A Type-Theoretic Framework for Efficient and Safe Colocation of Periodic Real-Time Systems

Abstract

Abstract-Desirable application performance is typically guaranteed through the use of Service Level Agreements (SLAs) that specify fixed fractions of resource capacities that must be allocated for unencumbered use by the application. The mapping between what constitutes desirable performance and SLAs is not unique: multiple SLA expressions might be functionally equivalent. Having the flexibility to transform SLAs from one form to another in a manner that is provably safe would enable hosting solutions to achieve significant efficiencies. This paper demonstrates the promise of such an approach by proposing a type-theoretic framework for the representation and safe transformation of SLAs. Based on that framework, the paper describes a methodical approach for the inference of efficient and safe mappings of periodic, real-time tasks to the physical and virtual hosts that constitute a hierarchical scheduler. Extensive experimental results support the conclusion that the flexibility afforded by safe SLA transformations has the potential to yield significant savings.

Colocation as a Service: Strategic and Operational Services for Cloud Colocation

“…For example, if services to be cohosted are of a periodic, real-time nature, then the identification of groupings of tenants that can be efficiently colocated would require the development of new functionalities and services, such as those envisioned in our recent work on the colocation of periodic real-time systems [38].…”

MORPHOSYS: Efficient Colocation of QoS-Constrained Workloads in the Cloud

“…We believe that providing this capability is crucial for the deployment of QoS-constrained workloads while at the same time ensuring efficient utilization of resources. Resource Allocation in Cloud Settings: Efficient scheduling of workloads in the cloud settings is an active topic of research [44,45] with central schedulers that focus on soft constraints such as data locality [46], deadlines [47,48], resource guarantees [49] or fairness [50]. Carvalho et al [51] analyze historical cloud workload data and motivate for the introduction of a new class of cloud resource offerings.…”

Formal Verification of SLA Transformations

“…3 This necessitates the use of searching and pruning heuristics that explore the solution space -namely, what SLA transform(s) to apply to each task, and how to partition the resulting set of tasks. Details of heuristics that were shown to be fairly effective (achieving significant packing efficiencies when compared to random co-location) and practical (scaling up to dozens of tasks per host) are given in [5], [11].…”

“…By "calculus", we mean a model that enables reasoning for the purpose of establishing SLA relationships/equivalencies. In this paper, we use a specific calculus that is particularly suited for periodic real-time systems [5]. In particular, our calculus is defined for a setting in which the "Task Workload" consists of a set of periodic real-time tasks, and the set of "Available Resources" are fixedcapacity hosts, which are individually scheduled using a Rate Monotonic Scheduler (RMS) [6].…”