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NIT6042 Thesis Assignment Help And Solution

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Course Code: NIT6042
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Mitra and Acharya (2003) define knowledge discovery in database (KDD) as a series of processes for a pattern recognition among a huge amount of data and useful information extraction from these data (see Figure 2.1). These processes are as follows.

Understanding the application domain: Research for prior knowledge and definition for goals
Data extraction: Selection of dataset and variables 3. Data pre-processing: Increase of mining efficiency through data cleaning and transformation
Data mining: Several functions like classification, identification of association rules and revelation of functional dependencies

Interpretation: Interpretation of revealed patterns and visualisation of these patterns

As mentioned above, data mining is one process of KDD and also has several sub processes. It can also be identified as several definitions (Larose 2005).

Processes for finding meaningful correlation among data
Analysis for revealing relationship among data and its summarisation
Information extraction combined with “machine learning, pattern recognition and statistics” technologies

Most companies dealing with privacy data over the Internet provide their privacy policies to protect privacy of the individual. These privacy policies essentially include following concepts (Hamadi, Paik & Benatallah 2007).

Personal Data: Privacy policy statement defines what kinds of data is collected from users.
Purpose: Privacy policy statement declares what is the purpose of collecting such data.
Disclosure: It is sometimes mentioned that the collected data can be shared with third parties either to provide other services to users or to improve currently provided services.
Retention: This term means that how long collected privacy related data will be kept in a database.
If M and m are the maximum number of records and the minimum number of records in a node respectively, all leaf nodes have index records between m and M except the root node.
Each entry of leaf node has the smallest rectangle, called Minimum Bounding Rectangle (MBR), which contains the data object pointed by the index record.
All non-leaf nodes have the number of children nodes between m and M except root node.
Each entry of non-leaf node makes another MBR which contains MBR in all children nodes.
There are at least two children nodes in the root node if it is not a leaf node.


