Cloud Simulation Tool Research Articles

A Systematic Parameter Analysis of Cloud Simulation Tools in Cloud Computing Environments

To provide various applications in various domains, a large-scale cloud data center is required. Cloud computing enables access to nearly infinite computing resources on demand. As cloud computing grows in popularity, researchers in this field must conduct real-world experiments. Configuring and running these tests in an actual cloud environment is costly. Modeling and simulation methods, on the other hand, are acceptable solutions for emulating environments in cloud computing. This research paper reviewed several simulation tools specifically for cloud computing in the literature and presented the most effective simulation methods in this research domain, as well as an analysis of a variety of cloud simulation tools. Cloud computing tools such as CloudSim, CloudSim Plus, CloudAnalyst, iFogSim, and CloudReports were evaluated. Furthermore, a parametric evaluation of cloud simulation tools is presented based on the identified parameters. Several 5-parameter tests were performed to demonstrate the capabilities of the cloud simulator. These results show the value of our proposed simulation system. CloudSim, CloudSim Plus, CloudAnalyst, iFogSim, and CloudReports are used to evaluate host processing elements, virtual machine processing elements, cloudlet processing elements, userbase average, minimum, and maximum, and cloudlet ID Start Time, Finish Time, Average Start, and Average Finish for each simulator. The outcomes compare these five simulator metrics. After reading this paper, the reader will be able to compare popular simulators in terms of supported models, architecture, and high-level features. We performed a comparative analysis of several cloud simulators based on various parameters. The goal is to provide insights for each analysis given their features, functionalities, and guidelines on the way to researchers’ preferred tools.

Simulation and Evaluation of a Financial Option Based Model for Cloud Resources Management

Cloud computing, where the use of resources is seen as a service (Resources-as-a-Service (RaaS)) has developed extensively for executing computationally resource-intensive applications. As a result, commercial services of such resources are becoming norm of the day and pricing them have become an important problem in finance. Only a few economic models have been reported for pricing cloud resources. In this paper, a novel application of financial option pricing theory to the management of distributed computing resources especially for pricing is addressed. First, the importance of finance models for the given problem is highlighted following an explain on how option theory fits well to price the distributed computing cloud resources. Various cloud resources such as memory, storage, software, and compute cycles are seen as individual commodities and pricing of the resources is done in isolation and in combination of various resources. Second, `we design and develop pricing model and generate pricing results for usage of such commodities for various resources. In the absence of cloud resource pricing benchmarks/standards, firstly, cloud resources usage is simulated in order to justify the pricing model using CloudSim toolkit. In this part of the work, the integration of a financial option-based pricing model with CloudSim framework is implemented using a cloud simulation tool to price cloud compute resources. Secondly, the model is evaluated using cloud metadata obtained from Amazon Web services. The analysis of cloud resources utilization from simulation and real cloud trace data shows the feasibility of a financial option-based model for pricing cloud resources. With a large number of experiments carried out, a justification of the pricing model is obtained by comparing a simulated system to real cloud trace data based on the spot price for the cloud resources. Keywords: Financial Option, Strike Price, CloudSim, Quality of Service, Compute Commodities

A Survey on Multi-Objective Tasks and Workflow Scheduling Algorithms in Cloud Computing

The challenge in cloud services is scheduling and allocating resources due to the exponential growth in demand and diversity of cloud resources. Scheduling is to allocate tasks across cloud resources so that scheduling algorithms reduce power consumption and offer cloud providers maximum return by reducing execution time. Various QoS parameters (such as makespan, load balancing, costs, etc.) are considered for efficient scheduling to reduce workload and enhance performance. Through this framework, multi-objective scheduling is a decision-making problem with multiple attributes considering the trade-off between the conflicting and competing parameters mentioned in the SLA between users and providers. This paper summarizes various multi-objective scheduling algorithms that consider contradictory and competing parameters or constraints to be optimized simultaneously. These algorithms are finally tabulated, presenting their advantages and disadvantages with cloud simulation tools and other QoS related parameters.

