Real Cloud Platform Research Articles

CloudSim express: A novel framework for rapid low code simulation of cloud computing environments

AbstractCloud computing environment simulators enable cost‐effective experimentation of novel infrastructure designs and management approaches by avoiding significant costs incurred from repetitive deployments in real Cloud platforms. However, widely used Cloud environment simulators compromise on usability due to complexities in design and configuration, along with the added overhead of programming language expertise. Existing approaches attempting to reduce this overhead, such as script‐based simulators and graphical user interface (GUI) based simulators, often compromise on the extensibility of the simulator. Simulator extensibility allows for customization at a fine‐grained level, thus reducing it significantly affects flexibility in creating simulations. To address these challenges, we propose an architectural framework to enable human‐readable script‐based simulations in existing Cloud environment simulators while minimizing the impact on simulator extensibility. We implement the proposed framework for the widely used Cloud environment simulator, the CloudSim toolkit, and compare it against state‐of‐the‐art baselines using a practical use case. The resulting framework, called CloudSim Express, achieves extensible simulations while surpassing baselines with over a % reduction in code complexity and an 89.42% reduction in lines of code.

Non-interactive Boolean Searchable Asymmetric Encryption With Bilateral Access Control

Abstract Searchable asymmetric encryption (SAE) enables a client to search over a data owner’s encrypted data. Nevertheless, state-of-the-art SAE schemes allow a data owner to specify access control policy for a client, while they have not considered the threat case of a malicious data owner. To address the problem, this work presents a non-interactive SAE scheme with bilateral access control: (i) allowing data owner and client to both specify policies toward the other party; (ii) allowing client to perform arbitrary boolean queries with sub-linear search complexity. Technically, we extend Cash et al.’s highly scalable SSE into an asymmetric setting and introduce the property of data owner authenticity. By refining identity-based matchmaking encryption, we formalize the syntax and security definition of our SAE with identity-based bilateral access control. Moreover, the security of the proposed SAE can be reduced to discrete logistic assumption and decisional bilinear Diffie–Hellman assumption. As an enhanced extension, we present a non-interactive multi-client SAE scheme with fuzzy identity-based bilateral access control. In addition, we implement the proposed schemes in real cloud platform and evaluate their performance on a real-world dataset. The result confirms that our SAE schemes achieve bilateral access control for both data owner and client with highly acceptable efficiency.

Local search driven periodic scheduling for workflows with random task runtime in clouds

When data processing applications from various fields are deployed in cloud computing, workflow scheduling is vital in satisfying users’ requirements and improving cloud platforms’ performance. Up to the present, some works have put forward heuristic methods to handle dynamic and uncertain factors when scheduling workflows in cloud platforms. Although these heuristics can generate feasible schedules quickly, their performance can be further improved. It is noteworthy that not all the workflow tasks can be executed immediately due to their data dependencies. Then, their waiting time can be utilized to further optimize schedules generated by heuristic methods. Motivated by the above fact, this paper proposes a Local Search driven Periodic Scheduling, LSPS, for workflows having deadlines and random task runtime. Specifically, in each scheduling period, the LSPS only schedule the tasks to start running, thus shortening the length of local waiting queues on resources to alleviate the negative effects of dynamics and uncertainty. Moreover, we design a problem-specific local search strategy for LSPS to fully use the scheduling period to improve the quality of schedules iteratively. At last, in the context of real cloud platforms, four groups of compared experiments are carried out to measure the effectiveness of the proposed LSPS concerning monetary cost and resource efficiency.

WITHDRAWN: Development and deployment of real-time cloud applications on red hat OpenShift and IBM bluemix

Most computing services, hosted by a number of cloud service providers, are available on demand nowadays. There are a range of cloud computing realms and core fields that have been under scrutiny for a long time due to rising digital infrastructure demands and applications. The cloud service providers bill based on the space or service offered to the customer to get access to cloud data. It is not always possible in research and development to provide the actual cloud technology to conduct the experiments. It is not possible for any research scholar, educator or scientist to employ cloud providers at all times and then perform their algorithms or implementations. For research, production and testing purposes, the real time cloud platforms can be taken by the researchers with free tier. In this research manuscript, the inclusion of IBM Bluemix and Red Hat OpenShift clouds are done for the development and effectual deployment of web apps on cloud. Such services can be used by the researchers and academicians for high performance web applications on cloud with real time logs and analytics on assorted parameters.

