A packing problem approach to energy-aware load distribution in Clouds

Carli, Thomas; Henriot, Stéphane; Cohen, Johanne; Tomasik, Joanna

doi:10.1016/j.suscom.2015.08.001

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…Resource awareness is a prevalent topic in designing resource allocation algorithms for cloud environments. Carli et al [10] formulated a variant of the bin packing problem, called Variable-Sized Bin Packing with Cost and Item Fragmentation, which is energy-aware when attempting to pack cloud resource requests onto servers in both online and offline settings. Breitgand and Epstein [11] considered a variant of the bin packing problem called Stochastic Bin Packing (SBP) which is riskaware of network bandwidth consumption, and designed both online and approximation algorithms to solve it.…”

Section: B Related Workmentioning

confidence: 99%

Sharing-Aware Online Algorithms for Virtual Machine Packing in Cloud Environments

Rampersaud

Grosu

2015

2015 IEEE 8th International Conference on Cloud Computing

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Cloud service providers offer services to a large number of users by employing virtualization technologies. One of the challenges faced by the cloud providers using virtualized environments is the development of efficient algorithms for assigning Virtual Machine (VM) instances to servers such that the number of hosting servers is minimized. This is also known as the problem of VM Packing. In this paper, we design a family of sharing-aware online algorithms for solving the VM Packing problem that take into account the sharing of memory among collocated VMs. We introduce a new server resource scarcity metric which establishes an order among servers that are suitable for hosting an arriving VM request and use it in the design of the algorithms. We evaluate the performance of our sharing-aware online algorithms by performing an extensive set of experiments comparing them against several existing sharing-oblivious VM packing algorithms.Index Terms-online algorithms, sharing-aware VM packing, vector bin packing, virtual machines.

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Section: B Related Workmentioning

confidence: 99%

Sharing-Aware Online Algorithms for Virtual Machine Packing in Cloud Environments

Rampersaud

Grosu

2015

2015 IEEE 8th International Conference on Cloud Computing

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“…For instance, in [1], the authors have applied the Bin-packing algorithm for multi capacity Bin-packing to achieve task waiting time and degree of imbalance on cloud resources. In a similar work [2], the authors used the Bin-packing algorithm for cost-aware and fragmentation enabled consolidation of tasks to achieve minimum energy consumption. In a work by [3], the authors used a dynamic clustering algorithm to achieve throughput and execution time.…”

Section: Introductionmentioning

confidence: 99%

Modeling an Optimized Approach for Load Balancing in Cloud

et al. 2020

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Despite significant infrastructure improvements, cloud computing still faces numerous challenges in terms of load balancing. Several techniques have been applied in the literature to improve load balancing efficiency. Recent research manifested that load balancing techniques based on metaheuristics provide better solutions for proper scheduling and allocation of resources in the cloud. However, most of the existing approaches consider only a single or few QoS metrics and ignore many important factors. The performance efficiency of these approaches is further enhanced by merging with machine learning techniques. These approaches combine the relative benefits of load balancing algorithm backed up by powerful machine learning models such as Support Vector Machines (SVM). In the cloud, data exists in huge volume and variety that requires extensive computations for its accessibility, and hence performance efficiency is a major concern. To address such concerns, we propose a load balancing algorithm, namely, Data Files Type Formatting (DFTF) that utilizes a modified version of Cat Swarm Optimization (CSO) along with SVM. First, the proposed system classifies data in the cloud from diverse sources into various types, such as text, images, video, and audio using one to many types of SVM classifiers. Then, the data is input to the modified load balancing algorithm CSO that efficiently distributes the load on VMs. Simulation results compared to existing approaches showed an improved performance in terms of throughput (7%), the response time (8.2%), migration time (13%), energy consumption (8.5%), optimization time (9.7%), overhead time (6.2%), SLA violation (8.9%), and average execution time (9%). These results outperformed some of the existing baselines used in this research such as CBSMKC, FSALB, PSO-BOOST, IACSO-SVM, CSO-DA, and GA-ACO.

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Energy aware resource allocation of cloud data center: review and open issues

Akhter

Othman

2016

Cluster Comput

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A packing problem approach to energy-aware load distribution in Clouds

Cited by 6 publications

References 26 publications

Sharing-Aware Online Algorithms for Virtual Machine Packing in Cloud Environments

Sharing-Aware Online Algorithms for Virtual Machine Packing in Cloud Environments

Modeling an Optimized Approach for Load Balancing in Cloud

Energy aware resource allocation of cloud data center: review and open issues

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