Abstract
A computing cloud for research and educational sector should consider different performance factors like reducing VM-VM communication cost, reducing VM migrations, and reducing VM-shared resource communication cost (like a shared file system). This work proposes eduCloud, a cloud for educational institutions with optimised approaches for VM placement, virtual machine (VM) reallocation during cloud fragmentation and cloud consolidation. For reducing communication costs, eduCloud employs a graph-based algorithm which operates on affinity matrix that contains information about magnitude of communication within several VMs. Proposed approach is able to achieve a reduction of 16.44% on average in total communication cost as compared to approaches like vector dot. 5.73% reduction in total communication is achieved over tight fitting approaches like first fit and volume-based. Percentage of in-place unfulfilled demands is reduced by 5.9% as compared to tight fitting algorithms by under provisioning the physical machines maintaining cloud utilisation and resource wastage levels. eduCloud also incorporates reallocation and consolidation routines which decrease communication cost by 5.57%. The distributed version of eduCloud yields very inexpensive Job allocation times of 775.18 ms and 218.43 ms for communicating and non-communicating jobs, respectively.
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