Fuzzy Modeling and Control Based Virtual Machine Resource Management

Virtual machines (VMs) are powerful platforms for building agile datacenters and emerging cloud systems. However, resource management for a VM-based system is still a challenging task. First, the complexity of application workloads as well as the interference among competing workloads makes it difficult to understand their VMs’ resource demands for meeting their Quality of Service (QoS) targets; Second, the dynamics in the applications and system makes it also difficult to maintain the desired QoS target while the environment changes; Third, the transparency of virtualization presents a hurdle for guest-layer application and host-layer VM scheduler to cooperate and improve application QoS and system efficiency. This dissertation proposes to address the above challenges through fuzzy modeling and control theory based VM resource management. First, a fuzzy-logic-based nonlinear modeling approach is proposed to accurately capture a VM’s complex demands of multiple types of resources automatically online based on the observed workload and resource usages. Second, to enable fast adaption for resource management, the fuzzy modeling approach is integrated with a predictive-control-based controller to form a new Fuzzy Modeling Predictive Control (FMPC) approach which can quickly track the applications’ QoS targets and optimize the resource allocations under dynamic changes in the system. Finally, to address the limitations of black-box-based resource management solutions, a cross-layer optimization approach is proposed to enable cooperation between a VM’s host and guest layers and further improve the application QoS and resource usage efficiency. The above proposed approaches are prototyped and evaluated on a Xen-based virtualized system and evaluated with representative benchmarks including TPC-H, RUBiS, and TerraFly. The results demonstrate that the fuzzy-modeling-based approach improves the accuracy in resource prediction by up to 31.4% compared to conventional regression approaches. The FMPC approach substantially outperforms the traditional linear-model-based predictive control approach in meeting application QoS targets for an oversubscribed system. It is able to manage dynamic VM resource allocations and migrations for over 100 concurrent VMs across multiple hosts with good efficiency. Finally, the cross-layer optimization approach further improves the performance of a virtualized application by up to 40% when the resources are contended by dynamic workloads.

Preface to Models@run.time 2016

The complexity of adapting software during runtime has spawned interest in how models can be used to validate, monitor and adapt runtime behaviour. The use of models during runtime extends the use of modeling techniques beyond the design and implementation phases. The goal of this workshop is to look at issues related to developing appropriate modeldriven approaches to managing and monitoring the execution of systems and, also, to allow the system to reason about itself. We aim to continue the discussion of research ideas and proposals from researchers who work in relevant areas such as MDE, software architectures, reflection, and autonomic and self-adaptive systems, and provide a 'state-of-the-art' research assessment expressed in terms of challenges and achievements.

Autonomic Activities in the Execution of Scientific Workflows: Evaluation of the AWARD Framework

Workflows have been successfully applied to express the decomposition of complex scientific applications. However the existing tools still lack adequate support to important aspects namely, decoupling the enactment engine from tasks specification, decentralizing the control of workflow activities allowing their tasks to run in distributed infrastructures, and supporting dynamic workflow reconfigurations. We present the AWARD (Autonomic Workflow Activities Reconfigurable and Dynamic) model of computation, based on Process Networks, where the workflow activities (AWA) are autonomic processes with independent control that can run in parallel on distributed infrastructures. Each AWA executes a task developed as a Java class with a generic interface allowing end-users to code their applications without low-level details. The data-driven coordination of AWA interactions is based on a shared tuple space that also enables dynamic workflow reconfiguration. For evaluation we describe experimental results of AWARD workflow executions in several application scenarios, mapped to the Amazon (Elastic Computing EC2) Cloud.

A cluster-based implementation of a fault-tolerant parallel reduction algorithm using swarm-array computing

Recent research in multi-agent systems incorporate fault tolerance concepts. However, the research does not explore the extension and implementation of such ideas for large scale parallel computing systems. The work reported in this paper investigates a swarm array computing approach, namely ‘Intelligent Agents’. In the approach considered a task to be executed on a parallel computing system is decomposed to sub-tasks and mapped onto agents that traverse an abstracted hardware layer. The agents intercommunicate across processors to share information during the event of a predicted core/processor failure and for successfully completing the task. The agents hence contribute towards fault tolerance and towards building reliable systems. The feasibility of the approach is validated by simulations on an FPGA using a multi-agent simulator and implementation of a parallel reduction algorithm on a computer cluster using the Message Passing Interface.

