Autonomic Cloud Computing: Open Challenges and Architectural Elements

As Clouds are complex, large-scale, and heterogeneous distributed systems, management of their resources is a challenging task. They need automated and integrated intelligent strategies for provisioning of resources to offer services that are secure, reliable, and cost-efficient. Hence, effective management of services becomes fundamental in software platforms that constitute the fabric of computing Clouds. In this direction, this paper identifies open issues in autonomic resource provisioning and presents innovative management techniques for supporting SaaS applications hosted on Clouds. We present a conceptual architecture and early results evidencing the benefits of autonomic management of Clouds.Comment: 8 pages, 6 figures, conference keynote pape

Multi-Agent System Approach for Trustworthy Cloud Service Discovery

Accessing the advantages of cloud computing requires that a prospective user has proper access to trustworthy cloud services. It is a strenuous and laborious task to find resources and services in a heterogeneous network such as cloud environment. The cloud computing paradigm being a form of distributed system with a complex collection of computing resources from different domains with different regulatory policies but having a lot of values could enhance the mode of computing. However, a monolithic approach to cloud service discovery cannot help the necessities of cloud environment efficiently. This study put forward a distributive approach for finding sincere cloud services with the use of Multi-Agents System for ensuring intelligent cloud service discovery from trusted providers. Experiments were carried out in the study using CloudAnalyst and the results indicated that extending the frontiers MAS approach into cloud service discovery by way of integrating trust into the process improves the quality of service in respect of response time and scalability. A further comparative analysis of the Multi-Agents System approach for cloud service discovery to monolithic approach showed that Multi-Agents System approach is highly efficient, and highly flexible for trustworthy cloud service discovery

High-Performance Cloud Computing: A View of Scientific Applications

Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Compute resources, storage resources, as well as applications, can be dynamically provisioned (and integrated within the existing infrastructure) on a pay per use basis. These resources can be released when they are no more needed. Such services are often offered within the context of a Service Level Agreement (SLA), which ensure the desired Quality of Service (QoS). Aneka, an enterprise Cloud computing solution, harnesses the power of compute resources by relying on private and public Clouds and delivers to users the desired QoS. Its flexible and service based infrastructure supports multiple programming paradigms that make Aneka address a variety of different scenarios: from finance applications to computational science. As examples of scientific computing in the Cloud, we present a preliminary case study on using Aneka for the classification of gene expression data and the execution of fMRI brain imaging workflow.Comment: 13 pages, 9 figures, conference pape

Multidimensional Decision Model for Investment in Cloud Computing

Cloud computing is an exciting phenomenon currently gaining popularity. Many organizations consider the option of migrating some of their infrastructure to cloud. However, it is absolutely essential to understand the promises of cloud computing, and its actual capabilities offered by modern-day cloud providers beforehand. This paper investigates the phenomenon of cloud computing from the perspective of “cloudonomics”. It describes the basic premises of cloud computing; these foundations allow us to frame the main research goal of this study – the investigation of costs associated with utilizing cloud services offered by external providers in contrast to meeting the needs in-house. We discuss how these topics have been approached by other researchers, and we identify the directions that future research should follow in order to provide valuable information for both IS practitioners and IS researchers. Finally, we propose a discrete multidimensional decision model that helps researchers and decision-makers in evaluating services offered by cloud vendors

Balancing the Computation-Intensive Function and User Privacy Disclosure at Different Security Levels

We propose a structure for protection safeguarding outsourced utilitarian calculation crosswise over substantial scale numerous encoded areas, which we allude to as POFD. With POFD, a client can get the yield of a capacity processed over encoded information from different spaces while ensuring the security of the capacity itself, its info and its yield. In particular, we present two thoughts of POFD, the essential POFD and its improved rendition, keeping in mind the end goal to tradeoff the levels of security insurance and execution. We display three conventions, named Multi-space Secure Multiplication protocol (MSM), Secure Exponent Calculation protocol with private Base (SECB), and Secure Exponent Calculation protocol (SEC), as the core sub-protocol for POFD to safely process the outsourced work. Point by point security examination demonstrates that the proposed POFD accomplishes the objective of ascertaining a client characterized work crosswise over various scrambled spaces without protection spillage to unapproved parties. Our execution assessments utilizing reenactments exhibit the utility and the productivity of POFD

Implementation of Private Cloud Computing Using Integration of JavaScript and Python

This paper deals with the design and deployment of a novel library class in Python, enabling the use of JavaScript functionalities in Application Programming and the leveraging of this Library into development for third generation technologies such as Private Cloud Computing. The integration of these two prevalent languages provides us with a new level of compliance which helps in developing an understanding between Web Programming and Application Programming. An inter-browser functionality wrapping, which would enable users to have a JavaScript experience in Python interfaces directly, without having to depend on external programs, has been developed. The functionality of this concept is prevalent in the fact that Applications written in JavaScript and accessed on the browser now have the capability of interacting with each other on a common platform with the help of a Python wrapper. The idea is demonstrated by the integrating with the now ubiquitous Cloud Computing concept. With the help of examples, we have showcased the same and explained how the Library XOCOM can be a stepping stone to flexible cloud computing environment