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4. On Non-Preemptive VM Scheduling in the Cloud

   https://doi.org/10.1145/3219617.3219644  

   Psychas, Konstantinos; Ghaderi, Javad (January 2018, SIGMETRICS '18 Abstracts of the 2018 ACM International Conference on Measurement and Modeling of Computer Systems)

   We study the problem of scheduling VMs (Virtual Machines) in a distributed server platform, motivated by cloud computing applications. The VMs arrive dynamically over time to the system, and require a certain amount of resources (e.g. memory, CPU, etc) for the duration of their service. To avoid costly preemptions, we consider non-preemptive scheduling: Each VM has to be assigned to a server which has enough residual capacity to accommodate it, and once a VM is assigned to a server, its service cannot be disrupted (preempted). Prior approaches to this problem either have high complexity, require synchronization among the servers, or yield queue sizes/delays which are excessively large. We propose a non-preemptive scheduling algorithm that resolves these issues. In general, given an approximation algorithm to Knapsack with approximation ratio r , our scheduling algorithm can provide rβ fraction of the throughput region for β < r. In the special case of a greedy approximation algorithm to Knapsack, we further show that this condition can be relaxed to β<1. The parameters β and r can be tuned to provide a tradeoff between achievable throughput, delay, and computational complexity of the scheduling algorithm. Finally extensive simulation results using both synthetic and real traffic traces are presented to verify the performance of our algorithm. 

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5. Fixed-Parameter Analysis of Preemptive Uniprocessor Scheduling Problems

   Baruah, Sanjoy; Ekberg, Pontus; Singh, Abhishek (December 2022, Proceedings of the 43rd IEEE Real-Time Systems Symposium (RTSS 2022))