Resource Allocation in Networking and Computing Systems: A Security and Dependability Perspective

In recent years, there has been a trend to integrate networking and computing systems, whose management is getting increasingly complex. Resource allocation is one of the crucial aspects of managing such systems and is affected by this increased complexity. Resource allocation strategies aim to effectively maximize performance, system utilization, and profit by considering virtualization technologies, heterogeneous resources, context awareness, and other features. In such complex scenario, security and dependability are vital concerns that need to be considered in future computing and networking systems in order to provide the future advanced services, such as mission-critical applications. This paper provides a comprehensive survey of existing literature that considers security and dependability for resource allocation in computing and networking systems. The current research works are categorized by considering the allocated type of resources for different technologies, scenarios, issues, attributes, and solutions. The paper presents the research works on resource allocation that includes security and dependability, both singularly and jointly. The future research directions on resource allocation are also discussed. The paper shows how there are only a few works that, even singularly, consider security and dependability in resource allocation in the future computing and networking systems and highlights the importance of jointly considering security and dependability and the need for intelligent, adaptive and robust solutions. This paper aims to help the researchers effectively consider security and dependability in future networking and computing systems.publishedVersio

Performance Evaluation of Scheduling Algorithms for Real Time Cloud Computing Systems

Cloud computing shares data and oers services transparently among its users. With the increase in number of users of cloud the tasks to be scheduled increases. The performance of cloud depends on the task scheduling algorithms used in the scheduling components or brokering components. Scheduling of tasks on cloud computing systems is one of the research problem, Where the matching of machines and completion time of the tasks are considered. Tasks matching of machines problem is that, assume number of active hosts are Y, number of VMs in each host are Z. Maximum number of possible Virtual Machines(VMs) to schedule a single task is (y*z). If we need to schedule X tasks, number of possibilities are (y *z)^x. So scheduling of tasks is NP Hard problem. NP Hard means this scheduling of tasks on VMs not having polynomial time complexity, but it may have algorithm for verifying solution. Fault-tolerance becomes an important key to establish dependability in cloud computing system. In task scheduling, if task not completed in it's deadline ,then it is one type of fault in scheduling of tasks. In this thesis this type of faults are taken and try to overcome it. In this thesis we present a non-preemptive scheduling algorithm, By inserting the ideal time for postponing the task by ensuring the other task will completes its execution with in the deadline. In simulation the proposed algorithm maximizes the prot of 25%, throughput of 25% and minimizes the penalty of 20% over EDF

Fault aware task scheduling in cloud using min-min and DBSCAN

Cloud computing leverages computing resources by managing these resources globally in a more efficient manner as compared to individual resource services. It requires us to deliver the resources in a heterogeneous environment and also in a highly dynamic nature. Hence, there is always a risk of resource allocation failure that can maximize the delay in task execution. Such adverse impact in the cloud environment also raises questions on quality of service (QoS). Resource management for cloud application and service have bigger challenges and many researchers have proposed several solutions but there is room for improvement. Clustering the resources clustering and mapping them according to task can also be an option to deal with such task failure or mismanaged resource allocation. Density-based spatial clustering of applications with noise (DBSCAN) is a stochastic approach-based algorithm which has the capability to cluster the resources in a cloud environment. The proposed algorithm considers high execution enabled powerful data centers with least fault probability during resource allocation which reduces the probability of fault and increases the tolerance. The simulation is cone using CloudsSim 5.0 tool kit. The results show 25% average improve in execution time, 6.5% improvement in number of task completed and 3.48% improvement in count of task failed as compared to ACO, PSO, BB-BC (Bib = g bang Big Crunch) and WHO(Whale optimization algorithm)