Turnaround Time Minimization-Based Static Scheduling Model Using Task Duplication for Fine-Grained Parallel Applications onto Hybrid Cloud Environment

Cloud computing is one of the emerging technologies in last decade; this technology provides a service model to organizations and public users. Organization users and developers, they start and maintain their organization without any hardware and software infrastructure they can develop their company with the help of cloud technology. Cloud deployment is categorized into three types, private, public and hybrid cloud environment and here public and private cloud more secure comparatively hybrid cloud because when data is moving from private to public cloud security problem is occur. Data communication is most important task in a network environment so the proposed model is focus on data security in hybrid cloud environment. In a hybrid cloud the participation of private cloud and its nodes very important after that the data transmission is depends on active node, In this node selection is based on genetic algorithm. The result of proposed model is going to compare with private and public cloud security parameters.

Virtual computing resources are provided via a cloud system that is both clever and intelligent. Based on the user's request, computing resources are made available. A hybrid cloud is the best option for storing and accessing user data for cloud deployments. Maintaining security in a hybrid cloud environment is time-consuming. This study provides a novel strategy for securing data in the hybrid cloud by ensuring the user's data is protected. Users' data in a hybrid cloud is protected using cryptographic approaches provided in this approach. Using this strategy, users' data may be protected in public and private clouds using various encryption methods. The suggested data security paradigm offers various advantages to both consumers and providers in terms of data security. Three symmetric encryption methods are offered as a service in the cloud. The concept is implemented as a cloud-based application hosted in the cloud, and the effectiveness of three strategies is assessed. They are evaluated in terms of performance and security. Using the recommended encryption methods in a hybrid cloud environment is more efficient than using other methods. The proposed technique can be used for relational data. It can be modified and enhanced to process multimedia data.

With the development of cloud computing, hybrid cloud has attracted extensive attention. It is a flexible collaboration of private clouds and public clouds, which can meet the increasing demands of energy consumption while reduce overhead. However, the clouds in hybrid clouds have to share data with each other. Therefore, hybrid connection mechanism remains a big issue. VPN is a common solution used as a bridge for connecting private clouds and public clouds. Using VPN to construct a direct connected network is a simple solution to implement where all the clients in hybrid cloud connect to a specified OpenVPN server residing in public cloud. Unfortunately, it will result in many problems impacting the efficiency of network. In this paper, we propose a proxy based virtual network solution. To compare the network QoS of the proxy based virtual network solution to the direct connected virtual network solution, we designed three controlled experiments measuring the bandwidth and delay time of the intra-cloud and inter-cloud communication. Then we compare the network performance of direct connected virtual network solution and proxy based virtual network with a Hadoop application on a hybrid cloud environment. The experiment result indicates that the proxy based virtual network solution outperforms the direct connected virtual network solution.

Although some cloud providers look at the hybrid cloud as blasphemy, there are strong reasons for them to adopt it. Hybrid clouds offer the cost and scale benefits of public clouds while also offering the security and control of private clouds. Task scheduling, one of the most famous combinational optimization problems, plays a key role in the hybrid cloud environment. We propose a graph-based task scheduling algorithm. In order to achieve minimum cost, our algorithm takes into account not only the private resources but also the public resources. This paper also presents an extensive evaluation study and demonstrates that our proposed algorithms minimize the user's cost in a hybrid cloud environment.

Hybrid Cloud, which is a cloud deployment model that integrates the public cloud and private cloud, is gaining considerable attention recently. The key technology for building a Hybrid Cloud environment involves the integration of different types of clouds. In this paper, the concept of Hybrid CSB and a method to solve the cloud integration problem are proposed. We present a structure for recommending a service based on a pattern according to users' requirements. In addition, we propose a method for integrating the recommended services with an integration script and a script generation process. The recommendation and integration method proposed in this study is expected to be used as an underlying technology to facilitate the transition to the Hybrid Cloud environment.