Cloud computing concept is considered as a broad field in the IT environment. There are systems in cloud computing which are connected through communication network which includes internet. Gmail, Microsoft, Yahoo are some of the instances of cloud computing that are available the market. There are also many companies who provide services of cloud computing which includes Amazon, Microsoft Google, and many other services as well (Gupta et al. 2015). The technique involved in cloud computing has a vital role in area of information technology. It delivers flexible services to the people with high performance as well as on demand service. The concept of using clod is completely changing the life of the consumer by providing many new services in daily life. The consumer do not need to pay any attention to any of the processes if they avail the service of cloud computing.
Cloud computing concept delivers the service mostly over Internet using resources of Hardware and software (Dangi and Gaud 2016). The most usage of cloud service is done in the business sectors, in education sector and in many other sectors as well. The most important field of computer science and engineering and the IT industry is cloud computing. There are large numbers of telecom operators also who are interested in service of cloud computing as cloud computing always provides a service with extremely lower cost.
According to Lanjewar et al. (2014), there are many different approaches that cloud computing delivers. The approach of Software as a service is growing fast as this service of cloud computing provides different services such as web email applications, Facebook, Gmail and lots more. The SaaS service is available to the users through the service of internet. The second approach that the cloud computing provides is Platform as a Service. This cloud platform helps the users to create web applications very easily and reduces the cost, complexity and maintenance of the system. There are lot of tools of software development that are provided by PaaS approach of cloud computing. PaaS service involved in cloud computing provides all user with scalability, reliability, security in which it is built up. The third approach that the cloud service provides is the Infrastructure as a Service. This particular technology is a new model of cloud compared to SaaS technology and PaaS technology (Singh and Petriya 2013). The IaaS approach delivers a resource as operating system, network, as well as hardware. Full scalability is provided by Infrastructure as a Service. Service provided by IaaS gives the users with additional resources such as virtual machines, IP addresses, load balancers, and virtual LAN.
The Cloud community provides three types of deployment model. First deployment model of cloud computing is private cloud which gives the users to keep the data o information private within the organization, own cloud or might be within an institute (Dharane et al. 2015). The private cloud helps to store as well as manage the data of the organization in which it is deployed. The second deployment of cloud is public cloud. This particular cloud deployment is used a single user or an organization depending on their personal needs. While using a personal computer, security becomes a major problem. The system is generally used by many common cloud users and cloud service provides tenure of public cloud which has profit value, costing model, charging as well as own policy. The Hybrid cloud is the third deployment model involved in cloud computing. Hybrid cloud is known as combination of both private cloud as well as public cloud. These two cloud are joined with the propriety technology which allows application probability and data portability such as load balancing. The organization   hybrid cloud model can have the advantage of both the model of cloud computing and this hybrid deployment cloud model provides good service to its users.
Concept of IaaS Cloud Computing
As stated by Alam and Khan (2017), concept of adopting cloud computing for deploying scientific deployment has increased the interest of researchers to design more efficient algorithms of scheduling which is capable of utilizing Virtual Machines. For modifying execution environment in cloud environment, a powerful tool is considered which provides scheduling algorithms for scaling total number of resources that are available for making the performance better as well as cost efficient. IaaS (Infrastructure as a Service) cloud computing model allows to manage the workflow of systems by providing virtual pool of resource which are acquired, used as well as configured. The IaaS cloud structure is charged on pay per use basis. The providers of infrastructure as a service provides computing resources that are virtualized and has termed the resources as Virtual Resources. This IaaS infrastructure has predefined memory, storage, CPU, capacity and bandwidth with different types of resource bundles that are of different price (Rodriguez and Buyya 2017).  The IaaS cloud service is acquired and released easily and generally are charged only for the time frame that the user uses or on the billing period. The computing power is delivered by Virtual Machine and the IaaS cloud offers networking service as well as offer storage that are provided with necessary infrastructure for executing the workflow of the application.
Chakravarthi and Vijayakumar (2018) stated that IaaS is a form of cloud computing which gives virtualized resources of cloud computing over Internet. Agreeing with the fact Bala and Chana (2014) stated that the IaaS is the most famous of three know cloud service model, that is SaaS model and PaaS model. The cloud provider in IaaS cloud service model mainly hosts infrastructure components which presents on premises data center such as storage, networking hardware, hypervisor or virtualization layer and servers. The model of IaaS is effective for the workloads which are experimental, temporary and on such workloads that are expected to change unexpectedly. One such example of IaaS cloud is stated below. If an organization is using the service of IaaS cloud as its software product, the service of cloud model is considered to be a cost effective one and to host as well as test all its application is also very cheap with the IaaS cloud provider. After the testing and refinement of the new software, the organization may remove the software from the IaaS environment for conducting more traditional deployment in house. The software can be considered as a long term IaaS deployment in the organization which has basically less costly. Dangi and Gaud (2016) stated that the users of IaaS pay on the basis of usage that is calculated as per hour, month or week. Some of the IaaS cloud service users are charged on the basis of space they use for virtual machine. For deploying this IaaS there is a capital cost for the in house software as well hardware.
