VM Economics for Java Cloud Computing: An Adaptive and Resource-Aware Java Runtime with Quality-of-Execution

Abstract

Resource management in Cloud Computing has been dominated by system-level virtual machines to enable the management of resources using a coarse grained approach, largely in a manner independent from the applications running on these infrastructures. However, in such environments, although different types of applications can be running, the resources are delivered equally to each one, missing the opportunity to manage the available resources in a more efficient and application driven way. So, as more applications target managed runtimes, high level virtualization is a relevant abstraction layer that has not been properly explored to enhance resource usage, control, and effectiveness.We propose a VM economics model to manage cloud infrastructures, governed by a quality-of-execution (QoE) metric and implemented by an extended virtual machine. The Adaptive and Resource-Aware Java Virtual Machine (ARA-JVM) is a clusterenabled virtual execution environment with the ability to monitor base mechanisms (e.g. thread scheduling, garbage collection, memory or network consumptions) to assess application's performance and reconfigure these mechanisms in runtime according to previously defined resource allocation policies. Reconfiguration is driven by incremental gains in quality-of-execution (QoE), used by the VM economics model to balance relative resource savings and perceived performance degradation.Our work in progress, aims to allow cloud providers to exchange resource slices among virtual machines, continually addressing where those resources are required, while being able to determine where the reduction will be more economically effective, i.e., will contribute in lesser extent to performance degradation.

Partial Utility-Driven Scheduling for Flexible SLA and Pricing Arbitration in Clouds

“…With the advent of Cloud Computing, particularly with the Infrastructure-as-a-Service business model, resource scheduling in virtualized environments received a prominent attention from the research community [18], [19], [20], [21], [22], addressed as either a resource management or a fair allocation challenge. At the same time the research community has built simulation environments to more realistically explore new strategies while making a significant contribution to repeatable science [23], [16], [8].…”

Flexible SLAs in the Cloud with a Partial Utility-Driven Scheduling Architecture

“…With the advent of Cloud Computing, particularly with the Infrastructure-as-a-Service business model, resource scheduling in virtualized environments received a prominent attention from the research community [13], [6], [14], [15], [16], [17], addressed as either a resource management or a fair allocation challenge.…”

We are all economists now

“…The main limitations of this work are that it only applies to a single VM in isolation, and that there is no notion of throughput optimization in their formalism. Simão and Veiga [21] present a semi-formal description of an adaptive resource sharing scheme that has the explicit objective of maximizing yield across multiple VMs. However this preliminary work does not present a concrete implementation and is not explicit about the optimization technique used to maximize the yield metric.…”