Leveraging AI/GenAI and data processing techniques to extract data from technical documents has proliferated due to recent advances in LLM capabilities and open source tools. However, being able to contextualize suitable GenAI/LLMs capabilities coupled with enterprise constraints on cost, data regulation, and quality is still challenging. Especially, AI/GenAI resource-constrained enterprises must deal with complex domainspecific technical documents of assets and designs supplied by multiple vendors. This paper presents novel practical methods that incorporate contexts into the design and execution of activities for industrial technical document extraction applications. We consider resource-constrained environments in which enterprises are with edge GenAI/LLMs infrastructures and non-AI engineers. We devise context-aware composition and adaptation for extraction pipelines to deal with diverse attributes of GenAI/LLMs and extraction quality control. We experiment our methods with technical documents for telco operators.

Swarm-edge-cloud service-based applications (SESA) utilize UAV swarm nodes and edge-cloud computing infrastructures to perform complex tasks. These applications leverage distributed services across UAVs and edge resources, transitioning to cloud resources as needed, to support continuum computing, adapting to dynamic workloads, network conditions, and mission requirements. However, existing solutions lack robust mechanisms for dynamic policy enforcement in such environments. This paper introduces SAGELY (SwArm-edGE PoLicY), a framework for secure, efficient, and adaptive policy enforcement in dynamic SESA environments. SAGELY incorporates: (1) context-aware policy adaptation to adjust enforcement dynamically, (2) flexible policy enforcement across centralized and decentralized models, and (3) a pluggable service architecture for continuum context management. We present a testbed to enforce continuum policies and conduct experiments.

ABSTRACT Deploying end‐to‐end ML applications on edge resources becomes a viable solution to achieve performance and data regulations. With the microservice architecture, these applications can scale dynamically, improving service availability under dynamic workloads. However, orchestrating multiple end‐to‐end ML applications within heterogeneous edge environments must deal with numerous challenges while sharing computing resources. Prevalent orchestration tools/frameworks supporting edge ML serving are inefficient in provisioning methods due to constrained resources, diverse resource demands and utilization patterns. In this work, we present a provisioning method to optimize resource utilization for end‐to‐end ML applications on a heterogeneous edge. By profiling all microservices within the application, we estimate scales and allocate them on desired hardware platforms with sufficient resources when considering their runtime utilization patterns. We also provide several practical analyses on runtime monitoring metrics to detect and mitigate resource contentions, guaranteeing performance. The experiments with three real‐world ML applications demonstrate the practicality of our method on a heterogeneous edge cluster of Raspberry Pis and Jetson Developer Kits.

The Internet of Things (IoT) is becoming increasingly ubiquitous, acting as an important source of real-time data for various applications. By allowing data exchange between various parties along the IoT devices-Edge-Cloud computing continuum, the larger societal benefits of the IoT can be achieved. Assuring security and fostering confidence for IoT data sharing, however, is one of the biggest obstacles. Sharing real-time data originating from connected devices is crucial to real-world intelligent IoT applications, i.e., based on artificial intelligence/machine learning. Such IoT data sharing involves multiple parties for different purposes and is usually based on data contracts that might depend on the dynamic change of IoT data variety and velocity. We aim to support multiple parties (aka tenants) with dynamic contracts based on the data value for their specific contextual purposes. This work addresses these challenges by introducing a novel dynamic context-based policy enforcement framework to support IoT data sharing (on-Edge) based on dynamic contracts. Our enforcement framework allows IoT Data Hub owners to define extensible rules and metrics to govern the tenants accessing the shared data on the Edge based on policies defined with static and dynamic contexts. We have created an edge-centered architecture that enables multi-tenant use cases with tenant-specific application deployment and IoT-context-based data sharing on edge servers. Our proof-of-concept prototype for sharing sensitive data such as surveillance camera videos has illustrated our proposed framework. The experimental results demonstrated that our framework could soundly and timely enforce context-based policies at runtime with moderate overhead. Moreover, the context and policy changes are correctly reflected in the system in nearly real-time. We have addressed the need to enable multi-parties IoT (data) resources to be shared based on contracts, especially with dynamic IoT contexts, for tenant applications on the edge to allow their closer access to data.

Emerging generative and fine-tuning LLMs services have been widely benchmarked and used for various software development tasks. These LLMs services are powerful but have different output qualities for software development tasks and may not be able to deal with complex development tasks in edge-cloud software modernization and new developments due to their generative capabilities and lack of up-ro-date (domain) knowledge. Many queries and solutions related to target platforms, deploy-ment configurations, policies, data regulation, observability, to name just a few, are not well integrated with these LLMs, but are accessed by the developer through other sources. In this work, we discuss situations where the gaps between the needs and the offerings from LLMs can be compensated by Platform Knowledge, which captures knowledge about, e.g., software, service and infrastructure catalogs, architectural decision records and code patterns. We propose COLLMS - a framework for coordinating LLMs services and Platform Knowledge. At the starting point of the framework, we will discuss challenges for achieving the coordination centered around Platform Knowledge, LLMs management and integration, quality-aware coordination of LLMs, and observability and knowledge updating.

A long running data-intensive computational application acquires costly computing resources. With the emerging new architectures, like computing systems with multiple nodes of many-core CPUs and accelerators, while domain-specific tools and libraries employed in such an application leverage high parallelism on accelerators for intensive computations, the remaining resources can potentially be utilized for other application-related data operations. Such data operations, called opportunistic data operations in this work, must usually be carried out for post-processing or follow-up analytics based on results produced during the runtime of the application. These operations are not easily backfilled or preempted under the guidance of the domain scientist or by common task scheduling systems due to their complex dependencies.In this paper, we introduce a framework for domain scientists to identify and execute opportunistic data operation tasks. With a minimal specification or modification of the main application, the scientists can specify, monitor, and execute opportunistic tasks independently from the main application and the framework will detect underutilized resources to execute these tasks, thereby, optimizing utilization efficiency within the allocated resources. We present experiments to demonstrate the applicability of our framework on a magnetic field modeling running on the LUMI computing system.

