Optimal Allocation Of Virtual Machines Research Articles

Cost and response time optimization of edge architectures

The cloud service provider market has recently expanded its offerings by providing edge as a service. This involves offering resources equivalent to those already available in the cloud, but through data centers located closer to the end user, with the goal of improving service latencies. Application providers face the challenge of selecting appropriate resources, both from the edge and cloud, to deploy their applications in a way that minimizes deployment costs while satisfying latency requirements. This paper presents Edarop (EDge ARchitecture OPtimization), an innovative orchestration mechanism for the optimal allocation of virtual machines in geographically distributed edge and cloud infrastructures. Edarop is capable of handling different edge and cloud vendors, each offering various types of VMs in different regions, with different prices, and network latencies. It also supports multiple simultaneous applications with different latency requirements and load profiles. Edarop employs Integer Linear Programming (ILP) to ensure the globally optimal solution within a reasonable time frame for the considered use cases. Several variants of the mechanism are provided, depending on whether the objective is to minimize cost, response times, or both. These variants are compared to each other and to alternative approaches, with the results showing that, unlike other methods, Edarop consistently respects latency constraints while minimizing the proposed objectives.

Dynamic task scheduling in edge cloud systems using deep recurrent neural networks and environment learning approaches

The rapid growth of the cloud computing landscape has created significant challenges in managing the escalating volume of data and diverse resources within the cloud environment, catering to a broad spectrum of users ranging from individuals to large corporations. Ineffectual resource allocation in cloud systems poses a threat to overall performance, necessitating the equitable distribution of resources among stakeholders to ensure profitability and customer satisfaction. This paper addresses the critical issue of resource management in cloud computing through the introduction of a Dynamic Task Scheduling with Virtual Machine allocation (DTS-VM) strategy, incorporating Edge-Cloud computing for the Internet of Things (IoT). The proposed approach begins by employing a Recurrent Neural Network (RNN) algorithm to classify user tasks into Low Priority, Mid Priority, and High Priority categories. Tasks are then assigned to Edge nodes based on their priority, optimizing efficiency through the application of the Spotted Hyena Optimization (SHO) algorithm for selecting the most suitable edge node. To address potential overloads on the edge, a Fuzzy approach evaluates offloading decisions using multiple metrics. Finally, optimal Virtual Machine allocation is achieved through the application of the Stable Matching algorithm. The seamless integration of these components ensures a dynamic and efficient allocation of resources, preventing the prolonged withholding of customer requests due to the absence of essential resources. The proposed system aims to enhance overall cloud system performance and user satisfaction while maintaining organizational profitability. The effectiveness of the DTS-VM strategy is validated through comprehensive testing and evaluation, showcasing its potential to address the challenges posed by the diverse and expanding cloud computing landscape.

An efficient virtual machine allocation using single stage weapon target assignment model in cloud software‐defined network environment

SummaryWith the rapid growth of the Internet, technological advancements, and the increasing number of cloud users, a paradigm transformation towards separate data and control planes with a centralized control point is taking place. As a result of this separation, software‐defined networks (SDNs) have distinct software controllers for routers and the capacity to manage network behavior. Virtual machine placement (VMP) and virtual machine migration (VMM) are two examples of SDN usage in data centers. The issue of VMP in cloud environments is a significant challenge that has been widely researched but not fully resolved. In this research, we used the single stage weapon target assignment (SSWTA) to model the virtual machine allocation (VMA) for jobs in order to maximize the resource utilization of the physical machines (PMs) by reducing idle time. Furthermore, this research proposes three algorithms: job assignment algorithms, PM allocation (PMA) using the least recently used (LRU) policy, and optimal VMA. The proposed VMA's performance is compared to other related VMA models such as first fit decreasing, and it is determined to be efficient in terms of resource utilization.

Modelling and simulation for cost optimization and performance analysis of transactional applications in hybrid clouds

In the design process of a hybrid cloud with the purpose of cost optimization, finding the optimal distribution between the resources to be deployed in both private and public clouds is a complex task. To help the cloud designer in this task, optimizer and simulation tools can be used. In the research presented in this paper, a state-of-the-art optimizer tool, designed for the optimal allocation of virtual machines in public clouds for cost optimization, is analyzed for its use in hybrid cloud scenarios. In addition, based on the system and workload model implemented by this tool, a simulator is developed and presented. The simulator provides detailed run-time performance information corresponding to the virtual machine allocations generated by the optimizer tool, offering essential analysis capabilities to the cloud designer. A motivating scenario is used to show how the combined utilization of the optimizer and simulation tools can offer fundamental help in the design and configuration process of hybrid clouds. The research questions posed in the motivating scenario are addressed in a set of experiments, which provide meaningful insights into the capabilities of the tools. The tools and all the experimental data carried out in this research have been made publicly available.

