Service-Oriented and Cloud Computing

Policy update management is one of the key problems in the ciphertext policy-attribute-based encryption (CP-ABE) supporting access control in data outsourcing scenario. The problem is that the policy is tightly coupled with the encryption itself. Hence, if the policy is updated, the data owner needs to re-encrypt files and sends them back to the cloud. This incurs overheads including computation, communication, and maintenance cost at data owner side. The computation and communication overheads are even more costly if there are frequent changes of access control elements such as users, attributes and access rules. In this paper, we extend the capability of our access control scheme: C-CP-ARBE to be capable to support secure and flexible policy updating in data outsourcing environment. We propose a policy updating method and exploit a very lightweight proxy re-encryption (VL-PRE) technique to enable policies to be dynamically and effectively updated in the cloud. Finally, we demonstrate the efficiency and performance of our proposed scheme through our evaluation and implementation.

In today’s ever computerized society, Cloud Data Centers are packed with numerous online services to promptly respond to users and provide services on demand. In such complex environments, guaranteeing throughput of Virtual Machines (VMs) is crucial to minimize performance degradation for all applications. vmBBThrPred, our novel approach in this work, is an application-oblivious approach to predict performance of virtualized applications based on only basic Hypervisor level metrics. vmBBThrPred is different from other approaches in the literature that usually either inject monitoring codes to VMs or use peripheral devices to directly report their actual throughput. vmBBThrPred, instead, uses sensitivity values of VMs to cloud resources (CPU, Mem, and Disk) to predict their throughput under various working scenarios (free or under contention); sensitivity values are calculated by vmBBProfiler that also uses only Hypervisor level metrics. We used a variety of resource intensive benchmarks to gauge efficiency of our approach in our VMware-vSphere based private cloud. Results proved accuracy of 95 % (on average) for predicting throughput of 12 benchmarks over 1200 h of operation.

In the Cloud domain, to guarantee adaptation to the needs of users and providers, Service-Level-Agreements (SLAs) would benefit from mechanisms to capture the dynamism of services. The existing SLA languages attempt to address this challenge by focusing on renegotiation of the agreement terms, which is a heavy-weight process, not really suitable for dealing with cloud dynamism. In this paper, we propose an extension of SLAC, a SLA language for clouds that we have recently defined, with a mechanism that enable dynamic modifications of the service agreement. We formally describe this extension, implement it in the SLAC framework and analyse the impacts of dynamic SLAs in some applications. The advantages of dynamic SLAs are demonstrated by comparing their effect with that of static SLA and of the “renegotiation” approach.

This paper proposes a self-organizing fully decentralized solution for the service assembly problem, whose goal is to guarantee a good overall quality for the delivered services, ensuring at the same time fairness among the participating peers. The main features of our solution are: (i) the use of a gossip protocol to support decentralized information dissemination and decision making, and (ii) the use of a reinforcement learning approach to make each peer able to learn from its experience the service selection rule to be followed, thus overcoming the lack of global knowledge. Besides, we explicitly take into account load-dependent quality attributes, which lead to the definition of a service selection rule that drives the system away from overloading conditions that could adversely affect quality and fairness. Simulation experiments show that our solution self-adapts to occurring variations by quickly converging to viable assemblies maintaining the specified quality and fairness objectives.

The continuous growth of the Internet of Things together with the complexity of modern information systems results in several challenges for modeling, provisioning, executing, and maintaining systems that are capable of adapting themselves to changing situations in dynamic environments. The properties of the workflow technology, such as its recovery features, makes this technology suitable to be leveraged in such environments. However, the realization of situation-aware mechanisms that dynamically adapt process executions to changing situations is not trivial and error prone, since workflow modelers cannot reflect all possibly occurring situations in complex environments in their workflow models. In this paper, we present a method and concepts to enable modelers to create traditional, situation-independent workflow models that are automatically transformed into situation-aware workflow models that cope with dynamic contextual situations. Our work builds upon the usage of workflow fragments, which are dynamically selected during runtime to cope with prevailing situations retrieved from low-level context sensor data. We validate the practical feasibility of our work by a prototypical implementation of a Situation-aware Workflow Management System (SaWMS) that supports the presented concepts.

Service-orientation has revolutionized the way applications are constructed and provisioned. To this end, a proliferation of web services is being increasingly available. To exploit such services, an accurate service discovery process is required with a suitable performance focusing both on functional and quality of service (QoS) aspects. In fact, QoS is the main distinguishing factor for the plethora of functionally-equivalent services available in the internet. Accuracy in service discovery comes via exploiting formal techniques and ontologies in particular. Satisfactory performance levels can be reached via using smart methods that intelligently organise the service advertisement space. In this paper, we propose smart ontology-based QoS-aware service discovery algorithms that exploit ontology subsumption as a means of matching QoS requests and offers. These algorithms exploit a variety of methods to structure the service advertisement space. Based on the empirical evaluation conducted, our proposed algorithms outperform the state-of-the-art in certain circumstances. To this end, ontology-based subsumption is indeed a promising technique to realise QoS-based service matchmaking.

Service-orientation is increasingly adopted by application and service developers, leading to a plethora of services becoming increasingly available. To enable constructing applications from such services, respective service description and discovery must be supported by considering both functional and non-functional aspects as they play a significant role in the service management lifecycle. However, research in service discovery has mainly focused on one aspect and not both of them. As such, this paper investigates the issues involved in considering both functional and non-functional aspects in service discovery. In particular, it proposes different ways via which aspect-specific algorithms can be combined to generate a complete service discovery system. It also proposes a specific unified service discovery architecture. Finally, it evaluates the proposed algorithms’ performance to give valuable insights to the reader.