WITHDRAWN: High performance research implementations with third party cloud platforms and services

Abstract Cloud computing is one of the evolving areas where distributed services are requested without the physical networks at the customer end. The physical tools of cloud computing are mounted at remote locations and implemented. This thesis focuses on deployment trends for researchers and professionals of real time cloud systems and platforms, in the area of cloud computing and associated technology. This research work is focusing on the integration of third party cloud platforms and services for the high performance computing in assorted domains. The integration of widely used cloud platforms are done so that the research implementations can be done effectively on assorted parameters.

AutoScaleSim: A simulation toolkit for auto-scaling Web applications in clouds

Auto-scaling of Web applications is an extensively investigated issue in cloud computing. To evaluate auto-scaling mechanisms, the cloud community is facing considerable challenges on either real cloud platforms or custom test-beds. Challenges include – but not limited to – deployment impediments, the complexity of setting parameters, and most importantly, the cost of hosting and testing Web applications on a massive scale. Hence, simulation is presently one of the most popular evaluation solutions to overcome these obstacles. Existing simulators, however, fail to provide support for hosting, deploying and subsequently auto-scaling of Web applications. In this paper, we introduce AutoScaleSim, which extends the existing CloudSim simulator, to support auto-scaling of Web applications in cloud environments in a customizable, extendable and scalable manner. Using AutoScaleSim, the cloud community can freely implement/evaluate policies for all four phases of auto-scaling mechanisms, that is, Monitoring, Analysis, Planning and Execution. AutoScaleSim can also be used for evaluating load balancing algorithms similarly. We conducted a set of experiments to validate and carefully evaluate the performance of AutoScaleSim in a real cloud platform, with a wide range of performance metrics.

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.

A Reinforcement Learning-Based Mixed Job Scheduler Scheme for Grid or IaaS Cloud

Job scheduling is a necessary prerequisite for performance optimization and resource management in the cloud computing system. Focusing on accurate scaled cloud computing environment and efficient job scheduling under Virtual Machine (VM) resource and Server Level Agreement (SLA) constraints, we introduce the architecture of cloud computing platform and optimization job scheduling scheme in this study. The system model is comprised of clearly defined separate constituent parts, including portal, job scheduler, and resources pool. By analyzing the execution process of user jobs, we designed a novel job scheduling scheme based on reinforcement learning to minimize the makespan and Average Waiting Time (AWT) under the VM resource and deadline constraints, and employ parallel multi-age parallel technologies to balance the exploration and exploitation in learning process and accelerate the convergence of Q -learning algorithm. Both simulation and real cloud platform experiment results demonstrate the efficiency of the proposed job scheduling scheme.

An Intelligent Approach to Allocating Resources within an Agent-Based Cloud Computing Platform

The cloud computing paradigm has the ability to adapt to new technologies and provide consistent cloud services. These features have led to the widespread use of the paradigm, making it necessary for the underlying computer infrastructure to cope with the increased demand and the high number of end users. Platforms often use classical mathematical models for this purpose, helping assign computational resources to the services provided to the final user. Although this kind of model is valid and widespread, it can be refined through intelligent techniques. Therefore, this research presents a novel system consisting of a multi-agent system, which integrates a case-based reasoning system. The resulting system dynamically allocates resources within a cloud computing platform. This approach, which is distributed and scalable, can learn from previous experiences and produce better results in each resource allocation. A model of the system has been implemented and tested on a real cloud platform with successful results.

