Real-time Cloud Services Research Articles

A Light Weight Optimal Resource Scheduling Algorithm for Energy Efficient and Real Time Cloud Services

Abstract: Cloud computing has become an essential technology for providing various services to customers. Energy consumption and response time are critical factors in cloud computing systems. Therefore, scheduling algorithms play a vital role in optimizing resource usage and improving energy efficiency. In this paper, we propose a lightweight optimal scheduling algorithm for energy-efficient and real-time cloud services. Our algorithm considers the trade-off between energy consumption and response time and provides a solution that optimizes both factors. We evaluate our algorithm on a cloud computing testbed using the Cloud Sim simulation toolkit and compare it with load balancing round robin, ant colony, genetic algorithm, and analytical algorithm. The results show that our proposed algorithm outperforms these algorithms in terms of energy efficiency and response time.

Social Media Application Development in Android with Firebase

Sharing thoughts and information through internet has become crucial these days. An online writing enables the users to communicate with other people in a fast and convenient way. Considering this, the online writing application must be able to share the content or any other useful text in a faster way with minimum delay or with no delay. Firebase is one of the platform that provides a real-time database and cloud services which allows the developer to create these applications with ease. Instant writing can be considered as a platform to maintain communication. Android offers a better platform to develop various applications for instant writing compared to other platforms such as iOS. The main objective of this paper is to present a software application where people across the globe with similar interests can connect with each other and share their thoughts, insight, and emotions. The application developed will enable the writers to share their amazing content with the readers and also with the fellow writers. This application will be based on Android with the backend provided by Google Firebase.

A Lightweight Optimal Scheduling Algorithm for Energy-Efficient and Real-Time Cloud Services

To support ever-chainging user needs such as large storage volumes, web search, and high-performance computing, numerous companies have expanded their systems to cloud computing servers. Cloud environment systems generally consume large amounts of electrical power, leading to tremendously high operational costs. In addition, they require computing infrastructures to run various real-time applications such as financial analysis, cloud gaming, and web-based real-time services. To represent performance guarantees, the negotiated agreements in real-time computing, expressed as deadline (or latency), can be specified by <i>service level agreements</i> of cloud services between users and cloud server providers. Thus, a number of research works have started focusing on reducing the energy consumption and simultaneously satisfying the temporal constraint in a cloud environment. Although we previously proposed an optimal real-time scheduling algorithm for multiprocessors, it is difficult to use it for cloud environments handling a large number of cloud services because of the high computational complexity of <inline-formula> <tex-math notation="LaTeX">$\Omega (N^{3}logN)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> is the number of tasks. Thus, we introduce a real-time task scheduling algorithm for cloud computing servers, which alleviates the computational complexity of <inline-formula> <tex-math notation="LaTeX">$O(N^{2})$ </tex-math></inline-formula> from the complexity of the previous algorithm using a novel flow network-based optimization method. To the best of our knowledge, our scheduling algorithm in a cloud environment, which ensures optimality for real-time tasks and achieves energy savings using dynamic power management simultaneously, is the first in the problem domain. We show that the proposed scheduling algorithm guarantees an optimal schedule for real-time tasks and achieves energy savings simultaneously. Our experimental results show that the proposed algorithm outperforms the latest existing algorithms in terms of both time complexity and energy efficiency.

An analytical approach to real-time cloud services on IoT-based applications for smart city planning

This paper illustrates the cloud-based services on next generation smart living technology, first by providing the concept of smart technology of the next generation, followed by the wide area of application in this sector. Next, the current generation technological enhancement, IoT (Internet of Things) is brought into the smart living. The entire IoT-based Smart Application sector has then been divided into five categories: Power and Energy Sector, Transport Sector, Healthcare Sector, Retail Sector and Education Sector. Each sector has been analysed in detail with respect to possible real-time cloud services, which can be incorporated into the respective area. Consequently, existing cloud services, current trends, limitations and future scopes have been discussed followed by recommendations in each section. For example, for IoT based application in power and energy sector, contribution includes finding the limitations of cloud service have been found such as unoptimised communication scheme, data complexity, interoperability, cyber security risk and data integrity issue. To address these limitations respectively, a policy to ensure backward and horizontal compatibility, proposal to increase local processing, data compression and prediction, implementation of big data techniques, authentication and encryption, planning and redundancy have been included as recommendation. For other four categories, an intense analysis has been carried out in similar fashion. Finally, the recommendation has been added for each category for the next barrier scope of research.

An analytical approach to real-time cloud services on IoT-based applications for smart city planning

An analytical approach to real-time cloud services on IoT-based applications for smart city planning

An efficient management scheme of blockchain-based cloud user information using probabilistic weighting

As the number of users using cloud services has increased in recent years, the number of users leaving the group or changing their personal information is often the case. In this paper, a blockchain-based cloud user information management scheme is proposed using stochastic weights to confirm that the information of users receiving cloud services has not been tampered with. The proposed scheme uses color keys to reliably distinguish blockchains of user information in a cloud environment. The quality of service is improved by pasting user attribute information generated using color keys to the front of the blockchain. The proposed scheme uses different color key information and attribute information, so they have the advantage of effectively processing user information in different cloud environments. It also supports lower overhead and higher efficiency compared to traditional techniques that individually add authentication keys to users by authenticating information from multiple users who receive cloud services with color keys. In addition, the proposed scheme allows the provision of reliable real-time cloud services by creating a stochastic sum of weights for the first and last users that make up the cloud. As a result of the performance analysis, the proposed scheme reduced the time of blockchain generation by $$7.9\%$$ on average by linking user information to the probability value of user information. Efficiency in managing blockchain-based user information over overlay networks was up to $$11.5\%$$ better than using RSA alone, with an average $$7.7\%$$ lower overhead.

