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CEO Eric Schmidt says that big corporations will change their IT models, but it may take 10 to 20 years.

Cloud computing datacenters provide millions of virtual machines in actual cloud markets. In this context, Virtual Machine Placement (VMP) is one of the most challenging problems in cloud infrastructure management, considering the large number of possible optimization criteria and different formulations that could be studied. Considering the on-demand model of cloud computing, the VMP problem should be optimized dynamically to efficiently attend typical workload of modern applications. This work proposes several dynamic environments for solving the VMP from the providers' perspective based on the most relevant dynamic parameters studied so far in the VMP literature. A complete set of environments and workload traces examples are presented in this work.

In this paper, we propose a design for novel and experimental cloud computing systems. The proposed system aims at enhancing computational, communicational and annalistic capabilities of road navigation services by merging several independent technologies, namely vision-based embedded navigation systems, prominent Cloud Computing Systems (CCSs) and Vehicular Ad-hoc NETwork (VANET). This work presents our initial investigations by describing the design of a global generic system. The designed system has been experimented with various scenarios of video-based road services. Moreover, the associated architecture has been implemented on a small-scale simulator of an in-vehicle embedded system. The implemented architecture has been experimented in the case of a simulated road service to aid the police agency. The goal of this service is to recognize and track searched individuals and vehicles in a real-time monitoring system remotely connected to moving cars. The presented work demonstrates the potential of our system for efficiently enhancing and diversifying real-time video services in road environments.

We propose a game theoretic framework for task allocation in mobile cloud computing that corresponds to offloading of compute tasks to a group of nearby mobile devices. Specifically, in our framework, a distributor node holds a multidimensional auction for allocating the tasks of a job among nearby mobile nodes based on their computational capabilities and also the cost of computation at these nodes, with the goal of reducing the overall job completion time. Our proposed auction also has the desired incentive compatibility property that ensures that mobile devices truthfully reveal their capabilities and costs and that those devices benefit from the task allocation. To deal with node mobility, we perform multiple auctions over adaptive time intervals. We develop a heuristic approach to dynamically find the best time intervals between auctions to minimize unnecessary auctions and the accompanying overheads. We evaluate our framework and methods using both real world and synthetic mobility traces. Our evaluation results show that our game theoretic framework improves the job completion time by a factor of 2-5 in comparison to the time taken for executing the job locally, while minimizing the number of auctions and the accompanying overheads. Our approach is also profitable for the nearby nodes that execute the distributor's tasks with these nodes receiving a compensation higher than their actual costs.

Chinese scientist have unveiled a so-called "super camera." It is five times more detailed than the human eye and equipped with AI facial recognition, real time monitoring and cloud computing technology.

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Cloud computing is a newly emerging distributed system which is evolved from Grid computing. Task scheduling is the core research of cloud computing which studies how to allocate the tasks among the physical nodes, so that the tasks can get a balanced allocation or each task's execution cost decreases to the minimum, or the overall system performance is optimal. Unlike task scheduling based on time or cost before, aiming at the special reliability requirements in cloud computing, we propose a non-cooperative game model for reliability-based task scheduling approach. This model takes the steady-state availability that computing nodes provide as the target, takes the task slicing strategy of the schedulers as the game strategy, then finds the Nash equilibrium solution. And also, we design a task scheduling algorithm based on this model. The experiments can be seen that our task scheduling algorithm is better than the so-called balanced scheduling algorithm.

Cloud computing systems, in which clients rent and share computing resources of third party platforms, have gained widespread use in recent years. Furthermore, cloud computing for mobile systems (i.e., systems in which the clients are mobile devices) have too been receiving considerable attention in technical literature. We propose a new method of delegating computations of resource-constrained mobile clients, in which multiple servers interact to construct an encrypted program known as garbled circuit. Next, using garbled inputs from a mobile client, another server executes this garbled circuit and returns the resulting garbled outputs. Our system assures privacy of the mobile client's data, even if the executing server chooses to collude with all but one of the other servers. We adapt the garbled circuit design of Beaver et al. and the secure multiparty computation protocol of Goldreich et al. for the purpose of building a secure cloud computing for mobile systems. Our method incorporates the novel use of the cryptographically secure pseudo random number generator of Blum et al. that enables the mobile client to efficiently retrieve the result of the computation, as well as to verify that the evaluator actually performed the computation. We analyze the server-side and client-side complexity of our system. Using real-world data, we evaluate our system for a privacy preserving search application that locates the nearest bank/ATM from the mobile client. We also measure the time taken to construct and evaluate the garbled circuit for varying number of servers, demonstrating the feasibility of our secure and verifiable cloud computing for mobile systems.