The growing complexity of end-to-end Machine Learning (ML) serving across the edge-cloud continuum has raised the necessity for runtime explainability to support service optimizations, transparency, and trustworthiness. That involves many challenges in managing ML service quality and engineering runtime explainability based on ML service contracts. Currently, consumers use ML services almost as a black box with insufficient explainability for not only inference decisions but also other contractual aspects, such as data/service quality and costs. The generic explainability for ML models is inadequate to explain the runtime ML usage for individual consumers. Moreover, ML-specific metrics have not been addressed in existing service contracts. In this work, we introduce a novel contract-based runtime explainability framework for end-to-end ensemble ML serving. The framework provides a comprehensive engineering toolset, including explainability constraints in ML contracts, report schemas, and interactions between ML consumers and the components of the ML serving for evaluating service quality with contract-based explanations. We develop new monitoring probes to measure ML-specific metrics on data quality, inference confidence, inference accuracy, and capture runtime ML usage. Finally, we present essential quality analyses via an observation agent. That interprets ML inferences and evaluates contributions of ML inference microservices, assisting ML serving optimization. The agent also integrates ML algorithms for detecting relations among metrics, supporting constraint developments. We demonstrate our work with two real-world applications for malware and object detection.

The Digital Twin (DT) paradigm has been largely adopted for many smart systems in various domains. Due to the heterogeneous and distributed nature of the physical twins, these systems increasingly incorporate disparate security tools, especially those based on service-based AI/ML capabilities. That presents numerous challenges in achieving a comprehensive understanding of security analytics and explainability in security operations carried out by ML-based security services, which require continuous monitoring and optimization to remain effective. This paper aims to support security service integration and automated analyses with enhanced explainability in DTs. We introduce a novel framework that unifies runtime contexts to facilitate security services unification and operation interpretation in security orchestration. We define a workflow and provide necessary services for generating security reports across physical and logical layers. Leveraging a centralized knowledge service, we let security analysts incorporate domain knowledge in automating incident reasoning and security enforcement at the logical layer. We demonstrate our explainability framework on a DT of an Industry 4.0 toy factory with two ML-based security services detecting network anomalies. Our experiments show a significant reduction in manual effort for orchestrating security incident analysis and mitigation.

With the rapid development of 5G technology, the service demand in various application scenarios is continuously increasing. Mobile edge computing (MEC) has become a popular computing paradigm by placing services and corresponding computing resources to edge servers to satisfy the low latency demands of users. However, edge servers lack a stable infrastructure for protection and limited storage space and computing power. Considering the reliability and stability of the edge system, efficiently placing resources and offloading tasks to the edge servers has become an urgent challenge. In this paper, we consider resource placement and task offloading strategies under different time scales to optimize the service response time in a dynamic edge system environment. We established the Markov model to obtain a quantitative relationship between system reliability and latency, and analyze the time required for resource and task offloading. Then, we propose the resource placement and task offloading (RPTO) algorithms under different time scales based on deep reinforcement learning (DRL) techniques with the aim of minimizing the cost of service providers in the long term. The experimental results demonstrate that our approach effectively tackles the challenges of joint resource placement and task offloading in the MEC.

In a large-scale mobile network, due to the diversity of data characteristics, detection purposes of operation teams, and analytics and machine learning algorithm abilities, building big data anomaly detection pipelines without considering different analytics and team situations may not yield expected quality of analytics, including detection relevancy, performance and quality. This is especially for analytics subjects, such as mobile network zones, of which characteristics are dynamic and contextual. Moreover, due to the lack of labeled data and the high cost of creating labeled data, building anomaly detection analytics models based on (supervised) deep learning or advanced models is even more challenging from various aspects of effort, cost and deployment. In this paper, we present a novel framework that enables anomaly detection through context-aware, composable components to provide efficient detection pipelines suitable for lightweight, resource constrained and geographical operation teams. First, we identify and categorize different types of analytics feature contexts and evaluate existing algorithms suitable for these contexts, mapping anomaly detection algorithms, patterns and configurations for data pre-processing and unsupervised detection tasks in individual analytics functionality. These context-specific pipelines detect anomalies and their relevancy for dynamic analytics subjects such as mobile network zones. Then we develop dynamic configuration and combination techniques for such pipelines to produce highly relevant, multi-context detection of anomalies. Our framework provides flexibility and configurations for team contexts to carry out the anomaly detection in the team’s operations. We will demonstrate our work through real data gathered for a large-scale mobile network covering multiple types of sites with different geographical zones and equipment. We especially focus on district zones and user-defined zones as analytics subjects that must be managed by teams in our experiments.

The rapid development of IoT (Internet of Things) brings great convenience to people through the utilization of IoT applications, but also brings huge security challenges. Existing IoT security breaches show that many IoT devices have authentication flaws. Although many IoT authentication schemes were proposed, they are not applicable to recent smart IoT applications covering IoT device, back-end sever, and user-end mobile applications. To build the first line of defense for trending IoT systems, this paper proposes a new authentication scheme. The proposed scheme first models the entire life cycle of the IoT device for real-world scenarios of smart IoT systems, which contains factory manufacturing, daily usage, and system resetting. For each stage in the life cycle, the proposed scheme employs efficient symmetric key mechanisms to achieve the authentication between IoT device, back-end server, and mobile application. The proposed scheme supports both server-free local area network communication and sever-involved remote public area communication. Formal security verification shows that the proposed scheme resists existing attacks. The open-source experimental evaluations also show that the proposed scheme is efficient and promising for practical usage.

This paper presents our approach for supporting machine learning (ML)-based analytics of quality of experience (QoE) related issues in a variety of Radio Access Networks (V-RAN). We focus on key problems in a holistic analytics infrastructure for engineers without strong ML skills and powerful computing infrastructures. We characterize types of relevant data and existing data systems to follow a specific data mesh approach suitable for engineers. The paper presents key steps in establishing the participation of engineers and the acquisition of domain knowledge. We introduce models for representing analytics subjects and their dependencies, and for managing relevant ML techniques and methods for analytics subjects. We explain our work through examples from a large-scale mobile network of approximately 4 million subscribers.

