Parallelism and Scalability: A Solution Focused on the Cloud Computing Processing Service Billing

Nowadays, big media healthcare data processing in cloud has become an effective solution for satisfying QoS demands of medical users. It can support various healthcare services such as pre-processing, storing, sharing, and analysis of monitored data as well as acquiring context-awareness. However, to support energy and cost savings, the union of cloud data centers termed as cloud confederation can be an promising approach, which helps a cloud provider to overcome the limitation of physical resources. However, the key challenge in it is to achieve multiple contradictory objectives, e.g., meeting the required level of services defined in service level agreement, maintaining medial users'application QoS, etc. while maximizing profit of a cloud provider. In this paper, for executing heterogeneous big healthcare data processing requests from users, we develop a local and global cloud confederation model, namely FnF, that makes an optimal selection decision for target cloud data center(s) by exploiting Fuzzy logic. The FnF trades off in between profit of cloud provider and user application QoS in selecting federated data center(s). In addition, FnF enhances its decision accuracy by precisely estimating the resource requirements for the big data processing tasks using multiple linear regression. The proposed FnF model is validated through numerical as well as experimental evaluations. Simulation results depict the effectiveness and efficiency of the FnF model compared to state-of-the-art approaches.

When designing today's highly complicated systems consisting of several hardware and software modules, it is essential to estimate the performance such as worst-case or best-case execution time in early design stages. Such estimation is essential to explore architecture and hardware/software partitioning in system-level design. A maximum execution time estimated topologically without considering false-paths is longer than the real. In this paper, we propose an static estimation method of maximum execution time in system-level designs, considering false-paths. Also, we adopt an approximation approach in order to avoid the path explosion problem. The experimental results show that our method can provide much smaller estimated maximum execution time than the method without considering false-paths. At the same time, the results show us that the maximum execution time can be estimated to a very small range, by applying both simulation-based method and our static method.

The paper concerns issues related to evaluation of processing in computational clouds. The effectiveness of the computational clouds depends on many factors and different metrics can be used to asses effects of processing. In this work potentiality for evaluation of processing in cloud virtual machines that takes into account also financial cost constraints is investigated. There are considered KVM and Hyper–V based cloud platform configurations. In the paper the APPI index (Application Performance and Price Index) that can be used for selection of recommended configuration considering acceptable price of processing is defined. Presented experiments show the impact of use of the proposed metric on the choice of recommended platform for processing.

Negotiations were introduced in [6] as a model for concurrent systems with multiparty decisions. What is very appealing with negotiations is that it is one of the very few non-trivial concurrent models where several interesting problems, such as soundness, i.e. absence of deadlocks, can be solved in PTIME [3]. In this paper, we introduce the model of timed negotiations and consider the problem of computing the minimum and the maximum execution times of a negotiation. The latter can be solved using the algorithm of [10] computing costs in negotiations, but surprisingly minimum execution time cannot.This paper proposes new algorithms to compute both minimum and maximum execution time, that work in much more general classes of negotiations than [10], that only considered sound and deterministic negotiations. Further, we uncover the precise complexities of these questions, ranging from PTIME to varDelta _2^P-complete. In particular, we show that computing the minimum execution time is more complex than computing the maximum execution time in most classes of negotiations we consider.

Cloud computing(CC), which utilizes massively virtualized data centers to deliver quick and affordable computing solutions, has developed ‎into an established industrial standard that is growing quickly. To handle such a massive amount of data effectively, cloud computing mostly ‎relies on automation and dynamic resource management. In cloud computing, load balancing (LB) is a vital technique for maximizing ‎resource utilization and making sure that no resource is used up. Without the requirement for physical infrastructure, cloud ‎LB allows online platforms to adjust their resources in response to traffic demands. In a cloud environment, workload and resource allocation entail determining the best way to divide up work among several servers. For increasingly severe uncertainty problems, traditional LB ‎approaches are simple but ineffective; for this reason, meta-heuristic methods are employed. This algorithm is heuristic and is independent ‎of the complexity of the challenges. Meta-heuristics approaches based on Artificial Intelligence (AI) are employed to analyze real-time data ‎and intelligently distribute workload among servers. This ensures efficient operations by preventing bottlenecks and enabling proactive LB ‎decisions. The review offers a thorough analysis of meta-heuristics techniques based on artificial intelligence (AI) for static and dynamic LB ‎in both homogeneous and heterogeneous cloud systems.

