Titlebook: Resource Management in Distributed Systems; Anwesha Mukherjee,Debashis De,Rajkumar Buyya Book 2024 The Editor(s) (if applicable) and The A

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書目名稱Resource Management in Distributed Systems影響因子(影響力)




書目名稱Resource Management in Distributed Systems影響因子(影響力)學(xué)科排名




書目名稱Resource Management in Distributed Systems網(wǎng)絡(luò)公開度




書目名稱Resource Management in Distributed Systems網(wǎng)絡(luò)公開度學(xué)科排名




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書目名稱Resource Management in Distributed Systems被引頻次學(xué)科排名




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書目名稱Resource Management in Distributed Systems讀者反饋




書目名稱Resource Management in Distributed Systems讀者反饋學(xué)科排名

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,FSRmSTS—An Optimize Task Scheduling with a Hybrid Approach: Integrating FCFS, SJF, and RR with Medice schedulers. In order to balance waiting times for both short and long tasks, it uses a dynamic task quantum. It also splits the ready queue into two sub-queues according to task duration. Tasks are assigned to resources from each sub-queue in a mutually exclusive manner. The performance of the pr

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,Resource Targeted Cybersecurity Attacks in?Cloud Computing Environments,nderstand the attack methods, possible effects, and possible countermeasures for each of the listed attacks. At the end of the chapter, we provide a set of solution directions related to cloud targeted attacks.

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Load Balancing Using Swarm Intelligence in Cloud Environment,to find out which one should be better for load balancing. We also discuss about performance matrices that affect load balancing i.e., makespan time, throughput, waiting time, response time, and fault tolerance. There can be works that can use a combination of more than one swarm intelligence, as a

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,Cyber Attack Victim Separation: New Dimensions to?Minimize Attack Effects by?Resource Management,cers or moving target defense (MTD) driven methods. We see a heavy use of resource sharing in multi-tenant environments such as cloud infrastructures. Not having proper resource isolation may also result into collateral damages among the multi-tenant services in cloud based deployments. Resource con

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Deep Reinforcement Learning (DRL)-Based Methods for Serverless Stream Processing Engines: A Vision,maintain the desired performance goals. To address these challenges, this article proposes a vision for creating Deep Reinforcement Learning (DRL)-based methods for transforming stream processing engines into self-managed serverless solutions. This will lead to an increase in productivity as enginee

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2197-6503 and portability, security and privacy in resource management, reliable resource management, trustworthiness in resource management, fault tolerance in resource management, and simulation related to resource management..978-981-97-2646-2978-981-97-2644-8Series ISSN 2197-6503 Series E-ISSN 2197-6511

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