The Introduction Of Cloud Computing And Openstack Presented in 2016 Software Freedom Day Celebration held in Tehran SFD Website: https://sfd.fsug.ir/1395 Organized by Libre/Free Knowledge Foundation Place: Sharif University of Technology

Cloud computing is next generation computer technology to the world wide wed. This paper describes comparative study on Microsoft  Cloud and  Amazone cloud .  This paper  explains  providers  storages of data,services to the client for  enhancement of data,requirement of specification to use cloud technology and database support's to the client useage.

Green computing is characterized as the examination and practice of structuring, assembling, utilizing, and discarding PCs, servers, and related subsystems, for example, screens, printers, storage gadgets, and systems administration and interchanges frameworks proficiently and successfully with negligible or no effect on the earth. The objective of green computing is to diminish the utilization of hazardous materials, amplify energy proficiency during the items lifetime, and advance the recyclability of obsolete items and factory waste. Green computing can be accomplished by either Product Longevity Resource distribution or Virtualization or Power management. power is the bottleneck of improving the system execution. Among all industries, the information communication technology ICT industry is seemingly answerable for a bigger segment of the overall development in energy utilization. The objective of green cloud computing is to advance the recyclability or biodegradability of outdated items and factory waste by diminishing the utilization of hazardous materials and amplifying the energy productivity during the items lifetime. Stephen Fernandes "Energy Saving by Migrating Virtual Machine to Green Cloud Computing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30422.pdf Paper Url :https://www.ijtsrd.com/computer-science/distributed-computing/30422/energy-saving-by-migrating-virtual-machine-to-green-cloud-computing/stephen-fernandes

Green computing is characterized as the examination and practice of structuring, assembling, utilizing, and discarding PCs, servers, and related subsystems, for example, screens, printers, storage gadgets, and systems administration and interchanges frameworks proficiently and successfully with negligible or no effect on the earth. The objective of green computing is to diminish the utilization of hazardous materials, amplify energy proficiency during the items lifetime, and advance the recyclability of obsolete items and factory waste. Green computing can be accomplished by either Product Longevity Resource distribution or Virtualization or Power management. power is the bottleneck of improving the system execution. Among all industries, the information communication technology ICT industry is seemingly answerable for a bigger segment of the overall development in energy utilization. The objective of green cloud computing is to advance the recyclability or biodegradability of outdated items and factory waste by diminishing the utilization of hazardous materials and amplifying the energy productivity during the items lifetime. Stephen Fernandes "Energy Saving by Migrating Virtual Machine to Green Cloud Computing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30422.pdf Paper Url :https://www.ijtsrd.com/computer-science/distributed-computing/30422/energy-saving-by-migrating-virtual-machine-to-green-cloud-computing/stephen-fernandes

Green computing is characterized as the examination and practice of structuring, assembling, utilizing, and discarding PCs, servers, and related subsystems, for example, screens, printers, storage gadgets, and systems administration and interchanges frameworks proficiently and successfully with negligible or no effect on the earth. The objective of green computing is to diminish the utilization of hazardous materials, amplify energy proficiency during the items lifetime, and advance the recyclability of obsolete items and factory waste. Green computing can be accomplished by either Product Longevity Resource distribution or Virtualization or Power management. power is the bottleneck of improving the system execution. Among all industries, the information communication technology ICT industry is seemingly answerable for a bigger segment of the overall development in energy utilization. The objective of green cloud computing is to advance the recyclability or biodegradability of outdated items and factory waste by diminishing the utilization of hazardous materials and amplifying the energy productivity during the items lifetime. Stephen Fernandes "Energy Saving by Migrating Virtual Machine to Green Cloud Computing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30422.pdf Paper Url :https://www.ijtsrd.com/computer-science/distributed-computing/30422/energy-saving-by-migrating-virtual-machine-to-green-cloud-computing/stephen-fernandes

