Energy-Aware Load Balancing and Application Scaling for the Cloud Ecosystem

Paya, Ashkan; Marinescu, Dan C.

doi:10.1109/tcc.2015.2396059

Cited by 104 publications

(46 citation statements)

References 19 publications

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“…SLA monitoring with corrective measures in performance as well as the cost is not incorporated in this method. Energy‐aware load balancing method focuses only on energy conservation in homogeneous clusters. The hierarchical method based on Petri nets proposed by Fan et al considers only resource utilization rate and cost.…”

Section: Related Workmentioning

confidence: 99%

Service‐level agreement–aware scheduling and load balancing of tasks in cloud

Babu¹,

Samuel

2019

Softw Pract Exp

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Summary Cloud computing is an innovative computing paradigm designed to provide a flexible and low‐cost way to deliver information technology services on demand over the Internet. Proper scheduling and load balancing of the resources are required for the efficient operations in the distributed cloud environment. Since cloud computing is growing rapidly and customers are demanding better performance and more services, scheduling and load balancing of the cloud resources have become very interesting and important area of research. As more and more consumers assign their tasks to cloud, service‐level agreements (SLAs) between consumers and providers are emerging as an important aspect. The proposed prediction model is based on the past usage pattern and aims to provide optimal resource management without the violations of the agreed service‐level conditions in cloud data centers. It considers SLA in both the initial scheduling stage and in the load balancing stage, and it looks into different objectives to achieve the minimum makespan, the minimum degree of imbalance, and the minimum number of SLA violations. The experimental results show the effectiveness of the proposed system compared with other state‐of‐the‐art algorithms.

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Section: Related Workmentioning

confidence: 99%

Service‐level agreement–aware scheduling and load balancing of tasks in cloud

Babu¹,

Samuel

2019

Softw Pract Exp

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“…Tasks or jobs are dynamic in nature. [14][15][16][17] It is dependent on current load information. It is further classified into two types.…”

Section: Dynamic Load Balancing Algorithmmentioning

confidence: 99%

Magnum opus of an efficient hospitality technique for load balancing in cloud environment

Sakthivelmurugan

Vimala

Britto

2018

Concurrency and Computation

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Summary The load balancing approach in the cloud environment is an open issue in the recent days. It helps obtain better resource utilization in cloud environment. It also improves the performance of the system. A good load balancing algorithm in cloud ensures no under loading or over loading in a single host. This paper proposes an algorithm named Hospitality Load Balancing (HLB) algorithm, which aims to balance the load among virtual machines (VM) for increasing the performance and throughput. The proposed algorithm handles preemptive or nonpreemptive tasks, which may be independent. It helps to reduce make span and obtain low task migration. The HLB algorithm has been analyzed and compared with plenty of load balancing technique. An experimental result shows that it is an excellent and effective algorithm when compared with the existing load balancing algorithm. It also illustrates the performance of proposed load balancing scheme with analysis and proves that it attains low makespan and low task migration.

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“…Concerning the improvement of energy usage at the high level of systems, a large amount of work has been done in the literature, and existing approaches often fall into load balancing and scheduling, resources planning or consolidating categories [10,20,21], mainly at the physical resources level, and often integrating with cost optimization.…”

Section: Related Workmentioning

confidence: 99%

Characterizing Energy per Job in Cloud Applications

Gribaudo

Pernici

2016

Electronics

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Energy efficiency is a major research focus in sustainable development and is becoming even more critical in information technology (IT) with the introduction of new technologies, such as cloud computing and big data, that attract more business users and generate more data to be processed. While many proposals have been presented to optimize power consumption at a system level, the increasing heterogeneity of current workloads requires a finer analysis in the application level to enable adaptive behaviors and in order to reduce the global energy usage. In this work, we focus on batch applications running on virtual machines in the context of data centers. We analyze the application characteristics, model their energy consumption and quantify the energy per job. The analysis focuses on evaluating the efficiency of applications in terms of performance and energy consumed per job, in particular when shared resources are used and the hosts on which the virtual machines are running are heterogeneous in terms of energy profiles, with the aim of identifying the best combinations in the use of resources.

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Energy-Aware Load Balancing and Application Scaling for the Cloud Ecosystem

Cited by 104 publications

References 19 publications

Service‐level agreement–aware scheduling and load balancing of tasks in cloud

Service‐level agreement–aware scheduling and load balancing of tasks in cloud

Magnum opus of an efficient hospitality technique for load balancing in cloud environment

Characterizing Energy per Job in Cloud Applications

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