Machine Resource Allocation Research Articles

Efficient resource allocation in cloud environment using SHO-ANN-based hybrid approach

The cloud computing paradigm provides services to users in an on-demand fashion using high-speed Internet. This Internet-based computing paradigm provides resources on a rent basis without any fault. Virtual machine resource allocation is one of the challenging concerns in a cloud computing environment. The existing static, dynamic, and Meta-Heuristic approaches provide the solution to the virtual machine allocation problem. These techniques stuck with the local optimal solution. The slow convergence rate leads to the optimal solution locally and fails to provide the optimal solution Globally. This manuscript proposes a hybrid Spotted Hyena optimizer and artificial neural network, named the SHO-ANN technique, to provide a solution to the virtual machine assignment problem. The presented hybrid technique is evaluated and analyzed using performance metrics “Energy Consumption (Kwh) (8.54%), Host Utilization (24.8%), Average Execution Time(ms) (26.33%), SLA Violations (1.33%), and Number of Migrations (Counts) (19.73%)”. The spotted hyena optimizer is used to provide the vast data set to the ANN model for better accuracy. The hybrid approach provides an optimal solution globally with high convergence. The experimental results exhibit that the SHO-ANN outperforms the IqMc, SHO, and Genetic approaches using real workload scenarios and fabricated scenarios.

Smart city implementation based on Internet of Things integrated with optimization technology

A smart city is a system that uses technology means to create, implement, and promote environmentally friendly solutions to the problems caused by growing urbanisation. The smart city is a key answer that will allow us to continue with our regular lives during and after the epidemic. To better serve its citizens, a smart city takes advantage of modern technology and innovative solutions in areas including healthcare, educational, energy provision, transport, and communications. Applications of the Internet of Things have led to the development of "Smart Cities" all around the globe. In order to further the development of smart cities toward effective sharing and interconnection, this study will examine the smart city data technology based on the Internet of Things (IoT) and cloud computing (CC). Initially, we gather IoT data based on the smart cities. The gathered data is normalised. Machine learning sets the way for the IoT, which allows machines to communicate with one another without any human intervention. So we utilize machine learning algorithm called Advanced Random Forest (ARF). For multi-criteria optimization, we suggest Adaptive cloud computing virtual machine resource allocation technique (ACC-VMRA). We analyse and simulate the use of IoT and CC technologies in a smart city to verify its efficacy. The experimental finding demonstrates the recommended optimization algorithm's superior efficiency to other currently used techniques.

Virtual Machine Resource Allocation Optimization in Cloud Computing Based on Multiobjective Genetic Algorithm.

Cloud computing is an important milestone in the development of distributed computing as a commercial implementation, and it has good prospects. Infrastructure as a service (IaaS) is an important service mode in cloud computing. It combines massive resources scattered in different spaces into a unified resource pool by means of virtualization technology, facilitating the unified management and use of resources. In IaaS mode, all resources are provided in the form of virtual machines (VM). To achieve efficient resource utilization, reduce users' costs, and save users' computing time, VM allocation must be optimized. This paper proposes a new multiobjective optimization method of dynamic resource allocation for multivirtual machine distribution stability. Combining the current state and future predicted data of each application load, the cost of virtual machine relocation and the stability of new virtual machine placement state are considered comprehensively. A multiobjective optimization genetic algorithm (MOGANS) was designed to solve the problem. The simulation results show that compared with the genetic algorithm (GA-NN) for energy saving and multivirtual machine redistribution overhead, the virtual machine distribution method obtained by MOGANS has a longer stability time. Aiming at this shortage, this paper proposes a multiobjective optimization dynamic resource allocation method (MOGA-C) based on MOEA/D for virtual machine distribution. It is illustrated by experimental simulation that moGA-D can converge faster and obtain similar multiobjective optimization results at the same calculation scale.

Fair and Stable Matching Virtual Machine Resource Allocation Method

In order to unify the management and scheduling of cloud resources, cloud platforms use virtualization technology to re-integrate multiple computing resources in the cloud and build virtual units on physical machines to achieve dynamic provisioning of resources by configuring virtual units of various sizes. Therefore, how to reasonably determine the mapping relationship between virtual units and physical machines is an important research topic for cloud resource scheduling. In this paper, we propose a fair cloud virtual machine resource allocation method of using the stable matching theory. Our allocation method considers the allocation of resources from both user’s demand and cloud computing resource provider’s request. When multiple users apply for resources, firstly select a user by user priority, and then deal with this user’s task. Because the user priority is dynamic, so as to avoid a user’s long-term share of resources. This strategy makes user task scheduling is relatively fair. On the basis of weighing the fair allocation of user resources, the stable matching between physical machines and virtual machines is achieved. Our simulation experiments especially given that the main focus of the paper is not to develop a very novel algorithm, but to demonstrate our virtual machine resource allocation method, which effectively improves the average utilization rate of computing resources and reduces the operating costs of cloud providers.

