WANalytics

Cloud computing provides distributed computing resources such as servers, storage and applications to the end users through data centers. The data centers are geographically located at different locations. The client applications or requests being serviced by cloud service providers on “pay per use”. So, different pricing models are adapted to compute the cost and revenue of the data centers. The cost of VM is computed depending on its placement in the data centers through broker policies. So, the broker policies have a significant role in evaluating the cost of the VM which directly impacts on revenue of the service provider. In the computing competition, the VM cost should be minimized by which service demand will be maximized. Moreover, the response time and processing time of the data centers need to be minimized to posses commendatory Quality of Service. In this work, we propose a new service broker policy to minimize the total cost. The total cost considers the VM cost and the data transfer cost. The proposed mechanism also reduces the response time and data processing time of the data centers. The proposed policy is simulated in Cloud Analyst. We examine the performance of the proposed mechanism with ten different scenarios. Finally, we compare performance results with respect to VM cost, data transfer cost, total cost, processing time and response time of data centers with the existing policies and observe better than these.

Because of the solid performance of providing quality of service for various applications for decades, bandwidth reservation has been increasingly used in recent years for large amounts of data transfer to achieve guaranteed performance. However, effective scheduling strategy to achieve the trade-off between data transfer cost and data transfer performance still remains to be investigated. In this paper, we focus on the trade-off between cost and the most common performance parameter, i.e., completion time, of data transfers using bandwidth reservation in dedicated networks. We consider the scheduling of two types of bandwidth reservation requests regarding such trade-off: (1) to achieve the minimum data transfer cost given the data transfer deadline, and (2) to achieve the earliest data transfer completion time given the maximum data transfer cost. We propose two bandwidth reservation algorithms with rigorous optimality proofs to optimize the scheduling of these two types of bandwidth reservation requests. We then compare the proposed algorithms with two scheduling algorithms originating from one widely used scheduling algorithm in production networks, and the efficacy of the proposed optimal algorithms is verified through extensive simulations.

The network of traditional data center has been usually designed and constructed for the provision of user's equal access of data centre's resource or data. Therefore, network administrators have a strong tendency to manage user traffic from the viewpoint that the traffic has a similar size and characteristics. But, the emersion of big data begins to make data centers have to deal with 1015 byte-data transfer at once. Such a big data transfer can cause problems in network traffic management in the existed data center. And, the tiered network architecture of the legacy data center magnifies the magnitude of the problems. One of the well-known big data in science is from large hadron collider such as LHC in Swiss CERN. CERN LHC generates multi-peta byte data per year. From our experience of CERN data service, this paper showed the impact of network traffic affected by large-scale data stream using NS2 simulation, and then, suggested the evolution direction based on separating of large-scale data stream for the big data center's network architecture.

An increasing number of applications in scientific and other domains have moved or are in active transition to clouds, and the demand for the movement of big data between geographically distributed cloud-based data centers is rapidly growing. Many modern backbone networks leverage logically centralized controllers based on software-defined networking (SDN) to provide advance bandwidth reservation for data transfer requests. How to fully utilize the bandwidth resources of the links connecting data centers with guaranteed QoS for each user request is an important problem for cloud service providers. Most existing work focuses on bandwidth scheduling for a single request for data transfer or multiple requests using the same service model. In this work, we construct rigorous cost models to quantify user satisfaction degree and formulate a generic problem of bandwidth scheduling for multiple deadline-constrained data transfer requests of different types to maximize the request scheduling success ratio while minimizing the data transfer completion time of each request. We prove this problem to be NP-complete and design a heuristic solution. Extensive simulation results show that our scheduling scheme significantly outperforms existing methods in terms of user satisfaction degree and scheduling success ratio.