Recently, big data analytics has received important attention in a variety of application domains including business, finance, space science, healthcare, telecommunication and Internet of Things (IoT). Among these areas, IoT is considered as an important platform in bringing people, processes, data and things/objects together in order to enhance the quality of our everyday lives. However, the key challenges are how to effectively extract useful features from the massive amount of heterogeneous data generated by resource-constrained IoT devices in order to provide real-time information and feedback to the end-users, and how to utilize this data-aware intelligence in enhancing the performance of wireless IoT networks. Although there are parallel advances in cloud computing and edge computing for addressing some issues in data analytics, they have their own benefits and limitations. The convergence of these two computing paradigms, i.e., massive virtually shared pool of computing and storage resources from the cloud and real-time data processing by edge computing, could effectively enable live data analytics in wireless IoT networks. In this regard, we propose a novel framework for coordinated processing between edge and cloud computing/processing by integrating advantages from both the platforms. The proposed framework can exploit the network-wide knowledge and historical information available at the cloud center to guide edge computing units towards satisfying various performance requirements of heterogeneous wireless IoT networks. Starting with the main features, key enablers and the challenges of big data analytics, we provide various synergies and distinctions between cloud and edge processing. More importantly, we identify and describe the potential key enablers for the proposed edge-cloud collaborative framework, the associated key challenges and some interesting future research directions.

In the last few decades, a novel branch of intelligent computation algorithms has been inspired by a swarm intelligence theory which imitates the behavior of animals. These algorithms are successfully applied to solve many kinds of simple and complex problems in several fields such as optimization problems, pattern recognition, image processing, features section, and task scheduling in cloud and parallel computing. Cloud computing has not only become the preferred environment for several companies, but also helps others to overcome many server-related issues by utilizing characteristics such as reliability, flexibility, high scalability, and security. Therefore, many intelligent computation algorithms are used to improve this environment. In this chapter, an overview of swarm intelligence for solving the scheduling problems of tasks in cloud computing is presented, including particle swarm optimization, cat optimization algorithm, artificial bee colony, lion optimization algorithm, whale optimization algorithm, bat algorithm, gray wolf optimizer, cuckoo search algorithm, hybrid swarm algorithms, and multi-objective swarm optimization. All these algorithms are described and presented with their achievements in solving task scheduling issues in cloud computing.

Organizations like Amazon, Netflix, eBay, and Gameloft are forced to use cloud computing due to a lack of storage space. Flexibility, scalability, multi-portability, and elasticity are all advantages of cloud computing. Despite the fact that attempts have been made to improve cloud security, it still contains flaws like security and data availability that prevent individuals from using it. The term “fog computing” denotes to the extension of cloud computing with respect to network's edge. Fog computing expands storage in cloud computing, networking, and processing capabilities. Data access security is improving day by day, despite the fact that data is kept remotely. The volume of data is likewise growing by the day. Data secrecy is maintained by securing data utilizing multiple behaviour parameters. The technique of encrypting and modifying messages in order to build data securely and to protect from attacks is known as cryptology (secret writing). NIST recommends AES as a symmetric encryption standard. AES has been shown to be a very secure, quick, and powerful encryption method. AES is widely utilized due to its high level of competency and ease of use. This paper introduce a system that will provide capabilities such as automatic text encryption and decryption. For this, the AES -256 bit method is utilized. AES is used for encryption and decryption, in the proposed system it can encrypt both textual data and IoT sensor data collected from different sensors and decryption can be done with an secret key.

Cloud computing and big data: Technologies and applications

The advances in technologies of cloud computing and mobile computing enable the newly emerging mobile cloud computing paradigm. Three approaches have been proposed for mobile cloud applications: 1) extending the access to cloud services to mobile devices; 2) enabling mobile devices to work collaboratively as cloud resource providers; 3) augmenting the execution of mobile applications on portable devices using cloud resources. In this paper, we focus on the third approach in supporting mobile data stream applications. More specifically, we study the computation partitioning, which aims at optimizing the partition of a data stream application between mobile and cloud such that the application has maximum speed/throughput in processing the streaming data. To the best of our knowledge, it is the first work to study the partitioning problem for mobile data stream applications, where the optimization is placed on achieving high throughput of processing the streaming data rather than minimizing the make span of executions in other applications. We first propose a framework to provide runtime support for the dynamic partitioning and execution of the application. Different from existing works, the framework not only allows the dynamic partitioning for a single user but also supports the sharing of computation instances among multiple users in the cloud to achieve efficient utilization of the underlying cloud resources. Meanwhile, the framework has better scalability because it is designed on the elastic cloud fabrics. Based on the framework, we design a genetic algorithm to perform the optimal partition. We have conducted extensive simulations. The results show that our method can achieve more than 2X better performance over the execution without partitioning.

