Commercial Cloud Platforms Research Articles

Enabling customer-provided resources for cloud computing: Potentials,challenges, and implementation

Recent years have witnessed cloud computing as an efficient means for providing resources as a form of utility. Driven by the strong demands, industrial pioneers have offered commercial cloud platforms, mostly datacenter-based, which are known to be powerful and effective. Yet, as the cloud customers are pure consumers, their local resources, though abundant, have been largely ignored. In this paper, We present SpotCloud , a real working system that seamlessly integrates the customers’ local resources into the cloud platform, enabling them to sell, buy, and utilize these resources. We also investigate the potentials and challenges towards enabling customer-provided resources for cloud computing. Given that these local resources are highly heterogeneous and dynamic, we closely examine two critical challenges in this new context: (1) How can the customers be motivated to contribute or utilize such resources? and (2) How can high service availability be ensured out of the dynamic resources? We demonstrate a distributed market for potential sellers to flexibly and adaptively determine their resource prices through a repeated seller competition game. We also present an optimal resource provisioning algorithm that ensures service availability with minimized lease and migration costs. The evaluation results indicate it as a flexible and less expensive complement to the pure datacenter-based cloud.

Provable Multicopy Dynamic Data Possession in Cloud Computing Systems

Increasingly more and more organizations are opting for outsourcing data to remote cloud service providers (CSPs). Customers can rent the CSPs storage infrastructure to store and retrieve almost unlimited amount of data by paying fees metered in gigabyte/month. For an increased level of scalability, availability, and durability, some customers may want their data to be replicated on multiple servers across multiple data centers. The more copies the CSP is asked to store, the more fees the customers are charged. Therefore, customers need to have a strong guarantee that the CSP is storing all data copies that are agreed upon in the service contract, and all these copies are consistent with the most recent modifications issued by the customers. In this paper, we propose a map-based provable multicopy dynamic data possession (MB-PMDDP) scheme that has the following features: 1) it provides an evidence to the customers that the CSP is not cheating by storing fewer copies; 2) it supports outsourcing of dynamic data, i.e., it supports block-level operations, such as block modification, insertion, deletion, and append; and 3) it allows authorized users to seamlessly access the file copies stored by the CSP. We give a comparative analysis of the proposed MB-PMDDP scheme with a reference model obtained by extending existing provable possession of dynamic single-copy schemes. The theoretical analysis is validated through experimental results on a commercial cloud platform. In addition, we show the security against colluding servers, and discuss how to identify corrupted copies by slightly modifying the proposed scheme.

Verifying Outsourced Replicated Data in Cloud Computing Storage Systems

Storage-as-a-Service offered by cloud service providers (CSPs) enables customers to store and retrieve almost unlimited amount of data by paying fees metered in GB/month. For an increased level of scalability, availability and durability, some customers may want their data to be replicated on multiple servers across multiple data centers. In this paper, we propose a pairing-based provable multicopy data possession (PB-PMDP) scheme, which provides an evidence that all outsourced copies are actually stored and remain intact. Moreover, it allows authorized users (i.e., those who have the right to access the owner’s file) to seamlessly access the file copies stored by the CSP, and supports public verifiability. The proposed scheme is proved to be secure against colluding servers. We illustrate the performance of the PB-PMDP scheme through theoretical analysis, which is then validated by experimental results on a commercial cloud platform. The verification time of the proposed scheme is practically independent of the number of file copies. Additionally, we discuss how to identify corrupted copies by slightly modifying the proposed PB-PMDP scheme.

Evaluating Google Compute Engine with PROOF

The advent of private and commercial cloud platforms has opened the question of evaluating the cost-effectiveness of such solution for computing in High Energy Physics . Google Compute Engine (GCE) is a IaaS product launched by Google as an experimental platform during 2012 and now open to the public market. In this contribution we present the results of a set of CPU-intensive and I/O-intensive tests we have run with PROOF on a GCE resources made available by Google for test purposes. We have run tests on large scale PROOF clusters (up to 1000 workers) to study the overall scalability of coordinated multi-process jobs. We have studied and compared the performance of ephemeral and persistent storage with PROOF-Lite on the single machines and of standard PROOF on the whole cluster. We will discuss our results in perspective, in particular with respect to the typical analysis needs of an LHC experiment.

Enabling Smart Cloud Services Through Remote Sensing: An Internet of Everything Enabler

The recent emergence and success of cloud-based services has empowered remote sensing and made it very possible. Cloud-assisted remote sensing (CARS) enables distributed sensory data collection, global resource and data sharing, remote and real-time data access, elastic resource provisioning and scaling, and pay-as-you-go pricing models. CARS has great potentials for enabling the so-called Internet of Everything (IoE), thereby promoting smart cloud services. In this paper, we survey CARS. First, we describe its benefits and capabilities through real-world applications. Second, we present a multilayer architecture of CARS by describing each layer’s functionalities and responsibilities, as well as its interactions and interfaces with its upper and lower layers. Third, we discuss the sensing services models offered by CARS. Fourth, we discuss some popular commercial cloud platforms that have already been developed and deployed in recent years. Finally, we present and discuss major design requirements and challenges of CARS.

Large-scale analysis of neuroimaging data on commercial clouds with content-aware resource allocation strategies

The combined use of mice that have genetic mutations (transgenic mouse models) of human pathology and advanced neuroimaging methods (such as magnetic resonance imaging) has the potential to radically change how we approach disease understanding, diagnosis and treatment. Morphological changes occurring in the brain of transgenic animals as a result of the interaction between environment and genotype can be assessed using advanced image analysis methods, an effort described as ‘mouse brain phenotyping’. However, the computational methods involved in the analysis of high-resolution brain images are demanding. While running such analysis on local clusters is possible, not all users have access to such infrastructure and even for those that do, having additional computational capacity can be beneficial (e.g. to meet sudden high throughput demands). In this paper we use a commercial cloud platform for brain neuroimaging and analysis. We achieve a registration-based multi-atlas, multi-template anatomical segmentation, normally a lengthy-in-time effort, within a few hours. Naturally, performing such analyses on the cloud entails a monetary cost, and it is worthwhile identifying strategies that can allocate resources intelligently. In our context a critical aspect is the identification of how long each job will take. We propose a method that estimates the complexity of an image-processing task, a registration, using statistical moments and shape descriptors of the image content. We use this information to learn and predict the completion time of a registration. The proposed approach is easy to deploy, and could serve as an alternative for laboratories that may require instant access to large high-performance-computing infrastructures. To facilitate adoption from the community we publicly release the source code.

Auditing and analysis of network traffic in cloud environment

Cloud computing allows users to remotely store their data into the cloud and provides on-demand access to applications and services. The security of the outsourced data in the cloud is dependent on the security of the cloud computing system and network. Though there have been numerous efforts on securing data on the cloud computing system, evaluation of security of cloud networks is still a very challenging task. Specifically, the audit of the cloud computing network will provide insights onto the level of protection provided within and outside data centres. This paper presents key technologies for cloud auditing to assess the security level within and outside cloud data centres: cloud data geolocation and cloud network risk assessment. We evaluate their performance on commercial cloud platforms and our experimental results demonstrate that cloud auditing technologies provide users the ability to assess the security of the cloud network.