Performance-oriented Cloud Provisioning: Taxonomy and Survey

Cloud computing is being viewed as the technology of today and the future. Through this paradigm, the customers gain access to shared computing resources located in remote data centers that are hosted by cloud providers (CP). This technology allows for provisioning of various resources such as virtual machines (VM), physical machines, processors, memory, network, storage and software as per the needs of customers. Application providers (AP), who are customers of the CP, deploy applications on the cloud infrastructure and then these applications are used by the end-users. To meet the fluctuating application workload demands, dynamic provisioning is essential and this article provides a detailed literature survey of dynamic provisioning within cloud systems with focus on application performance. The well-known types of provisioning and the associated problems are clearly and pictorially explained and the provisioning terminology is clarified. A very detailed and general cloud provisioning classification is presented, which views provisioning from different perspectives, aiding in understanding the process inside-out. Cloud dynamic provisioning is explained by considering resources, stakeholders, techniques, technologies, algorithms, problems, goals and more.Comment: 14 pages, 3 figures, 3 table

Effective Resource and Workload Management in Data Centers

The increasing demand for storage, computation, and business continuity has driven the growth of data centers. Managing data centers efficiently is a difficult task because of the wide variety of datacenter applications, their ever-changing intensities, and the fact that application performance targets may differ widely. Server virtualization has been a game-changing technology for IT, providing the possibility to support multiple virtual machines (VMs) simultaneously. This dissertation focuses on how virtualization technologies can be utilized to develop new tools for maintaining high resource utilization, for achieving high application performance, and for reducing the cost of data center management.;For multi-tiered applications, bursty workload traffic can significantly deteriorate performance. This dissertation proposes an admission control algorithm AWAIT, for handling overloading conditions in multi-tier web services. AWAIT places on hold requests of accepted sessions and refuses to admit new sessions when the system is in a sudden workload surge. to meet the service-level objective, AWAIT serves the requests in the blocking queue with high priority. The size of the queue is dynamically determined according to the workload burstiness.;Many admission control policies are triggered by instantaneous measurements of system resource usage, e.g., CPU utilization. This dissertation first demonstrates that directly measuring virtual machine resource utilizations with standard tools cannot always lead to accurate estimates. A directed factor graph (DFG) model is defined to model the dependencies among multiple types of resources across physical and virtual layers.;Virtualized data centers always enable sharing of resources among hosted applications for achieving high resource utilization. However, it is difficult to satisfy application SLOs on a shared infrastructure, as application workloads patterns change over time. AppRM, an automated management system not only allocates right amount of resources to applications for their performance target but also adjusts to dynamic workloads using an adaptive model.;Server consolidation is one of the key applications of server virtualization. This dissertation proposes a VM consolidation mechanism, first by extending the fair load balancing scheme for multi-dimensional vector scheduling, and then by using a queueing network model to capture the service contentions for a particular virtual machine placement

Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges

[EN] If last decade viewed computational services as a utility then surely this decade has transformed computation into a commodity. Computation is now progressively integrated into the physical networks in a seamless way that enables cyber-physical systems (CPS) and the Internet of Things (IoT) meet their latency requirements. Similar to the concept of ¿platform as a service¿ or ¿software as a service¿, both cloudlets and fog computing have found their own use cases. Edge devices (that we call end or user devices for disambiguation) play the role of personal computers, dedicated to a user and to a set of correlated applications. In this new scenario, the boundaries between the network node, the sensor, and the actuator are blurring, driven primarily by the computation power of IoT nodes like single board computers and the smartphones. The bigger data generated in this type of networks needs clever, scalable, and possibly decentralized computing solutions that can scale independently as required. Any node can be seen as part of a graph, with the capacity to serve as a computing or network router node, or both. Complex applications can possibly be distributed over this graph or network of nodes to improve the overall performance like the amount of data processed over time. In this paper, we identify this new computing paradigm that we call Social Dispersed Computing, analyzing key themes in it that includes a new outlook on its relation to agent based applications. We architect this new paradigm by providing supportive application examples that include next generation electrical energy distribution networks, next generation mobility services for transportation, and applications for distributed analysis and identification of non-recurring traffic congestion in cities. The paper analyzes the existing computing paradigms (e.g., cloud, fog, edge, mobile edge, social, etc.), solving the ambiguity of their definitions; and analyzes and discusses the relevant foundational software technologies, the remaining challenges, and research opportunities.Garcia Valls, MS.; Dubey, A.; Botti, V. (2018). Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges. Journal of Systems Architecture. 91:83-102. https://doi.org/10.1016/j.sysarc.2018.05.007S831029