Quantum Computing at Google and in the Cloud An update on Google's quantum computing program and its open source tools. by Kevin D. Kissell At: FOSDEM 2019 https://video.fosdem.org/2019/AW1.121/google_qc.webm Google is a world leader in technology and a major contributor to a number of open source efforts. Google's quantum hardware and algorithms teams have achieved a number of quantum computing "firsts". This talk will present the Google quantum computing architecture, an update on current hardware implementations, and a description of the design and philosophy of Cirq, Google's open source programming language for near term "NISQ" (noisy intermediate-scale quantum) computers. Room: AW1.121 Scheduled start: 2019-02-02 16:55:00+01 Source: https://www.youtube.com/watch?v=pAug8JSfAvA Uploader: FOSDEM

Towards Predictable Cloud Computing – Andreas Polze, Hasso-Plattner-Institute The Cloud for University City Campus – Danilo Montesi, University of Bologna Cloud Computing Projects in Engineering – Harold Castro, Universidad de los Andes

Double auction (DA) algorithm is widely used for trading systems in cloud computing. Distinct buyers request different attributes for virtual machines. On the other hand, different sellers offer several types of virtual machines according to their correspondence bids. In DA, getting multiple equilibrium prices from distinct cloud providers is a difficult task, and one of the major problems is bidding prices for virtual machines, so we cannot make decisions with inconsistent data. To solve this problem, we need to find the best machine learning algorithm that anticipates the bid cost for virtual machines. Analyzing the performance of DA algorithm with machine learning algorithms is to predict the bidding price for both buyers and sellers. Therefore, we have implemented several machine learning algorithms and observed their performance on the bases of accuracy, such as linear regression (83%), decision tree regressor (77%), random forest (82%), gradient boosting (81%), and support vector regressor (90%). In the end, we observed that the Artificial Neural Network (ANN) provided an astonishing result. ANN has provided 97% accuracy in predicting bidding prices in DA compared to all other learning algorithms. It reduced the wastage of resources (VMs attributes) and soared both users' profits (buyers & sellers). Different types of models were analyzed on the bases of individual parameters such as accuracy. In the end, we found that ANN is effective and valuable for bidding prices for both users.

As cloud computing continues to proliferate, ensuring data security within cloud environments becomes increasingly critical. This paper explores the integration of blockchain technology as a means to enhance data security in cloud computing. Blockchain, with its decentralized and immutable ledger system, offers unique capabilities that address key security challenges such as data privacy, integrity, identity management, and compliance. Through a comprehensive analysis of blockchain's potential applications and integration strategies, this paper provides insights into how blockchain can bolster data security in cloud computing environments. Case studies and real-world implementations highlight successful integration efforts by major cloud service providers such as IBM, Microsoft, and Amazon. Additionally, this paper discusses the challenges and limitations associated with blockchain integration and explores future directions and emerging trends in the intersection of blockchain and cloud security. By leveraging blockchain technology, organizations can enhance their security posture in the cloud, ensuring the confidentiality, integrity, and availability of their sensitive data.

Cloud computing is a model for giving ondemand internet access to common pools of configurable computer assets (e.g., networks, computers, memory, apps, and applications) that might be instantly given and released with minimum administrative effort or participation from service providers. Clouds computer has risen to prominence as a key technical trend, with several experts anticipating that it would transform IT processes and the IT sector. Users may download software, storage, and software frameworks through the Internet using a variety of devices, including PCs, notebooks, cell phones, and PDAs, thanks to cloud services supplied by clouds computer companies. This article provides an introduction of clouds computer technologies installation techniques, classifications, and characteristics. The term "cloud computing" refers to a type of computer and communications technology that provides on-demand services. There is no precise meaning for the word Cloud Computing

This research paper aims at analysing the application of artificial intelligence and deep learning techniques in the cloud computing paradigm especially in virtualization and containerization. Since cloud computing has been rapidly integrated into organizations, it is necessary to address the problem of resource management. This study evaluates four deep learning models—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Autoencoders—across key performance metrics: which are accuracy, precision, recall, and F1 score, for example. The findings suggest that the CNN had the best accuracy of 92% for identifying performance bottlenecks, while the LSTM had the second-best accuracy of 91% for forecasting. The RNN and Autoencoder also had a good performance in terms of predicting resource utilization and detecting abnormal behavior respectively. Consequently, it is suggested that these algorithms can significantly enhance the operational, security, and resource management efficiency of cloud computing and these may be valuable for further research and practical applications

