Efficient task scheduling is considered as one of the main critical challenges in cloud computing. Task scheduling is an NP-complete problem, so finding the best solution is challenging, particularly for large task sizes. In the cloud computing environment, several tasks may need to be efficiently scheduled on various virtual machines by minimizing makespan and simultaneously maximizing resource utilization. We present a novel hybrid antlion optimization algorithm with elite-based differential evolution for solving multi-objective task scheduling problems in cloud computing environments. In the proposed method, which we refer to as MALO, the multi-objective nature of the problem derives from the need to simultaneously minimize makespan while maximizing resource utilization. The antlion optimization algorithm was enhanced by utilizing elite-based differential evolution as a local search technique to improve its exploitation ability and to avoid getting trapped in local optima. Two experimental series were conducted on synthetic and real trace datasets using the CloudSim tool kit. The results revealed that MALO outperformed other well-known optimization algorithms. MALO converged faster than the other approaches for larger search spaces, making it suitable for large scheduling problems. Finally, the results were analyzed using statistical t-tests, which showed that MALO obtained a significant improvement in the results.

A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments

Models describing the transient behavior of phase-change energy storage (PCES) units are presented. Simulation techniques are used in conjunction with these models to determine the performance of solar heating systems utilizing PCES. Both air-based and liquid-based systems are investigated. The effects of storage capacity, storage unit heat transfer characteristics, collector area and location on the system performance are investigated for systems utilizing sodium sulfate decahydrate and paraffin wax as storage media. Optimum ranges of storage sizes are recommended on the basis of systems' thermal performance. Comparison is made between systems utilizing PCES and those using sensible heat storage, viz. rock beds in air-based systems and water tanks in liquid-based systems. The variation of the solar supplied fraction of load with storage size and collector area is given for systems utilizing both types of storage. The effects of location and collector energy loss coefficient on the relative performance of PCES and sensible heat storage are also investigated.

Effects of phase-change energy storage on the performance of air-based and liquid-based solar heating systems

Autonomic computing investigates how systems can achieve (user) specified control outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments. 

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AI for Next Generation Computing: Emerging Trends and Future Directions

In recent years, cloud computing technology has attracted extensive attention from both academia and industry. The popularity of cloud computing was originated from its ability to deliver global IT services such as core infrastructure, platforms, and applications to cloud customers over the web. Furthermore, it promises on-demand services with new forms of the pricing package. However, cloud job scheduling is still NP-complete and became more complicated due to some factors such as resource dynamicity and on-demand consumer application requirements. To fill this gap, this paper presents a modified Harris hawks optimization (HHO) algorithm based on the simulated annealing (SA) for scheduling jobs in the cloud environment. In the proposed HHOSA approach, SA is employed as a local search algorithm to improve the rate of convergence and quality of solution generated by the standard HHO algorithm. The performance of the HHOSA method is compared with that of state-of-the-art job scheduling algorithms, by having them all implemented on the CloudSim toolkit. Both standard and synthetic workloads are employed to analyze the performance of the proposed HHOSA algorithm. The obtained results demonstrate that HHOSA can achieve significant reductions in makespan of the job scheduling problem as compared to the standard HHO and other existing scheduling algorithms. Moreover, it converges faster when the search space becomes larger which makes it appropriate for large-scale scheduling problems.

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Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm.

The cloud computing paradigm, as a novel computing resources delivery platform, has significantly impacted society with the concept of on-demand resource utilization through virtualization technology. Virtualization enables the usage of available physical resources in a way that multiple end-users can share the same underlying hardware infrastructure. In cloud computing, due to the expectations of clients, as well as on the providers side, many challenges exist. One of the most important nondeterministic polynomial time (NP) hard challenges in cloud computing is resource scheduling, due to its critical impact on the cloud system performance. Previously conducted research from this domain has shown that metaheuristics can substantially improve cloud system performance if they are used as scheduling algorithms. This paper introduces a hybridized whale optimization algorithm, that falls into the category of swarm intelligence metaheuristics, adapted for tackling the resource scheduling problem in cloud environments. To more precisely evaluate performance of the proposed approach, original whale optimization was also adapted for resource scheduling. Considering the two most important mechanisms of any swarm intelligence algorithm (exploitation and exploration), where the efficiency of a swarm algorithm depends heavily on their adjusted balance, the original whale optimization algorithm was enhanced by addressing its weaknesses of inappropriate exploitation–exploration trade-off adjustments and the premature convergence. The proposed hybrid algorithm was first tested on a standard set of bound-constrained benchmarks with the goal to more accurately evaluate its performance. After, simulations were performed using two different resource scheduling models in cloud computing with real, as well as with artificial data sets. Simulations were performed on the robust CloudSim platform. A hybrid whale optimization algorithm was compared with other state-of-the-art metaheurisitcs and heuristics, as well as with the original whale optimization for all conducted experiments. Achieved results in all simulations indicate that the proposed hybrid whale optimization algorithm, on average, outperforms the original version, as well as other heuristics and metaheuristics. By using the proposed algorithm, improvements in tackling the resource scheduling issue in cloud computing have been established, as well enhancements to the original whale optimization implementation.

Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm

A comprehensive survey for scheduling techniques in cloud computing

Internet of Things (IoT) is a new buzzword in information technology where real-world physical objects are made smart by integrating them with internet-enabled technologies. The things can sense information around them, communicate the sensed information over some protocol and employ the information to solve real-life problems. In IoT, several technologies are integrated under a common umbrella so that they can connect and exchange data over a network protocol. A huge amount of data is generated from diverse geographical locations with the consequent urge for fast aggregation of overall sensed information, leading to an increase in the need to store and process such data in a more efficient and effective manner. The traditional fields of embedded systems, WSN, real-time analytics, automation system, machine learning and others all contribute to enabling the IoT. This article is focused on discussing the various IoT technologies, protocols and their application and usage in our daily life. It also summarizes the current state-of-the-art IoT architecture in various spheres conventionally and all related terminologies that will give the forthcoming researchers a glimpse of IoT as a whole.

A review of Internet of Things: qualifying technologies and boundless horizon

Use of renewable energy and integration of existing technologies are the areas of research which need more ideas and analysis, so that diminishing energy resources and technological competitiveness can be confronted. Parabolic trough collectors (PTCs) are one of those technologies that relate to the above issues and need more attention. PTC is a member of concentrating solar power technology that aims at conversion of solar energy to thermal energy for purposes like electricity generation and steam generation for different applications. Other members of concentrating solar power technology are (i) linear Fresnel reflectors, (ii) central receiver (or solar tower) system with heliostat reflectors and (iii) parabolic dish. In this paper, only PTC which is the most established and proven technology available today for collecting solar energy is discussed with emphasis on its modeling. Based on this, an exhaustive review of articles published in the area of modeling and simulation of PTC with an appropriate focus on its applications has been made, and future directions are provided.

A Review of Parabolic Trough Collector and Its Modeling

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Anubhav Goel

Papers

A comprehensive survey for scheduling techniques in cloud computing

A review of Internet of Things: qualifying technologies and boundless horizon

A Review of Parabolic Trough Collector and Its Modeling

A review of the artificial neural network based modelling and simulation approaches applied to optimize reverse osmosis desalination techniques

Solar thermal system—an insight into parabolic trough solar collector and its modeling