Application of Cloud Computing Technology in Information System Construction

With the rapid development and improvement of information technology, information system construction has new requirements and goals. Cloud computing technology is an inevitable choice in the construction of information system. By taking cloud computing technology as the basic structure framework, the phenomenon of data information island can be broken. The purpose of this paper is to study the application of cloud computing in the construction of information system. Taking the management mode of university campus as an example, based on the research of cloud computing technology, virtual machine technology and Internet of things technology, this paper discusses the data resource management algorithm of smart campus. Aiming at the scheduling model, strategy and research objectives of cloud computing resources in data center, this paper proposes a method of initial allocation and adaptive dynamic scheduling of virtual machine resources. According to the multi-dimensional vector characteristics of virtual machine, this paper proposes a resource allocation algorithm based on multi-objective evolution, and gives four important strategies. The experimental results show that the four advantages and strategies can effectively reduce the number of virtual machine migration and reduce the adverse impact on the overall performance of cloud computing core network.

Smart city information processing under internet of things and cloud computing.

This study is to explore the smart city information (SCI) processing technology based on the Internet of Things (IoT) and cloud computing, promoting the construction of smart cities in the direction of effective sharing and interconnection. In this study, a SCI system is constructed based on the information islands in the smart construction of various fields in smart cities. The smart environment monitoring, smart transportation, and smart epidemic prevention at the application layer of the SCI system are designed separately. A multi-objective optimization algorithm for cloud computing virtual machine resource allocation method (CC-VMRA method) is proposed, and the application of the IoT and cloud computing technology in the smart city information system is further analysed and simulated for the performance verification. The results show that the multi-objective optimization algorithm in the CC-VMRA method can greatly reduce the number of physical servers in the SCI system (less than 20), and the variance is not higher than 0.0024, which can enable the server cluster to achieve better load balancing effects. In addition, the packet loss rate of the Zigbee protocol used by the IoT gateway in the SCI system is far below the 0.1% indicator, and the delay is less than 10 ms. Therefore, the SCI system constructed by this study shows low latency and high utilization rate, which can provide experimental reference for the later construction of smart city.

Proposing a Novel IoT Framework by Identifying Security and Privacy Issues in Fog Cloud Services Network

These days, the Internet of Things applications are growing progressively. However, the existing frameworks for IoT applications are notsatisfied the security, allocation, and provisioning requirements.We propose a novel secure IoT application framework. Fog cloud execution model is serverless computing which is run by the server in cloud provider and the allocation of machine resources is dynamically managed. The application which consumes the actual amount of resources, the pricing is based upon that rather on pre-purchase units of capacity. The proposed framework consists of different methods, such as secure mobility, resource allocation, provisioning, and prediction under blockchain technologies.

RETRACTED ARTICLE: Energy and CPU utilization of cloud virtual machine resource allocation using dynamic heuristic mitigate migration algorithm

Cloud computing systems face new challenges every day due to the large and complex structure of system clusters and the data processed by the systems. The ability to obtain resources flexibly is considered to be the main reason for adopting cloud computing systems. Resiliency primarily supports facility growth and shrinks virtual resources according to the needs of dynamic cloud consumers. The cloud infrastructure promises to provide efficient resource utilization for data centers. The CPU is one of the many resources a client can use in a cloud environment. However, ensuring resource allocation flexibility is one of the major challenges in cloud environments. Develop a Dynamic Heuristic Mitigate Migration (DHMM) to increase the utilization of resources that are effectively allocated between virtual machines via cloud servers. In this proposed method DHMM focuses the storage migration and CPU utilization approaches to improve the cloud performance. In order to evaluate the performance, the proposed algorithm is compared to the prior art. Experimental results show that the proposed framework proves that it has been found very effective to improve resource utilization in a cloud computing environment.

Single machine resource allocation scheduling problems with deterioration effect and general positional effect.

This paper investigates single machine scheduling problems where the actual processing time of a job is dependent on its starting time, processing position and the amount of resource allocation. We present two unified models and provide a bicriteria analysis for the general scheduling criteria and the total weighted resource consumption. We consider two different versions for treating the two criteria and show that the unified models can be applied to solve scheduling problems under various due window assignment considerations. We prove that two different versions of the problems can be solved in polynomial time, respectively.

