Computing at massive scale: Scalability and dependability challenges

Large-scale Cloud systems and big data analytics frameworks are now widely used for practical services and applications. However, with the increase of data volume, together with the heterogeneity of workloads and resources, and the dynamic nature of massive user requests, the uncertainties and complexity of resource management and service provisioning increase dramatically, often resulting in poor resource utilization, vulnerable system dependability, and user-perceived performance degradations. In this paper we report our latest understanding of the current and future challenges in this particular area, and discuss both existing and potential solutions to the problems, especially those concerned with system efficiency, scalability and dependability. We first introduce a data-driven analysis methodology for characterizing the resource and workload patterns and tracing performance bottlenecks in a massive-scale distributed computing environment. We then examine and analyze several fundamental challenges and the solutions we are developing to tackle them, including for example incremental but decentralized resource scheduling, incremental messaging communication, rapid system failover, and request handling parallelism. We integrate these solutions with our data analysis methodology in order to establish an engineering approach that facilitates the optimization, tuning and verification of massive-scale distributed systems. We aim to develop and offer innovative methods and mechanisms for future computing platforms that will provide strong support for new big data and IoE (Internet of Everything) applications

Reliable Computing Service in Massive-scale Systems Through Rapid Low-cost Failover

Large-scale distributed systems deployed as Cloud datacenters are capable of provisioning service to consumers with diverse business requirements. Providers face pressure to provision uninterrupted reliable services while reducing operational costs due to significant software and hardware failures. A widely adopted means to achieve such a goal is using redundant system components to implement user-transparent failover, yet its effectiveness must be balanced carefully without incurring heavy overhead when deployed-an important practical consideration for complex large-scale systems. Failover techniques developed for Cloud systems often suffer serious limitations, including mandatory restart leading to poor cost-effectiveness, as well as solely focusing on crash failures, omitting other important types, such as timing failures and simultaneous failures. This paper addresses these limitations by presenting a new approach to user-transparent failover for massive-scale systems. The approach uses soft-state inference to achieve rapid failure recovery and avoid unnecessary restart, with minimal system resource overhead. It also copes with different failures, including correlated and simultaneous events. The proposed approach was implemented, deployed and evaluated within Fuxi system, the underlying resource management system used within Alibaba Cloud. Results demonstrate that our approach tolerates complex failure scenarios while incurring at worst 228.5 microsecond instance overhead with 1.71 percent additional CPU usage

Improving System Reliability for Cyber-Physical Systems

Cyber-physical systems (CPS) are systems featuring a tight combination of, and coordination between, the system's computational and physical elements. Cyber-physical systems include systems ranging from critical infrastructure such as a power grid and transportation system to health and biomedical devices. System reliability, i.e., the ability of a system to perform its intended function under a given set of environmental and operational conditions for a given period of time, is a fundamental requirement of cyber-physical systems. An unreliable system often leads to disruption of service, financial cost and even loss of human life. An important and prevalent type of cyber-physical system meets the following criteria: processing large amounts of data; employing software as a system component; running online continuously; having operator-in-the-loop because of human judgment and an accountability requirement for safety critical systems. This thesis aims to improve system reliability for this type of cyber-physical system. To improve system reliability for this type of cyber-physical system, I present a system evaluation approach entitled automated online evaluation (AOE), which is a data-centric runtime monitoring and reliability evaluation approach that works in parallel with the cyber-physical system to conduct automated evaluation along the workflow of the system continuously using computational intelligence and self-tuning techniques and provide operator-in-the-loop feedback on reliability improvement. For example, abnormal input and output data at or between the multiple stages of the system can be detected and flagged through data quality analysis. As a result, alerts can be sent to the operator-in-the-loop. The operator can then take actions and make changes to the system based on the alerts in order to achieve minimal system downtime and increased system reliability. One technique used by the approach is data quality analysis using computational intelligence, which applies computational intelligence in evaluating data quality in an automated and efficient way in order to make sure the running system perform reliably as expected. Another technique used by the approach is self-tuning which automatically self-manages and self-configures the evaluation system to ensure that it adapts itself based on the changes in the system and feedback from the operator. To implement the proposed approach, I further present a system architecture called autonomic reliability improvement system (ARIS). This thesis investigates three hypotheses. First, I claim that the automated online evaluation empowered by data quality analysis using computational intelligence can effectively improve system reliability for cyber-physical systems in the domain of interest as indicated above. In order to prove this hypothesis, a prototype system needs to be developed and deployed in various cyber-physical systems while certain reliability metrics are required to measure the system reliability improvement quantitatively. Second, I claim that the self-tuning can effectively self-manage and self-configure the evaluation system based on the changes in the system and feedback from the operator-in-the-loop to improve system reliability. Third, I claim that the approach is efficient. It should not have a large impact on the overall system performance and introduce only minimal extra overhead to the cyberphysical system. Some performance metrics should be used to measure the efficiency and added overhead quantitatively. Additionally, in order to conduct efficient and cost-effective automated online evaluation for data-intensive CPS, which requires large volumes of data and devotes much of its processing time to I/O and data manipulation, this thesis presents COBRA, a cloud-based reliability assurance framework. COBRA provides automated multi-stage runtime reliability evaluation along the CPS workflow using data relocation services, a cloud data store, data quality analysis and process scheduling with self-tuning to achieve scalability, elasticity and efficiency. Finally, in order to provide a generic way to compare and benchmark system reliability for CPS and to extend the approach described above, this thesis presents FARE, a reliability benchmark framework that employs a CPS reliability model, a set of methods and metrics on evaluation environment selection, failure analysis, and reliability estimation. The main contributions of this thesis include validation of the above hypotheses and empirical studies of ARIS automated online evaluation system, COBRA cloud-based reliability assurance framework for data-intensive CPS, and FARE framework for benchmarking reliability of cyber-physical systems. This work has advanced the state of the art in the CPS reliability research, expanded the body of knowledge in this field, and provided some useful studies for further research