Pocket data mining: towards collaborative data mining in mobile computing environments

Pocket Data Mining (PDM) is our new term describing collaborative mining of streaming data in mobile and distributed computing environments. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data for decision making using data stream mining techniques has now been achievable owing to the increasing power of these handheld devices. Wireless communication among these devices using Bluetooth and WiFi technologies has opened the door wide for collaborative mining among the mobile devices within the same range that are running data mining techniques targeting the same application. This paper proposes a new architecture that we have prototyped for realizing the significant applications in this area. We have proposed using mobile software agents in this application for several reasons. Most importantly the autonomic intelligent behaviour of the agent technology has been the driving force for using it in this application. Other efficiency reasons are discussed in details in this paper. Experimental results showing the feasibility of the proposed architecture are presented and discussed.

(m,k)-Gur game: Exploiting message discards in order to increase the lifetime and spatial diversity in autonomic wireless sensor networks

The main concern in Wireless Sensor Networks (WSN) algorithms and protocols are the energy consumption. Thus, the WSN lifetime is one of the most important metric used to measure the performance of the WSN approaches. Another important metric is the WSN spatial coverage, where the main goal is to obtain sensed data in a uniform way. This paper has proposed an approach called (m,k)-Gur Game that aims a trade-off between quality of service and the increasement of spatial coverage diversity. Simulation results have shown the effectiveness of this approach. © 2012 IEEE.

An Aspect-Oriented Approach for Supporting Autonomic Reconfiguration of Software Architecture

The increasing complexity of current software systems is encouraging the development of self-managed software architectures, i.e. systems capable of reconfiguring their structure at runtime to fulfil a set of goals. Several approaches have covered different aspects of their development, but some issues remain open, such as the maintainability or the scalability of self-management subsystems. Centralized approaches, like self-adaptive architectures, offer good maintenance properties but do not scale well for large systems. On the contrary, decentralized approaches, like self-organising architectures, offer good scalability but are not maintainable: reconfiguration specifications are spread and often tangled with functional specifications. In order to address these issues, this paper presents an aspect-oriented autonomic reconfiguration approach where: (1) each subsystem is provided with self-management properties so it can evolve itself and the components that it is composed of; (2) self-management concerns are isolated and encapsulated into aspects, thus improving its reuse and maintenance. Povzetek: Predstavljen je pristop s samo-preoblikovanjem programske arhitekture.

A proposal for a modular and application-aware autonomic manager of private cloud infrastructures