Note on the time complexity of resource constrained scheduling with general truncated job-dependent learning effect

In a recent paper (He et al. in J Comb Optim 33(2):626–644, 2017), the authors considered single machine resource allocation scheduling with general truncated job-dependent learning effect. For the convex resource consumption function and limited resource cost, the problem is to minimize the weighted sum of makespan, total completion time, the total absolute deviation in completion time and resource consumption cost. They conjectured that this problem is NP-hard. In this note we show that this problem can be solved in $$O(n^3)$$ time. It is also shown that Lemma 4 in He et al. (2017) is incorrect by a counter-example.

Cooperative Game-Based Virtual Machine Resource Allocation Algorithms in Cloud Data Centers

With the growing demand of cloud services, cloud data centers (CDCs) can provide flexible resource provisioning in order to accommodate the workload demand. In CDCs, the virtual machine (VM) resource allocation problem is an important and challenging issue to provide efficient infrastructure services. In this paper, we propose a unified resource allocation scheme for VMs in the CDC system. To provide a fair-efficient solution, we concentrate on the basic concept of Shapley value and adopt its variations to effectively allocate CDC resources. Based on the characteristics of value solutions, we develop novel CPU, memory, storage, and bandwidth resource allocation algorithms. To practically implement our algorithms, application types are assumed as cooperative game players, and different value solutions are applied to optimize the resource utilization. Therefore, our four resource allocation algorithms are jointly combined as a novel fourfold game model and take various benefits in a rational way through the cascade interactions while solving comprehensively some control issues. To ensure the growing demand of cloud services, this feature can leverage the full synergy of different value solutions. To check the effectiveness and superiority of our proposed scheme, we conduct extensive simulations. The simulation results show that our algorithms have significant performance improvement compared to the existing state-of-the-art protocols. Finally, we summarize our cooperative game-based approach and discuss possible major research issues for the future challenges about the cloud-assisted DC resource allocation paradigm.

Due-date assignment scheduling involving job-dependent learning effects and convex resource allocation

ABSTRACTThis article addresses single machine resource allocation scheduling problems with learning effects, where learning effects mean job-dependent position-based learning effects. For the common due-date assignment (CON) and slack due-date assignment (SLK) methods, a bi-cost analysis of the scheduling cost and the total weighted resource consumption cost is provided. The objective is to determine the optimal job sequence and the resource allocation simultaneously, such that the scheduling cost (the total weighted resource consumption cost) is minimized subject to the total weighted resource consumption cost (the scheduling cost) being limited. Solution procedures are provided for the problems under consideration.

Flexible allocation on related machines with assignment restrictions

We introduce the problem of Flexible Allocation on Related Machines with Assignment Restrictions (FARM). In this problem the input consists of a set of machines and a set of jobs. Each machine has a finite capacity, and each job has a resource requirement interval, a profit per allocated unit of resource, and a set of machines that can potentially supply the requirement. A feasible solution is an allocation of machine resources to jobs such that: (i) a machine resource can be allocated to a job only if it is a potential supplier of this job, (ii) the amount of machine resources allocated by a machine is bounded by its capacity, and (iii) the amount of resources that are allocated to a job is either in its requirement interval or zero. Notice that a job can be serviced by multiple machines. The goal is to find a feasible allocation that maximizes the overall profit. We focus on r- FARM in which the required resource of a job is at most an r-fraction of (or r times) the capacity of each potential machine, for some r≥0. FARM is motivated by resource allocation problems arising in cellular networks and in cloud computing. Specifically, FARM models the problem of assigning clients to base stations in 4G cellular networks. We present a 2-approximation algorithm for 1- FARM and a 11−r-approximation algorithm for r-FARM, for any r∈(0,1). Both are based on the local ratio technique and on maximum flow computations. We observe that an LP-rounding 2-approximation algorithm for the Generalized Assignment Problem also applies to 1-FARM. Finally, we give an Ω(rlogr) lower bound on the approximation ratio for r-FARM (assuming P≠NP).