Among different types of changes, a specific type named long-tailed change (LTC), induced by wide-spectrum and sporadic events (hereafter long-tailed business events (LBEs), poses fresh challenges to available change management solutions in business process management. The disorder in economic and social life caused by the competition of COVID-19 epidemics and countermeasures all over the world fully demonstrates the impact of this new change management problem. Based on the principle of separation of concerns, this paper proposes a systematic framework to solve the above problem. The solution consists of a low-code mechanism for process adaptation and business policy conformance. As a result, front-line practitioners can quickly react to changes by using a domain-specific language (DSL) while a corresponding verification of functional and non-functional attributes maintains compliance with business constraints. We validate the solution through a case study of an e-commerce scenario during the COVID-19 pandemic.

Identifying mobile apps based on network traffic has multiple benefits for security and network management. However, it is a challenging task due to multiple reasons. First, network traffic is encrypted using an end-to-end encryption mechanism to protect data privacy. Second, user behavior changes dynamically when using different functionalities of mobile apps. Third, it is hard to differentiate traffic behavior due to common shared libraries and content delivery within modern mobile apps. Existing techniques managed to address the encryption issue but not the others, thus achieving low detection/classification accuracy. In this paper, we present MAppGraph, a novel technique to classify mobile apps, addressing all the above issues. Given a chunk of traffic generated by an app, MAppGraph constructs a communication graph whose nodes are defined by tuples of IP address and port of the services connected by the app, edges are established by the weighted communication correlation among the nodes. We extract information from packet headers without analyzing encrypted payload to form feature vectors of the nodes. We leverage deep graph convolution neural networks to learn the diverse communication behavior of mobile apps from a large number of graphs and achieve a fast classification. To validate our technique, we collect traffic of a hundred mobile apps on the Android platform and run extensive experiments with various experimental scenarios. The results show that MAppGraph significantly improves classification accuracy by up to 20% compared to recently developed techniques and demonstrates its practicality for security and network management of mobile services.

We research a new abstraction for resources for applications, called Application-specific Resource Ensembles (ASREs), across edge and cloud infrastructures. ASRE encapsulates diverse types of high-level resources, coupled with management APIs, that is provided for application-specific contexts. ASRE is designed with integrated mechanisms to manage its multidimensional quality through monitoring and control techniques. Instances of ASRE can be provisioned on-demand, with the elasticity and resilience capabilities, thus help to simplify the resource management in edge-cloud continuum.

Important service-level constraints in machine learning (ML) services must be communicated and agreed among relevant stakeholders. Due to the lack of studies and support, it is unclear which and how ML-specific attributes and constraints should be specified and assured in service contracts for ML services. This paper examines service contracts in the three stakeholders engagement model of ML services. We identify key ML-specific attributes that should be specified and monitored for the ML service provider, ML consumer and ML infrastructure provider. Based on that, we propose QoA4ML (Quality of Analytics for ML) as a framework to support ML-specific service contracts. QoA4ML includes an ML-specific service contract specification, monitoring utilities and a contract observability service. To illustrate the usefulness of QoA4ML, we present real-world examples for contract terms and policies, monitoring and contract evaluation with dynamic ML services in predictive maintenance.

More widespread adoption requires swarms of robots to be more flexible for real-world applications. Multiple challenges remain in complex scenarios where a large amount of data needs to be processed in real-time and high degrees of situational awareness are required. The options in this direction are limited in existing robotic swarms, mostly homogeneous robots with limited operational and reconfiguration flexibility. We address this by bringing elastic computing techniques and dynamic resource management from the edge-cloud computing domain to the swarm robotics domain. This enables the dynamic provisioning of collective capabilities in the swarm for different applications. Therefore, we transform a swarm into a distributed sensing and computing platform capable of complex data processing tasks, which can then be offered as a service. In particular, we discuss how this can be applied to adaptive resource management in a heterogeneous swarm of drones, and how we are implementing the dynamic deployment of distributed data processing algorithms. With an elastic drone swarm built on reconfigurable hardware and containerized services, it will be possible to raise the self-awareness, degree of intelligence, and level of autonomy of heterogeneous swarms of robots. We describe novel directions for collaborative perception, and new ways of interacting with a robotic swarm.

Exponentially increasing data volumes, coupled with new modes of analysis have created significant new opportunities for data scientists. However, the stochastic nature of many data science techniques results in tradeoffs between costs and accuracy. For example, machine learning algorithms can be trained iteratively and indefinitely with diminishing returns in terms of accuracy. In this paper we explore the cost-accuracy tradeoff through three representative examples: we vary the number of models in an ensemble, the number of epochs used to train a machine learning model, and the amount of data used to train a machine learning model. We highlight the feasibility and benefits of being able to measure, quantify, and predict cost accuracy tradeoffs by demonstrating the presence and usability of these tradeoffs in two different case studies.

Dynamic, distributed and open business forces enterprises to support various critical requirements, such as, timely reacting to changes, properly reusing business assets and smoothly collaborating with external partners. Existing approaches focus on mechanisms dealing with heterogeneity, but there is a lack of frameworks enabling legacy business processes performing collaboration in an open service environment. This paper proposes the L2L service framework featuring reactive IoT event messaging and coordinator-based collaborating between autonomous enterprises. Along with the emerging of coordinators, L2L empowers on-the-fly business process collaboration with dynamic changes. We present our experiments with a real-world scenario from the shipping industry of China.

We propose and implement a decentralized, intelligent air traffic flow management (ATFM) solution to improve the efficiency of air transportation in the ASEAN region as a whole. Our system, named BlockAgent, leverages the inherent synergy between multi-agent reinforcement learning (RL) for air traffic flow optimization; and the rising blockchain technology for a secure, transparent and decentralized coordination platform. As a result, BlockAgent does not require a centralized authority for effective ATFM operations. We have implemented several novel distributed coordination approaches for RL in BlockAgent. Empirical experiments with real air traffic data concerning regional airports have demonstrated the feasibility and effectiveness of our approach. To the best of our knowledge, this is the first work that considers blockchain-based, distributed RL for ATFM.

