A New Task Scheduling Algorithm in 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.

ABSTRACTCloud computing is a multifarious computing paradigm incorporating the benefits of service-oriented architecture and utility computing through virtualization. Hybrid cloud, an amalgamation of two or more public and/or private clouds, is gaining high popularity between users due to various reasons involving improved performance, flexible business operations, capacity expansion, optimized costs, and enhanced security. The efficient execution of fine-grained parallel applications onto hybrid cloud system becomes limited due to a number of factors. From the application point of view, it ranges from the dynamicity of the applications to their precedence and communication constraints while for the computational resources, it includes heterogeneity of processors and participating clouds with their interconnection topology. This work proposes a compile time hybrid cloud-based task duplication strategy to execute the fine-grained applications represented as directed acyclic graph (DAG) onto the hybrid cloud environment. The proposed strategy schedules the tasks based on a degree relative to the critical path in the DAG and tries to achieve lower bound of the DAG. Furthermore, it makes an effort to avoid redundant duplication by duplicating only the required parent tasks considering the available idle slots to minimize the execution time of the application. The experimental study reveals that the proposed strategy performs better than its peers in terms of achieving the lower bound more efficiently with lesser degree of duplication for fine-grained jobs. The strategy is highly useful for cloud environment as it results in lower cost of usage of resources with enhanced system utilization.

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.

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.

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.

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.

Deadline-constrained cost-aware workflow scheduling in hybrid cloud

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.

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.

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.

Federated Learning (FL) represents a transformative approach to machine learning in hybrid cloud environments, addressing critical challenges in data privacy and security while enabling collaborative model training across distributed environments. This comprehensive technical overview explores the architecture, implementation, and benefits of FL in hybrid cloud deployments. The article explores how organizations can leverage FL to maintain data sovereignty while achieving comparable or superior model performance to traditional centralized approaches. By analyzing various aspects including communication optimization, model initialization, local training, and global aggregation, this article demonstrates FL's effectiveness in preserving privacy while enabling cross-organizational collaboration. The article encompasses privacy preservation mechanisms, security protocols, and scalability considerations, providing insights into both current capabilities and future directions for enterprise implementations.

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.

The rapid adoption of cloud computing has significantly transformed the way organizations manage and store data, but it has also introduced a complex array of security challenges. As businesses increasingly migrate to public, private, and hybrid cloud environments, ensuring robust protection of sensitive data and compliance with regulatory frameworks has become paramount. This chapter delves into advanced cloud security and access control models, with a particular focus on the integration of cutting-edge encryption technologies, identity management solutions, and continuous compliance monitoring. Key aspects such as multi-cloud security, data sovereignty, and the role of emerging technologies like artificial intelligence (AI) in threat detection are explored to provide a comprehensive view of modern cloud security strategies. The chapter emphasizes the importance of dynamic, real-time security measures that can adapt to the fluid nature of cloud environments, ensuring organizations maintain data integrity, privacy, and compliance in an increasingly interconnected digital ecosystem. By examining these critical components of cloud security, this work aims to provide actionable insights into safeguarding digital infrastructures in a rapidly evolving technological landscape.