Traditional development methodologies that separate software design from application deployment have been replaced by approaches such as continuous delivery or DevOps, according to which deployment issues should be taken into account already at the early stages of development. This calls for the definition of new modeling and specification languages. In this paper we show how deployment can be added as a first-class citizen in the object-oriented modeling language ABS. We follow a declarative approach: programmers specify deployment constraints and a solver synthesizes ABS classes exposing methods like deploy (resp. undeploy) that executes (resp. cancels) configuration actions changing the current deployment towards a new one satisfying the programmer’s desiderata. Differently from previous works, this novel approach allows for the specification of incremental modifications, thus supporting the declarative modeling of elastic applications.

Previous work on optimizing resource provisioning in virtualized environments focused either on mapping virtual machines to physical machines (i.e., virtual machine placement) or mapping computational tasks to virtual machines (i.e., virtual machine selection). In this paper, we investigate how these two optimization problems influence each other. Our study shows that exploiting knowledge about the physical machines and about the virtual machine placement algorithm in the course of virtual machine selection leads to better overall results than considering the two problems in isolation.

Auto-scaling, i.e., acquiring and releasing resources automatically, is a central feature of cloud platforms. The key problem is how and when to add/remove resources in order to meet agreed service-level agreements. Many commercial solutions use simple approaches such as threshold-based ones. However, providing good thresholds for auto-scaling is challenging. Recently, machine learning approaches have been used to complement and even replace expert knowledge. We propose a dynamic learning strategy based on a fuzzy logic algorithm, which learns and modifies fuzzy scaling rules at runtime without requiring prior knowledge. The proposed algorithm is implemented and evaluated as an extension to the OpenStack cloud platform, integrating it with the Heat and Ceilometer components for orchestration and monitoring, respectively, using Heat Orchestration Templates. We specifically focus on implementation and experimentation aspects here. Our auto-scaling approach can handle various load traffic situations, delivering resources on demand while reducing infrastructure and management costs. The experimentals show promising performance in terms of resource adjustment to optimize SLA compliance (response time) while reducing cloud provider’s costs.

Current solutions for establishing federations of clouds require applications to be installed on the individual members of the federation, which have to devote a certain amount of resources to services for federation managing. Moreover, additional interoperability requirements may need to be satisfied by individual clouds in order to join a federation. This situation may negatively affect the decision whether to join a federation. In this paper we propose an alternative approach by viewing creation and management of a cloud federation as cloud services themselves, thus allowing a drastic simplification in the federation set-up process and the decoupling of the federation management services from the technologies adopted by the individual clouds, minimising technological complexity and intrusiveness in the individual cloud infrastructures, while increasing the flexibility and scalability of resources. We also point out that existing technologies, in particular containers, microservices, configurators, clusters and orchestrators, can be the basis for implementing a platform for generation and management of federations of individual clouds, in a way which facilitates optimisation of workload and scaling of applications via resource aggregation, and makes deploying and joining federations fast, easy, and transparent.

Decomposing a software system into smaller parts always has been a challenge in software engineering. It is particularly important to split distributed systems into loosely coupled and highly cohesive units. Service-oriented architectures and their microservices deployments tackle many related problems, but remain vague on how to cut a system into discrete, autonomous, network-accessible services. In this paper, we propose a structured, repeatable approach to service decomposition based on 16 coupling criteria distilled from the literature and industry experience. These coupling criteria form the base of Service Cutter, our method and tool framework for service decomposition. In the Service Cutter approach, coupling information is extracted from software engineering artifacts such as domain models and use cases and represented as an undirected, weighted graph to find and score densely connected clusters. The resulting candidate service cuts promise to reduce coupling between and promote high cohesion within services. In our validation activities, which included prototyping, action research and case studies, we successfully decomposed two sample applications with acceptable performance; most (but not all) test scenarios resulted in appropriate service cuts. These results as well as early feedback from members of the target audience in industry and academia suggest that our coupling criteria catalog and tool-supported service decomposition approach have the potential to assist a service architect’s design decisions in a viable and practical manner.

We analyse the economic interaction on the market for composed services. Typically, as providers of composed services, intermediaries interact on the sales side with users and on the procurement side with providers of single services. Thus, in how far a user request can be met often crucially depends on the prices and qualities of the different single services used in the composition. We study an intermediary who purchases two complementary single services and combines them. The prices paid to the service providers are determined by simultaneous multilateral Nash bargaining between the intermediary and the respective service provider. By using a function with constant elasticity of substitution (CES) to determine the quality of the composed service, we allow for complementary as well as substitutable degrees of the providers’ service qualities. We investigate quality investments of service providers and the corresponding evolution of the single service quality within a differential game framework.

With the increasing availability of various types of cloud services many organizations are becoming reliant on providers of cloud services to maintain the operation of their enterprise applications. Different types of reliability strategies designed to improve the availability of cloud services have been proposed and implemented. In this paper we have estimated the theoretical improvements in service availability that can be achieved using the Retry Fault Tolerance, Recovery Block Fault Tolerance and Dynamic Sequential Fault Tolerance strategies, and we have compared these estimates to experimentally obtained results. The experimental results obtained using our prototype Service Consumer Framework are consistent with the theoretical predictions, and indicate significant improvements in service availability when compared to invoking cloud services directly.

In modern software development, paradigms like component-based software engineering (CBSE) and service-oriented architectures (SOA) emphasize the construction of large software systems out of existing components or services. Therein, a service is a self-contained piece of software, which adheres to a specified interface. In a model-based software design, this interface constitutes our sole knowledge of the service at design time, while service implementations are not available. Therefore, correctness checks or detection of potential errors in service compositions has to be carried out without the possibility of executing services. This challenges the usage of standard software error localization techniques for service compositions. In this paper, we review state-of-the-art approaches for error localization of software and discuss their applicability to service compositions.