Agent-Based Resource Discovery In Peer-To-Peer Networks

Large-scale resource sharing environments like Peer to Peers (P2P) are intrinsically distributed, heterogeneous and dynamic. Without having an efficient discovery mechanism, it is impossible to employ available resources and their related potential services that are geographically dispersed in heterogeneous platforms. Agents as an abstract encapsulated system with useful characteristics such as mobility, autonomy and intelligence can be considered as an applicable idea in different parts of P2P environments. The resource discovery model in an agent based environment can be observed from two different perspectives, in order to being improved. First, a model can be reviewed by its underlying architecture and arrangement of the network nodes. Component architecture specifies how the various components are linked together and what the components are supposed to expect from one another. It’s crucial to study the underlying architecture as it is the base platform to apply the resource discovery mechanism on top and a proper node federation will affect and facilitate the resource discovery process. The second perspective, is revisiting roles and features of the agents involved in the agent-based resource discovery model and improving their capabilities in order to achieve a higher performance

Agent's role in grid environments during resource discovery: a review

The grid and agent communities both develop concepts and mechanisms for open distributed systems, albeit from different perspectives. Discovering the resources in grid environments may cause lots of challenges. In this paper, a number of previous works on resource discovery process are reviewed, compared and criticized with the main focus on comparing their perspective they used to reduce the bandwidth consumption. The challenges, advantaged and disadvantages are articulated based on two main approaches: models that, do the resource discovery without using agents and resource discovery models that used agents as an applicable technology. Each of these two approaches will be studied both in grid and peer to peer environments

Nutritional requirements and actual dietary intake of adult burn patients

Background: Nutritional support of the burn patient is essential to optimize, host immune defenses and to promote prompt wound healing. Furthermore, the increased needs in calorie requirement of burned patients, the composition of proteins, carbohydrates and fats in their diet is important. The purpose of this study was to evaluate energy, macronutrient and micronutrient intake and comparing with Reference Daily Intake (RDI) in adult burn patients. Methods: Sixty thermally injured patients who were hospitalized in a burn care Imam Reza hospital in Mashhad, Iran, were included in this cross-sectional study. Information about actual intake was collected by ‘nutrient intake analysis (NIA) through direct observation. Individual nutritional intakes were assessed with the use of nutritionist 4 software and Data was analyzed by SPSS version 18. Results: The mean of energy, carbohydrate and protein intake was significantly lower than the mean total energy requirement and carbohydrate and protein RDA (

ZSCAN10 deficiency causes a neurodevelopmental disorder with characteristic oto-facial malformations

Neurodevelopmental disorders are major indications for genetic referral and have been linked to more than 1500 loci including genes encoding transcriptional regulators. The dysfunction of transcription factors often results in characteristic syndromic presentations; however, at least half of these patients lack a genetic diagnosis. The implementation of machine learning approaches has the potential to aid in the identification of new disease genes and delineate associated phenotypes. Next generation sequencing was performed in seven affected individuals with neurodevelopmental delay and dysmorphic features. Clinical characterization included reanalysis of available neuroimaging datasets and 2D portrait image analysis with GestaltMatcher. The functional consequences of ZSCAN10 loss were modelled in mouse embryonic stem cells (mESCs), including a knockout and a representative ZSCAN10 protein truncating variant. These models were characterized by gene expression and western blot analyses, chromatin immunoprecipitation and quantitative PCR (ChIP-qPCR) and immunofluorescence staining. Zscan10 knockout mouse embryos were generated and phenotyped. We prioritized bi-allelic ZSCAN10 loss-of-function variants in seven affected individuals from five unrelated families as the underlying molecular cause. RNA-sequencing analyses in Zscan10−/− mESCs indicated dysregulation of genes related to stem cell pluripotency. In addition, we established in mESCs the loss-of-function mechanism for a representative human ZSCAN10 protein truncating variant by showing alteration of its expression levels and subcellular localization, interfering with its binding to DNA enhancer targets. Deep phenotyping revealed global developmental delay, facial asymmetry and malformations of the outer ear as consistent clinical features. Cerebral MRI showed dysplasia of the semicircular canals as an anatomical correlate of sensorineural hearing loss. Facial asymmetry was confirmed as a clinical feature by GestaltMatcher and was recapitulated in the Zscan10 mouse model along with inner and outer ear malformations. Our findings provide evidence of a novel syndromic neurodevelopmental disorder caused by bi-allelic loss-of-function variants in ZSCAN10

