Energy-efficient storage in virtual machine environments

Lei, Ye; Lu, Gen; Kumar, Sushanth; Gniady, Chris; Hartman, John H.

doi:10.1145/1735997.1736009

Cited by 12 publications

(5 citation statements)

References 16 publications

(5 reference statements)

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“…THE HARD DISK ANALYSIS Some hard drives also offer the ability to save energy by changing their working status [44]. For this reason there are some researchers that study the relationship between the I / O requirements and the opportunity to keep the hard drive in a suspended state [11], even in the context of virtual machines [45].…”

Section: The Network Device Analysismentioning

confidence: 99%

The Architecture of Kaligreen V2: A Middleware Aware of Hardware Opportunities to Save Energy

Valera

Dalmau²,

Roose³

et al. 2019

2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS)

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Nowadays, energy saving in the use of information technologies is a very important issue both from the economic and sustainability point of view. Many scientists investigate methods to save energy at different application levels (cloud: i.e. architectures, grid: i.e. middlewares and frameworks and hardware management: i.e. operating systems) and many of them agree on the strategy of executing programs, processes or virtual machines only using the time and resources that are strictly necessary. For this, it is necessary to plan strategies for deployment and relocation of processes; but always taking into account hardware repercussions and the knowledge of the architecture and applications behavior. On the other hand, it has already been demonstrated that the use of microservices brings numerous advantages in availability and efficiency; but we do not find many jobs that exploit this technique on the energy level. In this article, we present the architecture of a middleware for distributed microservices-based applications, which allows any negotiation-based scheduling algorithm to duplicate or move microservices from one device to another in a non-centralized way for energy savings, taking into account the consumption characteristics of the microservices and the capabilities that the hardware components offer.

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Section: The Network Device Analysismentioning

confidence: 99%

The Architecture of Kaligreen V2: A Middleware Aware of Hardware Opportunities to Save Energy

Valera

Dalmau²,

Roose³

et al. 2019

2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS)

View full text Add to dashboard Cite

show abstract

“…Moreover, the three approaches of Ye [13] towards the energy consumption of virtual hard drives use dynamic memory allocation for running virtual machines.…”

Section: Virtual Worldmentioning

confidence: 99%

“…It is important to provide equal shares of system resources in all test environments. However, it is not clear how this was achieved in some previous BareMetal and virtualization comparisons [13,14,18]. A hypervisor cannot practically use all system resources because the host operating system also requires a portion of these resources in order to maintain the hypervisor and other basic functions.…”

Section: Used Benchmarksmentioning

confidence: 99%

Energy efficiency with an application container

Demirkol¹,

Demirkol²

2018

Turk J Elec Eng & Comp Sci

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Efficient use of energy is an issue that the information technology (IT) world gives prominence to both in academia and industry. Cloud computing and the Internet of things, today's most popular subjects, make the efficient use of resources and energy even more important. Every year, millions of smart devices connected to the Internet increase the demand for data center capacity to provide service to those devices. This increases energy consumption in the IT sector.Thus, more efficient use of energy in these systems is of critical importance. The increase in the migration to cloud computing makes fast and efficient infrastructure-as-a-service and platform-as-a-service services provided by Internet service providers important. On the other hand, virtual machines have been used for a long time as an alternative to physical servers. The application containerization concept is shaping the virtualization world by offering faster deployment, reduced resource consumption, easier manageability, and reduced energy consumption, as demonstrated in this study. Our study shows that this new concept is more energy-efficient than virtualization technologies that are currently being used.

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“…Hypervisors, where VMs are hosted in the virtualization environment, are usually attached with a HDD based shared storage to save VM images. In this architecture, it is widely recognized that the IO performance is fundamental to the efficient use of virtualized resources [6,7]. SSD caching is a promising option to improve the IO performance of VMs as well as the hosted data-intensive applications such as elastic Hadoop applications.…”

Section: Introductionmentioning

confidence: 99%

IO dependent SSD cache allocation for elastic Hadoop applications

Tang¹,

Wang²,

Sun³

et al. 2018

Sci. China Inf. Sci.

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Elastic Hadoop applications consisting of multiple virtual machines (VMs) are widely used to support big data analysis and processing. In this scenario, flash-based solid state drive (SSD) is usually deployed on hypervisors and used as the cache to improve the IO performance. However, existing SSD caching schemes are mostly VM-centric, which focus on the low-level IO performance metrics of individual VMs. They may not lead to the optimized performance of elastic Hadoop applications, i.e., the job completion time (JCT), as the importance of VMs inside the application are different even though they have the similar low-level IO patterns. Considering the IO dependency among VMs and figuring out the importance, which we regard as the application-centric metrics, may potentially better improve the performance. We present IO dependency based requirement model, to characterize the requirement of SSD cache for each VM inside the elastic Hadoop application, and then use it in a genetic algorithm (GA) based approach to calculate the nearly optimal weights of VMs for allocating the per-VM SSD cache space and the capacity of the I/O operations per second (IOPS). Furthermore, we present a tool AC-SSD based on the approach and introduce the closed-loop adaptation to react to continuously changing workloads. The evaluation shows that by using AC-SSD, the JCT is reduced by up to 39% for IO sensitive workloads, up to 29% for continuously changing workloads, and over 12.5% for different scale of data comparing to the shared cache.

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Energy-efficient storage in virtual machine environments

Cited by 12 publications

References 16 publications

The Architecture of Kaligreen V2: A Middleware Aware of Hardware Opportunities to Save Energy

The Architecture of Kaligreen V2: A Middleware Aware of Hardware Opportunities to Save Energy

Energy efficiency with an application container

IO dependent SSD cache allocation for elastic Hadoop applications

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