Showing papers by "Hong-Linh Truong published in 2014"

Provisioning Software-Defined IoT Cloud Systems

Abstract: Cloud computing is ever stronger converging with the Internet of Things (IoT) offering novel techniques for IoT infrastructure virtualization and its management on the cloud. However, system designers and operations managers face numerous challenges to realize IoT cloud systems in practice, mainly due to the complexity involved with provisioning large-scale IoT cloud systems and diversity of their requirements in terms of IoT resources consumption, customization of IoT capabilities and runtime governance. In this paper, we introduce the concept of software-defined IoT units--a novel approach to IoT cloud computing that encapsulates fine-grained IoT resources and IoT capabilities in well-defined APIs in order to provide a unified view on accessing, configuring and operating IoT cloud systems. Our software-defined IoT units are the fundamental building blocks of software-defined IoT cloud systems. We present our framework for dynamic, on-demand provisioning and deploying such software-defined IoT cloud systems. By automating provisioning processes and supporting managed configuration models, our framework simplifies provisioning and enables flexible runtime customizations of software-defined IoT cloud systems. We demonstrate its advantages on a real-world IoT cloud system for managing electric fleet vehicles.

Quality of Context: models and applications for context-aware systems in pervasive environments

Abstract: Limitations of sensors and the situation of a specific measurement can affect the quality of context information that is implicitly collected in pervasive environments. The lack of information about Quality of Context (QoC) can result in degraded performance of context-aware systems in pervasive environments, without knowing the actual problem. Context-aware systems can take advantage of QoC if context producers also provide QoC metrics along with context information. In this paper, we analyze QoC and present our model for processing QoC metrics. We evaluate QoC metrics considering the capabilities of sensors, circumstances of specific measurement, requirements of context consumer, and the situation of the use of context information. We also illustrate how QoC metrics can facilitate in enhancing the effectiveness and efficiency of different tasks performed by a system to provide context information in pervasive environments.

ADVISE – A Framework for Evaluating Cloud Service Elasticity Behavior

Abstract: Complex cloud services rely on different elasticity control processes to deal with dynamic requirement changes and workloads. However, enforcing an elasticity control process to a cloud service does not always lead to an optimal gain in terms of quality or cost, due to the complexity of service structures, deployment strategies, and underlying infrastructure dynamics. Therefore, being able, a priori, to estimate and evaluate the relation between cloud service elasticity behavior and elasticity control processes is crucial for runtime choices of appropriate elasticity control processes. In this paper we present ADVISE, a framework for estimating and evaluating cloud service elasticity behavior. ADVISE gathers service structure, deployment, service runtime, control processes, and cloud infrastructure information. Based on this information, ADVISE utilizes clustering techniques to identify cloud elasticity behavior produced by elasticity control. Our experiments show that ADVISE can estimate the expected elasticity behavior, in time, for different cloud services thus being a useful tool to elasticity controllers for improving the quality of runtime elasticity control decisions.

SALSA: A Framework for Dynamic Configuration of Cloud Services

Abstract: Contemporary cloud services are constructed from different types of software and deployed on multiple cloud infrastructures, which offer various configuration options, and can change dynamically at runtime. Due to this complexity, such cloud services require substantial configuration efforts. Currently we lack techniques for automating the complex tasks and providing fine-grained configuration features for multi-cloud services. In this paper, we present a novel multi-level configuration approach for complex cloud services on multi-cloud environments. We develop techniques for automating configuration orchestration activities. Our solution enables the fine-grained configuration at different application abstraction levels and supports the dynamic change of cloud services at runtime. We provide the SALSA framework to implement our approach and demonstrate its usefulness with several real-world services.

Principles of Software-Defined Elastic Systems for Big Data Analytics

Abstract: Techniques for big data analytics should support principles of elasticity that are inherent in types of data and data resources being analyzed, computational models and computing units used for analyzing data, and the quality of results expected from the consumer. In this paper, we analyze and present these principles and their consequences for software-defined environments to support data analytics. We will conceptualize software-defined elastic systems for data analytics and present a case study in smart city management, urban mobility and energy systems with our elasticity supports.

A collaboration model for community-based Software Development with social machines

Abstract: Today's crowdsourcing systems are predominantly used for processing independent tasks with simplistic coordination. As such, they offer limited support for handling complex, intellectually and organizationally challenging labour types, such as software development. In order to support crowdsourcing of the software development processes, the system needs to enact coordination mechanisms which integrate human creativity with machine support. While workflows can be used to handle highly-structured and predictable labour processes, they are less suitable for software development methodologies where unpredictability is an unavoidable part the process. This is especially true in phases of requirement elicitation and feature development, when both the client and development communities change with time. In this paper we present models and techniques for coordination of human workers in crowdsourced software development environments. The techniques augment the existing Social Compute Unit (SCU) concept—a general framework for management of ad-hoc human worker teams—with versatile coordination protocols expressed in the Lightweight Social Calculus (LSC). This approach allows us to combine coordination and quality constraints with dynamic assessments of software-user's desires, while dynamically choosing appropriate software development coordination models.

