Location-Aware Energy-Efficient Workload Allocation in Geo Distributed Cloud Environment

Abstract

The proliferation of cloud computing relied on the virtualization of the compute and storage resources and provisioning them dynamically according to users' needs on a pay-per-use model. Massive cloud providers have geo-distributed cloud data centers to ensure service reliability, availability and satisfy user's need. Therefore, cloud management systems are necessary to increase the profit of cloud providers and to improve the quality-of service demanded by users. This paper focuses on an energyefficient method to solve the problem of allocating data-intensive workloads in geographically distributed data centers. The workload's tasks are characterized by large data transfer times than their execution times. The problem formulated as a nonlinear programming optimization problem. Then, to find an optimal solution to the problem, meta-heuristic genetic algorithm is proposed. The designed heuristic takes into account the cost of the data transfer time from the storage location to the compute servers as well as the workload makespan on the available hosts. Extensive simulations using the CloudSim simulator are conducted to evaluate the efficacy of the proposed allocation method and how it performs with respect to other methods in the literature. Our results show significant enhancements in energy consumption while respecting the user's QoS.

Bi-Objective Optimization of Bandwidth Resources and Energy Consumption for Efficient Virtual Machine Placement in Cloud Computing

“…ղ(𝑖, 𝑗) + 𝜀. ∆𝑡 𝑒𝑞 (11) Where ε is the pheromone volatilization factor, which indicates the degree of pheromone evaporation per unit of time. The value is given in the range of (0,1].…”

“…Furthermore, VM placement impacts network traffic, affecting performance due to potential congestion between distant VMs [16]. Virtualization, a foundational aspect of cloud computing, aims to optimize resource use but presents challenges in achieving eco-friendly cloud operations [11].…”

Resource Management for Minimizing Energy and Cost of Geo-Distributed Data Centers

“…Predicting the electricity price for GDCs in multi-regional Resource Management for Minimizing Energy & Cost of GDCs Moh Moh THAN http://wjst.wu.ac.th GDCs. Rawas and Zekri [16] proposed the Location-Aware and Energy-Efficient (LAEE) data-intensive workloads in GDCs. They combined the DVFS technique in LAEE to minimize the energy consumption of servers.…”

“…Each of the mentioned papers [13][14][15][16] focused on minimizing the turnaround time for each request, PC of servers, energy consumption, and cost of data centers, respectively. This paper proposes the resource management framework to minimize energy consumption and also cost of data centers while satisfying SLO.…”