Cyber laboratory for FPGA design experiments combines the actual laboratory and the remote laboratory in a seamless way in a hybrid cloud environment. The hybrid cloud consists of on premise private cloud to support device dependent services and a public cloud to support rest of the laboratory services. The on premise private cloud allows concurrent use of device dependent tasks, such as FPGA run services, by making use of the Web services. The elastic nature of the public cloud is suitable for coping with dynamically changing student experimental usages. The number of servers in the public cloud can be scale out or shrink according to the number of simultaneous students accesses. Expensive license CAD tools, such as Verilog-HDL synthesis tools, are allocated for a limited number of the servers in the public cloud and the private cloud, where Verilog-HDL synthesis Web services are realized to achieve remote sharing of these tools. For remote laboratory case, student virtual machines are allocated in the public cloud and remotely issue the FPGA run services and the Verilog-HDL Synthesis services. The existing faculty file/database and education support system serves as an adhesive agent to combine the on premise cloud and the public cloud to minimize the data transfer among them and to perform student VM migrations between remote and actual laboratories. The Cyber Laboratory achieves an efficient use of laboratory platforms and can reduce TCO (total cost of ownership), as the public cloud can cope with the dynamically changing experimental loads.

Cloud services are offered to consumers based on Service Level Agreements (SLAs) signed between Cloud Service Provider (CSP) and consumer. Due to on-demand provisioning of resources there is exponential growth of cloud consumers. Job scheduling is one of the areas that has attracted researchers to improve performance of cloud management system. Along with the on premise infrastructure, Small and Medium Enterprises (SMEs) also depend on public cloud infrastructure (leading to hybrid cloud) for seamless continuity of their businesses. In this context, ensuring SLAs and effective management of hybrid cloud resources are major challenging issues to be considered. Hence, there is a need for an effective scheduling algorithm which considers multiple objective functions like SLA (deadline), cost and energy while making scheduling decisions. Most of the state of the art schedulers in hybrid cloud environment considered single objective function. However, in real world, it is inadequate for scheduling effectiveness. To overcome this problem, we proposed an integrated framework which ensures SLAs (deadline), cost effectiveness and energy efficiency with an underlying scheduling algorithm known as SCE-TS. This algorithm is evaluated with different workloads and SLAs using a cloud platform. The empirical study revealed that the proposed framework improves scheduling efficiency in terms of meeting SLAs, cost and energy efficiency. It is evaluated and compared with the state of the art and found to be effective in making scheduling decisions in cloud environment.

Cloud computing is a key technology for online service providers. However, current online service systems experience performance degradation due to the heterogeneous and time-variant incoming of user requests. To address this kind of diversity, we propose a hierarchical approach for resource management in hybrid clouds, where local private clouds handle routine requests and a powerful third-party public cloud is responsible for the burst of sudden incoming requests. Our goal is to answer (1) from the online service provider’s perspective, how to decide the local private cloud resource allocation, and how to distribute the incoming requests to private and/or public clouds; and (2) from the public cloud provider’s perspective, how to decide the optimal prices for these public cloud resources so as to maximize its profit. We use a Stackelberg game model to capture the complex interactions between users, online service providers and public cloud providers, based on which we analyze the resource allocation in private clouds and pricing strategy in public cloud. Furthermore, we design efficient online algorithms to determine the public cloud provider’s and the online service provider’s optimal decisions. Simulation results validate the effectiveness and efficiency of our proposed approach.

A hybrid cloud becomes the preferred solution when enterprises deploy cloud service. Using new technology such as software-defined network (SDN) and network virtualization to form a network will be the trend of the future. This paper introduces the new safety protection opportunities brought by new network technologies, and analyzes the challenges and risks of hybrid cloud system using these technologies. Finally, it puts forward the corresponding solution.

Hybrid cloud environments have seen a rapid rise in recent years. An essential part of a hybrid cloud is its ability to orchestrate the allocation, provisioning, and management of different compute resources spanning multiple cloud systems, and drive these operations across multiple cloud systems in an automated way. The Orchestration and Automation Engines (OAEs) of a hybrid cloud must themselves be highly available for ensuring high resiliency of the hybrid cloud. We present our experience in providing resiliency to the OAEs of a real-world hybrid cloud in this paper. The presentation includes the resiliency architecture of the OAEs, solutions that deal with errors ranging from software component crash to configuration/metadata error and data corruption, experimental results and our lessons learned from the practical experience.

Due to the security and scalability features of hybrid cloud architecture, it can better meet the diverse requirements of users for cloud services. And a reasonable resource allocation solution is the key to adequately utilize the hybrid cloud. However, most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling, even ignoring the conflicts between its security privacy features and other requirements. Based on the above problems, a many-objective hybrid cloud task scheduling optimization model (HCTSO) is constructed combining risk rate, resource utilization, total cost, and task completion time. Meanwhile, an opposition-based learning knee point-driven many-objective evolutionary algorithm (OBL-KnEA) is proposed to improve the performance of model solving. The algorithm uses opposition-based learning to generate initial populations for faster convergence. Furthermore, a perturbation-based multipoint crossover operator and a dynamic range mutation operator are designed to extend the search range. By comparing the experiments with other excellent algorithms on HCTSO, OBL-KnEA achieves excellent results in terms of evaluation metrics, initial populations, and model optimization effects.

