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Cloud computing is becoming a very popular form of distributed computing, in which digital resources are shared via the Internet. The user is provided with an overview of many available resources. Cloud providers want to get the most out of their resources, and users are inclined to pay less for better performance. Task scheduling is one of the most important aspects of cloud computing. In order to achieve high performance from cloud computing systems, tasks need to be scheduled for processing by appropriate computing resources. The large search space of this issue makes it an NP-hard problem, and more random search methods are required to solve this problem. Multiple solutions have been proposed with several algorithms to solve this problem until now. This paper presents a hybrid algorithm called GSAGA to solve the Task Scheduling Problem (TSP) in cloud computing. Although it has a high ability to search the problem space, the Genetic Algorithm (GA) performs poorly in terms of stability and local search. It is therefore possible to create a stable algorithm by combining the general search capacities of the GA with the Gravitational Search Algorithm (GSA). Our experimental results indicate that the proposed algorithm can solve the problem with higher efficiency compared with the state-of-the-art. [ABSTRACT FROM AUTHOR]

In cloud computing, the major task is to make efficient use of resources. The growth in cloud environment opens door for vast research. Placement of virtual machine is a main problem in cloud environment. The ideal placement of virtual machine leads to power effectiveness and asset usage in distributed cloud computing. A multi-objective krill herd technique is proposed in this paper for placement of virtual machine. The objective is to effectively acquire an arrangement of non-dominated solutions that the entire limit add up to asset wastage and power utilization. The work is analysed with the examples from the literature. The experimental outcomes are compared with the existing multi-objective genetic procedure and multi-objective ant colony algorithm. Also, the results are compared with the 2 algorithms, First Fit Decreasing algorithm and Simplified Ant Colony algorithm. The experimental results shows that the proposed method is more efficient when compared to other existing methods. [ABSTRACT FROM AUTHOR]

Cloud is a multi-tenant paradigm providing resources as a service. With its easily available computing infrastructure, researchers are adopting cloud for experimental purposes. However, using the platform efficiently for parallel and distributed simulations comes with new challenges. One such challenge is that the simulations comprise logical processes executing on distributed nodes, traditionally, organized in a sequential pattern. This placement strategy leads to delays as frequently communicating processes might get placed farther from one another. In this paper, we proposed a framework to facilitate implementation and evaluation of process placement algorithms inside a three-tier cloud data center. Furthermore, we used the framework to test different process placement strategies based on classical clustering techniques, as well as, our proposed efficient locality-aware placement algorithm. Our evaluation results show a performance gain of 14.5 % for the algorithm in comparison with sequential process placement used in practice. [ABSTRACT FROM AUTHOR]

In order to promote the integrated application of the Internet of Things and cloud platform computing technology in intelligent manufacturing system, a cloud platform of cutting database was established in this paper. With the help of multi-sensor and distributed storage technology, mathematics model and data mining algorithm, functions of fast accessing and storing cutting data, intelligent modeling and analysis, efficient mining and optimizing processing parameters can be realized in the system, solving the key problems of analyzing cutting force and controlling surface quality in turning difficult-to-cut materials. Data equipment and users in the system were connected as a whole through wireless network, which can provide intelligent service of data querying, modeling, and optimizing through the cloud server. The study made up for the drawbacks in current intelligent database system such as oversimplified platform architecture, low level of intelligence, and low utilization of historical data, providing an efficient method of managing and applying big data in cutting process for the production system. [ABSTRACT FROM AUTHOR]

In this work, we introduce slot selection and co-allocation algorithms for parallel jobs in distributed computing with non-dedicated and heterogeneous resources. A single slot is a time span that can be assigned to a task, which is a part of a job. The job launch requires a co-allocation of a specified number of slots starting synchronously. The challenge is that slots associated with different resources of distributed computational environments may have arbitrary start and finish points that do not match. Some existing algorithms assign a job to the first set of slots matching the resource request without any optimization (the first fit type), while other algorithms are based on an exhaustive search. In this paper, algorithms for effective slot selection of linear complexity on an available slots number are studied and compared with known approaches. The novelty of the proposed approach consists of allocating alternative sets of slots. It provides possibilities to optimize job scheduling. [ABSTRACT FROM AUTHOR]