A Hybrid Fault-Tolerant Scheduling for Deadline-Constrained Tasks in Cloud Systems

Among multiple fault-tolerant strategies, resubmission, and replication are fundamental and widely recognized in distributed computing systems. In recent years, many algorithms based on replication or resubmission have been proposed. However, few of them consider these two techniques together, especially in Cloud systems. In this article, we propose a Hybrid Fault-Tolerant Scheduling Algorithm (HFTSA) for independent tasks with deadlines by integrating the above techniques in virtualized Cloud systems. During the task scheduling process, HFTSA selects fault-tolerant strategies from resubmission and replication for each accepted task based on the characteristics of both task and Cloud resources and then reserves suitable resources. During the task execution process, HFTSA adopts an online adjustment scheme for fault-tolerant strategies of some tasks if necessary while providing an online scheduling scheme for faults. Moreover, an elastic resource provisioning mechanism is designed and incorporated into HFTSA to dynamically adjust the provided resources to improve resource utilization. Experiments on a real cloud platform and a simulated platform are conducted to verify the effectiveness of the proposed HFTSA. The results demonstrate that HFTSA can provide an efficient fault-tolerant scheduling strategy for deadline-constrained tasks with high resource utilization and performs better than corresponding competitors.

Establishment of Urban Real Estate Archives Information Management Platform Based on BIM Cloud Technology

The modern information technology, represented by real estate file information model (BIM), cloud platform and big data, has developed rapidly in the development and management of urban real estate, and the basic framework of intelligent city (CIM) based on big data and information model has been gradually formed. There are many problems in urban real estate archives management in China, such as insufficient information, low visualization and intelligence, which can not meet the needs of various professional applications, including real estate evaluation and transaction, real estate maintenance and management, and the future needs of intelligent cities for urban real estate information sharing and intelligent management. By using BIM cloud technology, this study can solve the problem of 3D visualization and management information of real estate archives in the receiving and keeping of real estate archives. It plays an active role in realizing the intelligence of real estate archives and the construction of intelligent city in the future.

General spin-up time distribution for energy-aware IaaS cloud service models

Cloud computing services provided over the Internet are realized by servers in physically distributed data centers that consume tremendous power for operational and maintenance purposes. To minimize energy consumption, modern cloud systems adopt intelligent sever power switching with thresholds based on the current system load and are bounded by the number of idle servers. The time taken to power on physical or virtual servers, known as the spin-up time, can significantly impact the delay incurred in service delivery and elasticity of real cloud platforms. In this paper, we model and assess the asymptotic performance of an energy-aware cloud data center assuming general distribution for server spin-up time. The waiting time of a newly arriving request is defined in the service-level agreement (SLA) and for each busy server, the fixation time distribution derived from an absorbing birth-and-death process characterizes the impact of thresholds. Simulation results show that the proposed model calculates the probability of SLA violation for different threshold values with less than 0.5% error.

A Secure Virtual Machine Allocation Strategy Against Co-Resident Attacks

In the area of network development, especially cloud computing, security has been a long-standing issue. In order to better utilize physical resources, cloud service providers usually allocate different tenants on the same physical machine, i.e., physical resources such as CPU, memory, and network devices are shared among multiple tenants on the same host. Virtual machine (VM) co-resident attack, a serious threat in this sharing methodology, includes malicious tenants who tend to steal private data. Currently, most solutions focus on how to eliminate known specific side channels, but they have little effect on unknown side channels. Compared to eliminating side channels, developing a VM allocation strategy is an effective countermeasure against VM co-resident attack as it reduces the probability of VM co-residency, but research on this topic is still in its infancy. In this study, firstly, a novel, efficient, and secure VM allocation strategy named Against VM Co-resident attack based on Multi-objective Optimization Best Fit Decreasing (AC-MOBFD) is proposed, which simultaneously optimizes load balancing, energy consumption, and host resource utilization during VM placement. Subsequently, security of the proposed allocation strategy is measured using two metrics – VM attack efficiency and VM attack coverage. Extensive experiments on simulated and real cloud platforms, CloudSim and OpenStack, respectively, demonstrate that using our strategy, the attack efficiency of VM co-residency is reduced by 37.3% and VM coverage rate is reduced by 24.4% when compared to existing strategies. Finally, we compare the number of co-resident hosts with that of hosts in a real cloud platform. Experimental results show that the deviation is below 9.4%, which validates the feasibility and effectiveness of the presented strategy.

