Cloud Service Discovery

In this paper, we propose a Focused Crawler for Cloud service Discovery (FC4DC). Its service oriented architecture ensures an easier modification for rapid updating and better performance. Furthermore, the proposed crawler guarantees saving the search time and a better exploitation of the provider offerings thanks to a dedicated Cloud service description ontology. We finally present some experiments to evaluate the proposed crawler and demonstrate its effectiveness and efficiency.

In this paper, we propose a Focused Crawler for Cloud service Discovery (FC4DC). Its service oriented architecture ensures an easier modification for rapid updating and better performance. Furthermore, the proposed crawler guarantees saving the search time and a better exploitation of the provider offerings thanks to a dedicated Cloud service description ontology. We finally present some experiments to evaluate the proposed crawler and demonstrate its effectiveness and efficiency.

With the spread of services related to cloud environment, it is tiresome and time consuming for users to look for the appropriate service that meet with their needs. Therefore, finding a valid and reliable service is essential. However, in case a single cloud service cannot fulfil every user requirements, a composition of cloud services is needed. In addition, the need to treat uncertainty in cloud service discovery and composition induces a lot of concerns in order to minimize the risk. Risk includes some sort of either loss or damage which is possible to be received by a target (i.e., the environment, cloud providers or customers). In this paper, we will focus on the uncertainty application for cloud service discovery and composition. A set of existing approaches in literature are reviewed and categorized according to the risk modeling.

Cloud computing offers new features of sharing resources and applications to meet users' computing requirements. It is a model by which the users can access computing resources as services offered on the Internet (cloud services). Cloud service providers offer a highly diverse range of asymmetric cloud services with heterogeneous features, which makes it difficult for the users to find the best service that fits his needs. Many research studies have been done on cloud service discovery, and several models and solutions that applied different techniques have been proposed. This paper aims at presenting the state of the art in the area of cloud services discovery by exploring the current approaches, techniques, and models. Furthermore, it proposes a taxonomy of cloud service discovery approaches. An integrative review approach was used to explore the related literature. Then, by analyzing the existing cloud service discovery solutions, a taxonomy of discovery approaches was suggested based on several perspectives including the discovery environment and the discovery process methods. The proposed taxonomy allows easily classifying and comparing cloud services discovery solutions. Moreover, it may reveal issues and gaps for further research and expose new insights for more innovative and effective cloud services discovery solutions.

With the recent increase demand for cloud services, handling clients’ needs is getting increasingly challenging. Responding to all requesting clients with no exception could lead to security breaches, and since it is the provider’s responsibility to secure not only the offered cloud services but also the data, it is important to ensure clients reliability. Although filtering clients in the cloud is not so common, it is required to assure cloud safety. In this paper, we made use of multi agent system interaction mechanisms to handle the cloud interactions for the providers’ side, and of Particle Swarm Optimization to select the and determine the accurate trust weight and therefore to filtrate the clients by determining malicious and untrustworthy clients. The selection depends on previous knowledge and overall rating of trusted peers. The conducted experiments proves that the combination between MAS, PSO and trust outputs relevant results with different number of peers, the solution ...

With the spread of services related to cloud environment, it is tiresome and time consuming for users to look for the appropriate service that meet with their needs. Therefore, finding a valid and reliable service is essential. However, in case a single cloud service cannot fulfil every user requirements, a composition of cloud services is needed. In addition, the need to treat uncertainty in cloud service discovery and composition induces a lot of concerns in order to minimize the risk. Risk includes some sort of either loss or damage which is possible to be received by a target (i.e., the environment, cloud providers or customers). In this paper, we will focus on the uncertainty application for cloud service discovery and composition. A set of existing approaches in literature are reviewed and categorized according to the risk modeling.

Communicating by using information technology in various ways produces big amounts of data. Such data requires processing and storage. The cloud is an online storage model where data is stored on multiple virtual servers. Big data processing represents a new challenge in computing, especially in cloud computing. Data processing involves data acquisition, storage and analysis. In this respect, there are many questions including, what is the relationship between big data and cloud computing? And how is big data processed in cloud computing? The answer to these questions will be discussed in this paper, where the big data and cloud computing will be studied, in addition to getting acquainted with the relationship between them in terms of safety and challenges. We have suggested a term for big data, and a model that illustrates the relationship between big data and cloud computing.

