Current Issue : January - March Volume : 2020 Issue Number : 1 Articles : 5 Articles
Cloud computing is a paradigm that ensures the flexible, convenient and on-demand\nprovisioning of a shared pool of configurable network and computing resources. Its services can\nbe offered by either private or public infrastructures, depending on who owns the operational\ninfrastructure. Much research has been conducted to improve a cloudâ??s resource provisioning\ntechniques. Unfortunately, sometimes an abrupt increase in the demand for cloud services results in\nresource shortages affecting both providers and consumers. This uncertainty of resource demands\nby users can lead to catastrophic failures of cloud systems, thus reducing the number of accepted\nservice requests. In this paper, we present Bouncer--a workload admission control scheme for cloud\nservices. Bouncer works by ensuring that cloud services do not exceed the cloud infrastructureâ??s\nthreshold capacity. By adopting an application-aware approach, we implemented Bouncer on\nsoftware-defined network (SDN) infrastructure. Furthermore, we conduct an extensive study to\nevaluate our frameworkâ??s performance. Our evaluation shows that Bouncer significantly outperforms\nthe conventional service admission control schemes, which are still state of the art....
Security and privacy concerns represent a significant hindrance to the widespread adoption\nof cloud computing services. While cloud adoption mitigates some of the existing information\ntechnology (IT) risks, research shows that it introduces a new set of security risks linked to\nmulti-tenancy, supply chain and system complexity. Assessing and managing cloud risks can\nbe a challenge, even for cloud service providers (CSPs), due to the increased numbers of parties,\ndevices and applications involved in cloud service delivery. The limited visibility of security controls\ndown the supply chain, further exacerbates this risk assessment challenge. As such, we propose the\nCloud Supply Chain Cyber Risk Assessment (CSCCRA) model, a quantitative risk assessment model\nwhich is supported by supplier security posture assessment and supply chain mapping. Using the\nCSCCRA model, we assess the risk of a SaaS application, mapping its supply chain, identifying weak\nlinks in the chain, evaluating its security risks and presenting the risk value in monetary terms (£),\nwith this, promoting cost-effective risk mitigation and optimal risk prioritisation. We later apply the\nCore Unified Risk Framework (CURF) in comparing the CSCCRA model with already established\nmethods, as part of evaluating its completeness....
In spatial data with complexity, different clusters can be very contiguous, and the density\nof each cluster can be arbitrary and uneven. In addition, background noise that does not belong to\nany clusters in the data, or chain noise that connects multiple clusters may be included. This makes it\ndifficult to separate clusters in contact with adjacent clusters, so a new approach is required to solve\nthe nonlinear shape, irregular density, and touching problems of adjacent clusters that are common in\ncomplex spatial data clustering, as well as to improve robustness against various types of noise in\nspatial clusters. Accordingly, we proposed an efficient graph-based spatial clustering technique that\nemploys Delaunay triangulation and the mechanism of DBSCAN (density-based spatial clustering of\napplications with noise). In the performance evaluation using simulated synthetic data as well as real\n3D point clouds, the proposed method maintained better clustering and separability of neighboring\nclusters compared to other clustering techniques, and is expected to be of practical use in the field of\nspatial data mining....
The Jordan decomposition of matrix is a typical scientific and engineering computational task, but such computation involves\nenormous computing resources for large matrices, which is burdensome for the resource-limited clients. Cloud computing\nenables computational resource-limited clients to economically outsource such problems to the cloud server. However, outsourcing\nJordan decomposition of large-scale matrix to the cloud brings great security concerns and challenges since the matrices\nusually contain sensitive information. In this paper, we present a secure, verifiable, efficient, and privacy preserving algorithm for\noutsourcing Jordan decomposition of large-scale matrix. Security analysis shows that our algorithm is practically secure. Efficient\nverification algorithm is used to verify the results returned from the cloud....
An important issue in cloud computing is the balanced flow of big data centers, which\nusually transfer huge amounts of data. Thus, it is crucial to achieve dynamic, load-balanced data\nflow distributions that can take into account the possible change of states in the network. A number\nof scheduling techniques for achieving load balancing have therefore been proposed. To the best of\nmy knowledge, there is no tool that can be used independently for different algorithms, in order to\nmodel the proposed system (network topology, linking and scheduling algorithm) and use its own\nprobability-based parameters to test it for good balancing and scheduling performance. In this paper,\na new, Probabilistic Model (ProMo) for data flows is proposed, which can be used independently with\na number of techniques to test the most important parameters that determine good load balancing\nand scheduling performance in the network. In this work, ProMo is only used for testing with two\nwell-known dynamic data flow scheduling schemes, and the experimental results verify the fact that\nit is indeed suitable for testing the performance of load-balanced scheduling algorithms....
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