Recientemente, el paradigma de la computación en la nube ha recibido mucho interés por parte tanto de la industria como del mundo académico. Las infraestructuras cloud públicas están posibilitando nuevos modelos de negocio y ayudando a reducir costes. Sin embargo, una compañía podría desear ubicar sus datos y servicios en sus propias instalaciones, o tener que atenerse a leyes de protección de datos. Estas circunstancias hacen a las infraestructuras cloud privadas ciertamente deseables, ya sea para complementar a las públicas o para sustituirlas por completo. Por desgracia, las carencias en materia de estándares han impedido que las soluciones para la gestión de infraestructuras privadas se hayan desarrollado adecuadamente. Además, la multitud de opciones disponibles ha creado en los clientes el miedo a depender de una tecnología concreta (technology lock-in). Una de las causas de este problema es la falta de alineación entre la investigación académica y los productos comerciales, ya que aquella está centrada en el estudio de escenarios idealizados sin correspondencia con el mundo real, mientras que éstos consisten en soluciones desarrolladas sin tener en cuenta cómo van a encajar con los estándares más comunes o sin preocuparse de hacer públicos sus resultados. Con objeto de resolver este problema, propongo un sistema de gestión modular para infraestructuras cloud privadas enfocado en tratar con las aplicaciones en lugar de centrarse únicamente en los recursos hardware. Este sistema de gestión sigue el paradigma de la computación autónoma y está diseñado en torno a un modelo de información sencillo, desarrollado para ser compatible con los estándares más comunes. Este modelo divide el entorno en dos vistas, que sirven para separar aquello que debe preocupar a cada actor involucrado del resto de información, pero al mismo tiempo permitiendo relacionar el entorno físico con las máquinas virtuales que se despliegan encima de él. En dicho modelo, las aplicaciones cloud están divididas en tres tipos genéricos (Servicios, Trabajos de Big Data y Reservas de Instancias), para que así el sistema de gestión pueda sacar partido de las características propias de cada tipo. El modelo de información está complementado por un conjunto de acciones de gestión atómicas, reversibles e independientes, que determinan las operaciones que se pueden llevar a cabo sobre el entorno y que es usado para hacer posible la escalabilidad en el entorno. También describo un motor de gestión encargado de, a partir del estado del entorno y usando el ya mencionado conjunto de acciones, la colocación de recursos. Está dividido en dos niveles: la capa de Gestores de Aplicación, encargada de tratar sólo con las aplicaciones; y la capa del Gestor de Infraestructura, responsable de los recursos físicos. Dicho motor de gestión obedece un ciclo de vida con dos fases, para así modelar mejor el comportamiento de una infraestructura real. El problema de la colocación de recursos es atacado durante una de las fases (la de consolidación) por un resolutor de programación entera, y durante la otra (la online) por un heurístico hecho ex-profeso. Varias pruebas han demostrado que este acercamiento combinado es superior a otras estrategias. Para terminar, el sistema de gestión está acoplado a arquitecturas de monitorización y de actuadores. Aquella estando encargada de recolectar información del entorno, y ésta siendo modular en su diseño y capaz de conectarse con varias tecnologías y ofrecer varios modos de acceso. ABSTRACT The cloud computing paradigm has raised in popularity within the industry and the academia. Public cloud infrastructures are enabling new business models and helping to reduce costs. However, the desire to host company’s data and services on premises, and the need to abide to data protection laws, make private cloud infrastructures desirable, either to complement or even fully substitute public oferings. Unfortunately, a lack of standardization has precluded private infrastructure management solutions to be developed to a certain level, and a myriad of diferent options have induced the fear of lock-in in customers. One of the causes of this problem is the misalignment between academic research and industry ofering, with the former focusing in studying idealized scenarios dissimilar from real-world situations, and the latter developing solutions without taking care about how they f t with common standards, or even not disseminating their results. With the aim to solve this problem I propose a modular management system for private cloud infrastructures that is focused on the applications instead of just the hardware resources. This management system follows the autonomic system paradigm, and is designed around a simple information model developed to be compatible with common standards. This model splits the environment in two views that serve to separate the concerns of the stakeholders while at the same time enabling the traceability between the physical environment and the virtual machines deployed onto it. In it, cloud applications are classifed in three broad types (Services, Big Data Jobs and Instance Reservations), in order for the management system to take advantage of each type’s features. The information model is paired with a set of atomic, reversible and independent management actions which determine the operations that can be performed over the environment and is used to realize the cloud environment’s scalability. From the environment’s state and using the aforementioned set of actions, I also describe a management engine tasked with the resource placement. It is divided in two tiers: the Application Managers layer, concerned just with applications; and the Infrastructure Manager layer, responsible of the actual physical resources. This management engine follows a lifecycle with two phases, to better model the behavior of a real infrastructure. The placement problem is tackled during one phase (consolidation) by using an integer programming solver, and during the other (online) with a custom heuristic. Tests have demonstrated that this combined approach is superior to other strategies. Finally, the management system is paired with monitoring and actuators architectures. The former able to collect the necessary information from the environment, and the later modular in design and capable of interfacing with several technologies and ofering several access interfaces.

Towards a standards-based autonomic context management system

Pervasive computing applications must be sufficiently autonomous to adapt their behaviour to changes in computing resources and user requirements. This capability is known as context-awareness. In some cases, context-aware applications must be implemented as autonomic systems which are capable of dynamically discovering and replacing context sources (sensors) at run-time. Unlike other types of application autonomy, this kind of dynamic reconfiguration has not been sufficiently investigated yet by the research community. However, application-level context models are becoming common, in order to ease programming of context-aware applications and support evolution by decoupling applications from context sources. We can leverage these context models to develop general (i.e., application-independent) solutions for dynamic, run-time discovery of context sources (i.e., context management). This paper presents a model and architecture for a reconfigurable context management system that supports interoperability by building on emerging standards for sensor description and classification.