ACTAS: Adaptive Composition and Trading with Agents for Services

Mainly in business domains, the vision of gaining flexible, adaptive service environments is based on the standardization and practical proliferation of (Semantic) Web Services, ontologies, and agents. The standards of Web Services and their Service-oriented Architectures (SOA) became the standard paradigm for software component integration. Dynamic changes and the permanently increasing amount of available e-services of different domains are a challenge of Service Discovery and Composition. Mediation between different approaches and expert knowledge is often necessary for the composition of services of different domains. Semantic enhancements, Autonomic Service Discovery, and the research for more holistic concepts for the classification of e-services are current attempts of overcoming this challenge, in order to reach the ultimate goal of Autonomic SOC

MACHS: Mitigating the Achilles Heel of the Cloud through High Availability and Performance-aware Solutions

Cloud computing is continuously growing as a business model for hosting information and communication technology applications. However, many concerns arise regarding the quality of service (QoS) offered by the cloud. One major challenge is the high availability (HA) of cloud-based applications. The key to achieving availability requirements is to develop an approach that is immune to cloud failures while minimizing the service level agreement (SLA) violations. To this end, this thesis addresses the HA of cloud-based applications from different perspectives. First, the thesis proposes a component’s HA-ware scheduler (CHASE) to manage the deployments of carrier-grade cloud applications while maximizing their HA and satisfying the QoS requirements. Second, a Stochastic Petri Net (SPN) model is proposed to capture the stochastic characteristics of cloud services and quantify the expected availability offered by an application deployment. The SPN model is then associated with an extensible policy-driven cloud scoring system that integrates other cloud challenges (i.e. green and cost concerns) with HA objectives. The proposed HA-aware solutions are extended to include a live virtual machine migration model that provides a trade-off between the migration time and the downtime while maintaining HA objective. Furthermore, the thesis proposes a generic input template for cloud simulators, GITS, to facilitate the creation of cloud scenarios while ensuring reusability, simplicity, and portability. Finally, an availability-aware CloudSim extension, ACE, is proposed. ACE extends CloudSim simulator with failure injection, computational paths, repair, failover, load balancing, and other availability-based modules

Engineering self-awareness with knowledge management in dynamic systems: a case for volunteer computing

The complexity of the modem dynamic computing systems has motivated software engineering researchers to explore new sources of inspiration for equipping such systems with autonomic behaviours. Self-awareness has recently gained considerable attention as a prominent property for enriching the self-adaptation capabilities in systems operating in dynamic, heterogeneous and open environments. This thesis investigates the role of knowledge and its dynamic management in realising various levels of self-awareness for enabling self­adaptivity with different capabilities and strengths. The thesis develops a novel multi-level dynamic knowledge management approach for managing and representing the evolving knowledge. The approach is able to acquire 'richer' knowledge about the system's internal state and its environment in addition to managing the trade-offs arising from the adaptation conflicting goals. The thesis draws on a case from the volunteer computing, as an environment characterised by openness, heterogeneity, dynamism, and unpredictability to develop and evaluate the approach. This thesis takes an experimental approach to evaluate the effectiveness of the of the dynamic knowledge management approach. The results show the added value of the approach to the self-adaptivity of the system compared to classic self­adaptation capabilities

Gestion mémoire dans une infrastructure répartie

De nos jours, de plus en plus d'organisations déploient des infrastructures matérielles telles que des clusters ou des grilles. Elles sont utilisées pour héberger des services internet communs tels que l'email, les réseaux sociaux ou le commerce électronique ou pour exécuter des applications scientifiques telles que les simulations nucléaires ou les prédictions météorologiques. La capacité de traitement et de stockage demandée pour répondre à la charge de travail de ces applications ne peut être fournie que par le biais de ces infrastructures matérielles. Ces infrastructures matérielles embarquent des systèmes d'exploitation, qui peuvent potentiellement coopérer dans le but de gérer au mieux les ressources disponibles. Ces systèmes gèrent alors l'allocation des ressources aux applications en fonction des besoins de ces dernières. Ces systèmes visent à garantir la qualité de service et en même temps à gérer de façon optimale les ressources dans le but de limiter les coûts, notamment l'énergie. La communauté scientifique s'est intéressée à la problématique de la gestion des ressources. De nombreuses approches ont été proposées et des solutions ont été mises en œuvre. En réalisant un état de l'art de ces approches, nous constatons que la plupart d'entre elles s'intéressent à la gestion des nœuds dans l'objectif de répartir les calculs d'une façon adéquate pour exploiter de manière optimale la charge processeur. La gestion globale de la mémoire dans de telles infrastructures n'a pas été suffisamment étudiée. En effet, la mémoire est souvent considérée comme une ressource avec une capacité théoriquement illimitée grâce aux mécanismes de swap, mais ces derniers ont des conséquences importantes sur les performances des applications et le coût de fonctionnement de l'infrastructure. Dans cette thèse, nous étudions la conception et l'implantation d'un service de gestion globale de la mémoire dans une infrastructure matérielle. Ce service de gestion de mémoire doit éviter le gaspillage de mémoire et ne doit pas pénaliser les performances des applications hébergées. Nous proposons un service de swap à distance permettant à une machine, plutôt que swapper sur son disque local, de swapper sur la mémoire distante d'une autre machine ayant de la mémoire disponible. Les pages distantes peuvent être déplacées dynamiquement afin d'équilibrer la charge entre les machines. Ceci permet de mutualiser la mémoire et d'économiser les ressources. Un prototype a été implémenté et évalué. ABSTRACT : Nowadays, more and more organizations are deploying large scale infrastructures such as clusters or grids. They are used to host common Internet services such as email, social networks, e-commerce applications or to run scientific applications such as nuclear simulations and weather predictions. Processing power and storage capacities satisfying the workload of these applications can only be provided by such infrastructure. The operating systems deployed on these nodes manage the allocation of application resources and can potentially cooperate in order to manage the available resources according of the application needs. The scientific community is usually interested in the resource management problematic. Many approaches have been proposed and solutions have been implemented. However, we find out that most of them focus on the node management in order to adequately distribute calculations to optimally exploit the CPU load. The global memory management in such infrastructures has not been enough studied. Indeed, memory is often considered as a resource with a theoretically unlimited capacity thanks to the swap capabilities, but swapping has a significant impact on the system performance and the operation cost. In this thesis, we study the design and the implementation of a global memory service management in a large scale infrastructure. This memory management service must avoid wasting memory resources and should not penalize the performance of hosted applications. It is based on remote swap mechanisms. A prototype has been implemented and evaluated