Biallelic MED27 variants lead to variable ponto-cerebello-lental degeneration with movement disorders

MED27 is a subunit of the Mediator multiprotein complex, which is involved in transcriptional regulation. Biallelic MED27 variants have recently been suggested to be responsible for an autosomal recessive neurodevelopmental disorder with spasticity, cataracts and cerebellar hypoplasia. We further delineate the clinical phenotype of MED27-related disease by characterizing the clinical and radiological features of 57 affected individuals from 30 unrelated families with biallelic MED27 variants. Using exome sequencing and extensive international genetic data sharing, 39 unpublished affected individuals from 18 independent families with biallelic missense variants in MED27 have been identified (29 females, mean age at last follow-up 17 ± 12.4 years, range 0.1-45). Follow-up and hitherto unreported clinical features were obtained from the published 12 families. Brain MRI scans from 34 cases were reviewed. MED27-related disease manifests as a broad phenotypic continuum ranging from developmental and epileptic-dyskinetic encephalopathy to variable neurodevelopmental disorder with movement abnormalities. It is characterized by mild to profound global developmental delay/intellectual disability (100%), bilateral cataracts (89%), infantile hypotonia (74%), microcephaly (62%), gait ataxia (63%), dystonia (61%), variably combined with epilepsy (50%), limb spasticity (51%), facial dysmorphism (38%) and death before reaching adulthood (16%). Brain MRI revealed cerebellar atrophy (100%), white matter volume loss (76.4%), pontine hypoplasia (47.2%) and basal ganglia atrophy with signal alterations (44.4%). Previously unreported 39 affected individuals had seven homozygous pathogenic missense MED27 variants, five of which were recurrent. An emerging genotype-phenotype correlation was observed. This study provides a comprehensive clinical-radiological description of MED27-related disease, establishes genotype-phenotype and clinical-radiological correlations and suggests a differential diagnosis with syndromes of cerebello-lental neurodegeneration and other subtypes of 'neuro-MEDopathies'

Capacity Management Approaches for Compute Clouds

Cloud computing provides the illusion of a seamless, infinite resource pool with flexibleon-demand accessibility. However, behind this illusion there are thousands ofservers and peta-bytes of storage, running tens of thousands of applications accessedby millions of users. The management of such systems is non-trivial because theyface elastic demand, have heterogeneous resources, must fulfill diverse managementobjectives, and are vast in scale.Autonomic computing techniques can be used to tackle the complex problem ofresource management in cloud data centers by introducing self-managing elementsknown as autonomic managers. Each autonomic manager should be capable of managingitself while simultaneously contributing to the fulfillment of high level systemwideobjectives. A wide range of approaches and mechanisms can be used to defineand design these autonomic managers as well as to organize them and coordinate theiractions in order to achieve specific goals.This thesis investigates autonomic approaches for cloud resource management thataim to optimize the cloud infrastructure layer with respect to various high level objectives.The resource management problem is formulated as a problem of optimizationwith respect to one or more management objectives such as cost, profitability, or datacenter utilization, as well as performance concerns such as response time, quality ofservice, and rejection rates. The aim of the reported investigations is to address theproblems of cost-efficient elastic resource provisioning, unified management of cloudresources, and scalability in cloud resource management. This is achieved by introducingthree new concepts in capacity management: the Repacking, Holistic, and Peerto Peer approaches