Emerging edge/fog computing models have fostered new types of applications whose software components and dependent services are provisioned across distributed edge and cloud infrastructures. The design of mobile edge cloud systems is complex, thus it is important to understand suitable deployment models and test them. Since mobile edge cloud computing and its deployments are quite new, there is a lack of techniques and knowledge about possible deployments, configurations, and performance evaluation. In this paper, we present our experiences on studying the impact of performance and data quality for mobile edge cloud systems. We use a mobile edge cloud cornering assistance (MECCA) application to examine various performance and data quality impact. In this paper, we explain how by using MECCA to test performance and data quality, we draw key issues and steps in analytics of edge cloud applications and lessons learned for mobile edge computing application testing.

Systems for big Internet of Things (IoT) data analytics are extremely complex. Different software components at different software stacks from different infrastructures and providers are involved in handling different types of data. Various types of incidents may occur during execution of such systems due to problems in software stacks, the data itself, and processing algorithms. Here incidents reflect unexpected context-specific situations that might happen within data themselves, machine learning algorithms, data analytics pipelines, and underlying big data services and computing platforms. It is important to address any incident that prevents the pipeline running correctly or producing the expected quality of analytics. In this paper, we show the need to characterize incidents for IoT data analytics in the cloud with real-world examples. We characterize incidents based on various aspects in IoT data analytics, including analytics phases, status of data, software services, and stakeholders. We introduce a meta-model for capturing knowledge about incidents.

With the rise of Internet of Things, end-users expect to obtain data from well-connected smart devices and stations through data services being provisioned in distributed architectures. Such services could be aggregated in a number of smart ways to provide the end-users and third-party applications with sophisticated data (e.g., weather data coupled with soil pollution), resulting in a growing number of service offerings to be requested. Service offerings that have been shortlisted for a certain data request (e.g., rainfall in a particular farming site) need to be ranked according to the end-users' preference. Service level agreements, i.e., the mutual responsibilities between the service provider and its consumers, address this sort of preference. Unfortunately, provisioning quality-aware services under this term still stays on the sidelines. In this paper, we propose a novel service architecture where the service level agreements shall be: (i) accumulated overtime on IoT service transactions; (ii) compiled when aggregating IoT services; (iii) used as a ranking criterion for suggesting IoT service offerings. We demonstrate our new approach in the service provisioning of agricultural datasets taken from a farming site of the Mekong Delta in Vietnam.

Interoperability for IoT is a challenging problem because it requires us to tackle (i) cross-system interoperability issues at the IoT platform sides as well as relevant network functions and clouds in the edge systems and data centers and (ii) cross-layer interoperability, e.g., w.r.t. data formats, communication protocols, data delivery mechanisms, and performance. However, existing solutions are quite static w.r.t software deployment and provisioning for interoperability. Many middle-ware, services and platforms have been built and deployed as interoperability bridges but they are not dynamically provisioned and reconfigured for interoperability at runtime. Furthermore, they are often not considered together with other services as a whole in application-specific contexts. In this paper, we focus on dynamic aspects by introducing the concept of Resource Slice Interoperability Hub (rsiHub). Our approach leverages existing software artifacts and services for interoperability to create and provision dynamic resource slices, including IoT, network functions and clouds, for addressing application-specific interoperability requirements. We will present our key concepts, architectures and examples toward the realization of rsiHub.

Many advances have been introduced recently for service-oriented computing and applications (SOCA). The Internet of Things (IoT) has been pervasive in various application domains. Fog/Edge computing models have shown techniques that move computational and analytics capabilities from centralized data centers where most enterprise business services have been located to the edge where most customer’s Things and their data and actions reside. Network functions between the edge and the cloud can be dynamically provisioned and managed through service APIs. Microservice architectures are increasingly used to simplify engineering, deployment and management of distributed services in not only cloud-based powerful machines but also in light-weighted devices. Therefore, a key question for the research in SOCA is how do we leverage existing techniques and develop new ones for coping with and supporting the changes of data and computation resources as well as customer interactions arising in the era of IoT and Fog/Edge computing. In this editorial paper, we attempt to address this question by focusing on the concept of ensembles for IoT, network functions and clouds.

Modern Cyber-Physical Systems (CPS) and Internet of Things (IoT) systems consist of both loosely and tightly interactions among various resources in IoT networks, edge servers and cloud data centers. These elements are being built atop virtualization layers and deployed in both edge and cloud infrastructures. They also deal with a lot of data through the interconnection of different types of networks and services. Therefore, several new types of uncertainties are emerging, such as data, actuation, and elasticity uncertainties. This triggers several challenges for testing uncertainty in such systems. However, there is a lack of novel ways to model and prepare the right infrastructural elements covering requirements for testing emerging uncertainties. In this paper, first we present techniques for modeling CPS/IoT Systems and their uncertainties to be tested. Second, we introduce techniques for determining and generating deployment configuration for testing in different IoT and cloud infrastructures. We illustrate our work with a real-world use case for monitoring and analysis of Base Transceiver Stations.

Recent emergence of IoT Cloud systems has fostered proliferation of various applications mainly driven by urgent need to respond to volume, velocity and variety of data generated by IoT Cloud, but also to enable timely propagation of actuation decisions, crucial for business operation, to the Edge of the infrastructure. In such systems, utilizing currently untapped Edge resources such as sensory gateways, and enabling the IoT devices as first-class execution environments plays a crucial role. However, enabling virtually exclusive access to the underlying devices, e.g., field bus sensors and supporting flexible, application-specific customizations for such devices still remain a challenge. In this paper, we introduce Data- and Control Points - a novel programming model and framework for developing applications specifically tailored for resource-constrained Edge devices. Our framework offers programming constructs that enable applications to define custom configurations for and their own view of the underlying devices. By providing an illusion of an exclusive access to the underlying sensors and actuators, our framework supports execution of multiple applications within a single Edge device.

Scalable applications deployed in public clouds can be built from a combination of custom software components and public cloud services. To meet performance and/or cost requirements, such applications can scale-out/in their components during run-time. When higher performance is required, new component instances can be deployed on newly allocated cloud services (e.g., virtual machines). When the instances are no longer needed, their services can be deallocated to decrease cost. However, public cloud services are usually billed over predefined time and/or usage intervals, e.g., per hour, per GB of I/O. Thus, it might not be cost efficient to scale-in public cloud applications at any moment in time, without considering their billing cycles. In this work we aid developers of scalable applications for public clouds to monitor their costs, and develop cost-aware scalability controllers. We introduce a model for capturing the pricing schemes of cloud services. Based on the model we determine and evaluate the application's costs depending on its used cloud services and their billing cycles. We further evaluate cost efficiency of cloud applications, analyzing which application component is cost efficient to deallocate and when. We evaluate our approach on a scalable platform for IoT, deployed in Flexiant, one of the leading European public cloud providers. We show that cost-aware scalability can achieve higher application stability and performance, while reducing its operation costs.