This research paper aims at analysing the application of artificial intelligence and deep learning techniques in the cloud computing paradigm especially in virtualization and containerization. Since cloud computing has been rapidly integrated into organizations, it is necessary to address the problem of resource management. This study evaluates four deep learning models—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Autoencoders—across key performance metrics: which are accuracy, precision, recall, and F1 score, for example. The findings suggest that the CNN had the best accuracy of 92% for identifying performance bottlenecks, while the LSTM had the second-best accuracy of 91% for forecasting. The RNN and Autoencoder also had a good performance in terms of predicting resource utilization and detecting abnormal behavior respectively. Consequently, it is suggested that these algorithms can significantly enhance the operational, security, and resource management efficiency of cloud computing and these may be valuable for further research and practical applications

Cryptographic keys are long strings of random bits generated using specialized algorithms and help secure data by making it unpredictable to any adversary. Cryptographic keys are used in various cryptographic algorithms in many domains, i.e., Cloud computing, Internet-of-Things (IoT), Fog computing, and others. The key generation algorithms are essential in cryptographic data encryption and decryption algorithms. This work proposed a cryptographic key generation algorithm based on Shannon entropy and the Salp Swarm algorithm (SSA) for generating randomized keys. The proposed Cryptographic Key Generation algorithm utilizes the dynamic movement of salps to create high-quality, robust, and randomized keys against attacks. The transfer function and quantization method convert a salp into a cryptographic key. The proposed Cryptographic Key Generation algorithm has been evaluated on four transfer functions against three state-of-the-art swarm intelligence metaheuristics, i.e., particle swarm optimization, BAT, and grey wolf optimization algorithms. The keys of eight different bit lengths, i.e., 512, 256, 192, 128, 96, 80, 64, were generated and evaluated due to their applications in the different encryption algorithms, i.e., AES, DES, PRESENT, SIMON, SPECK, and 3DES. The simulation study confirms that the proposed key generation algorithm effectively produces secure cryptographic keys.

Task scheduling is an essential component of any cloud computing architecture that seeks to cater to the requirements of its users in the most effective manner possible. It is essential in the process of assigning resources to new jobs while simultaneously optimising performance. Effective job scheduling is the only method by which it is possible to achieve the essential goals of any cloud computing architecture, including high performance, high profit, high utilisation, scalability, provision efficiency, and economy. This article gives a framework based on chaotic grey wolf optimization (CGWO) for efficiently scheduling tasks in cloud fog computing. Task scheduling is done with CGWO, ant colony optimization (ACO), and min-max algorithms. CloudSim is used to implement task scheduling algorithms. Makespan time required by CGWO algorithm for 500 tasks is 73.27 seconds. CGWO is taking minimum resources to accomplish the tasks in comparison to ACO and min-max methods. Response time of CGWO is also 3745.2 seconds. CGWO is performing better in terms of Makespan time, response time and resource utilization among the methods used in the experimental work. 

Industrial control systems (ICSs) provide important services in national critical infrastructure but are increasingly the subject of cyberattacks. The need for ease of maintenance and operational convenience encourages using cloud services, increasing their security vulnerabilities, and knowing what threats to expect that would help in defending cloud-based ICSs. This thesis tested an ICS honeypot (decoy system) called GridPot that was deployed in a third-party cloud environment and simulated a microgrid distribution system. We compared data from a GridPot instance deployed on an in-house server with three cloud-deployed GridPot instances with varying configurations. Overall results showed that the cloud-deployed GridPots had comparable traffic to the non-cloud GridPot, but it yielded less ICS-specific traffic, though what occurred appeared more deliberate. Nearly all attacks on the cloud-deployed GridPots showed little sophistication about ICS protocols. Our results further confirmed that cloud-based honeypot owners must maintain awareness of cloud service providers that recycle IP addresses to avoid exploits on previously used IP addresses. We conclude that ICS honeypots in the cloud are an effective tool for collecting cyberattack intelligence, and they do not appear to discourage attacks by being in the cloud.

