Identifying Shades of Green: The SPECpower Benchmarks

Lange, Klaus-Dieter

doi:10.1109/mc.2009.84

Cited by 93 publications

(41 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The heterogeneous hosts available in datacenter are set up based on assumptions that Google had certain kinds of commonly available machines in their datacenters in May 2011, when the trace was captured [19]. Table 2: Host characteristics and number suggested to be in Google's cluster in May 2011 [19] The power consumption values for these hosts are taken from SPEC power benchmarks [20]. The tasks are submitted according to arrivals in the Google dataset.…”

Section: Experimental Settingmentioning

confidence: 99%

An Energy Aware Cost Recovery Approach for Virtual Machine Migration

Zakarya

Gillam

2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Datacenters provide an IT backbone for today's business and economy, and are the principal electricity consumers for Cloud computing. Various studies suggest that approximately 30% of the running servers in US datacenters are idle and the others are under-utilized, making it possible to save energy and money by using Virtual Machine (VM) consolidation to reduce the number of hosts in use. However, consolidation involves migrations that can be expensive in terms of energy consumption, and sometimes it will be more energy efficient not to consolidate. This paper investigates how migration decisions can be made such that the energy costs involved with the migration are recovered, as only when costs of migration have been recovered will energy start to be saved. We demonstrate through a number of experiments, using the Google workload traces for 12,583 hosts and 1,083,309 tasks, how different VM allocation heuristics, combined with different approaches to migration, will impact on energy efficiency. We suggest, using reasonable assumptions for datacenter setup, that a combination of energy-aware fill-up VM allocation and energy-aware migration, and migration only for relatively long running VMs, provides for optimal energy efficiency.

show abstract

Section: Experimental Settingmentioning

confidence: 99%

An Energy Aware Cost Recovery Approach for Virtual Machine Migration

Zakarya

Gillam

2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…For the power model, p i MAX is set to 259W according to Ref. [23] and, c is set to 66% according to Ref. [21].…”

Section: Parameter Settingsmentioning

confidence: 99%

Green-Aware Virtual Machine Migration Strategy in Sustainable Cloud Computing Environments

Wang¹,

Zhang²,

Yang³

et al. 2017

Cloud Computing - Architecture and Applications

View full text Add to dashboard Cite

As cloud computing develops rapidly, the energy consumption of large-scale datacenters becomes unneglectable, and thus renewable energy is considered as the extra supply for building sustainable cloud infrastructures. In this chapter, we present a green-aware virtual machine (VM) migration strategy in such datacenters powered by sustainable energy sources, considering the power consumption of both IT functional devices and cooling devices. We define an overall optimization problem from an energy-aware point of view and try to solve it using statistical searching approaches. The purpose is to utilize green energy sufficiently while guaranteeing the performance of applications hosted by the datacenter. Evaluation experiments are conducted under realistic workload traces and solar energy generation data in order to validate the feasibility. Results show that the green energy utilization increases remarkably, and more overall revenues could be achieved.

show abstract

“…Energy efficiency can be defined as the number of instructions in billions executed per Watt hour (Bash et al, 2011). The Standard and Performance Evaluation Corporation (SPEC) community released SPECpower metric to measure power consumption (Lange, 2009). SPECpower is a Java application that generates a set of transactions completed per second.…”

Section: Power Consumptionmentioning

confidence: 99%

Evaluation Metrics for VM Allocation Mechanisms in Desktop Clouds

Alwabel¹,

Walters²,

Wills³

2015

Proceedings of the 2nd International Workshop on Emerging Software as a Service and Analytics

View full text Add to dashboard Cite

Abstract:Desktop Cloud computing is the idea of benefiting from computing resources around us to build a Cloud system in order to have better usage of these resources instead of them being idle. However, such resources are prone to failure at any given time without prior knowledge. Such failure events have a can negative impact on the outcome of a Desktop Cloud system. This paper proposes metrics that can evaluate the behaviour of Virtual Machine (VM) allocation mechanisms in the presence of node failures. The metrics are throughput, power consumption and availability. Three VM allocation mechanisms (Greedy, FCFS and RoundRobin mechanisms) are evaluated using the given metrics.

show abstract

Identifying Shades of Green: The SPECpower Benchmarks

Cited by 93 publications

References 0 publications

An Energy Aware Cost Recovery Approach for Virtual Machine Migration

An Energy Aware Cost Recovery Approach for Virtual Machine Migration

Green-Aware Virtual Machine Migration Strategy in Sustainable Cloud Computing Environments

Evaluation Metrics for VM Allocation Mechanisms in Desktop Clouds

Contact Info

Product

Resources

About