— Hollywood movies like the ―Flight‖, ―Star Trek: Into
Darkness‖ showcase the magical photorealistic effects produced
using the 3D animation techniques. In the 3D studios the animation
scene files undergo a process called as rendering, where the 3D wire
frame models are converted into 3D photorealistic images. As the
rendering process is both a computationally intensive and a time
consuming task, the cloud based rendering in cloud render farms is
gaining popularity among the animators. The advantages of using the
cloud based render farms are that it enables the on demand scalability
of the render nodes on the Pay-as-you-go model. The animators could
choose from either an IaaS cloud service that provides only the
render nodes or a PaaS Render farm service that provides the
complete rendering environment which includes the render nodes,
software licensing, render job management software etc. Though
cloud renderfarm services are beneficial, the animators and 3D
studios hesitate to move from their traditional offline renderfarms to
cloud renderfarms as they lack the knowledge, expertise in using the
cloud technology for rendering. They also lack the time to compare
the render farm service providers based on the Quality of Service
offered by them, negotiate the QoS and monitor whether the agreed
upon QoS is actually offered by the renderfarm service providers. In
this paper we propose a Cloud Service Broker (CSB) framework
called the RenderSelect that helps in the dynamic selection,
negotiation and monitoring of the cloud based render farm services.
The method of selecting the render farm services based on the
Service Measurement Index (SMI) suggested by the CSMIC (Cloud
Service Measurement Index Consortium) and ranking the services
using the AHP Multi Criteria Decision Making (MCDM) Method is
illustrated in detail with an example.

Among numerous computing services in cloud computing Billing is one of the utmost promising field. The users always expect the best services with minimal cost and a trustworthy manner from their service provider. Existing billing systems are limited in terms of verification or computational overhead, even third party allowed the user to interact with the CSP. As a result third party cannot verify that either user's record or the CSP's record is correct. In this paper, we propose a Verification as a service (VaaS) as a medication for these limitations. Cloud infrastructure for VaaS is solely managed by the Verification Service Provider (VSP) without any intervention of the users, as well as the Service Providers. This service can be used for any type of verification at the user side as well as the provider's side. Here we only implement a part of a service i.e. VAMS (Verification & Approval Management System) module for the verification of the billing at the user side. This module is not only used as a third party for bill verification purposes, but as a faster bill generator with low computational cost and low overheads.

With the spread of services related to cloud environment, it is tiresome and time consuming for users to look for the appropriate service that meet with their needs. Therefore, finding a valid and reliable service is essential. However, in case a single cloud service cannot fulfil every user requirements, a composition of cloud services is needed. In addition, the need to treat uncertainty in cloud service discovery and composition induces a lot of concerns in order to minimize the risk. Risk includes some sort of either loss or damage which is possible to be received by a target (i.e., the environment, cloud providers or customers). In this paper, we will focus on the uncertainty application for cloud service discovery and composition. A set of existing approaches in literature are reviewed and categorized according to the risk modeling.

With the spread of services related to cloud environment, it is tiresome and time consuming for users to look for the appropriate service that meet with their needs. Therefore, finding a valid and reliable service is essential. However, in case a single cloud service cannot fulfil every user requirements, a composition of cloud services is needed. In addition, the need to treat uncertainty in cloud service discovery and composition induces a lot of concerns in order to minimize the risk. Risk includes some sort of either loss or damage which is possible to be received by a target (i.e., the environment, cloud providers or customers). In this paper, we will focus on the uncertainty application for cloud service discovery and composition. A set of existing approaches in literature are reviewed and categorized according to the risk modeling.

With the spread of services related to cloud environment, it is tiresome and time consuming for users to look for the appropriate service that meet with their needs. Therefore, finding a valid and reliable service is essential. However, in case a single cloud service cannot fulfil every user requirements, a composition of cloud services is needed. In addition, the need to treat uncertainty in cloud service discovery and composition induces a lot of concerns in order to minimize the risk. Risk includes some sort of either loss or damage which is possible to be received by a target (i.e., the environment, cloud providers or customers). In this paper, we will focus on the uncertainty application for cloud service discovery and composition. A set of existing approaches in literature are reviewed and categorized according to the risk modeling.