Concept of Scientific Workflow Scheduling
Workflow scheduling is scientifically stated as a problem that maps all the task to their proper resource and thus allows all tasks which satisfies the criteria involved in the performance. According to Fakhfakh, Kacem and Kacem (2014), workflow scheduling has some sequence of connected tasks involved in it. Workflow concentrates on the automation of procedures to achieve a permanent goal which involves of carefully passes all data as well as files between participants and the information is transferred as per the predefined set of rules. A particular workflow basically includes structuring involved applications in DAG (Directed Acyclic Graph) from where all nodes is represented by its task and al the edges are represented by the dependencies in between nodes of applications. There is single workflow that are involved in group of tasks that can communicate with some other task in workflow. The scheduling workflow is supported by the Workflow Management System. There are involvement of resources that are basically invented by workflow scheduling and then allocates the tasks on some suitable resource. The work of workflow scheduling is to manage the flow of work in a business or in an organization. If workflow scheduling is done properly, then performance of the system increases automatically. There are various different types of scheduling algorithm that are included in the workflow management which helps to increase the performance of system.
Figure 1: Workflow in form of Graph
(Source: Gupta et al. 2015)
Bardsiri and Hashemi (2014) stated that workflow scheduling as the major problem in the environment of cloud. According to Bardsiri and Hashemi, there are many different scheduling algorithms involved in workflow management of cloud computing. All the scheduling algorithms has different parameters involved in them. All the workflow algorithms may not have execution time involved in them. This becomes a research gap which states that there is a need of scheduling algorithm which is capable of minimizing the execution time of the resources involved in cloud computing. With the environment of cloud, the workflow uses different types of different services of cloud so that it is capable of facilitating the executing of workflow.
Taxonomy of Workflow Scheduling
Kaur (2015) stated that there is a problem in workflow scheduling that has caused many researchers to carry out this research study. There are many heuristics developed that helps in tasks scheduling in all distributed environments. Main input that is related to workflow algorithm is considered as abstract workflow that defines tasks which does not specify the location of resources where the task are generally executed. Masdari et al. (2016) has also stated that Abstract workflow are categorized into two different categories. One is deterministic abstract workflow and the other is non-deterministic abstract workflow. In any deterministic model, all the dependencies of the task as well as input of data are included from first. Whereas, in a non-deterministic abstract workflow, the task and the data are done at run time.
Below diagram shows the categorization of workflow scheduling defined by Chakravarthi and Vijayakumar (2018). The workflow scheduling are then divided in two similar categories. The best effort based scheduling as well as QoS (Quality of Service) are based on this scheduling. The scheduling mainly focuses on the minimization of the make span by ignoring all the QoS constraint of the users. While the scheduling that is based on QoS constraint basically minimizes performance of the system under quality of service constraints. This helps in minimizing the cost and time that is under the deadline constraint and under budget constraint respectively.
Figure 2: Taxonomy of Workflow Scheduling
(Source: Chakravarthi and Vijayakumar (2018))
From the above figure 2, it can be seen that best effort based scheduling is further divided two parts heuristics based best effort or meta-heuristic based best effort. According to Di Stefano, Morana and Zito (2014), the Best Effort Scheduling mainly optimizes one objective as well as ignoring some factors which includes monetary cost or different QoS requirements. Chakravarthi and Vijayakumar (2018) defines that the main objective of best effort scheduling algorithm minimizes the make span of the workflow. Make span of the workflow application is basically total time that is taken execute the workflow. So, for minimizing the time of execution includes workflow that works in best effort scheduling algorithm. Further the heuristic based algorithm only fits to a specific type of problem and the meta-heuristics algo are generally based on the meta-heuristic method that provides basic solution to develop some specific heuristic so that they can fit in to some specific problem (Zhang et al. 2013). The QoS constraint of the workflow scheduling are based on two factors time and cost. So, QoS constraint of workflow scheduling is divided into two parts, deadline constraint related to time and budget constraint related to cost. Chakravarthi and Vijayakumar (2018) has defined time as the total time that is executed in all tasks in a particular workflow. The cost is represented as total expense used for the executing workflow.
Workflow Scheduling Parameters
According to Zhao et al. (2016), there are two nature of workflow algorithm. One is heuristic in nature and the other is meta-heuristic in nature. The algorithms based on heuristic nature are mainly based on priority and is problem centric. User generally uses own knowledge that makes priority of the workflow applications and all the cloud resource involved. The second nature of workflow is the meta-heuristic workflow algorithm which do not need the interface of humans at all. This particular workflow provides the user an overall solution to the application of the workflow.
Milani and Navimipour (2016) there are many workflow scheduling parameter that helps in controlling the tasks in IaaS cloud. The parameters includes:

Execution Time: This parameter includes the time taken by CPU to execute that particular job.
Response Time: Response parameter indicates all response time that is included when a particular system starts while responding for that task which is already submitted.
Makespan: The Makespan time is represented by time difference between task completion time and submission request.
Energy Consumption: The total power that is consumed by the resources for during the service.
Throughput: The production rate of the tasks and the number of tasks that are completed for particular time unit is known as the throughput.
Scalability: Scalability refers to the growing of system according to the increasing demand of tasks or the increasing number of data.
Resource Utilization: Resource Utilization includes keeping all resources busy as much as possible.
Load Balancing: This process involves keeping the resources as well as the servers in balance pushing load from one resource to another (Ramezani and Hussain 2014).
Fault Tolerance: Fault tolerance includes surety that the services are available and reliable.
Reliability: Reliability involves the trust value of resource, Cloud Service Provider, and services that the user receives from the cloud.

Conceptual Framework of Work Scheduling in IaaS Cloud
Figure 3: Framework of Workflow Scheduling in Cloud Computing
(Source: Cho et al. (2015))
From the above diagram, it can be stated that all the scheduling resource involved in cloud are mainly divided in six factors. Cost aware resource scheduling, efficiency aware resource scheduling, energy aware resource scheduling, load balancing resource scheduling, QoS aware resource scheduling, and utilization aware resource scheduling. There are many algorithms are well that are stated below which are used for scheduling the workflow of the cloud application.

Name of Algorithm


Nature of Algorithm






Cost-Based Scheduling of Scientific Workflow Applications On Utility Grids


Execution costs

Deadline, Task Dependencies

Cloud Computing



A Multiple QoS Constrained Scheduling Strategy of Multiple Workflows for Cloud Computing (MQMW)