Cyber-physical Systems (CPS) have components deployed both in the physical world, and in computing environments, such as smart buildings or factories. Elastic Cyber-physical Systems (eCPS) are adaptable CPS capable of aligning their resources, cost, and quality to varying demand. However, failures can appear at run-time in the physical or software resources used by the eCPS. Failures can have different origins, from hardware failure, to management operations, software bugs, or resource congestion. While static verification methods can determine failure sources, they are less applicable to eCPS with complex hardware and software stacks. To this end, in this paper we introduce an approach and supporting platform for verifying at run-time eCPS health, and evaluate it on an eCPS for analysis of streaming data from smart environments.

Recent developments of computing systems allow humans to participate not only as service consumers but also as service providers. The interweaving of human-based computing into machine-based computing systems becomes apparent in smart city settings, where human-based services together with software-based services and thing-based services (e.g., sensor-as-a-service) are orchestrated for solving complex problems, leading to the creation of the so-called Cyber-PhySical-Social Systems (CPSSs). Monitoring such CPSSs is essential for system planning, management, and governance. However, due to the diversity of the involved building blocks, it is challenging to monitor such systems. In this paper, we present metric models and the associated Quality of Data (QoD) to elastically monitor the execution metrics of a centralized coordinated CPSS. We develop a monitoring framework for capturing and analyzing runtime metrics occurring on various facets of the coordinated CPSS. Furthermore, we present the implementation of our monitoring framework, and showcase monitoring features in a simulated system using real world infrastructure maintenance scenarios.

This article introduces a dynamic cloud-based marketplace of near-realtime human sensing data (MARSA) for different stakeholders to sell and buy near-realtime data. MARSA is designed for environments where information technology (IT) infrastructures are not well developed but the need to gather and sell near-realtime data is great. To this end, we present techniques for selecting data types and managing data contracts based on different cost models, quality of data, and data rights. We design our MARSA platform by leveraging different data transferring solutions to enable an open and scalable communication mechanism between sellers (data providers) and buyers (data consumers). To evaluate MARSA, we carry out several experiments with the near-realtime transportation data provided by people in Ho Chi Minh City, Vietnam, and simulated scenarios in multicloud environments.

Cloud applications can benefit from the on-demand capacity of cloud infrastructures, which offer computing and data resources with diverse capabilities, pricing, and quality models. However, state-of-the-art tools mainly enable the user to specify “if-then-else” policies concerning resource usage and size, resulting in a cumbersome specification process that lacks expressiveness for enabling the control of complex multilevel elasticity requirements. In this article, first we propose SYBL, a novel language for specifying elasticity requirements at multiple levels of abstraction. Second, we design and develop the rSYBL framework for controlling cloud services at multiple levels of abstractions. To enforce user-specified requirements, we develop a multilevel elasticity control mechanism enhanced with conflict resolution. rSYBL supports different cloud providers and is highly extensible, allowing service providers or developers to define their own connectors to the desired infrastructures or tools. We validate it through experiments with two distinct services, evaluating rSYBL over two distinct cloud infrastructures, and showing the importance of multilevel elasticity control.

Emerging IoT cloud systems create unified IoT cloud infrastructures that offer large pools of elastic resources, which need to be governed through their entire lifecycle. However, numerous uncertainties are inherently present in such infrastructures, mainly due to the novel interactions of IoT elements, network elements, cloud resources and humans. They pose a plethora of challenges for the governance of such IoT cloud systems. In this paper we introduce U-GovOps -- a novel framework for dynamic, on-demand governance of elastic IoT cloud systems under uncertainty. We introduce a declarative policy language to simplifythe development of uncertainty-and elasticity-aware governance strategies. Based on that we develop runtime mechanisms, which enable mitigating the uncertainties by monitoring and governing the IoT cloud systems through specified strategies. We evaluate our approach using a real-life case study in the domain of predictive maintenance.

In this paper, we explore the benefits of automatically determining the degree of parallelism used to perform genetic mutation calling in a hybrid cloud environment. We propose algorithms to automatically control both the hiring of hybrid cloud resources and the selection of the degree of parallelism employed in analysis tasks executed against that cloud. Using the Broad Institute's Genome Analysis Toolkit as a case study, we then conduct profile-driven simulation studies to characterise the circumstances in which our algorithms are beneficial or deleterious compared to simple, conventional baseline algorithms. We find that there are a wide range of cloud workload scenarios where our algorithms outperform the baselines, and thereby argue that automatic control of cloud scaling and task parallelism, using techniques like those proposed, are likely to be beneficially applicable to real-world biocomputing.

There exists a huge amount of vertical applications that are developed for isolated computing environments. Due to increasing demand for additional resources there is a clear need to adapt these applications to the distributed environments. However, this is not an easy task and numerous variants are possible. Moreover, in this transition a new quality requirements become important, such as application elasticity. Application elasticity has to be built into a software system to enable smooth cost optimization at the run-time. In this paper, we provide a framework for evaluating different transformation variants of vertical Java EE multi-tiered applications into elastic cloud applications. With support of this framework the software developer is guided how to transform its application achieving optimal elasticity strategy. The framework is evaluated on slicing and evaluating elasticity of existing SaaS multi-tiered Java application used in Croatian market.

Developing and operating IoT cloud systems require novel features for deploying, controlling, monitoring and testing both IoT units and cloud services in an integrated environment spanning different infrastructures. In this paper, we demonstrate iCOMOT -- a novel toolset offering these features. Using iCOMOT we can perform various activities, such as dynamically reconfiguration of sensors, communication protocols, and cloud services in an elastic manner, suitable for testing and assuring quality of IoT cloud systems configurations. We will demonstrate our iCOMOT with a real-world predictive maintenance case study.