Retraction Note: The abnormal precipitation in rainy season based on genetic algorithm and the security of big data in network

In this paper, cloudsim cloud simulation tool is studied, and its running environment is reasonably configured. Taking the platform as the background, the improved genetic algorithm proposed in this paper is used for simulation experiment that is to optimize the genetic algorithm through simulated annealing behavior. Through the simulation experiment of the improved genetic algorithm, it can be known that the genetic algorithm designed in this paper can satisfy multiple customers one by one and has a certain improvement effect on cloud service quality. In this paper, the problem of rainfall anomaly in city D is studied. The results show that the rainfall anomaly of city D is related to solar activity, 500hPa height field in winter and SST in the Central South Pacific from June to August of the previous year. It is found that there is almost no rainfall during the high solar activity years, while there is frequent rainfall near the low solar activity years. Finally, according to the homomorphic encryption mechanism, this paper studies the protection of data confidentiality in computer network security and the multistorage technology of big data. In this research scheme, by adding a certain number of virtual objects to two different computing information dummies, we successfully protect the data such as the number of items owned by the data owner, the content of the items owned, and the item objects called by the user. In addition, this paper uses bilinear mapping and RS coding technology to create related protocols to solve some problems, such as infinite verification of big data integrity, error data recovery, and protection of data owner’s computing resources. Through the design of solutions to the above problems, this paper successfully completed the protection of big data resources and network security based on cloud computing environment.

RETRACTED ARTICLE: The abnormal precipitation in rainy season based on genetic algorithm and the security of big data in network

In this paper, cloudsim cloud simulation tool is studied, and its running environment is reasonably configured. Taking the platform as the background, the improved genetic algorithm proposed in this paper is used for simulation experiment that is to optimize the genetic algorithm through simulated annealing behavior. Through the simulation experiment of the improved genetic algorithm, it can be known that the genetic algorithm designed in this paper can satisfy multiple customers one by one and has a certain improvement effect on cloud service quality. In this paper, the problem of rainfall anomaly in city D is studied. The results show that the rainfall anomaly of city D is related to solar activity, 500hPa height field in winter and SST in the Central South Pacific from June to August of the previous year. It is found that there is almost no rainfall during the high solar activity years, while there is frequent rainfall near the low solar activity years. Finally, according to the homomorphic encryption mechanism, this paper studies the protection of data confidentiality in computer network security and the multistorage technology of big data. In this research scheme, by adding a certain number of virtual objects to two different computing information dummies, we successfully protect the data such as the number of items owned by the data owner, the content of the items owned, and the item objects called by the user. In addition, this paper uses bilinear mapping and RS coding technology to create related protocols to solve some problems, such as infinite verification of big data integrity, error data recovery, and protection of data owner’s computing resources. Through the design of solutions to the above problems, this paper successfully completed the protection of big data resources and network security based on cloud computing environment.

Fairness of Task Allocation in Crowdsourcing Workflows

Fairness plays a vital role in crowd computing by attracting its workers. The power of crowd computing stems from a large number of workers potentially available to provide high quality of service and reduce costs. An important challenge in the crowdsourcing market today is the task allocation of crowdsourcing workflows. Requester-centric task allocation algorithms aim to maximize the completion quality of the entire workflow and minimize its total cost, which are discriminatory for workers. The crowdsourcing workflow needs to balance two objectives, namely, fairness and cost. In this study, we propose an alternative greedy approach with four heuristic strategies to address such an issue. In particular, the proposed approach aims to monitor the current status of workflow execution and use heuristic strategies to adjust the parameters of task allocation. We design a two-phase allocation model to accurately match the tasks with workers. The F-Aware allocates each task to the worker that maximizes the fairness and minimizes the cost. We conduct extensive experiments to quantitatively evaluate the proposed algorithms in terms of running time, fairness, and cost by using a customer objective function on the WorkflowSim, a well-known cloud simulation tool. Experimental results based on real-world workflows show that the F-Aware, which is 1% better than the best competitor algorithm, outperforms other optimal solutions in finding the tradeoff between fairness and cost.

Trust-aware Task Allocation in Collaborative Crowdsourcing Model

Abstract Task allocation plays a vital role in crowd computing by determining its performance. The power of crowd computing stems from a large number of workers potentially available to provide high quality of service and reduce costs. An important challenge in the crowdsourcing market today is the task allocation of crowdsourcing workflows. Task allocation aims to maximize the completion quality of the entire workflow and minimize its total cost. Trust can affect the quality of the produced results and costs. Selecting workers with high levels of trust could provide better solution to the workflow and increase the budget. Crowdsourcing workflow needs to balance the two conflicting objectives. In this paper, we propose an alternative greedy approach with four heuristic strategies to address the issue. In particular, the proposed approach aims to monitor the current status of workflow execution and use heuristic strategies to adjust the parameters of task allocation. We design a two-phase allocation model to accurately match the tasks with workers. T-Aware allocates each task to the worker that maximizes the trust level, while minimizing the cost. We conduct extensive experiments to quantitatively evaluate the proposed algorithms in terms of running time, task failure ratio, trust and cost using a customer objective function on WorkflowSim, a well-known cloud simulation tool. Experimental results based on real-world workflows show that T-Aware outperforms other optimal solutions on finding the tradeoff between trust and cost, which is 3 to 6% better than the best competitor algorithm.

