Call Limit-Based Composite Service Selection

International audienceAPIs allow companies to export, via the Internet, their skills and know-how, or even to open up new markets and new media for sale. But to fully exploit the advantages of these services, customers, mainly developers, must be equipped with tools giving the possibility of being able to assemble different services together. Fortunately, the notion of service composition is quite advanced, and different tools exist to compose services. However, as APIs with similar functionality are expected to be provided by competing providers, the key challenge is to find the most relevant compositions. This issue has been addressed in the context of QoS-based composite service selection. The downside, in practice, customers choose services based on the number of call limits. In this paper, we propose an approach to select the most relevant compositions based on the notion of call limit. Specifically, we show how the call limits of the individual services can be aggregated to obtain the call limits of a given composition. Then, we introduce the notion of minimal budget skyline, which comprises the most interesting compositions that fit within the customer's budget. In addition, we develop two algorithms, based on effective pruning strategies, to efficiently compute the minimal budget skyline. Finally, we present a thorough experimental evaluation of our approach

Service selection and transactional management for web service composition

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Full Solution Indexing and Efficient Compressed Graph Representation for Web Service Composition

Service-oriented computing enhances business scalability and flexibility; providers who expect to benefit from it may bring explosive growth of web services. Searching an optimal composition solution with both functional and non-functional requirements is a computationally demanding problem: the time and space requirements may be infeasible due to the high number of available services. In this thesis, we study QoS-aware service composition problems which satisfy functional requirements as well as non-functional requirements. We use optimization algorithms to enhance accuracy of our searching algorithms. In the first approach, we propose a database-based approach to search a service composition solution. Current in-memory methods are limited by expensive and volatile physical memory, to deal with this problem, we want to use the large space available in relational database on persistence disk. In our database-based approach, all possible service combinations are generated beforehand and stored in a relational database. When a user request comes, SQL queries are composed to search in the database and K best solutions are returned. We test the performance of the proposed approach with a service challenge data set; experiment results demonstrate that this approach can always successfully find top-K valid solutions.We offer three main contributions in this approach. First, we overcome the disadvantages of in-memory composition algorithms, such as volatile and expensive, and provide a solution suitable to cloud environments. Second, we fetch top-K solutions in case the optimal solution is not available as backup solutions to the user. Third, compared with other pre-computing composition methods, we use a single SQL query: there is no need to eliminate spurious services iteratively. Then, we propose the application of a skyline operator to reduce the search space and improve the scalability. Skyline analysis returns all of the elements that are not dominated by another element. We use skyline analysis to find a set of candidate services referred to as "skyline services", therefore, less competitive services are reduced. This allows us to find a solution for a large composition problem with less storage and increased speed. In reality, different users may have same requests, we are motivated to pick some popular requests and generate paths for fast delivery. These paths are stored in a separate table of the relational database. When a user request comes, we first search to find a nearly ready-made solution. Only as a last resort do we search the table with whole paths to find a solution. Finally, to deal with the problem that the search space may explore, we apply a compressed data structure to represent the service composition graph. The goal is to allow algorithms running in in-memory over larger graphs. In this approach, we use compact K2-trees to represent the service composition graph. When a user request comes, we search the K2-tree for a satisfactory solution. We use an array to store values in the last level of the compact tree, which represents relationships between services and concepts. In our algorithms, we find services' inputs (resp. outputs) by locating elements in this array directly, therefore, decompressing the graph is unnecessary. To the best of our knowledge, our work is the first attempt to consider compact structure in solving web service composition problems. Experiment results demonstrate that this approach takes less space and has good scalability when handling a large number of web services. We provide different approaches to search a solution for the user. If the user want to find an optimal solution with fewer services, he may use the database-based approach to search for a solution. If the user want to get a solution in a short time, he may choose the in-memory approach

Technical debt-aware and evolutionary adaptation for service composition in SaaS clouds