Virtual Machine Resource Allocation of Probabilistic Optimization Based on SME Algorithm

To further enhance optimizing effect of virtual machine resource allocation, virtual machine resource allocation algorithm of probabilistic optimization based on SME-FFD (Simulated Evolution – First Fit Decreasing ) is proposed aiming at NP hard optimization problem in the process of virtual machine resource allocation in cloud computing. First of all, an optimization evaluation scheme of virtual machine resource allocation is proposed, and strong climbing optimization ability of simulated evolutionary algorithm is adopted to carry out iterative evolution to selection, evaluation and ranking of virtual machine resource allocation ; after that, based on SME algorithm obtaining resource allocation ordering, the secondary allocation on virtual machine and physical host resources ranked is conducted using FFD to improve efficiency and effectiveness of resource allocation; in the end, experimental comparison is conducted in CloundSim grid lab in the University of Melbourne and gridbus cloud simulation platform, the results show that CPU usage rate of proposed algorithm reaches up to 47%, memory usage rate reaches up to 56%, therefore, it may effectively reduce physical machine usage quantity and realize the goal of energy consumption.

Decision models for capacity planning in a regeneration environment

This paper presents an approach to capacity planning and coordination for the regeneration of complex investment goods. In order to capture the specifics of the regeneration environment, Bayesian networks are utilised to improve the accuracy of the workload forecast and mathematical models are proposed for the long-, medium- and short-term capacity planning and coordination. The long- and medium-term models maximise the total profit through the optimum determination of the quantity of goods to be regenerated in-house or at the sites of external vendors, the number of regenerated goods to be stored and the extent to which penalties should be tolerated for delayed regeneration orders. The short-term model determines the optimal allocation of human and machine resources to different orders. The models are validated through the use of real-world data supplied by an industrial partner. Sensitivity analyses are conducted in order to gain insights into the model. The application of the models leads to significant improvements for the industry partner due to the reduction in regeneration costs as a result of implementing varying plant capacities over the course of a year.

A hybrid flowshop scheduling model considering dedicated machines and lot-splitting for the solar cell industry

This paper studies a solar cell industry scheduling problem, which is similar to traditional hybrid flowshop scheduling (HFS). In a typical HFS problem, the allocation of machine resources for each order should be scheduled in advance. However, the challenge in solar cell manufacturing is the number of machines that can be adjusted dynamically to complete the job. An optimal production scheduling model is developed to explore these issues, considering the practical characteristics, such as hybrid flowshop, parallel machine system, dedicated machines, sequence independent job setup times and sequence dependent job setup times. The objective of this model is to minimise the makespan and to decide the processing sequence of the orders/lots in each stage, lot-splitting decisions for the orders and the number of machines used to satisfy the demands in each stage. From the experimental results, lot-splitting has significant effect on shortening the makespan, and the improvement effect is influenced by the processing time and the setup time of orders. Therefore, the threshold point to improve the makespan can be identified. In addition, the model also indicates that more lot-splitting approaches, that is, the flexibility of allocating orders/lots to machines is larger, will result in a better scheduling performance.

A hybrid approach based on the variable neighborhood search and particle swarm optimization for parallel machine scheduling problems—A case study for solar cell industry

Abstract This paper studies a solar cell industry scheduling problem which is similar to the traditional hybrid flow shop scheduling (HFS). In a typical HFS with parallel machines problem, the allocation of machine resources for each order should be scheduled in advance and then the optimal multiprocessor task scheduling in each stage could be determined. However, the challenge in solar cell manufacturing is the number of machines can be dynamically adjusted to complete the job within the shortest possible time. Therefore, the paper addresses a multi-stage HFS scheduling problem with characteristics of parallel processing, dedicated machines, sequence-independent setup time, and sequence-dependent setup time. The objective is to schedule the job production sequence, number of sublots, and dynamically allocate sublots to parallel machines such that the makespan time is minimized. The problem is formulated as a mixed integer linear programming (MILP) model. A hybrid approach based on the variable neighborhood search and particle swarm optimization (VNPSO) is developed to obtain the near-optimal solution. Preliminary computational study indicates that the developed VNPSO not only provides good quality solutions within a reasonable amount of time but also outperforms the classic branch and bound method and the current industry heuristic practiced by the case company.

Preemptive Open-Shop Scheduling: Network Flow Based Algorithm

Preemptive open-shop scheduling problem was studied, and a network flow based algorithm was presented. Firstly, based on the characteristics of the preemptive open-shop, the scheduling problem was formulated as a mixed-integer programming model with the objective to minimize the make-span. The maximum flow model of the preemptive open-shop was developed to model the machine resource allocation and time constraints. Moreover a new preflow push algorithm for the maximum flow model was put forward. Based on the solution of machine resource allocation problem got by preflow push algorithm, the sequences of the tasks processed by each machine were determined by calculating the matrix of the processing times and decrementing set. Finally, the validity of the developed scheduling algorithm is illustrated by randomly generated example.