31,824 research outputs found
On Asymptotic Optimality of Dual Scheduling Algorithm In A Generalized Switch
Generalized switch is a model of a queueing system where parallel servers are interdependent and have time-varying service capabilities. This paper considers the dual scheduling algorithm that uses rate control and queue-length based scheduling to allocate resources for a generalized switch. We consider a saturated system in which each user has infinite amount of data to be served. We prove the asymptotic optimality of the dual scheduling algorithm for such a system, which says that the vector of average service rates of the scheduling algorithm maximizes some aggregate concave utility functions. As the fairness objectives can be achieved by appropriately choosing utility functions, the asymptotic optimality establishes the fairness properties of the dual scheduling algorithm.
The dual scheduling algorithm motivates a new architecture for scheduling, in which an additional queue is introduced to interface the user data queue and the time-varying server and to modulate the scheduling process, so as to achieve different performance objectives. Further research would include scheduling with Quality of Service guarantees with the dual scheduler, and its application and implementation in various versions of the generalized switch model
Learning While Scheduling in Multi-Server Systems with Unknown Statistics: MaxWeight with Discounted UCB
Multi-server queueing systems are widely used models for job scheduling in
machine learning, wireless networks, crowdsourcing, and healthcare systems.
This paper considers a multi-server system with multiple servers and multiple
types of jobs, where different job types require different amounts of
processing time at different servers. The goal is to schedule jobs on servers
without knowing the statistics of the processing times. To fully utilize the
processing power of the servers, it is known that one has to at least learn the
service rates of different job types on different servers. Prior works on this
topic decouple the learning and scheduling phases which leads to either
excessive exploration or extremely large job delays. We propose a new
algorithm, which combines the MaxWeight scheduling policy with discounted upper
confidence bound (UCB), to simultaneously learn the statistics and schedule
jobs to servers. We prove that under our algorithm the asymptotic average queue
length is bounded by one divided by the traffic slackness, which is order-wise
optimal. We also obtain an exponentially decaying probability tail bound for
any-time queue length. These results hold for both stationary and nonstationary
service rates. Simulations confirm that the delay performance of our algorithm
is several orders of magnitude better than previously proposed algorithms
A BRANCH AND BOUND ALGORITHM FOR WORKFLOW SCHEDULING
Nowadays, people are connected to the Internet and use different Cloud solutions to store, process and deliver data. The Cloud consists of a collection of virtual servers that promise to provision on-demand computational and storage resources when needed. Workflow data is becoming an ubiquitous term in both science and technology and there is a strong need for new tools and techniques to process and analyze large-scale complex datasets that are growing exponentially. scientific workflow is a sequence of connected tasks with large data transfer from parent task to children tasks. Workflow scheduling is the activity of assigning tasks to execution on servers and satisfying resource constraints and this is an NP-hard problem. In this paper, we propose a scheduling algorithm for workflow data that is derived from the Branch and Bound Algorithm
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