An ever increasing number of customers might want to store their information to public cloud servers (PCSs) alongside the quick improvement of distributed computing. New security issues must be understood keeping in mind the end goal to enable more customers to process their information in broad daylight cloud. Right when the client is bound to get to PCS, he will name its middle person to process his data and exchange them. On the other hand, remote data trustworthiness checking is furthermore a basic security issue with no attempt at being subtle disseminated stockpiling. It impacts the clients to check whether their outsourced data is kept set up without downloading the whole data. From the security issues, we propose a novel intermediary situated information transferring and remote information uprightness checking model in personality based open key cryptography: character based intermediary arranged information transferring and remote information respectability checking in broad daylight cloud .We give the formal de?nition, framework model, and security demonstrates. At that point, a convention is outlined utilizing the bilinear pairings. The proposed convention is provably secure in view of the hardness of computational Dif?eHellman issue. Our convention is likewise efficient and ?exible. In light of the first customer's approval, the proposed convention can understand private remote information uprightness checking. J. Gopinadh"Data Outsourcing with Secure Data Auditing in Cloud Computing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12949.pdf http://www.ijtsrd.com/computer-science/other/12949/data-outsourcing-with-secure-data-auditing-in-cloud-computing/j-gopinadh

House Oversight Committee - Cloud Computing (Part 1) - On Thursday, July 1, 2010, the House Committee on Oversight and Government Reform and the Government Management, Organization, and Procurement Subcommittee held a hearing entitled, "Cloud Computing: Benefits and Risks of Moving Federal IT into the Cloud." The hearing took place at 10:00 a.m. in room 2154 Rayburn House Office Building. Video provided by the U.S. House of Representatives.

House Oversight Committee - Cloud Computing (Part 6) - On Thursday, July 1, 2010, the House Committee on Oversight and Government Reform and the Government Management, Organization, and Procurement Subcommittee held a hearing entitled, "Cloud Computing: Benefits and Risks of Moving Federal IT into the Cloud." The hearing took place at 10:00 a.m. in room 2154 Rayburn House Office Building. Video provided by the U.S. House of Representatives.

House Oversight Committee - Cloud Computing (Part 6) - On Thursday, July 1, 2010, the House Committee on Oversight and Government Reform and the Government Management, Organization, and Procurement Subcommittee held a hearing entitled, "Cloud Computing: Benefits and Risks of Moving Federal IT into the Cloud." The hearing took place at 10:00 a.m. in room 2154 Rayburn House Office Building. Video provided by the U.S. House of Representatives.

Maintaining an accurate Common Operational Picture (COP) is a strategic requirement for efficient and successful missions in both disaster response and battlefield scenarios. Past practices include utilizing cellular, radio, and computer-based communication methods and updating individual maps accordingly. A drawback of these practices has been the interoperability of these devices as well as accurate reporting and documentation among different entities of the effort. Recent advances in technology have led to the utilization of collaborative maps for maintaining a COP amongst command centers. Despite the advantages this technique offers, it does not address the difficulties surrounding receiving reports from field entities as well as ensuring that these entities also have good situational awareness. In this thesis, we will develop and demonstrate the feasibility of a cloud-based system, known herein as the Smartphone Assisted Readiness, Command, and Control System (SPARCCS), to advance the situational awareness capability of rapid response personnel. This system will allow for Android smartphones and a central web-based cloud application to communicate. We will use our web-based cloud application as the command center to effectively display information and communicate with Android smartphones to create a common operating picture, thereby improving situational awareness. The goal of this research is to explore smartphone capabilities in conjunction with cloud computing to determine how they can extend the benefits of collaborative maps to mobile users while simultaneously ensuring that command centers are receiving accurate, up-to-date reports from the field.

The Department of Defense (DoD) is planning an aggressive move toward cloud computing technologies. This concept has been floating around the private information technology sector for a number of years and has benefited organizations with cost savings, increased efficiencies, and flexibility by sharing computer resources through networked connections. The push for cloud computing has been driven by the 25 Point Implementation Plan to Reform Federal Information Technology Management that highlighted the shift to a cloud first policy. The cloud first policy has driven the DoD, specifically the Marine Corps, toward cloud computing technologies making this relatively new paradigm inevitable. The Marine Corps has provided its cloud computing guidance through its Private Cloud Computing Environment Strategy. However, the urgency for the Marine Corps to implement a cloud computing architecture that will support enhanced logistical systems in an expeditionary environment needs to be tempered by a comprehensive evaluation of current cloud computing technologies, virtualization technologies, and local versus remote logistical data types and sub-sets. This thesis seeks as its goal to explore and analyze current cloud computing architectures and virtualization technologies to determine and develop a cloud computing architecture that best supports expeditionary logistics for the Marine Corps.