Multi workflows
with multiple parameter

Task having minimum surplus time/cost & minimum covariance

Cloud Computing


Real World

Scheduling Scientific Workflows Elastically For Cloud Computing


Execution Time,


Cloud Computing


Cloud Environment

Scheduling Technique of Data-Intensive Application Workflows In Cloud Computing


Broadcast time,
access cost, execution cost

Network Delay

Cloud Computing


Not needed

Energy Efficient Workflow Job Scheduling for Green Cloud



Minimize VM overhead, Energy Consumptions, and CO2 emission

Frequency Scaling

Cloud Computing



An Approach to Workflow Scheduling using Priority in Cloud Computing Environment


Cost, Service Utilization


Cloud Computing


Real World

Involvement of Load Balancing in Workflow Scheduling of IaaS Cloud Computing
Adhikari and Amgoth (2018) defined load balancing is a cloud computing process where the workload of all the resources in a particular node is shifted with their resources to the other node in a network without mitigating other nodes that are running in the system. Load balancer, which is a hardware based system used to scale the web applications. The IP address is assumed by the load balancer first so that all the communication goes through load balancer. The concept of load balancer is then connected to many similar web servers in the back end. The load balancer that is hardware based is mainly designed to handle all the high level loads to scale them easily (Chen, Chen and Kuo 2017). The first and foremost aim of load balancer is to understand clearly the requirements of the customer, the information or the data that are to be sent or are to be received without consuming more time.
 Load balancing involved in cloud computing is considered as major problem solving technique. Without load balancers the users would get time consuming responses of the applications involved. Soni and Kalra (2014) has stated load balancing as a method which increases the performance of the Distributed System. Govindaraju and Duran-Limon (2016) has stated that in this load balancing method, mainly the method of transferring load is done among different processors of the distributed system so that the job response time is improved. Agreeing with the fact Babu and Samuel (2016) also added that load balancing algorithm increases the resource utilization not considering the condition of the processors that are overloaded and other processors are idle or does loaded work at any instant of time in system. Load balancers helps to calculate different terms like reducing the communication delays, reduces the execution time, as well as increases the throughput and resource utilization in the  resources of cloud computing. Dharane et al. (2015) in their journal has stated load balancing as of two types- static and dynamic load balancers. Static load balancing requires the previous knowledge related to the system and is not dependent on system of progress state. Run time changes facilities are not provided by permanent load balancing. On other hand, the dynamic load balancing is an algorithm that depends on the progress of the system. Previous state is not considered by dynamic load balancing (Paya and Marinescu 2017). This algorithm depends on progress of the system. Concept of Dynamic Load balancing is more better compared static load balancing algorithm. Easy run time changes are possible dynamic load balancing system.
Load Balancing Framework
Figure 4: Framework of Load Balancing
(Source: Ren, Pang and Cheng (2017))
The load balancing frame work is shown in the figure above. Zhang et al. (2013) has defined the framework of load balancing in the following way. Virtual Machine Migration Act (VMMC) mainly acts a resource pool in load balancing framework for hosting the virtual machines. The main node helps to collect data of performance, sends controlled instructions to all NMMC based strategy of load balancing and also establishes all models. The CPU load and the memory capacity is considered in the framework of load balancing. The load collection and the modelling part is master node is collected as data of performance included in virtual machines as well as in physical nodes in the VMMC. Load collection and modelling part is considered as master node whic collects the data of performance periodically for virtual machines and physical nodes that are included in VMMC. The performance of data mainly includes utilization of CPU, network input output and memory input output. From the above diagram it can be seen that the data have three purposes, first is to establish load model for all the virtual machine in the VMMC. Second is establishing a relational model between resource of physical host and virtual machine load. The third is to maintain the present load state in physical host of VMMC load balancing. The factor  of load balancing is defined by a formula that is stated below.
Issues involved in Load Balancing Algorithm in Cloud Computing Algorithms
With all the above stated advantage stated above by many authors, Toosi and Buyya (2015) has stated the issues of using load balancing algorithms. There are many problems with the algorithms of load balancing. Customer satisfaction is considered as an important factors and it must be taken care of while distributing work load in different nodes of the workflow schedule. With the flexibility, low cost and accessibility advantages of load balancing, there are many disadvantages as well. The below stated issues does not allow the load balancing techniques to scale up the increasing demand of cloud computing. To mitigate the below stated issues, many load balancing algorithms are stated which gets maximum resource utilization, get high throughput and can lower the response time of the system.
Geographical Distributions of the Nodes 
This is mainly used for large scaled applications such as Twitter, or Facebook. For maintaining the efficiency with system as well as handling the fault tolerance, distributed system of processors are very helpful for environment of cloud computing (Dharane et al. 2015). Geographical distribution is very much needed for the overall performance for calculating the actual time of the cloud environment.
Static vs. Dynamic Nature of Algorithms 
The main problem that lies in designing the load balancing algorithm is mainly based on nature of system that involves static algorithm as well as dynamic algorithm. Static algorithm works very well on the previous data or information and ignores the present state of system that it is working on. Sudden failure of the resource is the main disadvantage of using static algorithm. Dynamic algorithm work better compared to static algorithm (Pawar, Lilhore and Agrawal 2017). This algorithm mainly works on current state of system. There is no involvement of previous information in dynamic system. This algorithms give better fault tolerance compared to static algorithm.
Complexity of Algorithm 
Parida and Panchal (2018) stated that the complexity of the algorithm plays an important role in workflow scheduling algorithm which affects the overall performance of the resources in the cloud computing network. In complexity terms, the algorithm of load balancing is very simple but regarding the mitigation time, response time and fault tolerance, the algorithm of load balancing is very poor.
From the above discussion it can be summarized that increasing the cloud computing and increasing data rate involved in cloud computing, the workflow scheduling has become a major issue that decreases performance of cloud computing. To improve performance involved in cloud computing, the environment of cloud basically uses the load balancing technique and job scheduling technique. In this paper above, all the details involved in cloud computing starting from concept of IaaS involved in cloud computing to all issues related in workflow scheduling of cloud computing are stated clearly reviewing different papers and analyzing different author’s view. This paper mainly focus on the workflow scheduling and how load balancing can help in mitigating the workflow problem scheduling in cloud. For load balancing, there are different policies which uses different types or techniques including cloud partition, dynamic load policy, and static load policy for solving the problem of cloud computing. Two types of workflow scheduling are involved in cloud computing which include heuristics workflow and meta-heuristics workflow scheduling. Mainly the time quantum policy is used for sharing the process of virtual machine. The concept of sharing virtual machine that is discussed in this paper helps in improved machine utilization of the cloud resource. This paper focus on the concept of load balancing which reduces the usage of resource and the cost of economy. The recent techniques of load balancing are discussed in this paper which helps in proper allocation of job without considering time complexity as well as scalability that is involved in cloud computing. There is further a load balancing technique that is based on time quantum as well as probability function which can be discussed in next research study.
This main issue highlighted in this paper is the issue of cloud computing which is stated as load balancing. If the system involved in cloud computing are over loaded, the performance of the system deteriorate making the technology of cloud computing unsuccessful.  So, load balancing algorithm is always needed for utilizing the resources efficiently. All such load balancing algorithm are discussed in this paper, along with their applicability as well as the limitations they provide in the cloud environment. The load balancing algorithms are compared with each other in this paper. From the above stated theories and load balancing techniques, more efficient load balancing technique will be needed with the increasing amount of data that is being released from data centers of cloud.
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Alam, M. and Khan, Z.A., 2017. Issues and Challenges of Load Balancing Algorithm in Cloud Computing Environment. Indian Journal of Science and Technology, 10(25).
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Chakravarthi, K.K. and Vijayakumar, V., 2018. Workflow Scheduling Techniques and Algorithms in IaaS Cloud: A Survey. International Journal of Electrical and Computer Engineering (IJECE), 8(2), pp.853-866.
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Pawar, N., Lilhore, U.K. and Agrawal, N., 2017. A Hybrid ACHBDF Load Balancing Method for Optimum Resource Utilization In Cloud Computing.
Paya, A. and Marinescu, D.C., 2017. Energy-aware load balancing and application scaling for the cloud ecosystem. IEEE Transactions on Cloud Computing, 5(1), pp.15-27.
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The process of developing a successful business entity requires a multidimensional analysis of several factors that relate to the internal and external environment in commerce. The areas covered in this current unit are essential in transforming the business perspective regarding the key commerce factors such as ethics, technology, culture, entrepreneurship, leadership, culture, and globalization (Nzelibe, 1996; Barza, 2…

