SparkGrid:区块链辅助的安全查询调度和基于spark的Apache网格环境下服务实时迁移的动态风险评估

IET Blockchain Pub Date : 2025-02-14 DOI:10.1049/blc2.70004
Gangasandra Mahadevaiah Kiran, Narasimaiah Nalini
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引用次数: 0

摘要

网格计算是一种新兴技术,它支持异构数据收集和向用户提供服务。由于传入的异构请求数量很大,网格计算需要有效的调度来减少执行时间,满足服务水平协议(SLA)和服务质量(QoS)要求。为此,我们提出了SprakGrid方法来减少执行时间并满足SLA和QoS要求。提出的工作包括四个连续的阶段,解释如下:首先,我们使用基于椭圆曲线的混沌理论算法进行用户认证以确保用户的合法性,该算法生成一个密钥并将其存储在区块链中。其次,考虑到3P的参数,利用软角色评论家算法对资源发现进行查询调度,该调度由spark环境执行,spark环境根据服务请求调度最优资源。第三,我们进行风险评估和请求删除,其中工作人员的风险节点由主节点评估。为了解决攻击者的资源浪费问题,本文采用香农熵对风险值进行动态评估。根据风险评估,将请求分为正常和恶意两类。第四,利用基于多约束的帝企鹅优化技术实现业务实时迁移,将恶意请求从源节点删除,正常请求从源节点迁移到目标节点。最后,通过GridSim进行了仿真,仿真结果表明,与其他先进的方法相比,所提出的SparkGrid方法具有优越的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

SparkGrid: Blockchain Assisted Secure Query Scheduling and Dynamic Risk Assessment for Live Migration of Services in Apache Spark-Based Grid Environment

SparkGrid: Blockchain Assisted Secure Query Scheduling and Dynamic Risk Assessment for Live Migration of Services in Apache Spark-Based Grid Environment

Grid computing is an emerging technology that enables the heterogeneous collection of data and provision of services to users. Due to the high amount of incoming heterogeneous requests, grid computing needs efficient scheduling to reduce execution time and satisfy service level agreement (SLA) and quality of service (QoS) requirements. For that purpose, we proposed the SprakGrid method to reduce execution time and satisfy SLA, and QoS requirements. The proposed work includes four consecutive phases which are explained as follows, in first we perform user authentication to ensure the legitimacy of the users using the elliptic curve-based chaos theory algorithm which generates a secret key and stores it in the blockchain. In the second we perform query scheduling for resource discovery using the soft actor critic algorithm by considering 3P's parameters which is performed by spark environment that schedules optimal resources based on the service request. Third, we perform a risk assessment and request dropping, in which the risk nodes of workers are evaluated by the master node. To address the resource wastage by attackers, this research dynamically evaluates the risk value using Shannon entropy. Based on the risk assessment the requests are classified into two classes such as normal and malicious. Fourth we perform service live migration, in which the malicious requests are dropped and normal requests are migrated from the source node to the target node using multi-constraints based emperor penguin optimization. Finally, simulation is performed by GridSim and the simulation results demonstrate that the proposed SparkGrid method achieves superior performance compared to other state-of-the-art methods.

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