CloudMPL:a domain specific language for describing management policies for an autonomic cloud infrastructure

To benefit from the advantages that Cloud Computing brings to the IT industry, management policies must be implemented as a part of the operation of the Cloud. Among others, for example, the specification of policies can be used for the management of energy to reduce the cost of running the IT system or also for security policies while handling privacy issues of users. As cloud platforms are large, manual enforcement of policies is not scalable. Hence, autonomic approaches for management policies have recently received a considerable attention. These approaches allow specification of rules that are executed via rule-engines. The process of rules creation starts by the interpretation of the policies drafted by high-rank managers. Then, technical IT staff translate such policies to operational activities to implement them. Such process can start from a textual declarative description and after numerous steps terminates in a set of rules to be executed on a rule engine. To simplify the steps and to bridge the considerable gap between the declarative policies and executable rules, we propose a domain-specific language called CloudMPL. We also design a method of automated transformation of the rules captured in CloudMPL to the popular rule-engine Drools. As the policies are changed over time, code generation will reduce the time required for the implementation of the policies. In addition, using a declarative language for writing the specifications is expected to make the authoring of rules easier. We demonstrate the use of the CloudMPL language into a running example extracted from a management energy consumption case study.

Actionable pattern analysis in computing and business intelligence systems

With advances in science and technology, computing and business intelligence (BI) systems are steadily becoming more complex with an increasing variety of heterogeneous software and hardware components. They are thus becoming progressively more difficult to monitor, manage and maintain. Traditional approaches to system management have largely relied on domain experts through a knowledge acquisition process that translates domain knowledge into operating rules and policies. It is widely acknowledged as a cumbersome, labor intensive, and error prone process, besides being difficult to keep up with the rapidly changing environments. In addition, many traditional business systems deliver primarily pre-defined historic metrics for a long-term strategic or mid-term tactical analysis, and lack the necessary flexibility to support evolving metrics or data collection for real-time operational analysis. There is thus a pressing need for automatic and efficient approaches to monitor and manage complex computing and BI systems. To realize the goal of autonomic management and enable self-management capabilities, we propose to mine system historical log data generated by computing and BI systems, and automatically extract actionable patterns from this data. This dissertation focuses on the development of different data mining techniques to extract actionable patterns from various types of log data in computing and BI systems. Four key problems—Log data categorization and event summarization, Leading indicator identification , Pattern prioritization by exploring the link structures , and Tensor model for three-way log data are studied. Case studies and comprehensive experiments on real application scenarios and datasets are conducted to show the effectiveness of our proposed approaches.

Abnormally increased iron concentration in basal ganglia in Shy-Drager syndrome MR imaging and autonomic study: RM imagem e estudo autonômico

Report of an early case of Shy-Drager syndrome in a 67 year-old woman patient. Autonomic failure was diagnosed by functional evaluation as well as laboratory tests. MR imaging disclosed a prominent putamina hypodensity in T2-weighted images at high field strength due to iron increased depositing in this basal ganglia. MR imaging evidences confirm Shy-Drager syndrome diagnosis, and contributes for differential diagnosis of idiopathic hypotension (pure autonomic failure) in special in SDS early cases.

Hemodynamic, morphometric and autonomic patterns in hypertensive rats - renin-angiotensin system modulation

BACKGROUND: Spontaneously hypertensive rats develop left ventricular hypertrophy, increased blood pressure and blood pressure variability, which are important determinants of heart damage, like the activation of renin-angiotensin system. AIMS: To investigate the effects of the time-course of hypertension over 1) hemodynamic and autonomic patterns (blood pressure; blood pressure variability; heart rate); 2) left ventricular hypertrophy; and 3) local and systemic Renin-angiotensin system of the spontaneously hypertensive rats. METHODS: Male spontaneously hypertensive rats were randomized into two groups: young (n=13) and adult (n=12). Hemodynamic signals (blood pressure, heart rate), blood pressure variability (BPV) and spectral analysis of the autonomic components of blood pressure were analyzed. LEFT ventricular hypertrophy was measured by the ratio of LV mass to body weight (mg/g), by myocyte diameter (μm) and by relative fibrosis area (RFA, %). ACE and ACE2 activities were measured by fluorometry (UF/min), and plasma renin activity (PRA) was assessed by a radioimmunoassay (ng/mL/h). Cardiac gene expressions of Agt, Ace and Ace2 were quantified by RT-PCR (AU). RESULTS: The time-course of hypertension in spontaneously hypertensive rats increased BPV and reduced the alpha index in adult spontaneously hypertensive rats. Adult rats showed increases in left ventricular hypertrophy and in RFA. Compared to young spontaneously hypertensive rats, adult spontaneously hypertensive rats had lower cardiac ACE and ACE2 activities, and high levels of PRA. No change was observed in gene expression of Renin-angiotensin system components. CONCLUSIONS: The observed autonomic dysfunction and modulation of Renin-angiotensin system activity are contributing factors to end-organ damage in hypertension and could be interacting. Our findings suggest that the management of hypertensive disease must start before blood pressure reaches the highest stable levels and the consequent established end-organ damage is reached.