A novel QoS negotiation model based on intelligent learning technique in clouds

As more and more high-end applications have been deployed on cloud platforms, the quality-of-service (QoS) of cloud platform has become an important issue that need to be addressed. To achieve desirable QoS, an efficient negotiation model plays a critical role because cloud application often requires plenty of resource during their execution. In this paper, we propose a novel QoS negotiation model which applies intelligent learning technique to adjust the negotiation policy, in this way, QoS agreement between application and resource provider can be quickly achieved even when multiple resource requests are involved concurrently. The proposed negotiation model is implemented in an integrated QoS negotiation framework, in which negotiation agents help to make resource reservations while allocations agents are responsible for committing those reservation requests. The proposed framework is tested in a real cloud platform, and the results indicate that it can significantly improve the negotiation efficiency comparing with other negotiation methods. In addition, the experimental results also show that by using the proposed negotiation model, large-scale workflow applications can reduce their makespan by about 7%~11%.

ACE: Availability-Aware CloudSim Extension

In the interconnected globe where service delivery is the success measure, cloud high availability (HA) is an indispensable area for enterprises. An HA-aware cloud system provides different approaches to handle the outages. This includes geo-redundancy, failover schemes, and HA-aware placement solutions. However, using real-cloud platforms to model HA-aware approaches is hindered by the configuration settings. To this end, simulation tools, such as CloudSim, can be used to evaluate HA solutions and a cloud resiliency against failures. CloudSim allows implementing of scheduling policies, but it does not support HA properties. This paper provides availability-aware CloudSim extension (ACE). ACE extends CloudSim with a graphical and textual modeling to ensure simplicity and reusability of cloud scenarios. ACE has added HA-aware modeling (HA metrics and failure/redundancy/interdependency models) and HA-aware scheduling (HA-aware placements, failover, repair, and load balancing policies) into CloudSim. With ACE, the creation of cloud scenarios is facilitated, and multiple HA-aware deployment solutions can be evaluated under different stochastic and deterministic events. ACE can assess the impact of different redundancy/failure models, and other performance policies to extract HA-aware lessons. In this paper, ACE is assessed on a cloud application to evaluate different redundancy/failure models and provide availability analysis of the HA-aware placement solution.

A multi-objective decision support framework for virtual machine placement in cloud data centers: a real case study

Cloud computing (CC) is the concept of accessing to computing resources: servers, networks, storage, and applications, on demand through a network. This new paradigm has led to the birth of several data centers worldwide offering cloud services across millions of virtual machines. In fact, virtual machine placement (VMP) is considered as one of the greatest challenges for cloud providers to optimize their platforms in terms of physical machines number which reduces power costs and resources wastage. In this work, we propose an efficient framework based on multi-objective genetic algorithm (GA) and Bernoulli simulation that aims to minimize simultaneously used hosts and resource wastage in each PM on a CC platform. We operationalized our GA in a real case study related to the real cloud platform of the Office of the Merchant Marine and Ports of Tunisia (OMMP). This framework not only helped this company to optimize the VMP of their outsourced backup site, but also to minimize the operating expenses dedicated to the target platform. The proposed algorithm is tested on the OMMP’s data center, and experimental results show that the proposed technique significantly outperforms the compared methods especially in terms of VMP quality.

Possibilities of transfer process data from PLC to Cloud platforms based on IoT

This article is based on efficient and user-friendly remote control of the real device via internet by PLC with using Cloud platforms. For control it is used Industrial IoT Gateway, which storages and processes data through several CLOUD platforms. The main aim is to introduce a solution for transmission of process data between PLC and Cloud platforms in laboratory of industrial automation and distributed control Systems within the VSB - Technical University of Ostrava. It can be used for this IoT concept mechatronic tasks in laboratory. Another important part is to compare latency of process data between a real device (mechatronic tasks) and CLOUD platforms. Demonstration example of using this solution compares two Cloud Platform. The first commercial platform is from company IBM and second free platform is from company Ubidots. A part of the demonstration of reached results is also available visualization.