The advantages of composing and delivering software applications in the Cloud-Based Software as a Service (SaaS) model are offering cost-effective solutions with minimal resource management. However, several functionally-equivalent web services with diverse Quality of Service (QoS) values have emerged in the SaaS cloud, and the tenant-specific requirements tend to lead the difficulties to select the suitable web services for composing the software application. Moreover, given the changing workload from the tenants, it is not uncommon for a service composition running in the multi-tenant SaaS cloud to encounter under-utilisation and over-utilisation on the component services that affects the service revenue and violates the service level agreement respectively. All those bring challenging decision-making tasks: (i) when to recompose the composite service? (ii) how to select new component services for the composition that maximise the service utility over time? at the same time, low operation cost of the service composition is desirable in the SaaS cloud. In this context, this thesis contributes an economic-driven service composition framework to address the above challenges. The framework takes advantage of the principal of technical debt- a well-known software engineering concept, evolutionary algorithm and time-series forecasting method to predictively handle the service provider constraints and SaaS dynamics for creating added values in the service composition. We emulate the SaaS environment setting for conducting several experiments using an e-commerce system, realistic datasets and workload trace. Further, we evaluate the framework by comparing it with other state-of-the-art approaches based on diverse quality metrics

Supporting Multi-Criteria Decision Support Queries over Disparate Data Sources

In the era of big data revolution, marked by an exponential growth of information, extracting value from data enables analysts and businesses to address challenging problems such as drug discovery, fraud detection, and earthquake predictions. Multi-Criteria Decision Support (MCDS) queries are at the core of big-data analytics resulting in several classes of MCDS queries such as OLAP, Top-K, Pareto-optimal, and nearest neighbor queries. The intuitive nature of specifying multi-dimensional preferences has made Pareto-optimal queries, also known as skyline queries, popular. Existing skyline algorithms however do not address several crucial issues such as performing skyline evaluation over disparate sources, progressively generating skyline results, or robustly handling workload with multiple skyline over join queries. In this dissertation we thoroughly investigate topics in the area of skyline-aware query evaluation. In this dissertation, we first propose a novel execution framework called SKIN that treats skyline over joins as first class citizens during query processing. This is in contrast to existing techniques that treat skylines as an add-on, loosely integrated with query processing by being placed on top of the query plan. SKIN is effective in exploiting the skyline characteristics of the tuples within individual data sources as well as across disparate sources. This enables SKIN to significantly reduce two primary costs, namely the cost of generating the join results and the cost of skyline comparisons to compute the final results. Second, we address the crucial business need to report results early; as soon as they are being generated so that users can formulate competitive decisions in near real-time. On top of SKIN, we built a progressive query evaluation framework ProgXe to transform the execution of queries involving skyline over joins to become non-blocking, i.e., to be progressively generating results early and often. By exploiting SKIN\u27s principle of processing query at multiple levels of abstraction, ProgXe is able to: (1) extract the output dependencies in the output spaces by analyzing both the input and output space, and (2) exploit this knowledge of abstract-level relationships to guarantee correctness of early output. Third, real-world applications handle query workloads with diverse Quality of Service (QoS) requirements also referred to as contracts. Time sensitive queries, such as fraud detection, require results to progressively output with minimal delay, while ad-hoc and reporting queries can tolerate delay. In this dissertation, by building on the principles of ProgXe we propose the Contract-Aware Query Execution (CAQE) framework to support the open problem of contract driven multi-query processing. CAQE employs an adaptive execution strategy to continuously monitor the run-time satisfaction of queries and aggressively take corrective steps whenever the contracts are not being met. Lastly, to elucidate the portability of the core principle of this dissertation, the reasoning and query processing at different levels of data abstraction, we apply them to solve an orthogonal research question to auto-generate recommendation queries that facilitate users in exploring a complex database system. User queries are often too strict or too broad requiring a frustrating trial-and-error refinement process to meet the desired result cardinality while preserving original query semantics. Based on the principles of SKIN, we propose CAPRI to automatically generate refined queries that: (1) attain the desired cardinality and (2) minimize changes to the original query intentions. In our comprehensive experimental study of each part of this dissertation, we demonstrate the superiority of the proposed strategies over state-of-the-art techniques in both efficiency, as well as resource consumption

Multi User Context-Aware Service Selection for Mobile Environments - A Heuristic Technique

Modern service systems build on top of service dominant designs which encompass contextualization (value-in-context) and collaboration (value-in-use) between users and service providers. Processes in this domain often require the consideration of both context information (e.g., location or time of day) and multiple participating users where each user probably has its own preferences and constraints (e.g., restricted overall budget). However, selecting a suitable service provider for each action of a process, especially when some of these actions are conducted together by several users, can be a complex decision problem in multi user context-aware service systems. Consequently, exact approaches are not fit to solve such a service selection problem in appropriate time. Thus, the paper proposes a heuristic technique applying a decomposition of the users’ global constraints and a local service selection. In this way, the aim is to determine a feasible service composition for each participating user while taking the users’ individual preferences and constraints as well as context information into account. The evaluation of the heuristic technique shows, based on a real-world scenario in the tourism domain, that the proposed approach is able to achieve close-to-optimal solutions while efficiently scaling with problem size and therefore can support decision makers in multi user context-aware service Systems

Service recommendation and selection in centralized and decentralized environments.