To optimize the cost and performance of complex cloud services under dynamic requirements, workflows and diverse cloud offerings, we rely on different elasticity control processes. An elasticity control process, when being enforced, produces effects in different parts of the cloud service. These effects normally evolve in time and depend on workload characteristics, and on the actions within the elasticity control process enforced. Therefore, understanding the effects on the behavior of the cloud service is of utter importance for runtime decision-making process, when controlling cloud service elasticity. In this paper, we present a novel methodology and a framework for estimating and evaluating cloud service elasticity behaviors. To estimate the elasticity behavior, we collect information concerning service structure, deployment, service runtime, control processes, and cloud infrastructure. Based on this information, we utilize clustering techniques to identify cloud service elasticity behavior, in time, and for different parts of the service. Knowledge about such behavior is utilized within a cloud service elasticity controller to substantially improve the selection and execution of elasticity control processes. These elasticity behavior estimations are successfully being used by our elasticity controller, in order to improve runtime decision quality. We evaluate our framework with three real-world cloud services in different application domains. Experiments show that we are able to estimate the behavior in 89.5% of the cases. Moreover, we have observed improvements in our elasticity controller, which takes better control decisions, and does not exhibit control oscillations.

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.

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.

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 crowd

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.

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.

Modeling collaboration processes is a challenging task. Existing modeling approaches are not capable of expressing the unpredictable, non-routine nature of human collaboration, which is influenced by the social context of involved collaborators. We propose a modeling approach which considers collaboration processes as the evolution of a network of collaborative documents along with a social network of collaborators. Our modeling approach, accompanied by a graphical notation and formalization, allows to capture the influence of complex social structures formed by collaborators, and therefore facilitates such activities as the discovery of socially coherent teams, social hubs, or unbiased experts. We demonstrate the applicability and expressiveness of our approach and notation, and discuss their strengths and weaknesses.

Elasticity in cloud computing is a complex problem, regarding not only resource elasticity but also quality and cost elasticity, and most importantly, the relations among the three. Therefore, existing support for controlling elasticity in complex applications, focusing solely on resource scaling, is not adequate. In this paper we present SYBL - a novel language for controlling elasticity in cloud applications - and its runtime system. SYBL allows specifying in detail elasticity monitoring, constraints, and strategies at different levels of cloud applications, including the whole application, application component, and within application component code. Based on simple SYBL elasticity directives, our runtime system will perform complex elasticity controls for the client, by leveraging underlying cloud monitoring and resource management APIs. We also present a prototype implementation and experiments illustrating how SYBL can be used in real-world scenarios.

Cloud computing has enabled a wide array of applications to be exposed as elastic cloud services. While the number of such services has rapidly increased, there is a lack of techniques for supporting cross-layered multi-level monitoring and analysis of elastic service behavior. In this paper we introduce novel concepts, namely elasticity space and elasticity pathway, for understanding elasticity of cloud services, and techniques for monitoring and evaluating them. We present MELA, a customizable framework, which enables service providers and developers to analyze cross-layered, multi-level elasticity of cloud services, from the whole cloud service to service units, based on service structure dependencies. Besides support for real-time elasticity analysis of cloud service behavior, MELA provides several customizable features for extracting functions and patterns that characterize that behavior. To illustrate the usefulness of MELA, we conduct several experiments with a realistic data-as-a-service in an M2M cloud platform.

Service process orchestration using workflow technologies has led to significant improvements in generating predicable outcomes by automating tedious manual tasks but suffer from challenges related to the flexibility required in work especially when humans are involved. Recently emerging trends in enterprises to explore social computing concepts have realized value in more agile work process orchestrations but tend to be less predictable with respect to outcomes. In this paper, we use IT services management, specifically, incident management for large scale systems, to investigate the interplay of workflow systems and social computing. We apply a recently introduced concept of social compute units (SCU), and flexible teams sourced based on various parameters such as skills, availability, incident urgency, etc. in the context of resolution of incidents in an IT service provider organization. Results from simulation-based experiments indicate that the combination of SCUs and workflow based processes can lead to significant improvement in key service delivery outcomes, with average resolution time per incident and number of SLO violations being at times as low as 53.7% and 38.1%, respectively of the corresponding values for pure workflow based incident management. Moreover, significant benefits may also be obtained through cross-skilling of practitioners via exposure to new skills in the context of collaborative work.

For solving complex problems, in many cases, software alone might not be sufficient and we need hybrid systems of software and humans in which humans not only direct the software performance but also perform computing and vice versa. Therefore, we advocate constructing "social computers" which combine software and human services. However, to date, human capabilities cannot be easily programmed into complex applications in a similar way like software capabilities. There is a lack of techniques to conceptualize and program human and software capabilities in a unified way. In this paper, we explore a new way to virtualize, provision and program human capabilities using cloud computing concepts and service delivery models. We propose novel methods for conceptualizing and modeling clouds of human-based services (HBS) and combine HBS with software-based services (SBS) to establish clouds of hybrid services. In our model, we present common APIs, similar to well-developed APIs for software services, to access individual and team-based compute units in clouds of HBS. Based on that, we propose a framework for utilizing SBS and HBS to solve complex problems. We present several programming primitives for hybrid services, also covering forming hybrid solutions consisting of software and humans. We illustrate our concepts via some examples of using our cloud APIs and existing cloud APIs for software.

Cloud computing based Data-as-a-Service (DaaS) has become popular. Several data assets have been released in DaaSes across different cloud platforms. Nevertheless, there are no well-defined ways to describe DaaSes and their associated data assets. On the one hand, existing DaaS providers simply use HTML documents to describe their service. This simple way of service description requires user to manually perform service lookup by reading the HTML documents to understand DaaSes as well as their provided data assets. On the other hand, existing service description techniques are not suitable for describing DaaSes because they consider only service information. The lack of well-structured/linked model to describe DaaSes hinders the automatic service lookup for DaaSes and the integration of DaaSes into data composition and analytic tools. In this paper, we propose DEMODS, a Description Model for DaaS, which introduces a general linked model to cover all basic information of a DaaS. Besides the basic DaaS description model, we also introduce an extended model that integrates existing work in describing quality of data, data and service contract, data dependency, and Quality of Service (QoS). We present a mechanism to incorporate DEMODS into both new and existing DaaSes. Finally, a prototype of DEMODS has been developed to evaluate the effectiveness of the proposed model.