CBAC4C: conflict‐based VM isolation control for cloud computing

AbstractFor businesses to benefit from the many opportunities of cloud computing, they must first address a number of security challenges, such as the potential leakage of confidential data to unintended third parties. An inter‐VM (where VM is virtual machine) attack, also known as cross‐VM attack, is one threat through which cloud‐hosted confidential data could be leaked to unintended third parties. An inter‐VM attack exploits vulnerabilities between co‐resident guest VMs that share the same cloud infrastructure. In an attempt to stop such an attack, this paper uses the principles of logical analysis to model a solution that provides physical separation of VMs belonging to conflicting tenants based on their levels of conflict. The derived mathematical model is founded on scientific principles and implemented using four conflict‐aware VM placement algorithms. The resultant algorithms consider a tenant's risk appetite and cost implications. The model offers guidance to VM placement and is validated using a proof of concept. A cloud simulation tool was used to test and evaluate the effectiveness and efficiency of the model. The findings reflect that the introduction of the proposed model introduced a time lag in the time it took to place VM instances. On top of this, it was also discovered that the number and size of the VM instances has an effect on the VM placement performance. The findings further illustrate that the conflict tolerance level of a VM has a direct impact on the time it took to place.

Model-Driven Simulation of Elastic OCCI Cloud Resources

Abstract Deploying a cloud configuration in a real cloud platform is mostly cost- and time- consuming, as large number of cloud resources have to be rented for the time needed to run the configuration. Thereafter, cloud simulation tools are used as a cheap alternative to test cloud configuration. However, most of the existing cloud simulation tools require extensive technical skills and do not support simulation of any kind of cloud resources. In this context, using a model-driven approach can be helpful as it allows developers to efficiently describe their needs at a high level of abstraction. To do, we propose, in this article, a model-driven engineering approach based on the Open Cloud Computing Interface(OCCI) standard metamodel and CloudSim toolkit. We firstly extend OCCI metamodel for the supporting simulation of any kind of cloud resources. Afterward, to illustrate the extensibility of our approach, we enrich the proposed metamodel by new simulation capabilities. As proof of concept, we study the elasticity and pricing strategies of Amazon Web Services (AWS). This article benefits from OCCIware Studio to design an OCCI simulation extension and to provide a simulation designer for designing cloud configurations to be simulated. We detail the approach process from defining an OCCI simulation extension until the generation and the simulation of the OCCI cloud configurations. Finally, we validate the proposed approach by providing a realistic experimentation to study its usability, the resources coverage rate and the cost. The results are compared with the ones computed from AWS.

Dynamically Partitioning Workflow over Federated Clouds for Optimising the Monetary Cost and Handling Run-Time Failures

Several real-world problems in domain of healthcare, large scale scientific simulations, and manufacturing are organised as workflow applications. Efficiently managing workflow applications on the Cloud computing data-centres is challenging due to the following problems: (i) they need to perform computation over sensitive data (e.g., Healthcare workflows) hence leading to additional security and legal risks especially considering public cloud environments and (ii) the dynamism of the cloud environment can lead to several run-time problems such as data loss and abnormal termination of workflow task due to failures of computing, storage, and network services. To tackle above challenges, this paper proposes a novel workflow management framework call Deploy on Federated Cloud Framework (DoFCF) that can dynamically partition scientific workflows across federated cloud (public/private) data-centres for minimising the financial cost, adhering to security requirements, while gracefully handling run-time failures. The framework is validated in cloud simulation tool (CloudSim) as well as in a realistic workflow-based cloud platform (e-Science Central). The results showed that our approach is practical and is successful in meeting users security requirements and reduces overall cost, and dynamically adapts to the run-time failures.

An Improved Task Allocation Strategy in Cloud using Modified K-means Clustering Technique

In the present era, cloud computing has earned much popularity, mainly because of its utilities and relevance with the current technological trends. It is an arrangement which is highly customizable and encapsulated for providing better computational services to its clients worldwide. In cloud computing, scheduling plays a pivotal role in the optimal utilization of resources. Prevalent priority based job scheduling strategies are silent in deciding scheduling scheme for tasks with the same priority and strive hard in appropriately allocating jobs to virtual machines. In the recent years, despite of much research in this field, these scheduling algorithms are unable to provide optimal solution and are lacking in one way or the other in their performance and efficiency. Work pertaining to the use of four criteria/credits for deciding priority, with modified K-means clustering technique is scant. Therefore, to eliminate the drawbacks of the prevalent or existing system and to enhance the performance and efficiency of cloud computing, a new credits based scheduling algorithm has been rendered. The proposed system considers four real time parameters/factors namely Task-Length, Task-Priority, Deadline and Cost, as credits and uses Modified K-means Clustering technique for categorizing the cloudlets and virtual machines (VMs). Results indicate that the suggested scheduling algorithm has excelled existing priority-based scheduling strategy and it has been empirically proven with experimental/simulated results in this paper. CloudSim 3.0.3, a Cloud Simulation Tool has been used to implement and test the proposed algorithm.

