Event-based awareness services for P2P groupware systems

P2P systems enable decentralised applications for supporting collaborating groups and communities, where the collaboration may involve both sharing of data and sharing of group processes among group members. In such applications, monitoring and awareness are critical functionalities required for an effective collaboration. However, to date there has been little research into providing generic, application-independent awareness in P2P groupware systems. We present a distributed event-based awareness approach for such systems that provides different forms of awareness through a set of interoperating, low-level awareness services. The user and technical requirements for the approach are motivated with reference to Project-Based Learning in a P2P environment. We describe the implementation of a superpeer P2P network on a Cloud platform and the provision of reliable awareness services (AaaS - Awareness as a Service) from the Cloud. We report on the outcomes of an empirical evaluation of the performance and scalability of the approach

Cloud scheduling optimization: a reactive model to enable dynamic deployment of virtual machines instantiations

This study proposes a model for supporting the decision making process of the cloud policy for the deployment of virtual machines in cloud environments. We explore two configurations, the static case in which virtual machines are generated according to the cloud orchestration, and the dynamic case in which virtual machines are reactively adapted according to the job submissions, using migration, for optimizing performance time metrics. We integrate both solutions in the same simulator for measuring the performance of various combinations of virtual machines, jobs and hosts in terms of the average execution and total simulation time. We conclude that the dynamic configuration is prosperus as it offers optimized job execution performance

Meta-scheduling Issues in Interoperable HPCs, Grids and Clouds

Over the last years, interoperability among resources has been emerged as one of the most challenging research topics. However, the commonality of the complexity of the architectures (e.g., heterogeneity) and the targets that each computational paradigm including HPC, grids and clouds aims to achieve (e.g., flexibility) remain the same. This is to efficiently orchestrate resources in a distributed computing fashion by bridging the gap among local and remote participants. Initially, this is closely related with the scheduling concept which is one of the most important issues for designing a cooperative resource management system, especially in large scale settings such as in grids and clouds. Within this context, meta-scheduling offers additional functionalities in the area of interoperable resource management, this is because of its great agility to handle sudden variations and dynamic situations in user demands. Accordingly, the case of inter-infrastructures, including InterCloud, entitle that the decentralised meta-scheduling scheme overcome issues like consolidated administration management, bottleneck and local information exposition. In this work, we detail the fundamental issues for developing an effective interoperable meta-scheduler for e-infrastructures in general and InterCloud in particular. Finally, we describe a simulation and experimental configuration based on real grid workload traces to demonstrate the interoperable setting as well as provide experimental results as part of a strategic plan for integrating future meta-schedulers

Health Access Broker: Secure, Patient-Controlled Management of Personal Health Records in the Cloud

Secure and privacy-preserving management of Personal Health Records (PHRs) has proved to be a major challenge in modern healthcare. Current solutions generally do not offer patients a choice in where the data is actually stored and also rely on at least one fully trusted element that patients must also trust with their data. In this work, we present the Health Access Broker (HAB), a patient-controlled service for secure PHR sharing that (a) does not impose a specific storage location (uniquely for a PHR system), and (b) does not assume any of its components to be fully secure against adversarial threats. Instead, HAB introduces a novel auditing and intrusion-detection mechanism where its workflow is securely logged and continuously inspected to provide auditability of data access and quickly detect any intrusions.Comment: Copy of the paper accepted at 13th International Conference on Computational Intelligence in Security for Information Systems (CISIS

New Zinc-Based Active Chitosan Films: Physicochemical Characterization, Antioxidant, and Antimicrobial Properties

The improvement of the antioxidant and antimicrobial activities of chitosan (CS) films can be realized by incorporating transition metal complexes as active components. In this context, bioactive films were prepared by embedding a newly synthesized acylpyrazolonate Zn(II) complex, [Zn(QPhtBu)2(MeOH)2], into the eco-friendly biopolymer CS matrix. Homogeneous, amorphous, flexible, and transparent CS@Znn films were obtained through the solvent casting method in dilute acidic solution, using different weight ratios of the Zn(II) complex to CS and characterized by powder X-ray diffraction (PXRD), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), Fourier transform infrared (FT-IR), Raman, and scanning electron microscopy (SEM) techniques. The X-ray single-crystal analysis of [Zn(QPhtBu)2(MeOH)2] and the evaluation of its intermolecular interactions with a protonated glucosamine fragment through hydrogen bond propensity (HBP) calculations are reported. The effects of the different contents of the [Zn(QPhtBu)2(MeOH)2] complex on the CS biological proprieties have been evaluated, proving that the new CS@Znn films show an improved antioxidant activity, tested according to the DPPH method, with respect to pure CS, related to the concentration of the incorporated Zn(II) complex. Finally, the CS@Znn films were tried out as antimicrobial agents, showing an increase in antimicrobial activity against Gram-positive bacteria (Staphylococcus aureus) with respect to pure CS, when detected by the agar disk-diffusion method

Meta-heuristically seeded genetic algorithm for independent job scheduling in grid computing

Grid computing is an infrastructure which connects geographically distributed computers owned by various organizations allowing their resources, such as computational power and storage capabilities, to be shared, selected, and aggregated. Job scheduling problem is one of the most difficult tasks in grid computing systems. To solve this problem efficiently, new methods are required. In this paper, a seeded genetic algorithm is proposed which uses a meta-heuristic algorithm to generate its initial population. To evaluate the performance of the proposed method in terms of minimizing the makespan, the Expected Time to Compute (ETC) simulation model is used to carry out a number of experiments. The results show that the proposed algorithm performs better than other selected techniques

A budget feasible peer graded mechanism for iot-based crowdsourcing

We develop and extend a line of recent works on the design of mechanisms for heterogeneous tasks assignment problem in ’crowdsourcing’. The budgeted market we consider consists of multiple task requesters and multiple IoT devices as task executers. In this, each task requester is endowed with a single distinct task along with the publicly known budget. Also, each IoT device has valuations as the cost for executing the tasks and quality, which are private. Given such scenario, the objective is to select a subset of IoT devices for each task, such that the total payment made is within the allotted quota of the budget while attaining a threshold quality. For the purpose of determining the unknown quality of the IoT devices we have utilized the concept of peer grading. In this paper, we have carefully crafted a truthful budget feasible mechanism for the problem under investigation that also allows us to have the true information about the quality of the IoT devices. Further, we have extended the set-up considering the case where the tasks are divisible in nature and the IoT devices are working collaboratively, instead of, a single entity for executing each task. We have designed the budget feasible mechanisms for the extended versions. The simulations are performed in order to measure the efficacy of our proposed mechanismPeer ReviewedPostprint (author's final draft

Knowledge discovery for scheduling in computational grids

International audienceScheduling in computational grids addresses the allocation of computing jobs to globally distributed compute resources. In a frequently changing resource environment, scheduling decisions have to be made rapidly. Depending on both the job properties and the current state of the resources, those decisions are different. Thus, the performance of grid scheduling systems highly depends on their adaptivity and flexibility in changing environments. Under these conditions, methods from knowledge discovery yielded significant success to augment and substitute conventional grid scheduling techniques. This paper presents a survey on approaches to extract, represent, and utilize knowledge to improve the grid scheduling performance. It aims to give researchers insight into techniques used for knowledge-supported scheduling in large-scale distributed computing environments