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We study a fundamental problem of how to schedule complex workflows in the cloud for applications such as data analytics. One of the main challenges is that such workflow scheduling problems involve many constraints, requirements and varied objectives and it is extremely difficult to find high-quality solutions. To meet the challenge, we explore using mixed integer programming (MIP) to formulate and...
As computational clouds offer increasingly sophisticated services, there is a dramatic increase in the variety and complexity of virtual machine (VM) placement problems. In this paper, we consider a VM placement problem with a special type of anti-colocation requirements—disk anti-colocation—which stipulate that, for every VM assigned to a PM (physical machine), its virtual disks should be spread...
In this paper, we investigate the issue of minimizing data center energy usage. In particular, we formulate a problem of virtual machine placement with the objective of minimizing the total power consumption of all the servers. To do this, we examine a CPU power consumption model and then incorporate the model into an mixed integer programming formulation. In order to find optimal or near-optimal...
Computational clouds have evolved to go beyond cost-effective on-demand hosting of IT resources and elasticity. The added features now include the ability to offer entire IT systems as a service that can quickly adapt to changing business environments. The new trend introduces challenges in datacenter resource management, including scalability, system-orientation, and optimization supporting both...
Today's virtualized resource management in datacenters is typically VM-centric, which has serious problems in scalability. We propose a shift from the traditional flat, fine-grained model towards a hierarchical, abstract model that facilitates decomposition of the complex resource allocation problem in a divide-and-conquer approach. In particular, this paper elaborates a pack-centric framework in...
In this paper, we introduce a pack-centric approach to data center resource management by abstracting a system as a pack of resources and considering the mapping of these packs onto physical data center resource groups, called swads. The assignment of packs/VMs to swads/PMs is formulated as an integer optimization problem that can capture constraints related to the available resources, data center...
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