Improving cloud architectures using UML profiles and M2T transformation techniques

In this paper, we present an approach with the goal to improve the underlying architecture of cloud systems. For this, we propose UML2Cloud, a framework targeted at modeling and checking cloud systems. The main core of UML2Cloud uses UML profiles to capture the main elements of a cloud system including, among other elements, its underlying architecture and the interaction with clients. Additionally, UML2Cloud uses Model-to-Text transformation techniques to automatically generate configuration documents representing complex cloud scenarios. In this work, we use these documents as input for a cloud simulation tool, called Simcan2Cloud, to simulate the behavior of different systems. Thus, the analysis of the performance results obtained from the simulations allows us to draw some conclusions about how to improve the efficiency of the studied clouds by adjusting the hardware resource configuration.

MOWS: Multi-objective workflow scheduling in cloud computing based on heuristic algorithm

Cloud computing is emerging with growing popularity in workflow scheduling, especially for scientific workflow. Deploying data-intensive workflows in the cloud brings new factors to be considered during specification and scheduling. Failure to establish intermediate data security may cause information leakage or data alteration in the cloud environment. Existing scheduling algorithms for the cloud disregard the interaction among tasks and its effects on application security requirements. To address this issue, we design a new systematic method that considers both tasks security demands and interactions in secure tasks placement in the cloud. In order to respect security and performance, we formulate a model for task scheduling and propose a heuristic algorithm which is based on task’s completion time and security requirements. In addition, we present a new attack response approach to reduce certain security threats in the cloud. To do so, we introduce task security sensitivity measurement to quantify tasks security requirements. We conduct extensive experiments to quantitatively evaluate the performance of our approach, using WorkflowSim, a well-known cloud simulation tool. Experimental results based on real-world workflows show that compared with existing algorithms, our proposed solution can improved the overall system security in terms of quality of security and security risk under a wide range of workload characteristics. Additionally, our results demonstrate that the proposed attack response algorithm can effectively reduce cloud environment threats.

Cloud-based Simulation Tools for Cloud Testing: A Review

Cloud computing has maintained popularity and attraction in the ICT industry. Many business organizations have embraced the benefits that it offers and many more are still working towards cloud computing. One of the many concerns many organizations and individuals have about cloud computing from its inception is how to test the services and opportunities offered by cloud computing. This concern is as a result of the cost of running experiments on real cloud system and the fact that real infrastructure limits experiments within the size of the infrastructure, thereby making it really difficult to reproduce test. The alternative solution to this concern is the use of cloud simulation tools which offer a cloud environment to test cloud services in a repeated and controlled manner at no cost. Many cloud simulation tools have been developed since the inception of cloud computing. These tools have their strengths and weakness in modelling and simulating cloud system. This paper reviews some simulation tools and compares them in terms of their underlying framework, programming language, GUI, availability, cost modelling, energy modelling, simulation time, federation policy and communication model

Cost Effective, Reliable and Secure Workflow Deployment over Federated Clouds

The significant growth in cloud computing has led to increasing number of cloud providers, each offering their service under different conditions – one might be more secure whilst another might be less expensive or more reliable. At the same time user applications have become more and more complex. Often, they consist of a diverse collection of software components, and need to handle variable workloads, which poses different requirements on the infrastructure. Therefore, many organisations are considering using a combination of different clouds to satisfy these needs. It raises, however, a non-trivial issue of how to select the best combination of clouds to meet the application requirements. This paper presents a novel algorithm to deploy workflow applications on federated clouds. First, we introduce an entropy-based method to quantify the most reliable workflow deployments. Second, we apply an extension of the Bell-LaPadula Multi-Level security model to address application security requirements. Finally, we optimise deployment in terms of its entropy and also its monetary cost, taking into account the cost of computing power, data storage and inter-cloud communication. We implemented our new approach and compared it against two existing scheduling algorithms: Extended Dynamic Constraint Algorithm (EDCA) and Extended Biobjective dynamic level scheduling (EBDLS). We show that our algorithm can find deployments that are of equivalent reliability but are less expensive and meet security requirements. We have validated our solution through a set of realistic scientific workflows, using well-known cloud simulation tools (WorkflowSim and DynamicCloudSim) and a realistic cloud based data analysis system (e-Science Central).