CoMoT -- A Platform-as-a-Service for Elasticity in the Cloud

Abstract: Platform-as-a-Service (PaaS) should support the design, deployment, execution, test and monitoring of native elastic systems constructed from elastic service units based on multi-dimensional elasticity requirements. In this paper, we discuss fundamental building blocks for enabling multi-dimensional elasticity programming of software-defined elastic systems. We describe CoMoT, a novel PaaS for elasticity in the cloud that is developed based on these fundamental building blocks.

On Controlling Cloud Services Elasticity in Heterogeneous Clouds

Abstract: Various complex cloud services have to be deployed in multiple heterogeneous clouds, due to the service requirements for particular functionalities from specific clouds. In order to control these cloud services, we need to monitor and control the various units deployed across multiple clouds, dealing with cloud-specific protocols to support an end-to-end cloud service perspective. In this paper we present an approach for multi-cloud control, which evaluates relationships among different units deployed across heterogeneous clouds, and generates action plans necessary for controlling service elasticity. We show experiments of the end-to-end control and sensitivity analysis for a service deployed across two different types of clouds.

Virtualizing Software and Human for Elastic Hybrid Services

Abstract: Human capabilities have been incorporated into IT systems for solving complex problems since several years. Still, it is very challenging to program human capabilities due to the lack of techniques and tools. In this paper, we will discuss techniques and frameworks for conceptualizing and virtualizing human capabilities under programmable units and for provisioning them using cloud service models. We will discuss how elastic composite applications can be built by combining programmable units of software-based and human-based services in the Vienna Elastic Computing Model.

On the Elasticity of Social Compute Units

Abstract: Advances in human computation bring the feasibility of utilizing human capabilities as services. On the other hand, we have witnessed emerging collective adaptive systems which are formed from heterogeneous types of compute units to solve complex problems. The recently introduced Social Compute Units (SCUs) present one type of these systems, which have human-based services as their core fundamental compute units. While, there is related work on forming SCUs and optimizing their performance with adaptation techniques, most of it is focused on static structures of SCUs. To provide better runtime performance and flexibility management for SCUs, we present an elasticity model for SCUs and mechanisms for their elastic management which allow for certain fluctuations in size, structure, performance and quality. We model states of elastic SCUs, present APIs for managing SCUs as well as metrics for controlling their elasticity with which it is possible to tailor their performance parameters at runtime within the customer-set constraints. We illustrate our contribution with an example algorithm.

On Analyzing Elasticity Relationships of Cloud Services

Abstract: With the increasing cloud popularity, substantial effort has been paid for the development of emerging elastic cloud services, consisting of different units distributed among virtual machines/containers in different clouds. Due to the software stack and deployment complexity in single and multi-cloud scenarios, developing and managing such services is impeded by a lack of tools and techniques for understanding the elasticity relationships among individual service units, which influence the service's overall elasticity. In this paper we characterize the elasticity relationships, and develop mechanisms for analyzing them, based on service monitoring information and elasticity requirements. From collected monitoring information we abstract the elasticity behavior of the whole cloud service and individual units, over which we design a customizable algorithm for relationships analysis. We illustrate our approach via several experiments with an elastic data service for M2M platforms, highlighting the importance of determining elasticity relationships for the development and operation of elastic services.

Context-Aware Programming for Hybrid and Diversity-Aware Collective Adaptive Systems

Abstract: Collective adaptive systems (CASs) have been researched intensively since many years. However, the recent emerging developments and advanced models in service-oriented computing, cloud computing and human computation have fostered several new forms of CASs. Among them, Hybrid and Diversity-aware CASs (HDA-CASs) characterize new types of CASs in which a collective is composed of hybrid machines and humans that collaborate together with different complementary roles. This emerging HDA-CAS poses several research challenges in terms of programming, management and provisioning. In this paper, we investigate the main issues in programming HDA-CASs. First, we analyze context characterizing HDA-CASs. Second, we propose to use the concept of hybrid compute units to implement HDA-CASs that can be elastic. We call this type of HDA-CASs \(h^2\) CAS (Hybrid Compute Unit-based HDA-CAS). We then discuss a meta-view of \(h^2\) CAS that describes a \(h^2\) CAS program. We analyze and present program features for \(h^2\) CAS in four main different contexts.