From cluster databases to cloud storage: Providing transactional support on the cloud

Durant les últimes tres dècades, les limitacions tecnològiques (com per exemple la capacitat dels dispositius d'emmagatzematge o l'ample de banda de les xarxes de comunicació) i les creixents demandes dels usuaris (estructures d'informació, volums de dades) han conduït l'evolució de les bases de dades distribuïdes. Des dels primers repositoris de dades per arxius plans que es van desenvolupar en la dècada dels vuitanta, s'han produït importants avenços en els algoritmes de control de concurrència, protocols de replicació i en la gestió de transaccions. No obstant això, els reptes moderns d'emmagatzematge de dades que plantegen el Big Data i el cloud computing—orientats a millorar la limitacions pel que fa a escalabilitat i elasticitat de les bases de dades estàtiques—estan empenyent als professionals a relaxar algunes propietats importants dels sistemes transaccionals clàssics, cosa que exclou a diverses aplicacions les quals no poden encaixar en aquesta estratègia degut a la seva alta dependència transaccional. El propòsit d'aquesta tesi és abordar dos reptes importants encara latents en el camp de les bases de dades distribuïdes: (1) les limitacions pel que fa a escalabilitat dels sistemes transaccionals i (2) el suport transaccional en repositoris d'emmagatzematge en el núvol. Analitzar les tècniques tradicionals de control de concurrència i de replicació, utilitzades per les bases de dades clàssiques per suportar transaccions, és fonamental per identificar les raons que fan que aquests sistemes degradin el seu rendiment quan el nombre de nodes i / o quantitat de dades creix. A més, aquest anàlisi està orientat a justificar el disseny dels repositoris en el núvol que deliberadament han deixat de banda el suport transaccional. Efectivament, apropar el paradigma de l'emmagatzematge en el núvol a les aplicacions que tenen una forta dependència en les transaccions és fonamental per a la seva adaptació als requeriments actuals pel que fa a volums de dades i models de negoci. Aquesta tesi comença amb la proposta d'un simulador de protocols per a bases de dades distribuïdes estàtiques, el qual serveix com a base per a la revisió i comparativa de rendiment dels protocols de control de concurrència i les tècniques de replicació existents. Pel que fa a la escalabilitat de les bases de dades i les transaccions, s'estudien els efectes que té executar diferents perfils de transacció sota diferents condicions. Aquesta anàlisi contínua amb una revisió dels repositoris d'emmagatzematge de dades en el núvol existents—que prometen encaixar en entorns dinàmics que requereixen alta escalabilitat i disponibilitat—, el qual permet avaluar els paràmetres i característiques que aquests sistemes han sacrificat per tal de complir les necessitats actuals pel que fa a emmagatzematge de dades a gran escala. Per explorar les possibilitats que ofereix el paradigma del cloud computing en un escenari real, es presenta el desenvolupament d'una arquitectura d'emmagatzematge de dades inspirada en el cloud computing la qual s’utilitza per emmagatzemar la informació generada en les Smart Grids. Concretament, es combinen les tècniques de replicació en bases de dades transaccionals i la propagació epidèmica amb els principis de disseny usats per construir els repositoris de dades en el núvol. Les lliçons recollides en l'estudi dels protocols de replicació i control de concurrència en el simulador de base de dades, juntament amb les experiències derivades del desenvolupament del repositori de dades per a les Smart Grids, desemboquen en el que hem batejat com Epidemia: una infraestructura d'emmagatzematge per Big Data concebuda per proporcionar suport transaccional en el núvol. A més d'heretar els beneficis dels repositoris en el núvol en quant a escalabilitat, Epidemia inclou una capa de gestió de transaccions que reenvia les transaccions dels clients a un conjunt jeràrquic de particions de dades, cosa que permet al sistema oferir diferents nivells de consistència i adaptar elàsticament la seva configuració a noves demandes de càrrega de treball. Finalment, els resultats experimentals posen de manifest la viabilitat de la nostra contribució i encoratgen als professionals a continuar treballant en aquesta àrea.Durante las últimas tres décadas, las limitaciones tecnológicas (por ejemplo la capacidad de los dispositivos de almacenamiento o el ancho de banda de las redes de comunicación) y las crecientes demandas de los usuarios (estructuras de información, volúmenes de datos) han conducido la evolución de las bases de datos distribuidas. Desde los primeros repositorios de datos para archivos planos que se desarrollaron en la década de los ochenta, se han producido importantes avances en los algoritmos de control de concurrencia, protocolos de replicación y en la gestión de transacciones. Sin embargo, los retos modernos de almacenamiento de datos que plantean el Big Data y el cloud computing—orientados a mejorar la limitaciones en cuanto a escalabilidad y elasticidad de las bases de datos estáticas—están empujando a los profesionales a relajar algunas propiedades importantes de los sistemas transaccionales clásicos, lo que excluye a varias aplicaciones las cuales no pueden encajar en esta estrategia debido a su alta dependencia transaccional. El propósito de esta tesis es abordar dos retos importantes todavía latentes en el campo de las bases de datos distribuidas: (1) las limitaciones en cuanto a escalabilidad de los sistemas transaccionales y (2) el soporte transaccional en repositorios de almacenamiento en la nube. Analizar las técnicas tradicionales de control de concurrencia y de replicación, utilizadas por las bases de datos clásicas para soportar transacciones, es fundamental para identificar las razones que hacen que estos sistemas degraden su rendimiento cuando el número de nodos y/o cantidad de datos crece. Además, este análisis está orientado a justificar el diseño de los repositorios en la nube que deliberadamente han