Concurrency Lock Issues in Relational Cloud Computing

The widespread popularity of Cloud computing as a preferred platform for the deployment of web applications has resulted in an enormous number of applications moving to the cloud, and the huge success of cloud service providers. Due to the increasing number of web applications being hosted in the cloud, and the growing scale of data which these applications store, process, and serve – scalable data management systems form a critical part of cloud infrastructures. There are issues related to the database security while database is on cloud. The major challenging issues are multi-tenancy, scalability and the privacy. This paper focuses on the problems faced in the data security of Relational Cloud. The problems faced by various types of tenants and the type of access into the database makes a rework on the security of data, by analyzing proper locking strategies on the records accessed from the database. Data security in cloud computing addresses the type of access mode by the users (for analytical or transaction purpose) and the frequency of data access from the physical location (in shared or no-shared disk mode). Accordingly, the various data locking strategies are studied and appropriate locking mechanism will be implemented for real-time applications as in e-commerce. Keywords: Relational Cloud, Multi-tenant, two-phase locking, concurrency control, data management

Cooperative Interval Caching in Clustered Multimedia Servers

In this project, we design a cooperative interval caching (CIC) algorithm for clustered video servers, and evaluate its performance through simulation. The CIC algorithm describes how distributed caches in the cluster cooperate to serve a given request. With CIC, a clustered server can accommodate twice (95%) more number of cached streams than the clustered server without cache cooperation. There are two major processes of CIC to find available cache space for a given request in the cluster: to find the server containing the information about the preceding request of the given request; and to find another server which may have available cache space if the current server turns out not to have enough cache space. The performance study shows that it is better to direct the requests of the same movie to the same server so that a request can always find the information of its preceding request from the same server. The CIC algorithm uses scoreboard mechanism to achieve this goal. The performance results also show that when the current server fails to find cache space for a given request, randomly selecting a server works well to find the next server which may have available cache space. The combination of scoreboard and random selection to find the preceding request information and the next available server outperforms other combinations of different approaches by 86%. With CIC, the cooperative distributed caches can support as many cached streams as one integrated cache does. In some cases, the cooperative distributed caches accommodate more number of cached streams than one integrated cache would do. The CIC algorithm makes every server in the cluster perform identical tasks to eliminate any single point of failure, there by increasing availability of the server cluster. The CIC algorithm also specifies how to smoothly add or remove a server to or from the cluster to provide the server with scalability

Cloud Computing and Quality of Service: Issues and Developments

Cloud computing is a dynamic information technology (IT) paradigm that delivers on demand computing resources to a user over a network infrastructure. The Cloud Service Provider (CSP) offers applications which can be accessed online to users. Such applications can be shared by more than one user. CSPs provides programming interfaces that allows customers to build and deploy applications on the cloud; as well as providing massive storage and computing infrastructure to users. Users usually have no control on how data is stored on the cloud or where the underlying resources are located. With this limited control, customers’ requirements and Quality of Service (QoS) expectations from CSPs are spelt out using a Service Level Agreement (SLA). It is thus imperative to have the adequate QoS guarantees from a CSP. This paper examines trends in the area of Cloud computing QoS and provides a guide for future research. A review and survey of existing works in literature is done in order to identify these Cloud QoS trends. The finding is that the ultimate expectation of any QoS metrics or model is the related to cost concern for both the CSP and user

A survey on cost-effective context-aware distribution of social data streams over energy-efficient data centres

Social media have emerged in the last decade as a viable and ubiquitous means of communication. The ease of user content generation within these platforms, e.g. check-in information, multimedia data, etc., along with the proliferation of Global Positioning System (GPS)-enabled, always-connected capture devices lead to data streams of unprecedented amount and a radical change in information sharing. Social data streams raise a variety of practical challenges, including derivation of real-time meaningful insights from effectively gathered social information, as well as a paradigm shift for content distribution with the leverage of contextual data associated with user preferences, geographical characteristics and devices in general. In this article we present a comprehensive survey that outlines the state-of-the-art situation and organizes challenges concerning social media streams and the infrastructure of the data centres supporting the efficient access to data streams in terms of content distribution, data diffusion, data replication, energy efficiency and network infrastructure. We systematize the existing literature and proceed to identify and analyse the main research points and industrial efforts in the area as far as modelling, simulation and performance evaluation are concerned

An Algorithm for Network and Data-aware Placement of Multi-Tier Applications in Cloud Data Centers

Today's Cloud applications are dominated by composite applications comprising multiple computing and data components with strong communication correlations among them. Although Cloud providers are deploying large number of computing and storage devices to address the ever increasing demand for computing and storage resources, network resource demands are emerging as one of the key areas of performance bottleneck. This paper addresses network-aware placement of virtual components (computing and data) of multi-tier applications in data centers and formally defines the placement as an optimization problem. The simultaneous placement of Virtual Machines and data blocks aims at reducing the network overhead of the data center network infrastructure. A greedy heuristic is proposed for the on-demand application components placement that localizes network traffic in the data center interconnect. Such optimization helps reducing communication overhead in upper layer network switches that will eventually reduce the overall traffic volume across the data center. This, in turn, will help reducing packet transmission delay, increasing network performance, and minimizing the energy consumption of network components. Experimental results demonstrate performance superiority of the proposed algorithm over other approaches where it outperforms the state-of-the-art network-aware application placement algorithm across all performance metrics by reducing the average network cost up to 67% and network usage at core switches up to 84%, as well as increasing the average number of application deployments up to 18%.Comment: Submitted for publication consideration for the Journal of Network and Computer Applications (JNCA). Total page: 28. Number of figures: 15 figure

Piloting Multimodal Learning Analytics using Mobile Mixed Reality in Health Education

© 2019 IEEE. Mobile mixed reality has been shown to increase higher achievement and lower cognitive load within spatial disciplines. However, traditional methods of assessment restrict examiners ability to holistically assess spatial understanding. Multimodal learning analytics seeks to investigate how combinations of data types such as spatial data and traditional assessment can be combined to better understand both the learner and learning environment. This paper explores the pedagogical possibilities of a smartphone enabled mixed reality multimodal learning analytics case study for health education, focused on learning the anatomy of the heart. The context for this study is the first loop of a design based research study exploring the acquisition and retention of knowledge by piloting the proposed system with practicing health experts. Outcomes from the pilot study showed engagement and enthusiasm of the method among the experts, but also demonstrated problems to overcome in the pedagogical method before deployment with learners