SNM660 Evidence Based Practice
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Course Code: SNM660
University: The University Of Sheffield

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Country: United Kingdom

Critical reflection on the objective, design, methodology and outcome of the research undertaken Assessment-I
Smoking and tobacco addiction is one of the few among the most basic general restorative issues, particularly to developed nations such as the UK. It has been represented that among all risk segments smoking is the fourth driving purpose behind infections and other several ailments like asthma, breathing and problems in the l…
Australia Maidstone Management Business management with marketing University of New South Wales Masters in Business Administration 

BSBHRM513 Manage Workforce Planning
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Course Code: BSBHRM513
University: Tafe NSW

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Country: Australia

Task 1
1.0 Data on staff turnover and demographics
That includes the staffing information of JKL industries for the fiscal year of 2014-15, it can be said that the company is having problems related to employee turnover. For the role of Senior Manager in Sydney, the organization needs 4 managers; however, one manager is exiting. It will make one empty position which might hurt the decision making process. On the other hand, In Brisba…

MKT2031 Issues In Small Business And Entrepreneurship
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Course Code: MKT2031
University: University Of Northampton

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Country: United Kingdom

Entrepreneurial ventures
Entrepreneurship is the capacity and willingness to develop, manage, and put in order operations of any business venture with an intention to make profits despite the risks that may be involved in such venture. Small and large businesses have a vital role to play in the overall performance of the economy. It is, therefore, necessary to consider the difference between entrepreneurial ventures, individual, and c…
Turkey Istanbul Management University of Employee Masters in Business Administration 

MN506 System Management
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Course Code: MN506
University: Melbourne Institute Of Technology

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Country: Australia

An operating system (OS) is defined as a system software that is installed in the systems for the management of the hardware along with the other software resources. Every computer system and mobile device requires an operating system for functioning and execution of operations. There is a great use of mobile devices such as tablets and Smartphones that has increased. One of the widely used and implemented operating syste…
Australia Cheltenham Computer Science Litigation and Dispute Management University of New South Wales Information Technology 


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11174 Introduction To Management

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