dejado de lado el soporte transaccional. Efectivamente, acercar el paradigma del almacenamiento en la nube a las aplicaciones que tienen una fuerte dependencia en las transacciones es crucial para su adaptación a los requerimientos actuales en cuanto a volúmenes de datos y modelos de negocio. Esta tesis empieza con la propuesta de un simulador de protocolos para bases de datos distribuidas estáticas, el cual sirve como base para la revisión y comparativa de rendimiento de los protocolos de control de concurrencia y las técnicas de replicación existentes. En cuanto a la escalabilidad de las bases de datos y las transacciones, se estudian los efectos que tiene ejecutar distintos perfiles de transacción bajo diferentes condiciones. Este análisis continua con una revisión de los repositorios de almacenamiento en la nube existentes—que prometen encajar en entornos dinámicos que requieren alta escalabilidad y disponibilidad—, el cual permite evaluar los parámetros y características que estos sistemas han sacrificado con el fin de cumplir las necesidades actuales en cuanto a almacenamiento de datos a gran escala. Para explorar las posibilidades que ofrece el paradigma del cloud computing en un escenario real, se presenta el desarrollo de una arquitectura de almacenamiento de datos inspirada en el cloud computing para almacenar la información generada en las Smart Grids. Concretamente, se combinan las técnicas de replicación en bases de datos transaccionales y la propagación epidémica con los principios de diseño usados para construir los repositorios de datos en la nube. Las lecciones recogidas en el estudio de los protocolos de replicación y control de concurrencia en el simulador de base de datos, junto con las experiencias derivadas del desarrollo del repositorio de datos para las Smart Grids, desembocan en lo que hemos acuñado como Epidemia: una infraestructura de almacenamiento para Big Data concebida para proporcionar soporte transaccional en la nube. Además de heredar los beneficios de los repositorios en la nube altamente en cuanto a escalabilidad, Epidemia incluye una capa de gestión de transacciones que reenvía las transacciones de los clientes a un conjunto jerárquico de particiones de datos, lo que permite al sistema ofrecer distintos niveles de consistencia y adaptar elásticamente su configuración a nuevas demandas cargas de trabajo. Por último, los resultados experimentales ponen de manifiesto la viabilidad de nuestra contribución y alientan a los profesionales a continuar trabajando en esta área.Over the past three decades, technology constraints (e.g., capacity of storage devices, communication networks bandwidth) and an ever-increasing set of user demands (e.g., information structures, data volumes) have driven the evolution of distributed databases. Since flat-file data repositories developed in the early eighties, there have been important advances in concurrency control algorithms, replication protocols, and transactions management. However, modern concerns in data storage posed by Big Data and cloud computing—related to overcome the scalability and elasticity limitations of classic databases—are pushing practitioners to relax some important properties featured by transactions, which excludes several applications that are unable to fit in this strategy due to their intrinsic transactional nature. The purpose of this thesis is to address two important challenges still latent in distributed databases: (1) the scalability limitations of transactional databases and (2) providing transactional support on cloud-based storage repositories. Analyzing the traditional concurrency control and replication techniques, used by classic databases to support transactions, is critical to identify the reasons that make these systems degrade their throughput when the number of nodes and/or amount of data rockets. Besides, this analysis is devoted to justify the design rationale behind cloud repositories in which transactions have been generally neglected. Furthermore, enabling applications which are strongly dependent on transactions to take advantage of the cloud storage paradigm is crucial for their adaptation to current data demands and business models. This dissertation starts by proposing a custom protocol simulator for static distributed databases, which serves as a basis for revising and comparing the performance of existing concurrency control protocols and replication techniques. As this thesis is especially concerned with transactions, the effects on the database scalability of different transaction profiles under different conditions are studied. This analysis is followed by a review of existing cloud storage repositories—that claim to be highly dynamic, scalable, and available—, which leads to an evaluation of the parameters and features that these systems have sacrificed in order to meet current large-scale data storage demands. To further explore the possibilities of the cloud computing paradigm in a real-world scenario, a cloud-inspired approach to store data from Smart Grids is presented. More specifically, the proposed architecture combines classic database replication techniques and epidemic updates propagation with the design principles of cloud-based storage. The key insights collected when prototyping the replication and concurrency control protocols at the database simulator, together with the experiences derived from building a large-scale storage repository for Smart Grids, are wrapped up into what we have coined as Epidemia: a storage infrastructure conceived to provide transactional support on the cloud. In addition to inheriting the benefits of highly-scalable cloud repositories, Epidemia includes a transaction management layer that forwards client transactions to a hierarchical set of data partitions, which allows the system to offer different consistency levels and elastically adapt its configuration to incoming workloads. Finally, experimental results highlight the feasibility of our contribution and encourage practitioners to further research in this area