Risky Business podcast features news and in-depth commentary from security industry luminaries. Hosted by award-winning journalist Patrick Gray, Risky Business has become a must-listen digest for information security professionals. This week's edition of Risky Business is sponsored by RSA Security and hosted by Vigabyte virtual hosting. In this week's podcast we take a look at cloud computing, which is all the rage all of a sudden. Andrew Walls from Gartner stops by to cut through the hype and talk about what cloud services could mean for the average CSO. One-time CANVAS developer and freelance security consultant Adam Boileau pops by to discuss the news and respond to the controversy over last week's Risky Business interview with Marcus Ranum. If you haven't heard, Marcus really had a go at exploit tools like CANVAS and CORE Impact. It really annoyed a lot of listeners, and this week we revisit the topic. ISP engineer Mark Newton also stops by to talk about the proposed "great firewall of Australia," and RSA's Greg Singh joins us to discuss the sudden swell in uptake of smart card technology. NOTE: At one point you'll hear me refer to Mark Newton as Matthew Newton. The mistake was mine... sorry, Mark!

This article is from Sensors (Basel, Switzerland) , volume 11 . Abstract Context-aware technologies can make e-learning services smarter and more efficient since context-aware services are based on the user’s behavior. To add those technologies into existing e-learning services, a service architecture model is needed to transform the existing e-learning environment, which is situation-aware, into the environment that understands context as well. The context-awareness in e-learning may include the awareness of user profile and terminal context. In this paper, we propose a new notion of service that provides context-awareness to smart learning content in a cloud computing environment. We suggest the elastic four smarts (E4S)—smart pull, smart prospect, smart content, and smart push—concept to the cloud services so smart learning services are possible. The E4S focuses on meeting the users’ needs by collecting and analyzing users’ behavior, prospecting future services, building corresponding contents, and delivering the contents through cloud computing environment. Users’ behavior can be collected through mobile devices such as smart phones that have built-in sensors. As results, the proposed smart e-learning model in cloud computing environment provides personalized and customized learning services to its users.

World-Language-Cloud-Computing dumped with WikiTeam tools.

Data-intensive computing consists of batch-processing workloads that scan massive data sets in parallel. The focus on data access, data movement, data ingest, and data production means that these workloads overwhelm the network and I/O capabilities of data centers and supercomputers. Major improvements in throughput are available by co-scheduling tasks that access the same data so that multiple tasks complete processing based on accessing and transferring the data a single time. Multiple tasks share I/O, network data transfer, cache space, and even computing with SIMD or vector processing. This talk will review the evolution of co-scheduling in data-intensive computing systems, including shared-scan scheduling for map/reduce workloads (Agrawal et al., VLDB 2008), data-driven batch processing for scientific databases (LifeRaft and JAWS), shared streaming-I/O for spatial workloads, and shared join processing for Pig programs and Nova workflows. ©2011 Microsoft Corporation. All rights reserved.

Cloud Computing 

The cloud framework is extensively employed in the medical industry for a broad range of applications including medical information storage, distribution, as well as administration. Given the advantages of cloud computing, several medical companies are exploring implementing such techniques to address various difficulties inside the medical sector. It evolved into an essential component of healthcare delivery. It may help medical companies concentrate on their activities, and medical assistance, including clinical management. This provides a safer approach for sharing confidential material with hospitals as well as third-party studies and medical institutes. Nevertheless, because the structure of cloud technology emerges as well as develops fast, especially theoretical and practical implications, significant legal/contractual, economical, customer satisfaction, connectivity, cybersecurity, and confidentiality problems remain in the research phase. In this article, we explain several cloud-based computing services, as well as implementation strategies, also highlight important problems. This study also focuses on safe and secure information exchange options within cloud technology for medical domains.

On 21 May 2013, DGSTJFD requested advice from the Canadian National Lead of The Technical Cooperation Panel (TTCP) Command, Control, Communications and Intelligence(C3I) Action Group 2 (AG2) on Cloud Computing regarding how cloud computing could be used in a tactical environment to garner important Command and Control (C2) and situational awareness information. In response to this request, and based on the recommendations from an AG2 Technical Report on coalition cloud computing, scientists in the Cyber Operations and Signals Warfare (COSW) Section of Defence Research and Development Canada (DRDC)contracted a preliminary study to assess the use of tactical clouds in enhancing warfighter effectiveness. The study produced a number of diverse use-cases, architectures, and scenarios for tactical cloud computing.