As we are living in the era of big data, huge volumes of a wide variety of complex data-which can be of different levels of veracity-are generated or collected at a high velocity from rich sources of data in various real-life applications. A rich source of these big data sources is the Internet of Things (IoT), which include a collection of sensors, smartphones and other mobile devices, wearable devices, as well as other "things" that are capable to operate within the existing Internet infrastructure. Embedded in these big data are valuable knowledge and useful information. Hence, the research problem of data mining from big IoT data have drawn attention of many researchers as it aims to discover implicit, previously unknown and potentially useful information and knowledge from the data. For instance, frequent pattern mining finds sets of frequently co-occurring items in the IoT domains. Associative classification discovers rules revealing relationships among items within the frequent patterns and their associations with the corresponding class labels. Induction based classification uses decision tree or random forest to learn from old big IoT for classifying or making predictions on new data. Over the past quarter of a century, many serial, distributed, parallel, and MapReduce-based (Hadook-based and Spark-based) big data mining algorithms have been proposed. These algorithms are run in local computers, distributed and parallel environments, clusters, grids, clouds and/or data centers. In this paper, we review some of these algorithms, discuss issues and research prospective in mining classification patterns from these big IoT data in fog. Our case study on a real-life application shows the feasibility on classifying real-life big IoT data over fog for urban analytics.

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis

The volume of the coming data in HEP is growing. The volume of the data to be held for a long time is growing as well. Large volume of data – big data – is distributed around the planet. The methods, approaches how to organize and manage the globally distributed data storage are required. The distributed storage has several examples for personal needs like own-cloud.org, pydio.com, seafile.com, sparkleshare.org. For enterprise-level there is a number of systems: SWIFT - distributed storage systems (part of Openstack), CEPH and the like which are mostly object storage. When several data center’s resources are integrated, the organization of data links becomes very important issue especially if several parallel data links between data centers are used. The situation in data centers and in data links may vary each hour. All that means each part of distributed data storage has to be able to rearrange usage of data links and storage servers in each data center. In addition, for each customer of distributed storage different requirements could appear. The above topics are planned to be discussed in data storage proposal.

The big data era is presenting unprecedented opportunities for generating new revenues in various sectors ranging from health care, economics, life science, to manufacturing. Datacenters (DCs) are widely deployed to provision various application services as well as to process big data. Since more and more servers are installed in DCs, the cost of electricity incurs a financial burden for the DC operators. Many DCs are equipped with renewable energy to reduce the electricity bill. However, the locations of the energy demands do not match the locations of the renewable energy generation. This mismatch may be addressed by virtual machine (VM) migration. The DCs, which lack renewable energy, can migrate their workloads to other DCs, which have abundant renewable energy. In addition, DC operators always want to maximize revenue and minimize operation cost. In this paper, the problem of maximizing revenue and minimizing operation cost of a green DC network enabled with and without VM migration is formulated by integer linear programming. Simulation results show that optimal results can be reached for small size problems. Two heuristic algorithms are proposed to efficiently solve large size problems. To our best knowledge, this is the first study of the revenue driven VM management problem in green DC networks towards big data with VM migration.

While future terabit networks hold the promise of significantly improving big-data motion among geographically distributed data centers, significant challenges must be overcome even on today's 100 gigabit networks to realize end-to-end performance. Multiple bottlenecks exist along the end-to-end path from source to sink. Data storage infrastructure at both the source and sink and its interplay with the wide-area network are increasingly the bottleneck to achieving high performance. In this paper, we identify the issues that lead to congestion on the path of an end-to-end data transfer in the terabit network environment, and we present a new bulk data movement framework called LADS for terabit networks. LADS exploits the underlying storage layout at each endpoint to maximize throughput without negatively impacting the performance of shared storage resources for other users. LADS also uses the Common Communication Interface (CCI) in lieu of the sockets interface to use zero-copy, OS-bypass hardware when available. It can further improve data transfer performance under congestion on the end systems using buffering at the source using flash storage. With our evaluations, we show that LADS can avoid congested storage elements within the shared storage resource, improving I/O bandwidth, and data transfer rates across the high speed networks.

Cellular base band units of the future are expected to reside in a cloud data center which provides computation resources, content storage and caching, and a natural place to perform multi-user precoding, thus addressing both cost and performance concerns of cellular systems. Multi-user precoding relies on efficient user grouping schemes to maximize multiplexing gains. However, traditional user grouping schemes are unaware of data center constraints, and may induce a large number of data transfers across racks when fetching requested data to a certain rack for precoding. When congestion occurs in the data center network, the delay of data transfers across racks may exceed the channel coherence time. This would kill multi-user MIMO transmissions as channel state information becomes outdated. In this paper, we design a novel data-locality-aware user grouping schemes which preferentially group users whose requested data are located under the same rack. We also design user grouping algorithms which adapt to the congestion level in the cloud data center. Specifically, a regularized spectral efficiency maximization problem is proposed where the number of data transfers across racks is introduced as a regularization term. By adjusting the weight of the regularization term according to the congestion level, we gradually suppress data transfers across racks in forming user groups when congestion occurs. We reduce the above problem to a soft-capacitated facility location problem, and we devise a 2-approximation user grouping algorithm. At last, we conduct simulations which show that our proposed algorithm performs close to the optimal in practical scenarios, and study the tradeoff between higher spectral efficiency and lower data transfer cost.