Game-Theoretic Foundations for Forming Trusted Coalitions of Multi-Cloud Services in the Presence of Active and Passive Attacks

The prominence of cloud computing as a common paradigm for offering Web-based services has led to an unprecedented proliferation in the number of services that are deployed in cloud data centers. In parallel, services' communities and cloud federations have gained an increasing interest in the recent past years due to their ability to facilitate the discovery, composition, and resource scaling issues in large-scale services' markets. The problem is that the existing community and federation formation solutions deal with services as traditional software systems and overlook the fact that these services are often being offered as part of the cloud computing technology, which poses additional challenges at the architectural, business, and security levels. The motivation of this thesis stems from four main observations/research gaps that we have drawn through our literature reviews and/or experiments, which are: (1) leading cloud services such as Google and Amazon do not have incentives to group themselves into communities/federations using the existing community/federation formation solutions; (2) it is quite difficult to find a central entity that can manage the community/federation formation process in a multi-cloud environment; (3) if we allow services to rationally select their communities/federations without considering their trust relationships, these services might have incentives to structure themselves into communities/federations consisting of a large number of malicious services; and (4) the existing intrusion detection solutions in the domain of cloud computing are still ineffective in capturing advanced multi-type distributed attacks initiated by communities/federations of attackers since they overlook the attacker's strategies in their design and ignore the cloud system's resource constraints. This thesis aims to address these gaps by (1) proposing a business-oriented community formation model that accounts for the business potential of the services in the formation process to motivate the participation of services of all business capabilities, (2) introducing an inter-cloud trust framework that allows services deployed in one or disparate cloud centers to build credible trust relationships toward each other, while overcoming the collusion attacks that occur to mislead trust results even in extreme cases wherein attackers form the majority, (3) designing a trust-based game theoretical model that enables services to distributively form trustworthy multi-cloud communities wherein the number of malicious services is minimal, (4) proposing an intra-cloud trust framework that allows the cloud system to build credible trust relationships toward the guest Virtual Machines (VMs) running cloud-based services using objective and subjective trust sources, (5) designing and solving a trust-based maxmin game theoretical model that allows the cloud system to optimally distribute the detection load among VMs within a limited budget of resources, while considering Distributed Denial of Service (DDoS) attacks as a practical scenario, and (6) putting forward a resource-aware comprehensive detection and prevention system that is able to capture and prevent advanced simultaneous multi-type attacks within a limited amount of resources. We conclude the thesis by uncovering some persisting research gaps that need further study and investigation in the future

Exploring the motivation behind cybersecurity insider threat and proposed research agenda

Cyber exploitation and malicious activities have become more sophisticated. Insider threat is one of the most significant cyber security threat vector, while posing a great concern to corporations and governments. An overview of the fundamental motivating forces and motivation theory are discussed. Such overview is provided to identify motivations that lead trusted employees to become insider threats in the context of cyber security. A research agenda with two sequential experimental research studies are outlined to address the challenge of insider threat mitigation by a prototype development. The first proposed study will classify data intake feeds, as recognized and weighted by cyber security experts, in an effort to establish