2018 IEEE CLOUD COMPUTING

1.       A CLASSIFICATION AND COMPARISON FRAMEWORKFOR CLOUD SERVICE BROKERAGE ARCHITECTURES

ABSTRACT: Cloud service brokerage and related management and marketplace concepts have been identified as key concerns for future cloud technology development and research. Cloud service management is an important building block of cloud architectures that can be extended to act as a broker service layer between consumers and providers, and even to form marketplace services. We present a 3-pronged classification and comparison framework for broker platforms and applications. A range of specific broker development concerns like architecture, programming and quality are investigated. Based on this framework, selected management, brokerage and marketplace solutions will be compared, not only to demonstrate the utility of the framework, but also to identify challenges and wider research objectives based on an identification of cloud broker architecture concerns and technical requirements for service brokerage solutions. Cloud architecture concerns such as commoditisation and federation of integrated, vertical cloud stacks emerge. 

2.      ASSESSMENT OF THE SUITABILITY OF FOG COMPUTING IN THE CONTEXT OF INTERNET OF THINGS

ABSTRACT: This work performs a rigorous, comparative analysis of the fog computing paradigm and the conventional cloud computing paradigm in the context of the Internet of Things (IoT), by mathematically formulating the parameters and characteristics of fog computing – one of the first attempts of its kind. With the rapid increase in the number of Internetconnected devices, the increased demand of real-time, low-latency services is proving to be challenging for the traditional cloud computing framework. Also, our irreplaceable dependency on cloud computing demands the cloud data centers (DCs) always to be up and running which exhausts huge amount of power and yield tons of carbon dioxide (CO2) gas. In this work, we assess the applicability of the newly proposed fog computing paradigm to serve the demands of the latency-sensitive applications in the context of IoT. We model the fog computing paradigm by mathematically characterizing the fog computing network in terms of power consumption, service latency, CO2 emission, and cost, and evaluating its performance for an environment with high number of Internet-connected devices demanding realtime service. A case study is performed with traffic generated from the 100 highest populated cities being served by eight geographically distributed DCs. Results show that as the number of applications demanding real-time service increases, the fog computing paradigm outperforms traditional cloud computing. For an environment with 50% applications requesting for instantaneous, real-time services, the overall service latency for fog computing is noted to decrease by 50:09%. However, it is mentionworthy that for an environment with less percentage of applications demanding for low-latency services, fog computing is observed to be an overhead compared to the traditional cloud computing. Therefore, the work shows that in the context of IoT, with high number of latency-sensitive applications fog computing outperforms cloud computing. 

3.      ASSURANCE OF SECURITY AND PRIVACY REQUIREMENTS FOR CLOUD DEPLOYMENT MODELS

ABSTRACT: Despite of the several benefits of migrating enterprise critical assets to the Cloud, there are challenges specifically related to security and privacy. It is important that Cloud Users understand their security and privacy needs, based on their specific context and select cloud model best fit to support these needs. The literature provides works that focus on discussing security and privacy issues for cloud systems but such works do not provide a detailed methodological approach to elicit security and privacy requirements neither methods to select cloud deployment models based on satisfaction of these requirements by Cloud Service Providers. This work advances the current state of the art towards this direction. In particular, we consider requirements engineering concepts to elicit and analyze security and privacy requirements and their associated mechanisms using a conceptual framework and a systematic process. The work introduces assurance as evidence for satisfying the security and privacy requirements in terms of completeness and reportable of security incident through audit. This allows perspective cloud users to define their assurance requirements so that appropriate cloud models can be selected for a given context. To demonstrate our work, we present results from a real case study based on the Greek National Gazette. 

4.      CLOUD MESSAGE QUEUEING AND NOTIFICATION: CHALLENGES AND (BLOCKCHAIN) OPPORTUNITIES

ABSTRACT: The current mobile app development practice, like other large-scale network-centric software projects, is characterized by the use of event notification facilities supporting the exchange of effective and efficient data flows between the application’s front-end, usually located on customer’s terminal equipment, and the back-end services available within the cloud. In order to avoid the need of setting up the notification infrastructure from scratch for any new application, many cloud service providers and mobile system manufacturing companies provide cloud-based messaging solutions. Such solutions, however, are often characterized by vulnerabilities that can be exploited to compromise the mobile applications’ security by violating the privacy and integrity in their operations. In this work, we analyze these issues and posit the potential for the blockchain technology to mitigate such threats.

 5.      CLOUD-BASED FINE-GRAINED HEALTH INFORMATION ACCESS CONTROL FRAMEWORK FOR LIGHTWEIGHT IOT DEVICES WITH DYNAMIC AUDITING AND ATTRIBUTE REVOCATION

ABSTRACT: The eHealth trend has spread globally. Internet of Things(IoT) devices for medical service and pervasivePersonal Health Information (PHI) systemsplay important roles inthe eHealth environment. A cloud-based PHIsystem appearspromising but raises privacy and informationsecurity concerns. We propose a cloud-based fine-grained health information access control framework for lightweight IoT devices with data dynamics auditing and attribute revocation functions.   

