Avoid Deadlock Resource Allocation (ADRA) Model V VM-out-of-N PM: Avoid Deadlock Resource Allocation (ADRA) Model V VM-out-of-N PM

This paper presents an avoid deadlock resource allocation (ADRA) for model V VM-out-of-N PM since cloud computing is a new computing paradigm composed of grid computing, distributed computing and utility concepts. Cloud computing presents a different resource allocation paradigm than either grids or distributed systems. Cloud service providers dynamically scale virtualized computing resources as a service over the internet. Due to variable number of users and limited resources, cloud is prone to deadlock at very large scale. Resource allocation and the associated deadlock avoidance is problem originated in the design and the implementation of the distributed computing, grid computing. In this paper, a new concept of free space cloud is proposed to avoid deadlock by collecting available free resource from all allocated users. New algorithms are developed for allocating multiple resources to competing services running in virtual machines on a heterogeneous distributed platform.  An experiment is tested in CloudSim. The performance of resource pool manager is evaluated by using CloudSim and resource utilization and indicating good results

An effective method for clustering-based web service recommendation

Normally web services are classified by the quality of services; however, the term quality is not absolute and defined relatively. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, and availability. The limitation of the methods employing these parameters is that sometimes they are producing similar web services in recommendation lists. To address this research problem, the novel improved clustering-based web service recommendation method is proposed in this paper. This approach is mainly dealing with producing diversity in the results of web service recommendations. In this method, functional interest, quality of service (QoS) preference, and diversity features are combined to produce a unique recommendation list of web services to end-users. To produce the unique recommendation results, we propose a varied web service classification order that is clustering-based on web services’ functional relevance such as non-useful pertinence, recorded client intrigue importance, and potential client intrigue significance. Additionally, to further improve the performance of this approach, we designed web service graph construction, an algorithm of various widths clustering. This approach serves to enhance the exceptional quality, that is, the accuracy of web service recommendation outcomes. The performance of this method was implemented and evaluated against existing systems for precision, and f-score performance metrics, using the research datasets

Exploiting Context-Aware Event Data for Fault Analysis

Fault analysis in communication networks and distributed systems is a difficult process that heavily depends on system administrator’s experience and supporting tools. This process usually requires analytic techniques and several types of event data including log events, debug messages, trace obtained from these systems to investigate the root cause of faults. This paper introduces an approach of exploiting context-aware data and classification technique for improving this process. This approach uses both event data and context-aware data including CPU load, memory, processes, temperature, status to train a decision tree, and then applies the tree to assess suspected events. We have implemented and experimented the approach on the OpenStack cloud computing system with the Hadoop computing service and MELA event collection system. The experimental results reveal that the accuracy score of the approach reaches 85% on average. The paper also includes detailed analysis for the results

Security and risk analysis in the cloud with software defined networking architecture

Cloud computing has emerged as the actual trend in business information technology service models, since it provides processing that is both cost-effective and scalable. Enterprise networks are adopting software-defined networking (SDN) for network management flexibility and lower operating costs. Information technology (IT) services for enterprises tend to use both technologies. Yet, the effects of cloud computing and software defined networking on business network security are unclear. This study addresses this crucial issue. In a business network that uses both technologies, we start by looking at security, namely distributed denial-of-service (DDoS) attack defensive methods. SDN technology may help organizations protect against DDoS assaults provided the defensive architecture is structured appropriately. To mitigate DDoS attacks, we offer a highly configurable network monitoring and flexible control framework. We present a dataset shift-resistant graphic model-based attack detection system for the new architecture. The simulation findings demonstrate that our architecture can efficiently meet the security concerns of the new network paradigm and that our attack detection system can report numerous threats using real-world network data

Economic Dynamics with Renewable Resources and Pollution

This article considers a two-sector economy with externalities. In particular,the analysis involves an industrial sector whose production activities have negative effects on the regeneration of a natural resource in the other sector. Without the usual convexity or the super-modularity structure, we prove that the economy evolves to increase the \textit{net gain of stock}, and establish the conditions ensuring the convergence of the economy in the long run

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke

Arbitrage and asset market equilibrium in infinite dimensional economies with short-selling and risk-averse expected utilities

International audienceWe consider a model with an infinite number of states of nature, von Neumann–Morgenstern utilities, where agents have different probability beliefs and where short sells are allowed. We show that no-arbitrage conditions, defined for finite dimensional asset markets models, are not sufficient to ensure existence of equilibrium in presence of an infinite number of states of nature. However, if the individually rational utility set U is compact, we obtain an equilibrium. We give conditions which imply the compactness of U. We give examples of non-existence of equilibrium when these conditions do not hold