Accurate Fault Location using Deep Neural Evolution Network in Cloud Data Center Interconnection

Due to the threat of failure and the discrete distribution of data center users, the research of distributed cloud data center provides real-time cloud services with robustness, reliability and security. Faced with data center interconnection, network failures cause mass services delay and interruption, which do a great damage to cloud computing. Many researchers have studied fault location methods in data center interconnection, which are easy to trap in local optimum limited by search capability and reduce the accuracy of location, especially when confronted with large-scale alarm information. In this article, the deep neural evolution network is introduced to extract deep-hidden fault features from massive collected alarm information in cloud data center interconnection. It has the prominent capacity of global search without the constraint of gradient to realize the breakthrough of fault location accuracy. The fault location method based on deep neural evolution network (FL-DNEN) is applied which uses the alarm set and suspicious scope of fault getting from fault propagation model as input and export deterministic faults accurately. The emulations demonstrate that the proposed method dramatically improves the accuracy of fault location to 92 percent with large-scale alarm information, which improves the resilience of cloud data center interconnection dramatically.

Knowledge Transfer Using User-Generated Data within Real-Time Cloud Services

When automatic speech recognition (ASR) is provided as a cloud service, it is easy to collect voice and application domain data from users. Harnessing these data will facilitate the provision of more personalized services. In this paper, we demonstrate our transfer learning-based knowledge service that built with the user-generated data collected through our novel system that deliveries personalized ASR service. First, we discuss the motivation, challenges, and prospects of building up such a knowledge-based service-oriented system. Second, we present a Quadruple Transfer Learning (QTL) method that can learn a classification model from a source domain and transfer it to a target domain. Third, we provide an overview architecture of our novel system that collects voice data from mobile users, labels the data via crowdsourcing, utilises these collected user-generated data to train different machine learning models, and delivers the personalised real-time cloud services. Finally, we use the E-Book data collected from our system to train classification models and apply them in the smart TV domain, and the experimental results show that our QTL method is effective in two classification tasks, which confirms that the knowledge transfer provides a value-added service for the upper-layer mobile applications in different domains.

Energy-Efficient Adaptive Resource Management for Real-Time Vehicular Cloud Services

Providing real-time cloud services to Vehicular Clients (VCs) must cope with delay and delay-jitter issues. Fog computing is an emerging paradigm that aims at distributing small-size self-powered data centers (e.g., Fog nodes) between remote Clouds and VCs, in order to deliver data-dissemination real-time services to the connected VCs. Motivated by these considerations, in this paper, we propose and test an energy-efficient adaptive resource scheduler for Networked Fog Centers (NetFCs). They operate at the edge of the vehicular network and are connected to the served VCs through Infrastructure-to-Vehicular (I2V) TCP/IP-based single-hop mobile links. The goal is to exploit the locally measured states of the TCP/IP connections, in order to maximize the overall communication-plus-computing energy efficiency, while meeting the application-induced hard QoS requirements on the minimum transmission rates, maximum delays and delay-jitters. The resulting energy-efficient scheduler jointly performs: (i) admission control of the input traffic to be processed by the NetFCs; (ii) minimum-energy dispatching of the admitted traffic; (iii) adaptive reconfiguration and consolidation of the Virtual Machines (VMs) hosted by the NetFCs; and, (iv) adaptive control of the traffic injected into the TCP/IP mobile connections. The salient features of the proposed scheduler are that: (i) it is adaptive and admits distributed and scalable implementation; and, (ii) it is capable to provide hard QoS guarantees, in terms of minimum/maximum instantaneous rates of the traffic delivered to the vehicular clients, instantaneous rate-jitters and total processing delays. Actual performance of the proposed scheduler in the presence of: (i) client mobility; (ii) wireless fading; and, (iii) reconfiguration and consolidation costs of the underlying NetFCs, is numerically tested and compared against the corresponding ones of some state-of-the-art schedulers, under both synthetically generated and measured real-world workload traces.

Energy-Efficient Fault-Tolerant Scheduling Algorithm for Real-Time Tasks in Cloud-Based 5G Networks

Green computing has become a hot issue for both academia and industry. The fifth-generation (5G) mobile networks put forward a high request for energy efficiency and low latency. The cloud radio access network provides efficient resource use, high performance, and high availability for 5G systems. However, hardware and software faults of cloud systems may lead to failure in providing real-time services. Developing fault tolerance technique can efficiently enhance the reliability and availability of real-time cloud services. The core idea of fault-tolerant scheduling algorithm is introducing redundancy to ensure that the tasks can be finished in the case of permanent or transient system failure. Nevertheless, the redundancy incurs extra overhead for cloud systems, which results in considerable energy consumption. In this paper, we focus on the problem of how to reduce the energy consumption when providing fault tolerance. We first propose a novel primary-backup-based fault-tolerant scheduling architecture for real-time tasks in the cloud environment. Based on the architecture, we present an energy-efficient fault-tolerant scheduling algorithm for real-time tasks (EFTR). EFTR adopts a proactive strategy to increase the system processing capacity and employs a rearrangement mechanism to improve the resource utilization. Simulation experiments are conducted on the CloudSim platform to evaluate the feasibility and effectiveness of EFTR. Compared with the existing fault-tolerant scheduling algorithms, EFTR shows excellent performance in energy conservation and task schedulability.

Middleware strategies for clouds and grids in e‐Science

Middleware strategies for clouds and grids in e‐Science