Hotspot-Aware Workload Scheduling and Server Placement for Heterogeneous Cloud Data Centers

Jamal, M. Hasan; Chaudhry, Muhammad Tayyab; Tahir, Usama; Rustam, Furqan; Hur, Soojung; Ashraf, Imran

doi:10.3390/en15072541

Cited by 8 publications

(4 citation statements)

References 29 publications

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“…The research mainly focuses on reducing energy use and improving the distribution of workloads while completing more tasks at high performance; they established lower criteria utilizing the data canter’s overall workload utilization and employed ant colony optimization to minimize the frequency of VMs movements. Research 14 explored optimization and ML-based work for load distribution in the cloud environment. The proposed model utilizes a bee colony optimization method.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques

Simaiya,

Lilhore,

Sharma

et al. 2024

Sci Rep

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Virtual machine (VM) integration methods have effectively proven an optimized load balancing in cloud data centers. The main challenge with VM integration methods is the trade-off among cost effectiveness, quality of service, performance, optimal resource utilization and compliance with service level agreement violations. Deep Learning methods are widely used in existing research on cloud load balancing. However, there is still a problem with acquiring noisy multilayered fluctuations in workload due to the limited resource-level provisioning. The long short-term memory (LSTM) model plays a vital role in the prediction of server load and workload provisioning. This research presents a hybrid model using deep learning with Particle Swarm Intelligence and Genetic Algorithm (“DPSO-GA”) for dynamic workload provisioning in cloud computing. The proposed model works in two phases. The first phase utilizes a hybrid PSO-GA approach to address the prediction challenge by combining the benefits of these two methods in fine-tuning the Hyperparameters. In the second phase, CNN-LSTM is utilized. Before using the CNN-LSTM approach to forecast the consumption of resources, a hybrid approach, PSO-GA, is used for training it. In the proposed framework, a one-dimensional CNN and LSTM are used to forecast the cloud resource utilization at various subsequent time steps. The LSTM module simulates temporal information that predicts the upcoming VM workload, while a CNN module extracts complicated distinguishing features gathered from VM workload statistics. The proposed model simultaneously integrates the resource utilization in a multi-resource utilization, which helps overcome the load balancing and over-provisioning issues. Comprehensive simulations are carried out utilizing the Google cluster traces benchmarks dataset to verify the efficiency of the proposed DPSO-GA technique in enhancing the distribution of resources and load balancing for the cloud. The proposed model achieves outstanding results in terms of better precision, accuracy and load allocation.

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Section: Literature Reviewmentioning

confidence: 99%

A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques

Simaiya,

Lilhore,

Sharma

et al. 2024

Sci Rep

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“…For more granular workload placement, i.e., server deployment, some researchers proposed hot-aware server placement approaches oriented to reduce the outlet temperature of servers to save cooling energy [24,25] . As for the research on power resource optimization using temporal differences in workload power consumption patterns, Hsu et al [26] proposed SmoothOperator, which uses the k-means to cluster service instances or servers with periodic synchronous power consumption patterns , and uses heuristics for optimal placement to reduce the sum of peaks in power supply nodes.…”

Section: Initial Value Of Pheromone Cmentioning

confidence: 99%

A Server Placement Algorithm for Reducing Risk and Improving Power Utilization in Data Centers

Chen,

Huang,

Luo

et al. 2024

Tsinghua Sci. Technol.

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As the power demand in data centers is increasing, the power capacity of the power supply system has become an essential resource to be optimized. Although many data centers use power oversubscription to make full use of the power capacity, there are unavoidable power supply risks associated with it. Therefore, how to improve the data center power capacity utilization while ensuring power supply security has become an important issue.To solve this problem, we first define it and propose a risk evaluation metric called Weighted Power Supply Risk (WPSRisk). Then, a method, named Hybrid Genetic Algorithm with Ant Colony System (HGAACS) , is proposed to improve power capacity utilization and reduce power supply risks by optimizing the server placement in the power supply system. HGAACS uses historical power data of each server to find a better placement solution by population iteration. HGAACS possesses not only the remarkable local search ability of Ant Colony System (ACS), but also enhances the global search capability by incorporating genetic operators from Genetic Algorithm (GA). To verify the performance of HGAACS, we experimentally compare it with five other placement algorithms. The experimental results show that HGAACS can perform better than other algorithms in both improving power utilization and reducing the risk of power supply system.

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“…In 2022, M. Hasan Jamal et al [6] suggested a HAWDA and HASRA depending on thermal profiling considering outlet temperature detection. In order to reduce the peak output temperatures, HAWDA distributed workload on servers in a thermally efficient manner, while HASRA optimized server positioning in thermal hotspot areas.…”

Section: Related Workmentioning

confidence: 99%

“…The scheduling solution was optimized in terms of each metric specified in the QoS model using a QoS-driven CESS method [4]. It has been suggested to use an OP-MLB system, which combines a number of algorithms that cooperate to provide effective resource management for cloud environments [6] [7]. With more effective storage and V/F scaling improvement, the EARU model significantly reduces LLC disappointments and thus more effectively utilizes asset.…”

Section: Introductionmentioning

confidence: 99%

An AHP based Task Scheduling and Optimal Resource Allocation in Cloud Computing

Karimunnisa¹,

Yellamma²

2023

IJACSA

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Cloud systems by virtue characterize ultimate resource utilization with ever evolving user requirements facilitating adaptivity. With a scope of enhancing the QoS needs of user applications, numerous factors are considered for tunning among which Task scheduling promises to grab focus. The Task Scheduling mechanism ascertains improvement by distributing the subtasks to specific set of resources pertaining to prevailing Quality models. The work emphasizes the need for effective task scheduling and optimizing resource allocation by modelling a modified AHP (Analytical Hierarchy Process) driven approach. The proposed method guarantees the functionality in two phases pertaining to Task ranking and pipelined with Optimized scheduling algorithms resulting in maximization of resource utilization. The former phase of task ranking is aided by improved AHP with substantial usage of fuzzy clustering followed by an enhanced CUCMCA (Chimp Updated and Cauchy Mutated Coot Algorithm) algorithm for optimal resource allocation of cloud applications. The contributed model promises leveraged performance of 32% for memory usage, 33.5% for execution time, 29% for makespan and 18% for communication cost over pre-existing conventional models considered.

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Hotspot-Aware Workload Scheduling and Server Placement for Heterogeneous Cloud Data Centers

Cited by 8 publications

References 29 publications

A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques

A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques

A Server Placement Algorithm for Reducing Risk and Improving Power Utilization in Data Centers

An AHP based Task Scheduling and Optimal Resource Allocation in Cloud Computing

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