Flexible resource allocation for reliable virtual cluster computing systems

Abstract

Virtualization and cloud computing technologies now make it possible to create scalable and reliable virtual high performance computing clusters. Integrating these technologies, however, is complicated by fundamental and inherent differences in the way in which these systems allocate resources to computational tasks. Cloud computing systems immediately allocate available resources or deny requests. In contrast, parallel computing systems route all requests through a queue for future resource allocation. This divergence of allocation policies hinders efforts to implement efficient, responsive, and reliable virtual clusters.In this paper, we present a continuum of four scheduling polices along with an analytical resource prediction model for each policy to estimate the level of resources needed to operate an efficient, responsive, and reliable virtual cluster system. We show that it is possible to estimate the size of the virtual cluster system needed to provide a predictable grade of service for a realistic high performance computing workload and estimate the queue wait time for a partial or full resource allocation. Moreover, we show that it is possible to provide a reliable virtual cluster system using a limited pool of spare resources. The models and results we present are useful for cloud computing providers seeking to operate efficient and cost-effective virtual cluster systems.

Towards Efficient Resource Allocation for Heterogeneous Workloads in IaaS Clouds

“…Such heuristic adjusting is simple to implement, but the prediction accuracy is low. Model-based PM provisioning approaches [16], [12], [25], [15], on the other hand, are able to achieve more precise prediction. Lin et al [12] and Chen et al [25] both proposed algorithms that minimize the cost of data center to seek power-proportional PM provisioning.…”

“…Lin et al [12] and Chen et al [25] both proposed algorithms that minimize the cost of data center to seek power-proportional PM provisioning. Hacker et al [16] proposed a hybrid provisioning for both HPC and cloud workloads to cover their features in resource allocation (HPC jobs are all queued by the scheduling system, but jobs in public clouds use all-or-nothing policy). However, these approaches only consider CPU as the dominant resource in single-dimensional resource allocation.…”

“…Secondly, the complexity of resource allocation considering multiple resource types will be significantly increased. The complexity of provisioning algorithms for homogeneous resource allocation [15], [16] is already high and the computational time is long given the large number of PMs in data centers nowadays. The further consideration of multiple resources adds additional dimensions to the computation which will significantly increase the complexity.…”

Exploring portfolio scheduling for long-term execution of scientific workloads in IaaS clouds

“…The second phase simulates the sets of policies under the limitation of their time quotas (Algorithm 1 line [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. The policies in Smart, Stale, and P oor are simulated, in this order.…”

“…commercial clouds such as Amazon EC2 [11,17]. Other communities have converted their local clusters into public or private clouds [16,42].…”