As the amount of data explodes rapidly, more and more organizations tend to use data centers to make effective decisions and gain a competitive edge. Big data applications have gradually dominated the data centers workloads, and hence it has been increasingly important to understand their behaviour in order to further improve the performance of data centers. Due to the constantly increased gap between I/O devices and CPUs, I/O performance dominates the overall system performance, so characterizing I/O behaviour of big data workloads is important and imperative.In this paper, we select four typical big data workloads in broader areas from the BigDataBench which is a big data benchmark suite from internet services. They are Aggregation, TeraSort, Kmeans and PageRank. We conduct detailed deep analysis of their I/O characteristics, including disk read/write bandwidth, I/O devices utilization, average waiting time of I/O requests, and average size of I/O requests, which act as a guide to design highperformance, low-power and cost-aware big data storage systems.

The exponential rise of data/information and network resources made most of the organizations investing their resources to develop the biggest data centers (DCs) to accommodate. In the process, controlling the workload at the DCs with minimum response time, there is a need for an effective load-balancing system for which routing applications are playing an important role. Some of the routing applications based on software-defined networking (SDN) available in the market handling the traffic loads of these DCs efficiently. Using the centralized routing allows the network engineers to adjust different types of network elements such as switches, links, and ports dynamically based on the traffic loads. The data flow management at these DCs is managed by the routing applications sometimes face a non-conflicting way for the flow of data and towards the instructions for the switches as well. The security of routing applications plays an important role in safeguarding the control of data flow routing or scheduling from the attackers. The possibility of attackers controlling the traffic, servers, and switches at a DC is a serious threat and someone 2may even shut down the complete system/server. Such attacks may disrupt the routing application and also data flow management. In this chapter, various challenges and security analysis of SDN concerning to routing, cloud, Wi-Fi, and big data will be explored. Different types of design considerations for security requirements are discussed with considerable examples and code analysis. SDN cloud applications and security challenges that are related to the cloud are explained with detailed analysis. Some of the mitigation techniques are proposed for spoofing, data tampering, and repudiation issues. In addition, a detailed discussion on the big data using Apache Hadoop for processing and analyzing the large data sets will be explored.

Big Data Migration between Data Centers in Online Cloud Environment

As the infrastructure of cloud computing and big data, data centers have been deployed widely. Then, how to make full use of the computing and storage resources in data centers will be the focus. Data center networks are considered important solution for the problem above, which include intra-data center and inter-data center networks. Both of them will depend on the optical networking due to its advantages, such as high bandwidth, low latency, and low energy consumption. Data center interconnected by flexi-grid optical networks is a promising scenario to allocate spectral resources for applications in a dynamic, tunable and efficient control manner. Compared with inter-data center, optical interconnect in intra-data center networks is a more pressing need and promising scenario to accommodate these applications in a dynamic, flexible and efficient manner. OpenFlow based Software Defined Networking (SDN) is considered as a good technology, which is very suitable for data center networks. This paper mainly focuses on the data center optical networks based on software defined networking (SDN), which can control the heterogeneous networks with unified resource interface. Architecture and experimental demonstration of OpenFlow-based optical interconnects in intra-data center Networks and OpenFlow-based flexi-grid optical networks for inter-data center are presented in the paper respectively. Some future works are listed finally.

Power grid operation and maintenance decision-making reform is an important part of power system reform. With the construction of massive historical quasi real-time data management platform, the reform of power system is also advancing. However, in the face of massive data explosion, the business level and business logic become disorganized and redundant. Based on the actual situation of Shenzhen Power Supply Bureau, the sg-erp data center is composed of structured data center, massive data center, unstructured data center and power grid GIS data center. With the unprecedented growth of business application data, the data center can improve business logic and promote power system reform. The experimental results show that big data technology has a broad application prospect in the reform of power industry.