A JSON Token-Based Authentication and Access Management Schema for Cloud SaaS Applications

Cloud computing is significantly reshaping the computing industry built around core concepts such as virtualization, processing power, connectivity and elasticity to store and share IT resources via a broad network. It has emerged as the key technology that unleashes the potency of Big Data, Internet of Things, Mobile and Web Applications, and other related technologies, but it also comes with its challenges - such as governance, security, and privacy. This paper is focused on the security and privacy challenges of cloud computing with specific reference to user authentication and access management for cloud SaaS applications. The suggested model uses a framework that harnesses the stateless and secure nature of JWT for client authentication and session management. Furthermore, authorized access to protected cloud SaaS resources have been efficiently managed. Accordingly, a Policy Match Gate (PMG) component and a Policy Activity Monitor (PAM) component have been introduced. In addition, other subcomponents such as a Policy Validation Unit (PVU) and a Policy Proxy DB (PPDB) have also been established for optimized service delivery. A theoretical analysis of the proposed model portrays a system that is secure, lightweight and highly scalable for improved cloud resource security and management.Comment: 6 Page

Hybrid Job-driven Scheduling for Virtual MapReduce Clusters

It is cost-efficient for a tenant with a limited budget to establish a virtual MapReduce cluster by renting multiple virtual private servers (VPSs) from a VPS provider. To provide an appropriate scheduling scheme for this type of computing environment, we propose in this paper a hybrid job-driven scheduling scheme (JoSS for short) from a tenant's perspective. JoSS provides not only job level scheduling, but also map-task level scheduling and reduce-task level scheduling. JoSS classifies MapReduce jobs based on job scale and job type and designs an appropriate scheduling policy to schedule each class of jobs. The goal is to improve data locality for both map tasks and reduce tasks, avoid job starvation, and improve job execution performance. Two variations of JoSS are further introduced to separately achieve a better map-data locality and a faster task assignment. We conduct extensive experiments to evaluate and compare the two variations with current scheduling algorithms supported by Hadoop. The results show that the two variations outperform the other tested algorithms in terms of map-data locality, reduce-data locality, and network overhead without incurring significant overhead. In addition, the two variations are separately suitable for different MapReduce-workload scenarios and provide the best job performance among all tested algorithms.Comment: 13 pages and 17 figure

A graph database for persistent identifiers

The Handle Software manages references to resources of information. However, it does not support a search functionality. A prior implementation with Elasticsearch could not efficiently capture the complex structure of our dataset, especially the relationships between handles. In this paper, we apply a graph database together with Elasticsearch to provide more search capabilities to users. In addition, the graph can efficiently store meta-data provided during handle creation. Further use cases for this graph include redundancy detection (two or more handles pointing to the same URL), or bibliographic network analysis

Negotiation Model Supporting CoAllocation for Grid Scheduling

Abstract — In order to fulfill the complex resource requirements of some users in Grid environments, support for coallocation between different resource providers is needed. Here, it is quite difficult to coordinate these different services from different resource providers, because a Grid scheduler has to cope with different policies and objectives of the different resource providers and of the users. Agreement-based resource management is considered a feasible solution to solve many of these problems as it supports the reliable interaction between different providers and users. However, most current models do not well support co-allocation. Here, negotiation is needed to create such bi-lateral agreements between several Grid parties. Such a negotiation process should be automated with no or minimal human interaction, considering the potential scale of Grid systems and the amount of necessary transactions. Therefore, strategic negotiation models play an important role. In this paper, a negotiation models which supports the co-allocation between different resource providers are proposed and examined. First simulations have been conducted to evaluate the presented system. The results demonstrate that the proposed negotiation model are suitable and effective for Grid environments. I