Coordination-Aware Elasticity

Abstract: Enabling and controlling elasticity of cloud computing applications is a challenging issue. Elasticity programming directives have been introduced to delegate elasticity control to infrastructures and to separate elasticity control from application logic. Since coordination models provide a general approach to manage interaction and elasticity control entails interactions among cloud infrastructure components, we present a coordination-based approach to elasticity control, supporting delegation and separation of concerns at design and run-time, paving the way towards coordination-aware elasticity.

Sustainability Data and Analytics in Cloud-Based M2M Systems

Abstract: Recently, cloud computing technologies have been employed for large-scale machine-to-machine (M2M) systems, as they could potentially offer better solutions for managing monitoring data of IoTs (Internet of Things) and supporting rich sets of IoT analytics applications for different stakeholders. However, there exist complex relationships between monitored objects, monitoring data, analytics features, and stakeholders that require us to develop efficient ways to handle these complex relationships to support different business and data analytics processes in large-scale M2M systems. In this chapter, we analyze potential stakeholders and their complex relationships to data and analytics applications in M2M systems for sustainability governance. Based on that we present techniques for supporting M2M data and process integration, including linking and managing monitored objects, sustainability monitoring data and analytics applications, for different stakeholders who are interested in dealing with large-scale monitoring data in M2M environments. We present a cloud-based data analytics system for sustainability governance that includes a Platform-as-a-Service and an analytics framework. We also illustrate our prototype based on a real-world cloud system for facility monitoring and analytics.

Managing Incentives in Social Computing Systems with PRINGL

Abstract: Novel web-based socio-technical systems require incentives for efficient management and motivation of human workers taking part in complex collaborations Incentive management techniques used in existing crowdsourcing platforms are not suitable for intellectually-challenging tasks; platform-specific solutions prevent both workers from comparing working conditions across different platforms as well as platform owners from attracting skilled workers In this paper we present PRINGL, a domain-specific language for programming complex incentive strategies It promotes re-use of proven incentive logic and allows composing of complex incentives suitable for novel types of socio-technical systems We illustrate its applicability and expressiveness and discuss its properties and limitations

QUELLE – A Framework for Accelerating the Development of Elastic Systems

Abstract: A large number of cloud providers offer diverse types of cloud services for constructing complex ”cloud-native” software. However, there is a lack of supporting tools and mechanisms for accelerating the development of cloud-native software-defined elastic systems (SESs) based on elasticity capabilities of cloud services. In this paper we introduce QUELLE – a framework for evaluating and recommending SES deployment configurations. QUELLE presents models for describing the elasticity capabilities of cloud services and capturing elasticity requirements of SESs. Based on that QUELLE introduces novel functions and algorithms for quantifying the elasticity capabilities of cloud services. QUELLE’s algorithms can recommend SES deployment configurations from cloud services that both provide the required elasticity, and fulfill cost, quality, and resource requirements, and thus can be incorporated into different phases of the development of SESs. We present several experiments based on real-world cloud services for the development of an elastic machine-to-machine data-as-a-service system.

DRain: An Engine for Quality-of-Result Driven Process-Based Data Analytics

Abstract: The analysis of massive amounts of diverse data provided by large cities, combined with the requirements from multiple domain experts and users, is becoming a challenging trend. Although current process-based solutions rise in data awareness, there is less coverage of approaches dealing with the Quality-of-Result (QoR) to assist data analytics in distributed data-intensive environments. In this paper, we present the fundamental building blocks of a framework for enabling process selection and configuration through user-defined QoR at runtime. These building blocks form the basis to support modeling, execution and configuration of data-aware process variants in order to perform analytics. They can be integrated with different underlying APIs, promoting abstraction, QoR-driven data interaction and configuration. Finally, we carry out a preliminary evaluation on the URBEM scenario, concluding that our framework spends little time on QoR-driven selection and configuration of data-aware processes.

Social Interaction Analysis for Team Collaboration.

Abstract: Actor: Entity (human or computer) possessing a capability to act intelligently and process specific assignments (activities/tasks). Atomic task: Task that can be handled by an individual actor. CAS: Collective adaptive system. Collaboration system (platform): Information system supporting execution of collaborative processes. Collaborative process (collaboration): Joint effort of a (limited) number of actors with the goal of performing a task. A collaborative process has a limited duration and requires coordination among actors (due to task dependencies). Composite task: Task that must be handled by multiple actors due to size or complexity. A composite task can be broken down into atomic tasks. CSCW: Computer-supported cooperative work. HBS: Human-based service. Metric: Precisely defined, context-specific measure of some properties. QoD: Quality of data. QoS: Quality of service. SOA: Service-oriented architecture. SOC: Service-oriented computing. Task assignment: The art to divide a (composite) task into (sub)tasks and assign