A principal tenet of the scientific method is that experiments must be repeatable. This tenet relies on ceteris paribus (i.e., all other things being equal). As a scientific community, involved in data sciences, we mu...
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ISBN:
(纸本)9789755183817
A principal tenet of the scientific method is that experiments must be repeatable. This tenet relies on ceteris paribus (i.e., all other things being equal). As a scientific community, involved in data sciences, we must investigate ways to establish an environment where experiments can be repeated. We can no longer allude to where the data comes from, we must add rigor to the data collection and management process from which our analysis is conducted. This paper describes a computing environment to support repeatable scientific bigdata experimentation of world-wide scientific literature, and recommends a system that is housed at the Oak Ridge National Laboratory in order to provide value to investigators from government agencies, academic institutions, and industry entities. The described computing environment also adheres to the recently instituted digital data management plan, which involves all stages of the digital data life cycle including capture, analysis, sharing, and preservation, as mandated by multiple United States government agencies. It particularly focuses on the sharing and preservation of digital research data. The details of this computing environment are explained within the context of cloud services by the three layer classification of "Software as a Service", "Platform as a Service", and "Infrastructure as a Service".
Software-defined datacenters combine centralized resource management, software-defined networking, and virtualized infrastructure to meet diverse requirements of cloudcomputing. To fully realizing their capability in...
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ISBN:
(纸本)9781538619933
Software-defined datacenters combine centralized resource management, software-defined networking, and virtualized infrastructure to meet diverse requirements of cloudcomputing. To fully realizing their capability in traffic engineering and flow-based bandwidth management, it is critical for the switches to measure network traffic for both individual flows between virtual machines and aggregate flows between clusters of physical or virtual machines. This paper proposes a novel hierarchical traffic measurement scheme for software-defined datacenter networks. It measures both aggregate flows and individual flows that are organized in a hierarchy with an arbitrary number of levels. The measurement is performed based on a new concept of hierarchical virtual counter arrays, which record each packet only once by updating a single counter, yet the sizes of all flows that the packet belongs to will be properly updated. We demonstrate that the new measurement scheme not only supports hierarchical traffic measurement with accuracy, but does so with memory efficiency, using a fewer number of counters than the number of flows.
NoSQL data stores see considerable attention today in bigdata, cloud hosted environments because of their fault tolerance, distribution and high availability. Shared NoSQL data stores are preferred for their ability ...
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ISBN:
(纸本)9781479955480
NoSQL data stores see considerable attention today in bigdata, cloud hosted environments because of their fault tolerance, distribution and high availability. Shared NoSQL data stores are preferred for their ability to serve mUltiple tenants simultaneously which can improve resource utilization and lower management costs. Fair share in this setting can be a problem in that NoSQL data stores can be weak in preventing interference between tenants. We propose a methodology for multi-tenant fair share in a NoSQL store, in particular Cassandra. The approach uses an extended version of the deficit round robin algorithm to schedule tenant requests, and has local weight adjustment and slow tenant handling to improve the system throughput. Empirical results show that our approach is able to provide fair share for multi-tenancy.
In the Internet of Mobile Things (IoMT), bigdata storage and high-speed data processing capabilities are delegated to cloudcomputing, which effectively reduces the resource overhead of lightweight the IoMT devices. ...
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ISBN:
(纸本)9798350378412
In the Internet of Mobile Things (IoMT), bigdata storage and high-speed data processing capabilities are delegated to cloudcomputing, which effectively reduces the resource overhead of lightweight the IoMT devices. However, malicious users and cloud providers will forge and destroy the data stored in the cloud server. Therefore, how to ensure the security and integrity of the data is a challenge. To solve this problem, this paper proposes a verifiable commitment scheme for the Internet of Mobile Things. In particular, to ensure data privacy and security, the cloud server only partially decrypts the ciphertext while the final decryption is completed by the user. The commitment binds the final decryption key and data, which effectively achieves the distribution of the final decryption key and the verification of the final decrypted ciphertext. In addition, we provide a rigorous security proof and comparative performance analysis of the proposed scheme. The result shows that our scheme not only has more extensive security features, but also has better performance.
Active search has become a major way for Internet users to obtain information. To better understand information dissemination, we propose an active search-based susceptible forwarding immune(A-SFI) propagation dynamic...
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A growing number of global companies select Green clouddata Centers (GCDCs) to manage their delay-constrained applications. The fast growth of users' tasks dramatically increases the energy consumed by GCDC, e.g....