A Metaheuristic Framework for Dynamic Virtual Machine Allocation With Optimized Task Scheduling in Cloud Data Centers

Optimal allocation of virtual machines in a cloud computing environment for user-submitted tasks is a challenging task. Finding an optimal task scheduling solution is considered as NP-hard problem specifically for large task sizes in the cloud environment. The best solution involves scheduling the tasks to virtual machines data centre while minimizing the essential, influential and cost effective parameters such as energy usage, makespan and cost. In this direction, this work presents a metaheuristic framework called MDVMA for dynamic virtual machine allocation with optimized task scheduling in a cloud computing environment. The MDVMA focuses on developing a multi-objective scheduling method using non dominated sorting genetic algorithm (NSGA)-II algorithm-based metaheuristic algorithm for optimizing task scheduling with the aim of minimizing energy usage, makespan and cost simultaneously to provide trade-off to the cloud service providers as per their requirements. To evaluate the performance of the MDVMA approach, we compared the performances of two different scenarios of benchmark real-world workload data sets using the existing approaches, namely, Artificial Bee Colony (ABC) algorithm, Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) algorithm. Simulation results demonstrate that optimizing task scheduling leads to better overall results in terms of minimizing energy usage, makespan and cost of the cloud data center. Finally, the paper concludes metaheuristic algorithms as a promising method for task scheduling in a cloud computing environment.

Resource‐efficient load‐balancing framework for cloud data center networks

Cloud computing has drastically reduced the price of computing resources through the use of virtualized resources that are shared among users. However, the established large cloud data centers have a large carbon footprint owing to their excessive power consumption. Inefficiency in resource utilization and power consumption results in the low fiscal gain of service providers. Therefore, data centers should adopt an effective resource-management approach. In this paper, we present a novel load-balancing framework with the objective of minimizing the operational cost of data centers through improved resource utilization. The framework utilizes a modified genetic algorithm for realizing the optimal allocation of virtual machines (VMs) over physical machines. The experimental results demonstrate that the proposed framework improves the resource utilization by up to 45.21%, 84.49%, 119.93%, and 113.96% over a recent and three other standard heuristics-based VM placement approaches.

Performance analysis of ACO‐based improved virtual machine allocation in cloud for IoT‐enabled healthcare

SummaryThe Internet of Things (IoT)–enabled healthcare environment irregularly requires the resources from the Cloud to handle massive amounts of data, which impacts the response times of the Cloud. A typical healthcare application scenario could be to monitor the heart‐patients who require the highest response times, immediate attention of all the stakeholders, and very quick decisions. This paper replaces the default First‐Come‐First‐Serve (FCFS) Virtual Machine (VM) allocation scheme of the Cloud computing in CloudSim with the implementation of Ant Colony Optimization (ACO) by the efficient tuning of parameters. An IoT‐enabled healthcare environment is designed, which irregularly requires the resources from the Cloud to handle massive amounts of data to assess the impact of the response times of the Cloud. Several experiments have been carried out to assess the optimal VM allocation using ACO by varying different parameters like the variation of the number of ants, the strength of pheromone, the number of VMs, the number of hosts, the strength of computing power of processing elements, the number of users, and the size of the workloads. Experimental results indicate that the ACO optimally utilizes the resources in a Cloud when compared to the default FCFS strategy. The results indicate that on an average, the ACO gives 25% to 30% improved response times than FCFS in the given healthcare and Cloud scenarios.

Optimal Allocation of Virtual Machines in Cloud Computing

Virtualization is one of the core technologies used in cloud computing to provide services on demand for end users over the Internet. Most current research allocates virtual machines to physical machines based on CPU utilization. However, for many applications that require communication between services running on different servers, communication costs influence the overall performance. Therefore, this study focuses on the optimal allocation of virtual machines across multiple geographically dispersed data centers, with the objective of minimizing communication costs. The original problem can be constructed as a quadratic assignment problem that is a classical NP-hard combinatorial optimization problem. This study adopts an efficient deterministic optimization approach to reformulate the original problem as a mixed-integer linear program that may be solved to obtain a globally optimal solution. Since the required bandwidth matrix and communication cost matrix are symmetric, the mathematical model of virtual machine placement can be simplified. Several numerical examples drawn from the literature are solved to demonstrate the computational efficiency of the proposed method for determining the optimal virtual machine allocation in cloud computing.