predictive analytics of novel correlations of activities that may lead to cyber security incidents. It will also develop approach to identify how user activities can be compared against an established baseline, the user’s network cyber security pulse, with visualization of simulated users’ activities. Additionally, the second study will explain the process of assessing the usability of a developed visualization prototype that intends to present correlated suspicious activities requiring immediate action. Successfully developing the proposed prototype via feeds aggregation and an advanced visualization from the proposed research could assist in the mitigation of malicious insider threat

24th International Conference on Information Modelling and Knowledge Bases

In the last three decades information modelling and knowledge bases have become essentially important subjects not only in academic communities related to information systems and computer science but also in the business area where information technology is applied. The series of European – Japanese Conference on Information Modelling and Knowledge Bases (EJC) originally started as a co-operation initiative between Japan and Finland in 1982. The practical operations were then organised by professor Ohsuga in Japan and professors Hannu Kangassalo and Hannu Jaakkola in Finland (Nordic countries). Geographical scope has expanded to cover Europe and also other countries. Workshop characteristic - discussion, enough time for presentations and limited number of participants (50) / papers (30) - is typical for the conference. Suggested topics include, but are not limited to: 1. Conceptual modelling: Modelling and specification languages; Domain-specific conceptual modelling; Concepts, concept theories and ontologies; Conceptual modelling of large and heterogeneous systems; Conceptual modelling of spatial, temporal and biological data; Methods for developing, validating and communicating conceptual models. 2. Knowledge and information modelling and discovery: Knowledge discovery, knowledge representation and knowledge management; Advanced data mining and analysis methods; Conceptions of knowledge and information; Modelling information requirements; Intelligent information systems; Information recognition and information modelling. 3. Linguistic modelling: Models of HCI; Information delivery to users; Intelligent informal querying; Linguistic foundation of information and knowledge; Fuzzy linguistic models; Philosophical and linguistic foundations of conceptual models. 4. Cross-cultural communication and social computing: Cross-cultural support systems; Integration, evolution and migration of systems; Collaborative societies; Multicultural web-based software systems; Intercultural collaboration and support systems; Social computing, behavioral modeling and prediction. 5. Environmental modelling and engineering: Environmental information systems (architecture); Spatial, temporal and observational information systems; Large-scale environmental systems; Collaborative knowledge base systems; Agent concepts and conceptualisation; Hazard prediction, prevention and steering systems. 6. Multimedia data modelling and systems: Modelling multimedia information and knowledge; Contentbased multimedia data management; Content-based multimedia retrieval; Privacy and context enhancing technologies; Semantics and pragmatics of multimedia data; Metadata for multimedia information systems. Overall we received 56 submissions. After careful evaluation, 16 papers have been selected as long paper, 17 papers as short papers, 5 papers as position papers, and 3 papers for presentation of perspective challenges. We thank all colleagues for their support of this issue of the EJC conference, especially the program committee, the organising committee, and the programme coordination team. The long and the short papers presented in the conference are revised after the conference and published in the Series of “Frontiers in Artificial Intelligence” by IOS Press (Amsterdam). The books “Information Modelling and Knowledge Bases” are edited by the Editing Committee of the conference. We believe that the conference will be productive and fruitful in the advance of research and application of information modelling and knowledge bases. Bernhard Thalheim Hannu Jaakkola Yasushi Kiyok