With the increasing use of web services in everyday tasks we are entering an era of Internet of Services (IoS). Service discovery and selection in both centralized and decentralized environments have become a critical issue in the area of web services, in particular when services having similar functionality but different Quality of Service (QoS). As a result, selecting a high quality service that best suits consumer requirements from a large list of functionally equivalent services is a challenging task. In response to increasing numbers of services in the discovery and selection process, there is a corresponding increase of service consumers and a consequent diversity in Quality of Service (QoS) available. Increases in both sides leads to a diversity in the demand and supply of services, which would result in the partial match of the requirements and offers. Furthermore, it is challenging for customers to select suitable services from a large number of services that satisfy consumer functional requirements. Therefore, web service recommendation becomes an attractive solution to provide recommended services to consumers which can satisfy their requirements.In this thesis, first a service ranking and selection algorithm is proposed by considering multiple QoS requirements and allowing partially matched services to be counted as a candidate for the selection process. With the initial list of available services the approach considers those services with a partial match of consumer requirements and ranks them based on the QoS parameters, this allows the consumer to select suitable service. In addition, providing weight value for QoS parameters might not be an easy and understandable task for consumers, as a result an automatic weight calculation method has been included for consumer requirements by utilizing distance correlation between QoS parameters. The second aspect of the work in the thesis is the process of QoS based web service recommendation. With an increasing number of web services having similar functionality, it is challenging for service consumers to find out suitable web services that meet their requirements. We propose a personalised service recommendation method using the LDA topic model, which extracts latent interests of consumers and latent topics of services in the form of probability distribution. In addition, the proposed method is able to improve the accuracy of prediction of QoS properties by considering the correlation between neighbouring services and return a list of recommended services that best satisfy consumer requirements. The third part of the thesis concerns providing service discovery and selection in a decentralized environment. Service discovery approaches are often supported by centralized repositories that could suffer from single point failure, performance bottleneck, and scalability issues in large scale systems. To address these issues, we propose a context-aware service discovery and selection approach in a decentralized peer-to-peer environment. In the approach homophily similarity was used for bootstrapping and distribution of nodes. The discovery process is based on the similarity of nodes and previous interaction and behaviour of the nodes, which will help the discovery process in a dynamic environment. Our approach is not only considering service discovery, but also the selection of suitable web service by taking into account the QoS properties of the web services. The major contribution of the thesis is providing a comprehensive QoS based service recommendation and selection in centralized and decentralized environments. With the proposed approach consumers will be able to select suitable service based on their requirements. Experimental results on real world service datasets showed that proposed approaches achieved better performance and efficiency in recommendation and selection process.N/

Value- and debt-aware selection and composition in cloud-based service-oriented architectures using real options

This thesis presents a novel model for service selection and composition in Cloud-based Service-Oriented Architectures (CB-SOA), which is called CloudMTD, using real options, Dependency Structure Matrix (DSM) and propagation-cost metrics. CB-SOA architectures are composed of web services, which are leased or bought off the cloud marketplace. CB-SOA can improve its utility and add value to its composition by substituting its constituent services. The substitution decisions may introduce technical debt, which needs to be managed. The thesis defines the concept of technical debt for CB-SOA and reports on the available technical debt definitions and approaches in the literature. The formulation of service substitution problem and its technical debt valuation is based on options, which exploits Binomial Options Analysis. This thesis looks at different option types under uncertainty. This thesis is concerned with some scenarios that may lead to technical debt, which are related to web service selection and composition that has been driven by either a technical or a business objective. In each scenario, we are interested in three decisions (1) keep, (2) substitute or (3) abandon the current service. Each scenario takes into consideration either one or more QoS attribute dimension (e.g. Availability). We address these scenarios from an option-based perspective. Each scenario is linked to a suitable option type. A specific option type depends on the nature of the application, problem to be investigated, and the decision to be taken. In addition, we use Dependency Structure Matrix (DSM) in order to represent dependencies among web services in CB-SOA. We introduce time and complexity sensitive propagation-cost metrics to DSM to solve the problem. In addition, CloudMTD model informs the time-value of the decisions under uncertainty based on behavioral and structural aspects of CB-SOA