On the design of decentralized control architectures for workload consolidation in large-scale server clusters

ABSTRACT

This paper develops a fully decentralized control architecture to address the workload consolidation problem in large-scale server clusters wherein the cluster's processing capacity is dynamically tuned to satisfy the service level agreements (SLAs) associated with the incoming workload while consolidating the workload onto the fewest number of servers. In a decentralized setting, this problem is decomposed into simpler subproblems, each of which is mapped to a server and solved by a controller assigned to that server. Though control loops on different servers run independently of each other, they are implicitly coupled via the shared high-level performance goal and interactions between controllers may result in undesired system behavior such as SLA violations and frequent switching of cores on and off. Using the proposed architecture as the reference, we analyze how the organization of individual controllers within the control structure affects its overall performance for large clusters of up to thousand servers. Our studies indicate that the control structure, when organized as a causal system in which a precedence relation exists among the individual controllers, achieves a high degree of SLA satisfaction (> 98%) while significantly reducing the corresponding switching cost.

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Index Terms

• Computer systems organization

  • Dependable and fault-tolerant systems and networks

• Computing methodologies

  • Modeling and simulation

    • Model development and analysis

      • Modeling methodologies

• General and reference

  • Cross-computing tools and techniques

    • Design