Optimization of load balancing algorithm for green IT

Siddavatam, Irfan; Johri, Era; Patole, Deepti

doi:10.1145/1980022.1980321

Cited by 4 publications

(3 citation statements)

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“…There is a consumption relationship between the CPU, RAM and other components such as the video card or the motherboard (This last components are understood as constants directly proportional to the use of the CPU) [8]. This analysis can also be done by taking into account the daily execution time [6]. On the other hand, in the context of a host, consumption relations have been demonstrated between processing (high efficiency and idle state) and the transfer rate [17] (use of the network) and between the consumption of an entire system and the proper use of storage devices [18].…”

Section: Most Energy-expensive Hardware Componentsmentioning

confidence: 99%

“…Many authors have developed middlewares with the aim of saving energy by focusing on the use of the CPU, exposing different points of view, such as analyzing/scheduling virtual machines [5] [28] [29]; applying process load balancing algorithms between servers and establishing minimum allowed loads [6]; finding the process/application and CPU consumption relationship to apply load balancing algorithms [30] [8] [13] [18] [29] [24]. These perspectives have shown good results.…”

Section: Middlewares and Improvement Opportunitiesmentioning

confidence: 99%

“…In this last approach, computer scientist found the relationship between the load of node devices and the running processes. With this, a software module could be moved or stoped with the aim of allowing (1) a CPU to turn off its cores or decrease its frequency [8] [9] [10], (2) Decrease disk access [11] [12] and (3) eventually turn off or suspend the entire node [6].…”

Section: Introductionmentioning

confidence: 99%

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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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