Cluster Scheduling and Management for Large-Scale Compute Clouds

Cloud computing has become a powerful enabler for many IT services and new technolo-gies. It provides access to an unprecedented amount of resources in a fine-grained andon-demand manner. To deliver such a service, cloud providers should be able to efficientlyand reliably manage their available resources. This becomes a challenge for the manage-ment system as it should handle a large number of heterogeneous resources under diverseworkloads with fluctuations. In addition, it should also satisfy multiple operational require-ments and management objectives in large scale data centers.Autonomic computing techniques can be used to tackle cloud resource managementproblems. An autonomic system comprises of a number of autonomic elements, which arecapable of automatically organizing and managing themselves rather than being managedby external controllers. Therefore, they are well suited for decentralized control, as theydo not rely on a centrally managed state. A decentralized autonomic system benefits fromparallelization of control, faster decisions and better scalability. They are also more reliableas a failure of one will not affect the operation of the others, while there is also a lower riskof having faulty behaviors on all the elements, all at once. All these features are essentialrequirements of an effective cloud resource management.This thesis investigates algorithms, models, and techniques to autonomously managejobs, services, and virtual resources in a cloud data center. We introduce a decentralizedresource management framework, that automates resource allocation optimization and ser-vice consolidation, reliably schedules jobs considering probabilistic failures, and dynam-icly scales and repacks services to achieve cost efficiency.As part of the framework, we introduce a decentralized scheduler that provides andmaintains durable allocations with low maintenance costs for data centers with dynamicworkloads. The scheduler assigns resources in response to virtual machine requests andmaintains the packing efficiency while taking into account migration costs, topologicalconstraints, and the risk of resource contention, as well as fluctuations of the backgroundload.We also introduce a scheduling algorithm that considers probabilistic failures as part ofthe planning for scheduling. The aim of the algorithm is to achieve an overall job reliabil-ity, in presence of correlated failures in a data center. To do so, we study the impacts ofstochastic and correlated failures on job reliability in a virtual data center. We specificallyfocus on correlated failures caused by power outages or failure of network components onjobs running large number of replicas of identical tasks.Additionally, we investigate the trade-offs between vertical and horizontal scaling. Theresult of the investigations is used to introduce a repacking technique to automatically man-age the capacity required by an elastic service. The repacking technique combines thebenefits of both scaling strategies to improve its cost-efficiency.Datormoln har kommit att bli kraftfulla möjliggörare för många nya IT-tjänster. De ger tillgång till mycket storskaliga datorresurser på ett finkornigt och omedelbart sätt. För att tillhandahålla sådana resurser krävs att de underliggande datorcentren kan hantera sina resurser på ett tillförlitligt och effektivt sätt. Frågan hur man ska designa deras resurshanteringssystem är en stor utmaning då de ska kunna hantera mycket stora mängder heterogena resurser som i sin tur ska klara av vitt skilda typer av belastning, ofta med väldigt stora variationer över tid. Därtill ska de typiskt kunna möta en mängd olika krav och målsättningar för hur resurserna ska nyttjas. Autonomiska system kan med fördel användas för att realisera sådana system. Ett autonomt system innehåller ett antal autonoma element som automatiskt kan organisera och hantera sig själva utan stöd av externa regulatorer. Förmågan att hantera sig själva gör dem mycket lämpliga som komponenter i distribuerade system, vilka i sin tur kan bidra till snabbare beslutsprocesser, bättre skalbarhet och högre feltolerans. Denna avhandling fokuserar på algoritmer, modeller och tekniker för autonom hantering av jobb och virtuella resurser i datacenter. Vi introducerar ett decentraliserat resurshanteringssystem som automatiserar resursallokering och konsolidering, schedulerar jobb tillförlitligt med hänsyn till korrelerade fel, samt skalar resurser dynamiskt för att uppnå kostnadseffektivitet. Som en del av detta ramverk introducerar vi en decentraliserad schedulerare som allokerar resurser med hänsyn till att tagna beslut ska vara bra för lång tid och ge låga resurshanteringskostnader för datacenter med dynamisk belastning. Scheduleraren allokerar virtuella maskiner utifrån aktuell belastning och upprätthåller ett effektivt nyttjande av underliggande servrar genom att ta hänsyn till migrationskostnader, topologiska bivillkor och risk för överutnyttjande. Vi introducerar också en resursallokeringsalgoritm som tar hänsyn till korrelerade fel som ett led i planeringen. Avsikten är att kunna uppnå specificerade tillgänglighetskrav för enskilda tjänster trots uppkomst av korrelerade fel. Vi fokuserar främst på korrelerade fel som härrör från problem med elförsörjning och från felande nätverkskomponenter samt deras påverkan på jobb bestående av många identiska del-jobb. Slutligen studerar vi även hur man bäst ska kombinera horisontell och vertikal skalning av resurser. Resultatet är en process som ökar kostnadseffektivitet genom att kombinera de två metoderna och därtill emellanåt förändra fördelning av storlekar på virtuella maskiner