Ioannis Patiniotakis

Managing Imprecise Criteria in Cloud Service Ranking with a Fuzzy Multi-criteria Decision Making Method

The increase of cloud technology solutions has made the evaluation and selection of desired cloud services, a cumbersome task for the user. In particular, the lack of standard mechanisms that allow the comparison of cloud service specifications against user requirements taking into account the implicit uncertainty and vagueness is a major hindrance during the cloud service evaluation and selection. In this paper, we discuss an alternative classification of metrics used for ranking cloud services based on their level of fuzziness and present an approach that allows cloud service evaluation based on a heterogeneous model of service characteristics. Our approach allows the multi-objective assessment of cloud services in a unified way, taking into account precise and imprecise metrics. We use fuzzy numbers to model the imprecise service characteristics and vague user preferences and we validate a fuzzy AHP approach that solves the problem of service ranking.

An Aspect Oriented Approach for Implementing Situational Driven Adaptation of BPMN2.0 Workflows

To address the issue of business process adaptation, we focus on handling adaptation needs as cross-cutting concerns because they rely or must affect many parts of a business process. Our research objective is to enhance aspect-oriented business process management with event-driven capabilities for discovering situations requiring adaptations. To this end, we develop an aspect-oriented extension to BPMN2.0 and we couple it with an event-driven approach for detecting and reasoning about situations that require adaptation of business processes. We use event processing in order to monitor the process execution environment and, when execution violates some quality “threshold” or a problem arises, to detect it and trigger lookup for a suitable process adaptation, using a reasoning mechanism. We demonstrate that our approach is able to address simultaneously adaptation on process model and execution level.

Fuzzy UTASTAR: A method for discovering utility functions from fuzzy data

We propose Fuzzy UTASTAR, a method for inferring fuzzy utility functions from a partial preorder of options evaluated on multiple criteria. It is an extension of the well-known UTASTAR method capable to handle both ordinary (crisp) and fuzzy evaluation data. This property gives much flexibility to decision makers because the majority of real-life decision problems involve a considerable level of uncertainty that hinders them from assigning exact evaluations (scores) to options. In case all evaluation data are crisp the method behaves exactly as the original UTASTAR. The proposed method builds fuzzy additive value functions taking as input a partial preorder on a subset of the options, called reference set, along with their associated scores on the criteria. The resulting fuzzy utility functions can subsequently be used to estimate the (fuzzy) utility of each option, thus allowing their ranking, prioritization, selection or classification. The ranking of the options in partial preorder is as compatible as possible to the original one. The method is implemented into a decision support system and is applied to an example from the transportation domain. Results are found to be in concordance with those of the original method. To the best of our knowledge this is the first attempt to extend UTASTAR method to handle both crisp and fuzzy evaluation data.

Dynamic event subscriptions in distributed event based architectures

Highlights? Approach for recommending dynamically subscriptions to event sources. ? Goal-driven, hierarchical extension of Event Condition Action rules. ? Run-time enablement and disablement of Event Condition Action rules. The burst of wireless sensors networks transmitting events paved the way for infrastructures that link uniquely identifiable things to their virtual representations in the Internet. As the Internet of Things gains momentum for leveraging and offering information access to service-based applications and users, challenges associated with efficiently publishing, subscribing, processing and reacting to events emerge. This work focuses on the challenge of enabling efficient active capability in large, distributed event infrastructures. We aim to support dynamic recommendations of new event streams to which a service should subscribe for a meaningful period of time, in order to take advantage of situational information. We present a goal-driven, ECA-based hierarchical model, called Situation-Action-Network (SAN) and we implement the Event Subscription Recommender (ESR) software that uses SANs in order to produce dynamically new event subscriptions or cancel existing ones before they become obsolete, based on detected situations. We evaluated ESR in a scenario that involves events transmitted by marine vessels in order to efficiently enforce safety regulations on behalf of port authorities. The results of this evaluation were compared against a traditional ECA-based system and we found that ESR outperformed existing systems with respect to subscription efficiency.

