clouds, users may conduct a (web) search for usable services, extract service components, and recompose these components. The quality characteristics can be used to optimise the selection of usable services.

The current transition from Web 1.0 to Web 2.0 is not so much a change of the world wide web itself, but a change of usage. While Web 1.0 is mainly provider-driven, Web 2.0 is user-driven and content-centred. This also implies a fast change of content turning the web from a rather static medium into an active communication medium. Furthermore, the web is subject to even more changes emphasising content, commerce, community and context, personalization, and vertical search, which is branded as the next generation Web 3.0.In this situation the design of Web Information Systems (WISs) requires adaptations to the new developments, if not completely new methods. The tutorial wants to show that the co-design method for WISs did already support many of the features required in Web 2.0 applications. This is particularly stressed by the focus of user contributions to a community or service and the incorporation of collaboration. It will further outline, how the method has to be extended to be fully functional as a Web 2.0 applications development method.The tutorial comprises three parts, the first one dealing with the transition from Web 1.0 to Web 2.0 and beyond, the second one dealing with the co-design methodology with particular emphasis on service orientation, personalisation and contribution to communities, and the final one devoted to the composition of services and collaboration.Part I will give a brief description of three stages that websites have gone through and still will go through: author-driven Web 1.0, user-driven and content-centred Web 2.0, and Web 3.0, which is characterised by (4C+P+VS), where 4C means Content, Commerce, Community and Context, P means Personalization, and VS denotes Vertical Search. In particular, Enterprise 2.0, cloud computing, mashups and Web-Oriented architecture are discussed in detail in order to show the problems in WIS development and the need for WIS development methods. Part II presents the major blocks of our WIS development methodology dealing with strategic modelling of WISs, usage modelling of WISs by means of storyboarding, conceptual modelling of WISs by means of media types, and semantics and pragmatics of storyboarding. Particular attention is paid to services and contributions to communities. Part III focuses on service composition and collaboration showing how the WIS co-design techniques presented in Part II can be adopted to solutions to various Web 2.0 applications including E-Business, Enterprise 2.0, Mashups, thus picking up on the challenges that were highlighted in part I.

Data warehouses integrate and aggregate data from various sources to support decision making within an enterprise. Usually, it is assumed that data are extracted from operational databases used by the enterprise. Web warehousing relaxes this view permitting data sources to be located anywhere on the world-wide web. In this paper we even go one step further and propose that web warehousing should exploit data-intensive web services. For this we adopt the view of the recently introduced model of abstract state services (ASSs). An ASS combines a hidden database layer with an operation-equipped view layer, and can be anything from a simple function to a full-fledged Web Information System, thus provides an abstraction of web services that can be made available for use by other systems, e.g. via the web. We demonstrate how data warehouses and OLAP systems can be considered in a natural way as ASSs, i.e. selected data and functionality of a data warehouse may be made available in the form of a generalised web service, and consequently ASS composition can be used for web warehousing. In order to become workable the model of ASSs has to be extended in a way that allows services to be searched for. For this we propose federations of services together with an ontology that specifies their semantics.