HirePool: Optimizing Resource Reuse Based on a Hybrid Resource Pool in the Cloud

In a cloud environment, the primary way to optimize physical resources is to reuse a physical machine (PM) by consolidating complementary multiple virtual machines (VMs) on it. When considering VMs’ dynamically changing resource demands, one hot research topic revolves around reusing VM migration resources more efficiently. The challenge here is finding the best tradeoff between the VM migration optimization performance and complexity. On one hand, to improve the migration efficiency, VM migration planning is adopted to achieve efficient resource reuse while minimizing the number of VM migrations. On the other hand, the huge number of PMs and VMs in a cloud datacenter often adds considerable complexity to migration planning, which hampers the decision-making process in VM migration. To address these issues, this paper proposes a hybrid resource pool model to reduce the complexity of VM migration planning by limiting the scope of VM migration decisions. Then, based on this model, we use our novel resource-reuse optimization mechanism (called HirePool) to improve efficiency by maximizing resource usage with only a few VM migrations. Finally, we perform simulation tests and actual experiments running on a real cloud platform to evaluate HirePool. Results show that HirePool improves average resource usage by 13%, saves the number of PMs used by 12%, and reduces the average number of migrations (compared with contrast mechanisms) by 31%.

Reservation schemes for IaaS cloud broker: a time‐multiplexing way for different rental time

SummaryInfrastructure‐as‐a‐Service cloud providers always charge users in 2 ways: on‐demand billing and reservation billing with different discounts. On‐demand billing is more flexible for short‐term use but with higher cost, while reservation billing is much cheaper for long renting period but may cause inefficiency because of underutilized capacity. As middle agent, cloud broker always tries to reserve a large number of virtual machine (VM) instances from cloud providers to meet the requirement of all users, while making high profit through reducing its cost by leveraging long‐term reservations and time‐multiplexing of instances. Different from existing work, we believe that the rental time of each user's request should be served consecutively in one VM instance, rather than partitioned into different VM instances by cloud broker to enhance time‐multiplexing ratio. In this paper, we first try to smooth users' unordered and time‐overlapping requests using demand graph, based on which we propose two reservation algorithms for cloud broker, one for off‐line and the other for online. We also consider cloud broker with multiple service providers, finding that much more cost can be saved by leveraging the various reservation discounts of those providers. Extensive simulations have been done on the traces from real cloud platforms. Experiment results show that the broker cost can be substantially saved using our off‐line and online algorithms.

Geometric Range Search on Encrypted Spatial Data

Geometric range search is a fundamental primitive for spatial data analysis in SQL and NoSQL databases. It has extensive applications in location-based services, computer-aided design, and computational geometry. Due to the dramatic increase in data size, it is necessary for companies and organizations to outsource their spatial data sets to third-party cloud services (e.g., Amazon) in order to reduce storage and query processing costs, but, meanwhile, with the promise of no privacy leakage to the third party. Searchable encryption is a technique to perform meaningful queries on encrypted data without revealing privacy. However, geometric range search on spatial data has not been fully investigated nor supported by existing searchable encryption schemes. In this paper, we design a symmetric-key searchable encryption scheme that can support geometric range queries on encrypted spatial data. One of our major contributions is that our design is a general approach, which can support different types of geometric range queries. In other words, our design on encrypted data is independent from the shapes of geometric range queries. Moreover, we further extend our scheme with the additional use of tree structures to achieve search complexity that is faster than linear. We formally define and prove the security of our scheme with indistinguishability under selective chosen-plaintext attacks, and demonstrate the performance of our scheme with experiments in a real cloud platform (Amazon EC2).