Cloud  computing  delivers  services  over  the  internet  based  on  pay  as  you  use
financial  model.  Cloud  providers  publish  service  advertisements  in  different
format (text, files, table and images) on the Internet. On the other hand, cloud
consumers spend a lot of
time
and effort to find the appropriate cloud service
using
  public  search  engines.  Nowadays,  cloud  service  discovery  process
c
onsiders  as  an  important  challenge  for  cloud  service  consumers,  especially
with  massive  increase  in  the  number  of  available  cloud  services. 
In  this
paper,
  we  present  a  layered  architecture  for  cloud  service  discovery  system. 
Additi
onally, we present a hybrid approach for cloud service discovery based
on  semantic  and  numerical  similarity.  Furthermore,  we  present  domain
ontology  to  create
a  shared  understanding  for  cloud  service  domain.  Finally,
we  implement  proposed  approach  with  protégé  and  SPARQL.  Empirical
results show the correctness and completeness of proposed system.

Cloud computing is a paradigm for delivering ubiquitous, convenient, on-demand resources based on pay-as-you-go financial model. The Cloud Service Providers (CSPs) typically publish service descriptions, pricing policies and Service Level Agreement (SLA) rules on websites in various formats. With enormous growth of the cloud services and number of cloud providers discovering cloud services is a significant challenge and time-consuming task, especially when using general search mechanisms. Clients need to browse several websites online and across multiple cloud providers to select the appropriate cloud service. This paper presents new XML-Decentralized architecture to improve the effectiveness and efficiency of cloud service discovery. This architecture is based on Crawler Search Engine to collect the data from Cloud Service Providers. This data includes syntax and semantic information about cloud services. With new architecture Cloud Service Providers don't need to register service descriptions with every Cloud Service Discovery Systems (CSDSs) and only need to publish the service descriptions publicly. On the other hand, CSDS can directly access the service descriptions of Cloud Service Providers to index them. The cloud service discovery process is expected to be easier and faster by adopting the new architecture.

With the growth of cloud computing, vast numbers of services have been deployed on cloud. While it provides more solutions to the cloud consumer, it is challenging to find the relevant or most appropriate cloud service. Even though the current search engines return results based on keyword search and page ranking, human intervention is required to select and find the best suited services from the large set of cloud services. Hence to overcome the limitations with the current search scenario, this paper proposes the cloud ontology technique to make discovery of cloud services efficient. It utilizes a multi broker agent based discovery system that consults ontology while retrieving information about services. This approach can be efficiently used to retrieve similar or related cloud services in the cloud computing environment.

With the spread of services related to cloud environment, it is tiresome and time consuming for users to look for the appropriate service that meet with their needs. Therefore, finding a valid and reliable service is essential. However, in case a single cloud service cannot fulfil every user requirements, a composition of cloud services is needed. In addition, the need to treat uncertainty in cloud service discovery and composition induces a lot of concerns in order to minimize the risk. Risk includes some sort of either loss or damage which is possible to be received by a target (i.e., the environment, cloud providers or customers). In this paper, we will focus on the uncertainty application for cloud service discovery and composition. A set of existing approaches in literature are reviewed and categorized according to the risk modeling.

For the past few years, the evolution of cloud computing has been potentially becoming one of the major advances in the history of computing. But is cloud computing the saviour of business? Does it signal the demise of the corporate IT functionality entirely? However, if cloud computing has to achieve its potential, there is a need to have a clear understanding of various issues involved, both from the perspectives of the providers and the consumers related to the technology, management and business aspects. Objective of this research is to explore the strategic business, management and technical challenges existing in cloud systems. It is believed that adopting a methodology and suggesting a corresponding architectural framework would serve as a potential comprehensive conceptual tool, which shows path for mitigating challenges

With the spread of services related to cloud environment, it is tiresome and time consuming for users to look for the appropriate service that meet with their needs. Therefore, finding a valid and reliable service is essential. However, in case a single cloud service cannot fulfil every user requirements, a composition of cloud services is needed. In addition, the need to treat uncertainty in cloud service discovery and composition induces a lot of concerns in order to minimize the risk. Risk includes some sort of either loss or damage which is possible to be received by a target (i.e., the environment, cloud providers or customers). In this paper, we will focus on the uncertainty application for cloud service discovery and composition. A
set of existing approaches in literature are reviewed and categorized according to the risk modeling.