Article
Computer Science, Information Systems
Minhaj Ahmad Khan
Summary: The proposed normalization-based VM consolidation strategy aims to minimize energy consumption, SLA violations, and the number of VM migrations in cloud environments. The strategy utilizes resource parameters to identify overloaded hosts and compares the capacity of virtual machines and hosts. Experimental results demonstrate that this approach outperforms other well-known methods in reducing energy consumption, SLA violations, and the number of VM migrations.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Theory & Methods
Wenbin Yao, Zhuqing Wang, Yingying Hou, Xikang Zhu, Xiaoyong Li, Yamei Xia
Summary: In the cloud computing environment, unbalanced utilization of multi-dimensional resources in physical servers leads to inefficient resource utilization and energy wastage in data centers. We propose a load balancing strategy based on virtual machine consolidation (LBVMC) to reduce energy consumption and service level agreement (SLA) violations. We present algorithms for load state classification, migratable VM selection based on multi-dimensional resource utilization, and VM placement based on resource fitness and load correlation. Experimental results demonstrate the superior performance of LBVMC in reducing energy consumption and SLA violations.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Computer Science, Information Systems
Ilksen Caglar, Deniz Turgay Altilar
Summary: Energy efficiency is crucial for reducing environmental dissipation. This research proposes a novel efficient resource management algorithm at the data center level to meet dynamic workload requirements without migrating virtual machines.
JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS
(2022)
Article
Computer Science, Theory & Methods
Jing Zeng, Ding Ding, Kaixuan Kang, HuaMao Xie, Qian Yin
Summary: This paper proposes an ADVMC framework for energy-efficient cloud data centers, which includes two phases: a dynamic Influence Coefficient-based VM selection algorithm and a Prediction Aware DRL-based VM placement method. Experimental results show that the ADVMC approach can significantly reduce system energy consumption and decrease user SLA violation.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2022)
Article
Computer Science, Information Systems
Sundas Iftikhar, Mirza Mohammad Mufleh Ahmad, Shreshth Tuli, Deepraj Chowdhury, Minxian Xu, Sukhpal Singh Gill, Steve Uhlig
Summary: Cloud computing is a cost-effective and scalable solution for modern technology. Cloud providers face increased costs due to the shift of resource needs to cloud-based systems. By reducing energy consumption through intelligent task scheduling algorithms, cloud providers can improve cost reduction.
INTERNET OF THINGS
(2023)
Article
Computer Science, Information Systems
Vittorio Cozzolino, Leonardo Tonetto, Nitinder Mohan, Aaron Yi Ding, Jorg Ott
Summary: To address the issues of computational burden, high energy consumption, and poor performance in mobile augmented reality (AR) and virtual reality (VR) applications, this paper introduces Nimbus - a task placement and offloading solution that offloads deep learning tasks from the AR application pipeline to nearby GPU-powered edge devices. Our aim is to minimize the latency experienced by end-users and the energy costs on mobile devices. The evaluation results show that Nimbus can significantly reduce task latency and energy consumption for real-time object detection in AR applications.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2023)
Article
Computer Science, Software Engineering
Amarendhar Reddy Madireddy, Kongara Ravindranath
Summary: This article proposes a dynamic VM relocation system to study how to reduce energy consumption in cloud computing environments. The experimental results show that the system can efficiently balance loads and decrease overall energy consumption.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2023)
Article
Computer Science, Hardware & Architecture
V Roopa, K. Malarvizhi, S. Karthik
Summary: This paper presents an Efficient Energy-Aware Resource Management Model (EEARMM) that operates in a decentralized manner in the cloud environment. By reducing migration frequency and employing appropriate VM selection algorithms, the model achieves efficient resource utilization and energy efficiency. Performance evaluation and comparative analysis demonstrate the efficiency, feasibility, and scalability of the proposed model in resource and workload management.
COMPUTER SYSTEMS SCIENCE AND ENGINEERING
(2022)
Review
Computer Science, Information Systems
Leila Helali, Mohamed Nazih Omri
Summary: Virtualization technology plays a crucial role in cloud systems, providing solutions for key problems in resource management. Resource consolidation is a major technique used to develop more efficient management policies.
COMPUTER SCIENCE REVIEW
(2021)
Article
Computer Science, Information Systems
Sonia Bashir, Saad Mustafa, Raja Wasim Ahmad, Junaid Shuja, Tahir Maqsood, Abdullah Alourani
Summary: Cloud computing consumes a large amount of energy, leading to high expenditure, greenhouse gas emissions, and CO2 emissions. Existing energy-efficient techniques only consider the energy consumption of the CPU during task placement and ignore the energy consumption of memory and SLA violations. To address these issues, we propose two novel nature-inspired techniques based on artificial bee colony and particle swarm optimization, which consider the energy consumption of both CPU and memory during VM placement. We also provide SLA-aware variants to reduce SLA violations resulting from excessive task consolidation.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
K. Dinesh Kumar, E. Umamaheswari
Summary: The power consumption of datacenters is increasing rapidly, and it is estimated to reach approximately 8000 TWh by 2030 if cloud resources are not utilized effectively. VM consolidation technique is a prominent solution to effectively manage cloud resources, improve server performance, and reduce power consumption. However, unnecessary actions of VM consolidation can lead to degraded resource management, poor QoS, and SLA violations. This paper proposes a proactive VM consolidation technique using an improved LSTM network to effectively manage resources, reduce power consumption, and avoid SLA violations.
Article
Automation & Control Systems
Zheng Chang, Liqing Liu, Xijuan Guo, Quan Sheng
Summary: This article proposes a dynamic optimization scheme for IoT fog computing system with multiple mobile devices, where resources can be dynamically coordinated and allocated. Through Lyapunov optimization, a joint computation offloading and radio resource allocation algorithm is implemented to minimize system costs related to latency, energy consumption, and weights of devices.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Information Systems
Ehsan Ahvar, Shohreh Ahvar, Zoltan Adam Mann, Noel Crespi, Roch Glitho, Joaquin Garcia-Alfaro
Summary: Placement of application components on Edge Clouds (ECs) significantly impacts their energy consumption and carbon emissions, considering varying energy prices and carbon emission rates. DECA method combines prediction-based A* algorithm with Fuzzy Sets technique to optimize energy cost and carbon emissions intelligently, achieving a tradeoff between them.
Article
Computer Science, Information Systems
Navpreet Kaur Walia, Navdeep Kaur, Majed Alowaidi, Kamaljeet Singh Bhatia, Shailendra Mishra, Naveen Kumar Sharma, Sunil Kumar Sharma, Harsimrat Kaur
Summary: The proposed Hybrid Scheduling Algorithm (HS) based on Genetic Algorithm (GA) and Flower Pollination based Algorithm (FPA) outperforms existing scheduling algorithms in various parameters like completion time, resource utilization, cost of computation, and energy consumption for both cloud environments. Simulation results demonstrate that HS achieves better resource utilization, lower energy consumption, and shorter completion time compared to GA and FPA in both homogeneous and heterogeneous environments.
Article
Computer Science, Information Systems
Rongping Lin, Tianze Xie, Shan Luo, Xiaoning Zhang, Yong Xiao, Bill Moran, Moshe Zukerman
Summary: This study focuses on the computation offloading problem in collaborative edge computing networks and proposes a collaborative load shedding approach to optimize computation offloading and resource allocation, achieving more efficient computing services. Theoretical analysis and numerical results demonstrate that the distributed algorithm can achieve guaranteed long-term performance and improve the performance of computation offloading.
IEEE INTERNET OF THINGS JOURNAL
(2022)