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ISBN:
(纸本)9781538672358
A growing number of global companies select Green clouddata Centers (GCDCs) to manage their delay-constrained applications. The fast growth of users' tasks dramatically increases the energy consumed by GCDC, e.g., Google. The random nature of tasks brings a big challenge of scheduling tasks of each application with limited infrastructure resources of GCDCs. This work accurately computes a mathematical relation between task service rates and the number of tasks refusal in GCDC. Besides, it proposes a Temporal Task Scheduling (TTS) algorithm investigating the temporal variation in geo-distributed clouddata centers to schedule all tasks within their delay constraints. Furthermore, a novel dynamic hybrid meta-heuristic algorithm is developed for the formulated profit maximization problem, based on genetic simulated annealing and particle swarm optimization. The proposed algorithm can guarantee that differentiated service qualities can be provided with higher overall performance and lower energy cost. Trace-driven simulations demonstrate that larger throughput and profit is achieved than several existing scheduling algorithms.
Task scheduling and resource allocation are the key challenges of cloudcomputing. Compared with grid environment, data transfer is a big overhead for cloud workflows. So, the cost arising from data transfers between ...
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Task scheduling and resource allocation are the key challenges of cloudcomputing. Compared with grid environment, data transfer is a big overhead for cloud workflows. So, the cost arising from data transfers between resources as well as execution costs must also be taken into account during scheduling based upon user's Quality of Service (QoS) constraints. In this paper, we present Deadline Constrained Heuristic based Genetic Algorithms (HGAs) to schedule applications to cloud resources that minimise the execution cost while meeting the deadline for delivering the result. Each workflow's task is assigned priority using bottom-level (b-level) and top-level (t-level). To increase the population diversity, these priorities are then used to create the initial population of HGAs. The proposed algorithms are simulated and evaluated with synthetic workflows based on realistic workflows. The simulation results show that our proposed algorithms have a promising performance as compared to Standard Genetic Algorithm (SGA).
Development and improvement of intercloud environments is still in an early stage;correspondingly, analyzing adequate realization of many types of applications is essential. In the presented contribution we have chose...
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ISBN:
(纸本)9781467381031
Development and improvement of intercloud environments is still in an early stage;correspondingly, analyzing adequate realization of many types of applications is essential. In the presented contribution we have chosen an application with a high potential for value generation through cloud and cloud of clouds, i.e. the real estate business. The reason for this accumulated benefit that clouds may generate is threefold: (i) the objects in real estate business have high values, (ii) some phases during their life cycle cover very long time periods, and (iii) many different actors are involved along the value chain, many of them are SME's. We give a to-the-point insight into the real estate sector (life cycle, value, chain, actors) and demonstrate possible effects through clouds for selected stakeholders. We further give a brief overview of cloud-based products in the field of real estate for a well-defined market (i.e. DACH-countries) in order to demonstrate the current situation.
The era of technology is now shifting towards the cloudcomputing and today's computation tends to be provisioned as a service rather than a product. Recently cloudcomputing has become more portable and flexible ...
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ISBN:
(纸本)9781538621752
The era of technology is now shifting towards the cloudcomputing and today's computation tends to be provisioned as a service rather than a product. Recently cloudcomputing has become more portable and flexible in such way so that we call it having a super computer in our pockets. Despite the potential application of cloudcomputing, data security is still questionable in privacy issue due to insider threats and data breaches. After the internet of things (IoT) emerges, in bigdata arena both data security and storage optimization at the same time has been a crying need. In this paper, we propose an enhanced framework of security model including tokenization with a view to eradicating the privacy issue of sensor data and ensuring storage optimization. Tokenization provides a wider range of security by protecting data from malicious insider threats or data breaches in cloud. Our proposed tokenization process optimizes cloud storage instances as well with a little prior mining in order to convert large data sets into small ones.
data mining algorithms tacitly quite access to the data either at centralized or distributed form. Distributed data becomes a big challenge and cannot handle by a classical analytic tool. cloudcomputing can solve the...
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ISBN:
(纸本)9781509036967
data mining algorithms tacitly quite access to the data either at centralized or distributed form. Distributed data becomes a big challenge and cannot handle by a classical analytic tool. cloudcomputing can solve the issues of processing, storing, and analyzing the data at distributing locations within the cloud. However, a significant problem that is preventing free sharing of data is privacy and security issues, therefore obstructing data mining schemes. Lately, there is increasingly hard to find a solution to these problems. Due to the existing knowledge in a more distributed data and better for data mining issues. An important task of data mining and machine learning is classification, a widely used in classification is support vector machine (SVM) algorithms applicable in many various domains. In this paper, we proposes a privacy-preserving solution for SVM classification. Our workaround constructing a global SVM classification model from vertically partitioned distributed data at multi-parties based on Gram matrix, without revealing a party's data. We proposed an efficient and preserve privacy protocol for SVM classification on vertical partitioned data. Our experimental results, the accuracy of distributed SVM using Gram matrix up to 90% and the privacy not compromised.
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