-Security and isolation in cloud environments-Cyber infrastructure security-Design of algorithms and techniques for real-time assuredness in cloud computing-Map-reduce task assignment with data locality constraint-Trustworthiness estimation for workflow completion-Application-aware cloud network resource allocation

In this video, we have discussed what is cloud and cloud computing, types of clouds and cloud computing services, advantage and disadvantage of cloud computing.

In this video, we have discussed what is cloud and cloud computing, types of clouds and cloud computing services, advantage and disadvantage of cloud computing.

IBSA-Cloud-Computing-Project dumped with WikiTeam tools.

Perkembangan teknologi informasi yang pesat telah mendorong adopsi cloud computing sebagai paradigma baru dalam pengelolaan sumber daya komputasi. Teknologi ini menawarkan fleksibilitas, efisiensi, dan skalabilitas tinggi melalui akses layanan berbasis internet yang dapat diakses kapan saja dan di mana saja. artikel ini bertujuan untuk memberikan pemahaman dasar mengenai arsitektur cloud computing, termasuk model layanan utama seperti Infrastructure as a Service (IaaS), Platform as a Service (PaaS), dan Software as a Service (SaaS). Dalam artikel ini, akan dibahas mengenai dasar-dasar arsitektur dan model layanan cloud computing, termasuk pengertian, jenis, karakteristik utama, cara kerja, manfaat, serta model layanannya.

Mobile cloud computing is envisioned as a promising approach to augment computation capabilities of mobile devices for emerging resource-hungry mobile applications. In this paper, we propose a game theoretic approach for achieving efficient computation offloading for mobile cloud computing. We formulate the decentralized computation offloading decision making problem among mobile device users as a decentralized computation offloading game. We analyze the structural property of the game and show that the game always admits a Nash equilibrium. We then design a decentralized computation offloading mechanism that can achieve a Nash equilibrium of the game and quantify its efficiency ratio over the centralized optimal solution. Numerical results demonstrate that the proposed mechanism can achieve efficient computation offloading performance and scale well as the system size increases.

The first ever HITB keynote panel discussion on the Future of Mobile Malware and Cloud Computing Security Source: https://www.youtube.com/watch?v=MEd-Fq0Mmfs Uploader: Hack In The Box Security Conference

Discrete exponential operation, such as modular exponentiation and scalar multiplication on elliptic curves, is a basic operation of many public-key cryptosystems. However, the exponential operations are considered prohibitively expensive for resource-constrained mobile devices. In this paper, we address the problem of secure outsourcing of exponentiation operations to one single untrusted server. Our proposed scheme (ExpSOS) only requires very limited number of modular multiplications at local mobile environment thus it can achieve impressive computational gain. ExpSOS also provides a secure verification scheme with probability approximately 1 to ensure that the mobile end-users can always receive valid results. The comprehensive analysis as well as the simulation results in real mobile device demonstrates that our proposed ExpSOS can significantly improve the existing schemes in efficiency, security and result verifiability. We apply ExpSOS to securely outsource several cryptographic protocols to show that ExpSOS is widely applicable to many cryptographic computations.

Cloud computing is one of the dispersed and effective computing models, which offers tremendous opportunity to address scientific issues with big scale characteristics. Despite having such a dynamic computing paradigm, it faces several difficulties and falls short of meeting the necessary quality of services (QoS) standards. For sustainable cloud computing workflow, QoS is very much required and need to be addressed. Recent studies looked on quantitative fault-tolerant programming to reduce the number of copies while still achieving the reliability necessity of a process on the heterogeneous infrastructure as a service (IaaS) cloud. In this study, we create an optimal replication technique (ORT) about fault tolerance as well as cost-driven mechanism and this is known as optimal replication technique with fault tolerance and cost minimization (ORT-FTC). Here ORT-FTC employs an iterative-based method that chooses the virtual machine and its copies that have the shortest makespan in the situation of specific tasks. By creating test cases, ORT-FTC is tested while taking into account scientific workflows like CyberShake, laser interferometer gravitational-wave observatory (LIGO), montage, and sipht. Additionally, ORT-FTC is shown to be only slightly improved over the current model in all cases.

The first ever HITB keynote panel discussion on the Future of Mobile Malware and Cloud Computing Security Source: https://www.youtube.com/watch?v=P3W9TxfSGmk Uploader: Hack In The Box Security Conference