With diverse and rich offerings from cloud computing providers in the open cloud market, scientists have great opportunities to design and conduct complex applications by utilizing and combining computational resources, software components and data sources in elastic manners. While existing techniques focus mainly on resource elasticity in single cloud infrastructure, scientists expect to design their applications being elastic in multiple dimensions to ensure that they applications can operate on multiple clouds with minimum software engineering effort. In this paper we will focus on providing techniques for scientists to compose elastic high performance applications by utilizing traditional software components, user-provided components and cloud services. We characterize elastic compositions via their resource, quality, cost, available time and usage right elasticity, thus enabling scientists to evaluate and decide how to develop, deploy and control the compositions to match their elastic needs. To illustrate our approach, we will present several real-world application compositions for multi-cloud environments.

Rich types of data offered by data as a service(DaaS) in the cloud are typically associated with different and complex data concerns that DaaS service providers, data providers and data consumers must carefully examine and agree with before passing and utilizing data. Unlike service agreements, data agreements, reflecting conditions established on the basis of data concerns, between relevant stakeholders have got little attention. However, as data concerns are complex and contextual, given the trend of mixing data sources by automated techniques, such as data mash up, data agreements must be associated with data discovery, retrieval and utilization. Unfortunately, exchanging data agreements so far has not been automated and incorporated into service and data discovery and composition. In this paper, we analyze possible steps and propose interactions among data consumers, DaaS service providers and data providers in exchanging data agreements. Based on that, we present a novel service for composing, managing, analyzing data agreements for DaaS in cloud environments and data marketplaces.

Currently, rich and diverse data types have been increasingly provided using the Data-as-a-Service (DaaS) model, a form of cloud computing services. However, data offered by DaaS are constrained by several data concerns that, if not automatically being reasoned properly, will lead to a wrong way of using them. In this paper, we support the assumption that data concerns should be explicitly modeled and specified in data contracts to support concern-aware data selection and utilization. Instead of relying on a specific definition of data contracts, we analyze contemporary data contracts and we present an abstract model for data contracts. Based on the abstract model, we propose several techniques for evaluating data contracts that can be integrated into data service selection and composition frameworks. We also illustrate our approach with some real world scenarios.

In recent years scientific workflows have been used for conducting data-intensive and long running simulations. Such simulation workflows have processed and produced different types of data whose quality has a strong influence on the final outcome of simulations. Therefore being able to monitor and analyze quality of this data during workflow execution is of paramount importance, as detection of quality problems will enable us to control the execution of simulations efficiently. Unfortunately, existing scientific workflow execution systems do not support the monitoring and analysis of quality of data for multi-scale or multi-domain simulations. In this paper, we examine how quality of data can be comprehensively measured within workflows and how the measured quality can be used to control and adapt running workflows. We present a quality of data measurement process and describe a quality of data monitoring and analysis framework that integrates this measurement process into a workflow management system.

Achieving sustainability has become an important goal in the construction, refurbishment, operation and management of buildings. To this end, we need to achieve greater information exchange, especially, about practices and solutions for Energy Efficiency (EE) and the use of Renewable Energy Sources (RES) in buildings. However, in the building life-cycle, complex and disparate information sources are used by various stakeholders, thus understanding, integrating, managing and providing means for sharing such information is a challenging task. In this paper, we analyze the possibilities to capture, distill and disseminate expert know-how related to sustainable buildings, addressing the needs of the various stakeholders. A Sustainable Building Profile (SBP) is presented, which is a novel conceptual model designed to integrate information on EE and RES aspects of buildings. The SBP makes it possible to analyse the transformation of a particular building over time. Different stakeholders can use it to study various engineering, operation and maintenance problems in buildings related to energy efficiency.

The inadequate quality of context forces the context consumers in pervasive environments to reason about the quality and relevance of context to be confident of its worth to perform their functionality. The additional task of analyzing large volumes of context drastically affects the performance of the context consumers to adjust to dynamically changing situations. A single value that presents the quality and relevance of context information tailored to the needs of a particular context consumer may release them from spending resources on context quality analysis and let them concentrate on their main task. In this paper we present a novel technique to combine different Quality of Context (QoC) metrics to infer the value of confidence on context. Our technique also considers the requirements of a particular context consumer regarding QoC metrics while confidence inference. Confidence on context is further provided to the context consumers to select high quality context and use the confidence in their functionality. We have successfully evaluated our approach using two context consumer services and user context collected from a smart home pervasive environment.

The proliferation of Data as a Service (DaaS) available on the Internet and offered by cloud service providers indicates an increasing trend in providing data under Web services in e-science and business domains. While data usage and selection are dependent on different constraints established on the basis of several data concerns, for example, quality of data and data privacy, existing data service engineering approaches lack techniques to allow the evaluation, association and publishing of such concerns with data provided via DaaS. Furthermore, data sources behind DaaSs are not static but dynamically changing, thus requiring the evaluation and publishing of data concerns to be dynamic and on-the-fly as well. In this paper, we present a novel data concern-aware service engineering process for evaluating and publishing data concerns inside DaaS that covers different evaluation and publishing scopes, modes, and integration models. Based on our process, we present a framework and its implementation for the evaluation and publishing of quality of data metrics associated with data provided by DaaSs. In this paper, we also perform several experiments to demonstrate our framework.

Providing data as a service has not only fostered the access to data from anywhere at anytime but also reduced the cost of investment. However, data is often associated with various concerns that must be explicitly described and modeled in order to ensure that the data consumer can find and select relevant data services as well as utilize the data in the right way. In particular, the use of data is bound to various rules imposed by data owners and regulators. Although, technically Web services and database technologies allow us to quickly expose data sources as Web services, until now, research has not been focused on the description of data service concerns, thus hindering the discovery, selection and utilization of data services. In this paper, we analyze major concerns for data as a service, model these concerns, and discuss how they can be used to improve the search and utilization of data services.

