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Abstract
Cloud-based software services necessitate adaptive resource allocation with the promise of dynamic resource adjustment for guaranteeing the Quality-of-Service (QoS) and reducing resource costs. However, it is challenging to achieve adaptive resource allocation for software services in complex cloud environments with dynamic workloads. To address this essential problem, we propose an adaptive resource allocation strategy for cloud-based software services with workload-time windows. Based on the QoS prediction, the proposed strategy first brings the current and future workloads into the process of calculating resource allocation plans. Next, the particle swarm optimization and genetic algorithm (PSO-GA) is proposed to make runtime decisions for exploring the objective resource allocation plan. Using the RUBiS benchmark, the extensive simulation experiments are conducted to validate the effectiveness of the proposed strategy on improving the performance of resource allocation for cloud-based software services. The simulation results show that the proposed strategy can obtain a better trade-off between the QoS and resource costs than two classic resource allocation methods.
Highlights
In cloud computing, different resources including central processing units (CPUs), memories, and storage units in data centers are virtualized to provide users with renting services, which is expected to promise the on-demand resource provisioning and authorize users the basic access to massive data and cloud resources [1]–[3]
PERFORMANCE EVALUATION we evaluate the performance of the proposed particle swarm optimization (PSO)-genetic algorithm (GA) based resource allocation strategy for cloud-based software services and make comparisons with the other two classic methods
EXPERIMENTAL RESULTS Based on the above settings of simulation experiments, we evaluate the performance of the proposed particle swarm optimization and genetic algorithm (PSO-GA) based resource allocation strategy for cloud-based software services and make comparisons with the greedy algorithm and singlepoint optimal local random method
Summary
Different resources including central processing units (CPUs), memories, and storage units in data centers are virtualized to provide users with renting services, which is expected to promise the on-demand resource provisioning and authorize users the basic access to massive data and cloud resources [1]–[3]. CHEN et al.: PSO-GA Based Resource Allocation Strategy for Cloud-Based Software Services with Workload-Time Windows needed for meeting the requirements of response time In this scheme, the CPU utilization was regarded as a threshold, the workload status was checked regularly (e.g., every 1 or 2 minutes), and the number of VMs to be adjusted (i.e., add or delete) was calculated. A clustering-based heuristic approach for edge resource allocation was proposed in [12] to reduce the average service response time of applications These two works only considered the deterministic user demands without implementing the dynamic resource allocation. With the consideration of the above problems, inspired by the work in [26], we propose an adaptive resource allocation strategy for cloud-based software services with workload-time windows.
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