This paper explores secure data processing in hybrid clouds wherein local computing capability is exploited alongside public cloud services to deliver an efficient and secure data management solution. Hybrid clouds offer numerous advantages including the ability to selectively outsource data and computations based on sensitivity/confidentiality. Data processing in hybrid clouds must address two interrelated challenges: (i) data distribution: how is data distributed across public and private machines, and (ii) distributed query processing: how are queries executed efficiently without leaking sensitive data to untrusted public machines. This paper addresses these challenges and incorporates the respective solutions into an add-on tool for a Hadoop, Spark, and Hive based cloud computing infrastructure. Our results show performance advantages in using our strategy as compared to other secure alternatives, even when the percentage of sensitive data is as high as 50%.

Mobile devices play an integral role in our day lives and have brought the revolutionary change in business, education, and entertainment. Moreover, the emergence of cloud computing technology greatly extended the significance of smart device. On the other hand, the smart devices experience the problem when obtaining the multiple cloud services during the handoff process. In this paper, we propose an energy detection (ED) analytical model for handoff process that calculates the energy consumption for each handoff process in the cloud computing environment. Our ED analytic model is developed to examine the consumed energy for different handoff processes in cloud computing. The model helps the mobile users to get prior information for the status of the mobile when executing the handoff process. To reconfirm the validity of ED analytical model, we have test programmed in NS2. The results demonstrate that the ED analytical model efficiently detects the energy consumption of mobile devices during the handoff process in cloud computing environment.

The data storage and processing system based on cloud computing can take cloud server as the core, make use of sensor technology synthetically, and provide data storage and computing function for client. Combining GPRS/WiFi technology with Ali cloud server, this paper realizes cloud storage and cloud computing processing of remote important environment information and dynamic display of real-time / historical data in Android client. The experimental results show that the system runs stably and the data are reliable.

Due to economic benefits of cloud computing, consumers have rushed to adopt Cloud Computing. Apart from rushing into cloud, security concerns are also raised. These security concerns cause trust issue in adopting cloud computing. Enterprises adopting cloud, will have no more control over data, application and other computing resources that are outsourced from cloud computing provider. In this paper we propose a novel technique that will not leave consumer alone in cloud environment. Firstly we present theoretical analysis of selected state of the art technique and identified issues in IaaS cloud computing. Secondly we propose Distributed Trust Protocol for IaaS Cloud Computing in order to mitigate trust issue between cloud consumer and provider. Our protocol is distributed in nature that lets the consumer to check the integrity of cloud computing platform that is in the premises of provider's environment. We follow the rule of security duty separation between the premises of consumer and provider and let the consumer be the actual owner of the platform. In our protocol, user VM hosted at IaaS Cloud Computing uses Trusted Boot process by following specification of Trusted Computing Group (TCG) and by utilizing Trusted Platform Module (TPM) Chip of the consumer. The protocol is for the Infrastructure as a Service IaaS i.e. lowest service delivery model of cloud computing.

Cloud computing is a technology that provides secure storage space for the customer’s massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encryption and decryption algorithms are being deployed. In cloud computation, data processing, storage, and transmission can be done through laptops and mobile devices. Data Storing in cloud facilities is expanding each day and data is the most significant asset of clients. The important concern with the transmission of information to the cloud is security because there is no perceivability of the client’s data. They have to be dependent on cloud service providers for assurance of the platform’s security. Data security and privacy issues reduce the progression of cloud computing and add complexity. Nowadays; most of the data that is stored on cloud servers is in the form of images and photographs, which is a very confidential form of data that requires secured transmission. In this research work, a public key cryptosystem is being implemented to store, retrieve and transmit information in cloud computation through a modified Rivest-Shamir-Adleman (RSA) algorithm for the encryption and decryption of data. The implementation of a modified RSA algorithm results guaranteed the security of data in the cloud environment. To enhance the user data security level, a neural network is used for user authentication and recognition. Moreover; the proposed technique develops the performance of detection as a loss function of the bounding box. The Faster Region-Based Convolutional Neural Network (Faster R-CNN) gets trained on images to identify authorized users with an accuracy of 99.9% on training.