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Register a new userOpportunities in a Federated Cloud Marketplace
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Added by
Hamed Haddadi
Publication date
2014
fields
Informatics Engineering
and research's language is
English
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Recent measurement studies show that there are massively distributed hosting and computing infrastructures deployed in the Internet. Such infrastructures include large data centers and organizations computing clusters. When idle, these resources can readily serve local users. Such users can be smartphone or tablet users wishing to access services such as remote desktop or CPU/bandwidth intensive activities. Particularly, when they are likely to have high latency to access, or may have no access at all to, centralized cloud providers. Today, however, there is no global marketplace where sellers and buyers of available resources can trade. The recently introduced marketplaces of Amazon and other cloud infrastructures are limited by the network footprint of their own infrastructures and availability of such services in the target country and region. In this article we discuss the potentials for a federated cloud marketplace where sellers and buyers of a number of resources, including storage, computing, and network bandwidth, can freely trade. This ecosystem can be regulated through brokers who act as service level monitors and auctioneers. We conclude by discussing the challenges and opportunities in this space.
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With the proliferation of mobile applications, Mobile Cloud Computing (MCC) has been proposed to help mobile devices save energy and improve computation performance. To further improve the quality of service (QoS) of MCC, cloud servers can be deployed locally so that the latency is decreased. However, the computational resource of the local cloud is generally limited. In this paper, we design a threshold-based policy to improve the QoS of MCC by cooperation of the local cloud and Internet cloud resources, which takes the advantages of low latency of the local cloud and abundant computational resources of the Internet cloud simultaneously. This policy also applies a priority queue in terms of delay requirements of applications. The optimal thresholds depending on the traffic load is obtained via a proposed algorithm. Numerical results show that the QoS can be greatly enhanced with the assistance of Internet cloud when the local cloud is overloaded. Better QoS is achieved if the local cloud order tasks according to their delay requirements, where delay-sensitive applications are executed ahead of delay-tolerant applications. Moreover, the optimal thresholds of the policy have a sound impact on the QoS of the system.
Unmanned aerial vehicles (UAVs), or say drones, are envisioned to support extensive applications in next-generation wireless networks in both civil and military fields. Empowering UAVs networks intelligence by artificial intelligence (AI) especially machine learning (ML) techniques is inevitable and appealing to enable the aforementioned applications. To solve the problems of traditional cloud-centric ML for UAV networks such as privacy concern, unacceptable latency, and resource burden, a distributed ML technique, textit(i.e.), federated learning (FL), has been recently proposed to enable multiple UAVs to collaboratively train ML model without letting out raw data. However, almost all existing FL paradigms are still centralized, textit{i.e.}, a central entity is in charge of ML model aggregation and fusion over the whole network, which could result in the issue of a single point of failure and are inappropriate to UAV networks with both unreliable nodes and links. Thus motivated, in this article, we propose a novel architecture called DFL-UN (underline{D}ecentralized underline{F}ederated underline{L}earning for underline{U}AV underline{N}etworks), which enables FL within UAV networks without a central entity. We also conduct a preliminary simulation study to validate the feasibility and effectiveness of the DFL-UN architecture. Finally, we discuss the main challenges and potential research directions in the DFL-UN.
Bandwidth slicing is introduced to support federated learning in edge computing to assure low communication delay for training traffic. Results reveal that bandwidth slicing significantly improves training efficiency while achieving good learning accuracy.
The proliferation of cloud providers has brought substantial interoperability complexity to the public cloud market, in which cloud brokering has been playing an important role. However, energy-related issues for public clouds have not been well addressed in the literature. In this paper, we claim that the broker is also situated in a perfect position where necessary actions can be taken to achieve energy efficiency for public cloud systems, particularly through job assignment and scheduling. We formulate the problem by a mixed integer program and prove its NP-hardness. Based on the complexity analysis, we simplify the problem by introducing admission control on jobs. In the sequel, optimal job assignment can be done straightforwardly and the problem is transformed into improving job admission rate by scheduling on two coupled phases: data transfer and job execution. The two scheduling phases are further decoupled and we develop efficient scheduling algorithm for each of them. Experimental results show that the proposed solution can achieve significant reduction on energy consumption with admission rates improved as well, even in large-scale public cloud systems.
Cloud Computing is rising fast, with its data centres growing at an unprecedented rate. However, this has come with concerns over privacy, efficiency at the expense of resilience, and environmental sustainability, because of the dependence on Cloud vendors such as Google, Amazon and Microsoft. Our response is an alternative model for the Cloud conceptualisation, providing a paradigm for Clouds in the community, utilising networked personal computers for liberation from the centralised vendor model. Community Cloud Computing (C3) offers an alternative architecture, created by combing the Cloud with paradigms from Grid Computing, principles from Digital Ecosystems, and sustainability from Green Computing, while remaining true to the original vision of the Internet. It is more technically challenging than Cloud Computing, having to deal with distributed computing issues, including heterogeneous nodes, varying quality of service, and additional security constraints. However, these are not insurmountable challenges, and with the need to retain control over our digital lives and the potential environmental consequences, it is a challenge we must pursue.
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