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Cloud computing is a network model of on-demand access for sharing configurable computing resource pools. Compared with conventional service architectures, cloud computing introduces new security challenges in secure service management and control, privacy protection, data integrity protection in distributed databases, data backup, and synchronization. Blockchain can be leveraged to address these challenges, partly due to the underlying characteristics such as transparency, traceability, decentralization, security, immutability, and automation. We present a comprehensive survey of how blockchain is applied to provide security services in the cloud computing model and we analyze the research trends of blockchain-related techniques in current cloud computing models. During the reviewing, we also briefly investigate how cloud computing can affect blockchain, especially about the performance improvements that cloud computing can provide for the blockchain. Our contributions include the following: (i) summarizing the possible architectures and models of the integration of blockchain and cloud computing and the roles of cloud computing in blockchain; (ii) classifying and discussing recent, relevant works based on different blockchain-based security services in the cloud computing model; (iii) simply investigating what improvements cloud computing can provide for the blockchain; (iv) introducing the current development status of the industry/major cloud providers in the direction of combining cloud and blockchain; (v) analyzing the main barriers and challenges of integrated blockchain and cloud computing systems; and (vi) providing recommendations for future research and improvement on the integration of blockchain and cloud systems.

Hardware-accelerated cloud computing systems based on FPGA chips (FPGA cloud) or ASIC chips (ASIC cloud) have emerged as a new technology trend for power-efficient acceleration of various software applications. However, the operating systems and hypervisors currently used in cloud computing will lead to power, performance, and scalability problems in an exascale cloud computing environment. Consequently, the present study proposes a parallel hardware hypervisor system that is implemented entirely in special-purpose hardware, and that virtualizes application-specific multi-chip supercomputers, to enable virtual supercomputers to share available FPGA and ASIC resources in a cloud system. In addition to the virtualization of multi-chip supercomputers, the system’s other unique features include simultaneous migration of multiple communicating hardware tasks, and on-demand increase or decrease of hardware resources allocated to a virtual supercomputer. Partitioning the flat hardware design of the proposed hypervisor system into multiple partitions and applying the chip unioning technique to its partitions, the present study introduces a cloud building block chip that can be used to create FPGA or ASIC clouds as well. Single-chip and multi-chip verification studies have been done to verify the functional correctness of the hypervisor system, which consumes only a fraction of (10%) hardware resources.

Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous in recent years. This has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems. However, deploying machine learning models on such end-devices is nearly impossible. A typical solution involves offloading data to external computing systems (such as cloud servers) for further processing but this worsens latency, leads to increased communication costs, and adds to privacy concerns. To address this issue, efforts have been made to place additional computing devices at the edge of the network, i.e., close to the IoT devices where the data is generated. Deploying machine learning systems on such edge computing devices alleviates the above issues by allowing computations to be performed close to the data sources. This survey describes major research efforts where machine learning systems have been deployed at the edge of computer networks, focusing on the operational aspects including compression techniques, tools, frameworks, and hardware used in successful applications of intelligent edge systems.

E-commerce has changed the buying and selling process and the way people interact via the internet. One company that uses e-commerce is PT Patriot Memory Indonesia. PT Patriot Memory Indonesia sells well-known computer peripherals, including the Solid State Drive (SSD). PT Patriot Memory Indonesia wants to analyze customer feedback regarding SSD products sold in e-commerce, namely Shopee by using Text Network Analysis (TNA) which is one part of social computing. Social computing is a science that focuses on social behavior and social contexts using computing systems. One of the tools of social computing, namely Text Network Analysis (TNA), is a research technique that focuses on identifying and comparing network relationships between words, sentences, and systems to model interactions that generate new knowledge or information. In this study, TextNetwork Analysis will show consumer perceptions through the feedback it provideson buyer reviews. The opinions expressed by consumers in buyer reviews can be analyzed so that they can connect each word and create associations of consumer perceptions of a product. Thus, it can be seen the aspects that must be addressed by the company to improve consumer perceptions. The problem analyzed is the development of social computing in analyzing big data. Can the company take advantage of this information so that they know the perceptions of their consumers through the information in the customer feedback at Shopee. Through Text Network Analysis in social computing, researchers will know the association of each word of consumer perception and can see the perception that has the highest degree of a product and see its relationship with other perceptions. This study looks at consumer perceptions of Patriot SSD products at Shopee. The results of this study can help provide customer feedback information to PT Patriot Memory Indonesia. 

As a joint effort from various communities involved in the Worldwide LHC Computing Grid, the Operational Intelligence project aims at increasing the level of automation in computing operations and reducing human interventions. The distributed computing systems currently deployed by the LHC experiments have proven to be mature and capable of meeting the experimental goals, by allowing timely delivery of scientific results. However, a substantial number of interventions from software developers, shifters, and operational teams is needed to efficiently manage such heterogenous infrastructures. Under the scope of the Operational Intelligence project, experts from several areas have gathered to propose and work on “smart” solutions. Machine learning, data mining, log analysis, and anomaly detection are only some of the tools we have evaluated for our use cases. In this community study contribution, we report on the development of a suite of operational intelligence services to cover various use cases: workload management, data management, and site operations.

Abstract Cloud computing is an emerging paradigm that provides hardware, platform and software resources as services over the internet in a pay-as-you-go model. It is being increasingly used for hosting and executing service-based business processes. These business processes are exposed to dynamic evolution during their life-cycle due to the highly dynamic evolution of cloud environments. The main adopted technique is to couple cloud computing with autonomic management in order to build autonomic computing systems. Almost all the existing approaches on autonomic computing have been focused on modeling and implementing autonomic mechanisms without paying any attention to the optimization of the autonomic management cost. Therefore, in this paper, we propose a novel approach based on binary linear program for determining the optimal allocation of cloud resources to manage a service-based business process which guarantees the specific requirements of customers and minimizes the management monetary cost. Then, to validate our approach under realistic conditions and inputs, we extend the CloudSim simulator to model and simulate the behaviour of business processes and their management in a cloud environment. Experiments conducted on two real datasets highlight the effectiveness of our approach.