327 research outputs found
Pricing the Cloud: An Auction Approach
Cloud computing has changed the processing and service modes of information communication technology and has affected the transformation, upgrading and innovation of the IT-related industry systems. The rapid development of cloud computing in business practice has spawned a whole new field of interdisciplinary, providing opportunities and challenges for business management research.
One of the critical factors impacting cloud computing is how to price cloud services. An appropriate pricing strategy has important practical means to stakeholders, especially to providers and customers. This study addressed and discussed research findings on cloud computing pricing strategies, such as fixed pricing, bidding pricing, and dynamic pricing. Another key factor for cloud computing is Quality of Service (QoS), such as availability, reliability, latency, security, throughput, capacity, scalability, elasticity, etc. Cloud providers seek to improve QoS to attract more potential customers; while, customers intend to find QoS matching services that do not exceed their budget constraints.
Based on the existing study, a hybrid QoS-based pricing mechanism, which consists of subscription and dynamic auction design, is proposed and illustrated to cloud services. The results indicate that our hybrid pricing mechanism has potential to better allocate available cloud resources, aiming at increasing revenues for providers and reducing expenses for customers in practice
A Research Perspective on Data Management Techniques for Federated Cloud Environment
Cloud computing has given a large scope of improvement in processing, storage and retrieval of data that is generated in huge amount from devices and users. Heterogenous devices and users generates the multidisciplinary data that needs to take care for easy and efficient storage and fast retrieval by maintaining quality and service level agreements. By just storing the data in cloud will not full fill the user requirements, the data management techniques has to be applied so that data adaptiveness and proactiveness characteristics are upheld. To manage the effectiveness of entire eco system a middleware must be there in between users and cloud service providers. Middleware has set of events and trigger based policies that will act on generated data to intermediate users and cloud service providers. For cloud service providers to deliver an efficient utilization of resources is one of the major issues and has scope of improvement in the federation of cloud service providers to fulfill user’s dynamic demands. Along with providing adaptiveness of data management in the middleware layer is challenging. In this paper, the policies of middleware for adaptive data management have been reviewed extensively. The main objectives of middleware are also discussed to accomplish high throughput of cloud service providers by means of federation and qualitative data management by means of adaptiveness and proactiveness. The cloud federation techniques have been studied thoroughly along with the pros and cons of it. Also, the strategies to do management of data has been exponentially explored
DRIVE: A Distributed Economic Meta-Scheduler for the Federation of Grid and Cloud Systems
The computational landscape is littered with islands of disjoint resource providers including
commercial Clouds, private Clouds, national Grids, institutional Grids, clusters, and data centers.
These providers are independent and isolated due to a lack of communication and coordination,
they are also often proprietary without standardised interfaces, protocols, or execution environments.
The lack of standardisation and global transparency has the effect of binding consumers
to individual providers. With the increasing ubiquity of computation providers there is an opportunity
to create federated architectures that span both Grid and Cloud computing providers
effectively creating a global computing infrastructure. In order to realise this vision, secure and
scalable mechanisms to coordinate resource access are required. This thesis proposes a generic
meta-scheduling architecture to facilitate federated resource allocation in which users can provision
resources from a range of heterogeneous (service) providers.
Efficient resource allocation is difficult in large scale distributed environments due to the inherent
lack of centralised control. In a Grid model, local resource managers govern access to a
pool of resources within a single administrative domain but have only a local view of the Grid
and are unable to collaborate when allocating jobs. Meta-schedulers act at a higher level able to
submit jobs to multiple resource managers, however they are most often deployed on a per-client
basis and are therefore concerned with only their allocations, essentially competing against one
another. In a federated environment the widespread adoption of utility computing models seen in
commercial Cloud providers has re-motivated the need for economically aware meta-schedulers.
Economies provide a way to represent the different goals and strategies that exist in a competitive
distributed environment. The use of economic allocation principles effectively creates an
open service market that provides efficient allocation and incentives for participation.
The major contributions of this thesis are the architecture and prototype implementation of the
DRIVE meta-scheduler. DRIVE is a Virtual Organisation (VO) based distributed economic metascheduler
in which members of the VO collaboratively allocate services or resources. Providers
joining the VO contribute obligation services to the VO. These contributed services are in effect
membership “dues” and are used in the running of the VOs operations – for example allocation,
advertising, and general management. DRIVE is independent from a particular class of provider
(Service, Grid, or Cloud) or specific economic protocol. This independence enables allocation in
federated environments composed of heterogeneous providers in vastly different scenarios. Protocol
independence facilitates the use of arbitrary protocols based on specific requirements and
infrastructural availability. For instance, within a single organisation where internal trust exists,
users can achieve maximum allocation performance by choosing a simple economic protocol.
In a global utility Grid no such trust exists. The same meta-scheduler architecture can be used
with a secure protocol which ensures the allocation is carried out fairly in the absence of trust.
DRIVE establishes contracts between participants as the result of allocation. A contract describes
individual requirements and obligations of each party. A unique two stage contract negotiation
protocol is used to minimise the effect of allocation latency. In addition due to the co-op nature of
the architecture and the use of secure privacy preserving protocols, DRIVE can be deployed in a
distributed environment without requiring large scale dedicated resources.
This thesis presents several other contributions related to meta-scheduling and open service
markets. To overcome the perceived performance limitations of economic systems four high utilisation
strategies have been developed and evaluated. Each strategy is shown to improve occupancy,
utilisation and profit using synthetic workloads based on a production Grid trace. The
gRAVI service wrapping toolkit is presented to address the difficulty web enabling existing applications.
The gRAVI toolkit has been extended for this thesis such that it creates economically
aware (DRIVE-enabled) services that can be transparently traded in a DRIVE market without requiring
developer input. The final contribution of this thesis is the definition and architecture of
a Social Cloud – a dynamic Cloud computing infrastructure composed of virtualised resources
contributed by members of a Social network. The Social Cloud prototype is based on DRIVE
and highlights the ease in which dynamic DRIVE markets can be created and used in different
domains
Trustworthy Federated Learning: A Survey
Federated Learning (FL) has emerged as a significant advancement in the field
of Artificial Intelligence (AI), enabling collaborative model training across
distributed devices while maintaining data privacy. As the importance of FL
increases, addressing trustworthiness issues in its various aspects becomes
crucial. In this survey, we provide an extensive overview of the current state
of Trustworthy FL, exploring existing solutions and well-defined pillars
relevant to Trustworthy . Despite the growth in literature on trustworthy
centralized Machine Learning (ML)/Deep Learning (DL), further efforts are
necessary to identify trustworthiness pillars and evaluation metrics specific
to FL models, as well as to develop solutions for computing trustworthiness
levels. We propose a taxonomy that encompasses three main pillars:
Interpretability, Fairness, and Security & Privacy. Each pillar represents a
dimension of trust, further broken down into different notions. Our survey
covers trustworthiness challenges at every level in FL settings. We present a
comprehensive architecture of Trustworthy FL, addressing the fundamental
principles underlying the concept, and offer an in-depth analysis of trust
assessment mechanisms. In conclusion, we identify key research challenges
related to every aspect of Trustworthy FL and suggest future research
directions. This comprehensive survey serves as a valuable resource for
researchers and practitioners working on the development and implementation of
Trustworthy FL systems, contributing to a more secure and reliable AI
landscape.Comment: 45 Pages, 8 Figures, 9 Table
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