Influence of big-data based digital media on spiritual goal strivings and well-being: a media richness theory perspective

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyBig-Data characteristics and spirituality are seldom discussed together in the context of assistances provided by big-data based digital media on spiritual goal strivings (SGS). This study’s main aim is to investigate the significance of this relationship between big-data based digital media characteristics and SGS outcomes, and its impact on well-being. A theoretical integrated framework was developed underpinned by Media Richness Theory (MRT) to capture the influence of big-data based digital media characteristics on SGS outcomes. The research design of this epistemological study adopted positivism type of scientific enquiry; employing a deductive approach confining under quantitative research methods and used survey data collection technique. Non-probability self-selection sampling was used and a total of 987 valid responses were analysed by applying statistical tests and techniques following rigorous statistical Structural Equation Modelling (SEM) techniques using IBM AMOS. The results revealed the existence of significant relationship between the big-data based digital media characteristics and SGS outcomes. The study also reveals that digital media characteristics influences successes in SGS outcomes where certain aspects of digital media characteristics are shown to assist SGS towards accomplishments while some have shown to cause hindrances for SGS to be accomplished. The results also confirmed that success in SGS accomplishments increased vitality aspect of well-being. This information is vital for decision making, implementing and planning for various spiritual stakeholders mainly spiritual seekers, spiritual organisations and user experience (UX) - user interface (UI) designers of big-data based digital media developers. With this knowledge contribution, the stakeholders are able to make informed decisions and look for efficient strategies that would provide effective, reliable and sustainable assistances towards SGS accomplishments. The study provides theoretical contribution to the body of IS literature with an integrated and extended MRT conceptual framework providing the foundation for exploring the extended MRT instrument for future studies in similar thematic contexts by other researchers. Further, this study’s empirically validated evidence provides practical contribution in its effort to spiritual stakeholders with the confidence to adopt and develop effective strategies to implement big-data based digital systems in organisations with selective configuring and tuning to utilise the accelerating aspects of the medium for effective SGS accomplishments. UX and UI stakeholders will benefit significantly to be able to design and develop digital systems supporting SGS based on a deeper understanding of the certain factors from this study which indicates significant influence on SGS and to look for effective strategies in their development phase to accommodate the revealed concerns and assistances that would provide efficient, consistent and sustainable spiritual goal outcomes. Overall the findings in this study provide optimistic future for utilising the assistances provided by big-data based digital media capabilities for SGS accomplishments. Overall statistical results reveal that the advantages of assistances provided towards SGS outcomes outweighed the disadvantages of hindrances towards SGS outcomes

Self-management and Optimization Framework. OpenIoT Deliverable D512

This deliverable describes the OpenIoT self-management and optimization framework, in terms of algorithms and mechanisms that it comprises as well as in terms of their implementation over the OpenIoT platform and associated cloud infrastructure. As a first step the main operations and functionalities of the OpenIoT self-management and optimization infrastructure are described and related to the structure of management operations defined in state-of-the-art frameworks for autonomic computing and self-management. Along with a brief description of the optimization techniques that are employed in OpenIoT, an initial mapping of the various techniques on the OpenIoT architecture is performed