A model-based runtime environment for adapting communication systems

With increasing network sizes, mobility, and traffic, it becomes a challenging task to achieve goals such as continuously delivering a satisfying service quality. Self-adaptive approaches use feedback loops to adapt a managed resource at runtime according to changes in the execution context. Adding self-adaptive capabilities to communication systems-computer networks as well as supporting structures such as overlays or middleware-is a major research focus. However, making a communication system self-adaptive is a challenging task for communication system developers. First, the distributed nature of such systems requires the collection of monitoring information from multiple hosts and the adaptation of distributed components. Second, communication systems consist of heterogeneous components, which are, e.g., developed in different programming languages. Third, system developers typically lack knowledge about the development of self-adaptive systems. Hence, this work's overall goal is to allow system developers to focus on making a (legacy) communication system adaptive. Motivated by these observations, this thesis proposes a model-based runtime environment for adapting communication systems called REACT. In contrast to self-adaptation frameworks, which offer a standard way to build self-adaptive applications, we refer to REACT as a runtime environment, i.e., a platform that is additionally able to plan and execute adaptations based on user-specified adaptation behavior. REACT includes the support for decentralized adaptation logics and distributed systems, multiple programming languages, as well as tool support and assistance for developers. The developer support is achieved using model-based techniques for specifying the reconfiguration behavior of the adaptation logic. Also, this thesis proposes an easy-to-follow development process. As part of that, it is needed to monitor the reconfiguration behavior of the self-adaptive system. Hence, this work also presents two dashboard-based visualization approaches called CoalaViz and EnTrace for providing traceability of self-adaptive systems for system developers and administrators. This thesis follows a design science research methodology resulting in the design and implementation of the final artifacts. By that, this dissertation presents different REACT Loops, including specific ways to model and plan the adaptive behavior using satisfiability, mixed-integer linear programming, and constraint solvers. The prototypes of these approaches, including the two visualization solutions, are evaluated in multiple use cases. Therefore, this work provides an end-to-end solution for specifying the adaptive behavior, connecting a managed resource, deploying the system, as well as debugging and monitoring it

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

The SysMES Framework: System Management for Networked Embedded Systems and Clusters

Automated system management for large distributed and heterogeneous environments is a common challenge in modern computer sciences. Desired properties of such a management system are, among others, a minimal dependency on human operators for problem recognition and solution, adaptability to increasing loads, fault tolerance and the flexibility to integrate new management resources at runtime. Existing tools address parts of these requirements however there is no single integrated framework which possesses all mentioned characteristics. SysMES was developed as an integrated framework for automated monitoring and management of networked devices. In order to achieve the requirements of scalability and fault tolerance, a fully distributed and decentralized architecture has been chosen. The framework comprises a monitoring module, a rule engine and an executive module for the execution of actions. A formal language has been defined which allows administrators to define complex spatial and temporal rule conditions for failure states and according reactions. These rules are used in order to reduce the number and duration of manual interventions in the managed environment by automated problem solution. SysMES is based on standards ensuring interoperability and manufacturer independence. The object-oriented modeling of management resources allows several abstraction levels for handling the complexity of managing large and heterogeneous environments. Management resources can be extended and (re)configured without downtime for increased flexibility. Multiple tests and a reference installation demonstrate the suitability of SysMES for automated management of large heterogeneous environments