SMDP-Based Coordinated Virtual Machine Allocations in Cloud-Fog Computing Systems

Heterogeneous computing powered by remote clouds and local fogs is a promising technology to improve the performance of user terminals in the Internet of Things. In this paper, two semi-Markov decision process (SMDP)-based coordinated virtual machine (VM) allocation methods are proposed to balance the tradeoff between the high cost of providing services by the remote cloud and the limited computing capacity of the local fog. We first present a model-based planning method in which it is necessary to train the state transition probabilities and the expected time intervals between adjacent decision epochs. To facilitate training them, the SMDP is degraded into a continuous-time Markov decision process (CTMDP) in which the service requests and ongoing service completions follow a continuous-time Markov chain. The relative value iterative algorithm for the CTMDP is used to find an asymptotically optimal VM allocation policy. In addition, we also propose a model-free reinforcement learning (RL) method, where an optimal coordinated VM allocation policy is approximated by learning from the states and rewards of feedback. The simulation results show that the performance of the model-free RL method can converge to a level similar to that of the model-based planning method and outperform the greedy VM allocation method.

Optimal Virtual Machine Allocation and Migration Model Based on PCA-BFD

Optimal Virtual Machine Allocation and Migration Model Based on PCA-BFD

An evolutionary approach to schedule deadline constrained bag of tasks in a cloud

Bag of tasks (BoT) is an application model which consists of a large number of independent tasks. In cloud, computing power is offered as virtual machines (VMs) which differ in terms of speed, memory and cost. When such applications are executed on cloud, an optimal allocation of VMs is needed so that the application executes to completion within the deadline and the cost incurred is minimal. Here, the main challenge is to find an optimal trade-off between execution time and execution cost. Genetic algorithms (GA) are evolutionary algorithms which enable to solve multi-objective problems. This paper proposes a novel deadline constrained bi-objective genetic algorithm based scheduler (DBOGA) to schedule a BoT application onto a cloud. A new fitness function is defined. Exploration and exploitation of search space is carried out based on this. An extensive study on the applicability of DBOGA by considering various scenarios is explored.

An evolutionary approach to schedule deadline constrained bag of tasks in a cloud

Bag of tasks (BoT) is an application model which consists of a large number of independent tasks. In cloud, computing power is offered as virtual machines (VMs) which differ in terms of speed, memory and cost. When such applications are executed on cloud, an optimal allocation of VMs is needed so that the application executes to completion within the deadline and the cost incurred is minimal. Here, the main challenge is to find an optimal trade-off between execution time and execution cost. Genetic algorithms (GA) are evolutionary algorithms which enable to solve multi-objective problems. This paper proposes a novel deadline constrained bi-objective genetic algorithm based scheduler (DBOGA) to schedule a BoT application onto a cloud. A new fitness function is defined. Exploration and exploitation of search space is carried out based on this. An extensive study on the applicability of DBOGA by considering various scenarios is explored.

Which is the best algorithm for virtual machine placement optimization?

SummaryOne of the key problems for Infrastructure‐as‐a‐Service providers is finding the optimal allocation of virtual machines on the physical machines available in the provider's data center. Since the allocation has significant impact on operational costs as well as on the performance of the accommodated applications, several algorithms have been proposed for the virtual machine placement problem. So far, no objective comparison of the proposed algorithms has been provided; therefore, it is not known which one works best or what factors influence the performance of the algorithms. In this paper, we present an environment and methodology for such comparisons and compare 7 different algorithms using the proposed environment and methodology. Our results showcase differences of up to 66% between the effectiveness of different algorithms on the same real‐world workload traces, thus underlining the importance of objectively comparing the performance of competing algorithms.

Optimal allocation of virtual machines in multi-cloud environments with reserved and on-demand pricing

In the Cloud Computing market, a significant number of cloud providers offer Infrastructure as a Service (IaaS), including the capability of deploying virtual machines of many different types. The deployment of a service in a public provider generates a cost derived from the rental of the allocated virtual machines. In this paper we present LLOOVIA (Load Level based OpimizatiOn for VIrtual machine Allocation), an optimization technique designed for the optimal allocation of the virtual machines required by a service, in order to minimize its cost, while guaranteeing the required level of performance. LLOOVIA considers virtual machine types, different kinds of limits imposed by providers, and two price schemas for virtual machines: reserved and on-demand. LLOOVIA, which can be used with multi-cloud environments, provides two types of solutions: (1) the optimal solution and (2) the approximated solution based on a novel approach that uses binning applied on histograms of load levels. An extensive set of experiments has shown that when the size of the problem is huge, the approximated solution is calculated in a much shorter time and is very close to the optimal one. The technique presented has been applied to a set of case studies, based on the Wikipedia workload. These cases demonstrate that LLOOVIA can handle problems in which hundreds of virtual machines of many different types, multiple providers, and different kinds of limits are used.