Continuous Quality Assurance and Optimisation in Cloud-Based Virtual Enterprises

With the rise of cloud computing, enterprises increasingly rely for their daily operations on heterogeneous externally-sourced cloud services that span different levels of capability. Their IT environment is thus progressively transformed into an ecosystem of intertwined infrastructure, platform, and application services. To effectively manage the ensuing complexity, enterprises are anticipated to increasingly rely on cloud service brokerage (CSB). This work presents a conceptual architecture for a framework which provides solutions with respect to the quality assurance and optimisation dimensions of CSB in the context of virtual enterprises. The framework revolves around three general themes, namely governance and quality control, failure prevention and recovery, and optimisation.

Service adaptation recommender in the event marketplace: conceptual view

In this paper, we present the conceptual architecture of a highly scalable federated platform for processing vast number of events coming from distributed sources, in order to detect interesting situations that lead to service adaptation recommendations. The approach presents an Event Marketplace, a platform for mediating between event providers and (complex) event consumers in very large and heterogeneous environments which is enriched with timely reaction capabilities, through situational-driven service adaptations. We focus on an approach for detecting real-time interesting situations that lead to service adaptations.

Context-aware Security Models for PaaS-enabled Access Control

Enterprises are embracing cloud computing in order to reduce costs and increase agility in their everyday business operations. Nevertheless, due mainly to confidentiality, privacy and integrity concerns, many are still reluctant to migrate their sensitive data to the cloud. In this paper, firstly, we outline the construction of a suitable Context-aware Security Model, for enhancing security in cloud applications. Secondly, we outline the construction of an extensible and declarative formalism for representing policy-related knowledge, one which disentangles the definition of a policy from the code employed for enforcing it. Both of them will be employed for supporting innovative PaaS-enabled access control mechanisms.

PuLSaR: preference-based cloud service selection for cloud service brokers

Over the last few years, the vast increase of cloud service offerings that are available from heterogeneous cloud vendors, has made the evaluation and selection of desired cloud services, a cumbersome task for service consumers. In that respect, there is an increasing need for user guidance and intermediation during the service selection process but also during the cloud service consumption that should always refer to the best possible choice based on user preferences. In this paper, we discuss the Preference-based cLoud Service Recommender (PuLSaR) that uses a holistic multi-criteria decision making (MCDM) approach for offering optimisation as a brokerage service. The specification and implementation details of this proposed software mechanism are thoroughly discussed while the background method used is summarised. Both method and brokerage service allow for the multi-objective assessment of cloud services in a unified way, taking into account precise and imprecise metrics and dealing with their fuzziness. We cope with the fuzziness of imprecise metrics in the sense that this approach deals with linguistically expressed preferences and cloud service characteristics that lack a fixed or precise value and entail a level of vagueness which can only be captured using the Zadeh’s Fuzzy Set Theory. Furthermore, this paper reports on a number of experiments that were conducted in order to measure PuLSaR’s performance and scalability.

Preference-based cloud service recommendation as a brokerage service

As the multitude and complexity of cloud services increases, the role of cloud brokers in the cloud service ecosystems becomes increasingly important. In particular, the lack of standard mechanisms that allow for the comparison of cloud service specifications against user requirements taking into account the implicit uncertainty and vagueness is a major hindrance during the cloud service evaluation and selection. In this paper, we discuss the Preference-based cLoud Service Recommender (PuLSaR) that uses a holistic multi-criteria decision making (MCDM) approach for offering optimisation as brokerage service. The specification and implementation details of this dedicated software are thoroughly discussed while the background method used is summarised. Both method and brokerage service allow for the multi-objective assessment of cloud services in a unified way, taking into account precise and imprecise metrics and dealing with their fuzziness.

Context Management in Event Marketplaces

This paper refers to methods and tools for enabling context detection and management based on events. We propose a context model that builds on top of previous efforts and we give details about the mechanisms developed for context detection in event marketplaces. In addition, we show how simple or complex events can be used in combination with external services in order to derive higher level context with the use of Situation-Action-Networks (SANs). Specifically, we present two different approaches, one for detecting low level context and another one for deriving higher-level contextual information using SANs. We present an illustrative scenario for demonstrating the process of specialization of our generic context model and its instantiation based on real-time events.