Comprehensive study of multi-resource cloud simulation tools

Comprehensive study of multi-resource cloud simulation tools

A framework for simulating large scale cloud infrastructures

Cloud infrastructures are continuously growing in size, since more cloud nodes are added to already existing hyper-scale infrastructures. These hyper-scale infrastructures are also becoming heterogeneous as different types of accelerators are added in order to increase performance per watt for certain types of applications and allow for various HPC workloads to migrate to Cloud environments. The introduction of diverse workloads that migrate in the Cloud along with increasing volume of incoming tasks results in phenomena of network congestion, underutilization and resource fragmentation. Simulators are used to analyze, study and possibly improve Cloud environments. However, existing Cloud simulation tools lack the ability to handle heterogeneous resources and tasks that span across multiple Cloud nodes. Moreover, they are mostly sequential and cannot scale to large numbers of Cloud nodes. Furthermore, they do not support over-commitment, which is a common practice in real-world Cloud environments. A framework for simulating large numbers of heterogeneous cloud nodes organized in Cells and executing large numbers of HPC tasks is proposed. The framework is inherently parallel and designed for hybrid distributed memory parallel systems, supporting CPU, memory and network over-commitment. The simulation framework is based on a time advancing loop, allowing dynamic change of the granularity of the simulator and minimizing memory requirements, since data related to the current time-step is stored. Moreover, a latency model for the currency of data in the Gateway Service and Broker is also supported. Implementation details along with discussions concerning the extensibility of the framework are given. Numerical results for simulating large number of heterogeneous resources and incoming tasks are also presented.

AN QUALITY BASED ENHANCEMENT OF USER DATA PROTECTION VIA FUZZY RULE BASED SYSTEMS IN CLOUD ENVIRONMENT

So far, in cloud computing distinct customer is accessed and consumed enormous amount of services through web, offered by cloud service provider (CSP). However cloud is providing one of the services is, security-as-a-service to its clients, still people are terrified to use the service from cloud vendor. Number of solutions, security components and measurements are coming with the new scope for the cloud security issue, but 79.2% security outcome only obtained from the different scientists, researchers and other cloud based academy community. To overcome the problem of cloud security the proposed model that is, Quality based Enhancing the user data protection via fuzzy rule based systems in cloud environment, will helps to the cloud clients by the way of accessing the cloud resources through remote monitoring management (RMMM) and what are all the services are currently requesting and consuming by the cloud users that can be well analyzed with Managed service provider (MSP) rather than a traditional CSP. Normally, people are trying to secure their own private data by applying some key management and cryptographic based computations again it will direct to the security problem. In order to provide good quality of security target result by making use of fuzzy rule based systems (Constraint & Conclusion segments) in cloud environment. By using this technique, users may obtain an efficient security outcome through the cloud simulation tool of Apache cloud stack simulator.

A COMBINED BIN PACKING VM ALLOCATION AND MINIMUM LOADED VM MIGRATION APPROACH FOR LOAD BALANCING IN IAAS CLOUD DATACENTERS

Day by day increasing online computation and migration to the cloud, significantly increase the need of proper load balancing. Load balancing ensure service availability and performance to users. Downtime experienced by users is great issue which leads to violation of QoS requirements for users. Improving response time is one of the solution and some extend it helps to meet the SLA. The proposed system has the intention of achieving an improved load balancing performance to cloud data center, and a satisfying service response to users. By focusing on virtualization technology here consider both virtual machine allocation and migration processes to achieve a better load balancing solution. A VM allocation policy called Bin packing and a Minimum loaded VM migration policy based on load threshold are used together to achieve the goal in Infrastructure as a Service cloud environment. A cloud simulation tool called CloudReport is used to perform the simulation of the system. The experimental results show that the approach provides comparatively improved response time and completion time for users.

A Study on Cloud Simulation Tools

Cloud computing is an emerging technology, advancing quickly in IT due to the flexibility of using computing without buying any infrastructure but by using as pay-per-use model. It is risky to evaluate the performance of cloud environments as cloud computing adoption increases. The simulation technology has become a powerful tool in cloud computing research community to address these issues. Various cloud simulators have been precisely developed for testing and performance analysis of cloud computing environments. This paper presents a study, analysis and comparison of various existing cloud simulation tools.