Nowadays, the importance of Web services is steadily increasing in domains where interoperability is of paramount importance. This trend is especially observable in complex computer systems which consist of a large number of interacting distributed components, often implemented using Web services. Large-scale systems and high complexity usually result in higher error-proneness in the development process. This should be addressed as early as possible during the development of complex service-oriented systems, ideally before they are actually deployed on a distributed infrastructure. In this paper we present GENESIS - a software framework for solving this problem. Our framework allows automatic generation and steering of testbeds of complex Web services, thereby empowering developers to specify functional and non-functional properties of Web services, to generate and deploy Web service instances on remote hosting environments, to enhance the functionality of the framework with plug-ins, and to control the behavior of the testbed during runtime.

Grid workflows for e-science are complex and prone to failures. However, there is a lack of performance monitoring and analysis tools for supporting the user as well as workflow middleware to monitor and understand the performance of complex interactions among Grid applications, middleware and resources involved in workflow executions. In this paper, we present a novel integrated environment which supports online performance monitoring and analysis of service-oriented workflows. Performance monitoring and analysis of Grid workflows and infrastructure is conducted through a Web portal. Performance overheads of Grid workflows are analyzed in a systematic way, and performance problems can be detected during runtime. Moreover, we present several languages that alleviate the interaction among performance monitoring and analysis services and their clients. Our system has been integrated into the K-WfGrid knowledge-based workflow system. It plays a key role in supporting the user and developer to analyze their workflows and in providing performance knowledge for constructing and executing workflows.

QoS (Quality of Service) parameters play a key role in selecting Grid resources and optimizing resources usage efficiently. Although many works have focused on using QoS metrics, surprisingly few tools support the monitoring and analysis of QoS metrics of Grid services. This paper presents a novel framework which supports the monitoring and analysis of QoS metrics in the Grid. Our approach is that, firstly, we develop a classification of important QoS metrics for Grid services that should be monitored and analyzed. Secondly, sensors are developed to monitor QoS of disparate Grid services by using a peer-to-peer Grid monitoring middleware. The dependencies among Grid services are modeled. Based on that, several techniques are used to analyze QoS metrics of dependent Grid services

To understand the performance of grid workflows, performance analysis tools have to select, measure and analyze various performance metrics of the workflows. However there is a lack of a comprehensive study of performance metrics which can be used to evaluate the performance of a workflow executed in the grid. This paper presents performance metrics that performance monitoring and analysis tools should provide during the evaluation of the performance of grid workflows. Performance metrics are associated with many levels of abstraction. We introduce an ontology for describing performance data of grid workflows. We describe how the ontology can he utilized for monitoring and analyzing the performance of grid workflows.

Abstract Performance engineering of parallel and distributed applications is a complex task that iterates through various phases, ranging from modeling and prediction, to performance measurement, experiment management, data collection, and bottleneck analysis. There is no evidence so far that all of these phases should/can be integrated into a single monolithic tool. Moreover, the emergence of computational Grids as a common single wide‐area platform for high‐performance computing raises the idea to provide tools as interacting Grid services that share resources, support interoperability among different users and tools, and, most importantly, provide omnipresent services over the Grid. We have developed the ASKALON tool set to support performance‐oriented development of parallel and distributed (Grid) applications. ASKALON comprises four tools, coherently integrated into a service‐oriented architecture. SCALEA is a performance instrumentation, measurement, and analysis tool of parallel and distributed applications. ZENTURIO is a general purpose experiment management tool with advanced support for multi‐experiment performance analysis and parameter studies. AKSUM provides semi‐automatic high‐level performance bottleneck detection through a special‐purpose performance property specification language. The PerformanceProphet enables the user to model and predict the performance of parallel applications at the early stages of development. In this paper we describe the overall architecture of the ASKALON tool set and outline the basic functionality of the four constituent tools. The structure of each tool is based on the composition and sharing of remote Grid services, thus enabling tool interoperability. In addition, a data repository allows the tools to share the common application performance and output data that have been derived by the individual tools. A service repository is used to store common portable Grid service implementations. A general‐purpose Factory service is employed to create service instances on arbitrary remote Grid sites. Discovering and dynamically binding to existing remote services is achieved through registry services. The ASKALON visualization diagrams support both online and post‐mortem visualization of performance and output data. We demonstrate the usefulness and effectiveness of ASKALON by applying the tools to real‐world applications. Copyright © 2005 John Wiley & Sons, Ltd.

Abstract Many existing performance analysis tools lack the flexibility to control instrumentation and performance measurement for code regions and performance metrics of interest. Performance analysis is commonly restricted to single experiments. In this paper we present SCALEA, which is a performance instrumentation, measurement, analysis, and visualization tool for parallel programs that supports post‐mortem performance analysis. SCALEA currently focuses on performance analysis for OpenMP, MPI, HPF, and mixed parallel programs. It computes a variety of performance metrics based on a novel classification of overhead. SCALEA also supports multi‐experiment performance analysis that allows one to compare and to evaluate the performance outcome of several experiments. A highly flexible instrumentation and measurement system is provided which can be controlled by command‐line options and program directives. SCALEA can be interfaced by external tools through the provision of a full Fortran90 OpenMP/MPI/HPF frontend that allows one to instrument an abstract syntax tree at a very high‐level with C‐function calls and to generate source code. A graphical user interface is provided to view a large variety of performance metrics at the level of arbitrary code regions, threads, processes, and computational nodes for single‐ and multi‐experiment performance analysis. Copyright © 2003 John Wiley & Sons, Ltd.

In this paper we give an overview of SCALEA, which is a new performance analysis tool for OpenMP, MPI, HPF, and mixed parallel/distributed programs. SCALEA instruments, executes and measures programs and computes a variety of performance overheads based on a novel overhead classification. Source code and HW-profiling is combined in a single system which significantly extends the scope of possible overheads that can be measured and examined, ranging from HW-counters, such as the number of cache misses or floating point operations, to more complex performance metrics, such as control or loss of parallelism. Moreover, SCALEA uses a new representation of code regions, called the dynamic code region call graph, which enables detailed overhead analysis for arbitrary code regions. An instrumentation description file is used to relate performance information to code regions of the input program and to reduce instrumentation overhead. Several experiments with realistic codes that cover MPI, OpenMP, HPF, and mixed OpenMP/MPI codes demonstrate the usefulness of SCALEA.