Only symmetriccryptographyis required for IoT devices, such as wireless body sensors. A variant of ciphertext-policy attribute-based encryption, dual encryption,and Merkle hash treesare used to support fine-grained access control, efficient dynamic data auditing, batch auditing,and attribute revocation. Moreover, the proposed scheme also definesand handlesthe cloud reciprocityproblem wherein cloud service providerscanhelp each other avoid finesresulting fromdata loss. Security analysis and performance comparisons show thatthe proposed scheme is an excellent candidate for a cloud-based PHI system. 

6. CONSISTENT DISASTER RECOVERY FOR MICROSERVICES: THE BAC THEOREM

ABSTRACT: How do you back up a microservice? You dump its database. But how do you back up an entire application decomposed into microservices? In this article, we discuss the tradeoff between the availability and consistency of a microservice-based architecture when a backup of the entire application is being performed.

We demonstrate that service designers have to select two out of three qualities: backup, availability, and/or consistency (BAC). Service designers must also consider how to deal with consequences such as broken links, orphan state, and missing state. 

7.      A HOLISTIC APPROACH TO DISTRIBUTED DIMENSIONALITY REDUCTION OF BIG DATA

ABSTRACT: With the exponential growth of data volume, big data have placed an unprecedented burden on current computing infrastructure. Dimensionality reduction of big data attracts a great deal of attention in recent years as an efficient method to extract the core data which is smaller to store and faster to process. This paper aims at addressing the three fundamental problems closely related to distributed dimensionality reduction of big data, i.e. big data fusion, dimensionality reduction algorithm and construction of distributed computing platform. A chunk tensor method is presented to fuse the unstructured, semi-structured and structured data as a unified model in which all characteristics of the heterogeneous data are appropriately arranged along the tensor orders. A Lanczos based High Order Singular Value Decomposition algorithm is proposed to reduce dimensionality of the unified model. Theoretical analyses of the algorithm are provided in terms of storage scheme, convergence property and computation cost. To execute the dimensionality reduction task, this paper employs the Transparent Computing paradigm to construct a distributed computing platform as well as utilizes the linear predictive model to partition the data blocks. Experimental results demonstrate that the proposed holistic approach is efficient for distributed dimensionality reduction of big data. 

8.      ARCHITECTURAL PROTECTION OF APPLICATION PRIVACY AGAINST SOFTWARE AND PHYSICAL ATTACKS IN UNTRUSTED CLOUD ENVIRONMENT

ABSTRACT: In cloud computing, it is often assumed that cloud vendors are trusted; the guest Operating System (OS) and the Virtual Machine Monitor (VMM, also called Hypervisor) are secure. However, these assumptions are not always true in practice and existing approaches cannot protect the data privacy of applications when none of these parties are trusted. We investigate how to cope with a strong threat model which is that the cloud vendors, the guest OS, or the VMM, or both of them are malicious or untrusted, and can launch attacks against privacy of trusted user applications. This model is relevant because applications may be small enough to be formally verified, while the guest OS and VMM are too complex to be formally verified. Specifically, we present the design and analysis of an architectural solution which integrates a set of components on-chip to protect the memory of trusted applications from potential software and hardware based attacks from untrusted cloud providers, compromised guest OS, or malicious VMM. Full-system performance evaluation results show that the design only incurs 9% overhead  on average, which is a small performance price that is paid for the substantial security gain.  

9. SHAPLEY-VALUE MECHANISM FOR BANDWIDTH ON DEMAND BETWEEN DATACENTERS

ABSTRACT: Recent studies in cloud resource allocation and pricing have focused on computing and storage resources but not network bandwidth. Cloud users nowadays customarily deploy services across multiple geo-distributed datacenters, with significant interdatacenter traffic generated, paid by cloud providers to ISPs. An effective bandwidth allocation and charging mechanism is needed between the cloud provider and the cloud users. Existing volume based static charging schemes lack market efficiency. This work presents the first dynamic pricing mechanism for inter-datacenter on-demand bandwidth, via a Shapley value based auction. Our auction is expressive enough to accept bids as a flat bandwidth rate plus a time duration, or a data volume with a transfer deadline. We start with an offline auction, design an optimal end-to-end traffic scheduling approach, and exploit the Shapley value in computing payments. Our auction is truthful, individual rational, budget balanced and approximately efficient in social welfare. An online version of the auction follows, where decisions are made instantly upon the arrival of each user’s realtime transmission demand. We propose an efficient online traffic scheduling algorithm, and approximate the offline Shapley value based payments on the fly. We validate our mechanism design with solid theoretical analysis, as well as trace-driven simulation studies.