Background & Objective: Detecting and mitigating Distributed Denial of Service (DDoS) attacks is a serious problem. In addition, new features and network deployments such as Software- Defined Networking (SDN) may open the door for new threats that did not previously exist. : Recent publications and patent are reviewed to find new techniques developed for integrating different mechanisms to secure networks against DDoS. Methods: This work presents a simple model for integrating different mechanisms to secure both SDN and legacy network in a hybrid cloud environment, it is called FocusON. It aims at mitigating DDoS attacks of a victim network. In addition, separating network monitoring from its control aims at mitigating DDoS attacks of a victim network. Traffic pattern analysis is apart from attack detection mechanism that gives a conceptual representation of a specific kind of DDoS attacks. DDoS detection is a completely automated process. Once called, for the reaction, the active response will be taken against the real IP source of the attacker. : The communication time overhead was tested in order to evaluate the remote server response time in case of deploying our proposed model mechanisms and without our proposed model. : Here we introduce a response mechanism that consists of an analysis of event logs, traffic patterns, and IP traceback. The proposed model categorizes the underlying network according to the location into a victim network and the source of attack (public cloud). Results & Conclusion:: The proposed model implemented in a hybrid cloud environment using the network of SDN and legacy network. The experimental setup was built using our network lab connected to the Amazon public cloud.

NoSQL and relational databases have been integrated in response to the increasing demand for scalable and efficient data management in hybrid cloud environments. The differences in data structures and query processing methods between these databases present both challenges and opportunities when designing an optimized hybrid system. This study explores the integration of NoSQL and relational databases to maximize data storage, retrieval, and processing efficiency across multiple cloud systems. NoSQL databases excel in handling unstructured and semi-structured data, offering flexibility and scalability, whereas relational databases provide robust consistency and structured query capabilities. Despite the wide availability of relational databases, certain applications require the dynamic adaptability of NoSQL systems, making integration a viable solution. The research evaluates key performance parameters, including query execution speed, scalability, data consistency, and resource utilization. Cloudsim is used for simulation, allowing for an in-depth comparison between standalone and hybrid database models. Experimental results indicate that the proposed hybrid model improves query performance by 30%, reduces latency by 20%, and enhances scalability by 40% compared to using relational or NoSQL databases alone. The novelty of this approach lies in its ability to overcome the limitations of each database type by their strengths in a dynamic cloud environment. The results highlight the effectiveness of hybrid database integration in optimizing cloud-based data management while ensuring seamless operation, adaptability, and resource efficiency.

Appearance of Science Clouds enables scientists to facilitate large-scale scientific computational experiments over cloud environment. Many task computing (MTC) in computational science needs to certificate stable executions of applications even in rapid changes of vital status of physical resources and supports high performance resources in a long period. Auto-scaling approach on virtual machines (VM) increases efficient cloud resources management for the computational problem solving environment. Diverse auto-scaling methods which provide useful resource management presently are being debated and studied. However, most of the auto-scaling methods are just easily considered in performance metrics or execution deadline in specific workloads but not in various patterns of workflow. We propose an auto-scaling method, guaranteeing the execution of various patterns of workflow within deadline time in hybrid cloud environment. The experimental results show the method works dynamically and acceptably on hybrid cloud resources for various workflow patterns having random workload dependency.

In recent years, many service providers have started migrating their service offerings to cloud infrastructure. Sometimes, parts of the service workflow can however not be moved to cloud environments. This can occur due to client policies, or because some services are linked to physical client-site devices. The result of the migration is then a hybrid cloud environment, where part of the services are executed within the client network, while most of the processing is moved to the cloud. Migration to the cloud enables a more flexible deployment of services, but also increases the strain on underlying networks as most tasks are partially handled in a remote cloud, and no longer just in the local network. An important question that providers must answer before new service workflows are deployed is whether they can provide the workflow with sufficient quality of service, and whether the deployment will impact existing service workflows. In this paper we discuss strategies based on multi-commodity flow problems, a subset of graph flow problems that can be used to determine whether new service workflows can be sufficiently provisioned, and whether the addition of new workflows can negatively impact the performance of existing flows. We evaluate the proposed solution by comparing the performance of three approaches with respect to the number of successful workflows and with respect to their execution speed.