Resource pool management: Reactive versus proactive or let’s be friends

Abstract

The consolidation of multiple workloads and servers enables the efficient use of server and power resources in shared resource pools. We employ a trace-based workload placement controller that uses historical information to periodically and proactively reassign workloads to servers subject to their quality of service objectives. A reactive migration controller is introduced that detects server overload and underload conditions. It initiates the migration of workloads when the demand for resources exceeds supply. Furthermore, it dynamically adds and removes servers to maintain a balance of supply and demand for capacity while minimizing power usage. A host-load simulation environment is used to evaluate several different management policies for the controllers in a time effective manner. A case study involving three months of data for 138 SAP applications compares three integrated controller approaches with the use of each controller separately. The study considers trade-offs between i) required capacity and power usage, ii) resource access quality of service for CPU and memory resources, and iii) the number of migrations. Our study sheds light on the question of whether a reactive controller or proactive workload placement controller alone is adequate for resource pool management. The results show that the most tightly integrated controller approach offers the best results in terms of capacity and quality but requires more migrations per hour than the other strategies.

Adaptation in cloud resource configuration: a survey

“…However, setting the optimal threshold is not simple and typically requires workload knowledge. Some proposals used experimentation [39,57] to set threshold values. In [45], the authors proposed an alternative approach, where the customer defines the SLA and the IPs set the resource utilisation thresholds.…”

Selling T-shirts and Time Shares in the Cloud

“…In each interval it will consolidate workloads onto a minimum set of servers and power down unused servers to save power and associated cooling costs. A more detailed description can be found in [5], [6], [8].…”

“…Finally, we note that these consolidation results are optimistic since they use historic workloads over the past time periods for consolidation. Accommodating unexpected future rises and variability in demands typically require 10-20% more capacity as shown in [8]. This applies to both the t-shirt sizing and time share sizing resource management methods, but likely more so to the time share sizing method.…”

Resource and virtualization costs up in the cloud: Models and design choices

“…Without loss of generality, this paper considers the highest quality of service, which corresponds to a required capacity for workloads on a server that is the peak of their aggregate demand. The engine can be used to support studies on the advantages of consolidation and for operational management [16].…”