Simvastatin-induced cardiac autonomic control improvement in fructose-fed female rats

OBJECTIVE: Because autonomic dysfunction has been found to lead to cardiometabolic disorders and because studies have reported that simvastatin treatment has neuroprotective effects, the objective of the present study was to investigate the effects of simvastatin treatment on cardiovascular and autonomic changes in fructose-fed female rats. METHODS: Female Wistar rats were divided into three groups: controls (n=8), fructose (n=8), and fructose+ simvastatin (n=8). Fructose overload was induced by supplementing the drinking water with fructose (100 mg/L, 18 wks). Simvastatin treatment (5 mg/kg/day for 2 wks) was performed by gavage. The arterial pressure was recorded using a data acquisition system. Autonomic control was evaluated by pharmacological blockade. RESULTS: Fructose overload induced an increase in the fasting blood glucose and triglyceride levels and insulin resistance. The constant rate of glucose disappearance during the insulin intolerance test was reduced in the fructose group (3.4+ 0.32%/min) relative to that in the control group (4.4+ 0.29%/min). Fructose+simvastatin rats exhibited increased insulin sensitivity (5.4+0.66%/min). The fructose and fructose+simvastatin groups demonstrated an increase in the mean arterial pressure compared with controls rats (fructose: 124+2 mmHg and fructose+simvastatin: 126 + 3 mmHg vs. controls: 112 + 2 mmHg). The sympathetic effect was enhanced in the fructose group (73 + 7 bpm) compared with that in the control (48 + 7 bpm) and fructose+simvastatin groups (31+8 bpm). The vagal effect was increased in fructose+simvastatin animals (84 + 7 bpm) compared with that in control (49 + 9 bpm) and fructose animals (46+5 bpm). CONCLUSION: Simvastatin treatment improved insulin sensitivity and cardiac autonomic control in an experimental model of metabolic syndrome in female rats. These effects were independent of the improvements in the classical plasma lipid profile and of reductions in arterial pressure. These results support the hypothesis that statins reduce the cardiometabolic risk in females with metabolic syndrome.

Hyperglycemia can delay left ventricular dysfunction but not autonomic damage after myocardial infarction in rodents

Background: Although clinical diabetes mellitus is obviously a high risk factor for myocardial infarction (MI), in experimental studies disagreement exists about the sensitivity to ischemic injury of an infarcted myocardium. Recently, our group demonstrated that diabetic animals presented better cardiac function recovery and cellular resistance to ischemic injury than nondiabetics. In the present study, we evaluated the chronic effects of MI on left ventricular (LV) and autonomic functions in streptozotocin (STZ) diabetic rats. Methods: Male Wistar rats were divided into 4 groups: control (C, n = 15), diabetes (D, n = 16), MI (I, n = 21), and diabetes + MI (DI, n = 30). MI was induced 15 days after diabetes (STZ) induction. Ninety days after MI, LV and autonomic functions were evaluated (8 animals each group). Left ventricular homogenates were analyzed by Western blotting to evaluate the expression of calcium handling proteins. Results: MI area was similar in infarcted groups (similar to 43%). Ejection fraction and + dP/dt were reduced in I compared with DI. End-diastolic pressure was additionally increased in I compared with DI. Compared with DI, I had increased Na(+)-Ca(2+) exchange and phospholamban expression (164%) and decreased phosphorylated phospholamban at serine(16) (65%) and threonine(17) (70%) expression. Nevertheless, diabetic groups had greater autonomic dysfunction, observed by baroreflex sensitivity and pulse interval variability reductions. Consequently, the mortality rate was increased in DI compared with I, D, and C groups. Conclusions: LV dysfunction in diabetic animals was attenuated after 90 days of myocardial infarction and was associated with a better profile of calcium handling proteins. However, this positive adaptation was not able to reduce the mortality rate of DI animals, suggesting that autonomic dysfunction is associated with increased mortality in this group. Therefore, it is possible that the better cardiac function has been transitory, and the autonomic dysfunction, more prominent in diabetic group, may lead, in the future, to the cardiovascular damage.