WITCAT: A Workload Spike Targeted Cloud Management Solution

The cloud computing technology offers consistent access to large-scale computing capabilities, thereby bringing convenience to life. However, the virtualized cloud systems are still too vulnerable to maintain performance scalability and service agility once a task burst surges in without any warning. A mounting account of research has been conducted on proper strategies for accurate workload prediction as well as effective resource reservation and arrangement, but commonly cloud providers seek help to strategies that deploy excessive resources, adding overhead cost and sacrificing the cloud's advantage of scalability, or otherwise fail to reconfigure timely and properly, causing dissatisfaction and even financial loss, which are not expected by both cloud providers and clients.

In this paper, we present a holistic solution called Workload Spike Targeted Cloud Management Solution (WITCAT) for virtualized cloud systems with three fundamental modules as a whole, which was seldom proposed before. By learning historical taskflow patterns, WITCAT can effectively classify the arriving tasks into clusters that feature respective workload traits. Then two different prediction means are employed to continually forecast the arrival rate and attributes of workloads for respective clusters, under two different characteristic scenarios: normal scenarios and bursty scenarios. Last, we employ a reservation strategy, makes full use of the available resources, strengthening the effectiveness of cloud service provisioning under workload spike.

As far as our knowledge reaches, the contributions are three-fold.

• We improve the clustering method for task characterization, where a Mahalanobis-distance-bused k-means clustering is adopted to eliminate the relevance among tasks' attributes.

• We employ a traffic-oriented two-scenario integrated prediction method, with a control knob that monitors the increment of tasks and triggers prediction means alternation for different workload scenarios.

• We develop a prediction-based heuristic algorithm for resource reservation and provisioning, reserving enough space in CPU and memory ahead of time for bursts without disabling the cloud's scalibility.

We conduct extensive experiments using Google cloud traces and the results outperform other scheduling algorithms in guarantee ratio (25.8% improved), total energy consumption (17.3% saved) and resource utilization (18.2% improved), which further indicates the advantages of our proposed solution towards task traffic bursts.