A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

Abstract

Given the perpetual surging of cloud services' requests, energy consumption of cloud data centers with their related CO 2 emissions still represents major issues. Efficient use of cloud's resources becomes then the driven force ensuring both, the satisfaction of service-level agreements and the sobriety of cloud's energy consumption. This paper purports to survey for the first time, a comprehensive literature of some actual challenges facing various cloud's resources utilization scheduling approaches dealing with energy conservation. Indeed, cloud resources involve not only computing servers, but also a wide set of intra-and inter-cloud network's resources to be considered. These resources are mainly provisioned through two wellknown technologies: virtualization and/or containerization. As existing studies in the area of cloud resources provisioning are generally categorized into different groups, this research depicts a complete taxonomy of energy-efficient cloud resources scheduling. In this same perspective, we survey some recent efforts made in energy-efficient virtual and containerized resources, by emphasizing first the most used reactive then proactive techniques for managing the whole cloud resources energy efficiency scheduling.

Towards Software-Defined-Networking-based IoT: A Systematic Literature Review on Management Frameworks and Open Challenges

“…2) Energy Management: SDN offers a better solution for green networking, which has become essential in network design and implementation for economic and environmental benefits [68]. It should be noted that, when introduced, security implementations in IoT increase energy consumption since security systems enact computations and communications that consume more power in the network [52], [69]. In [25] and [42], the authors address SDN/NFV-based security approaches.…”

“…It is also used to systematically distribute the network's load to improve network capacity and quality of service (QoS). Therefore, with load balancing technology, the IoT network's overall efficiency can be significantly improved [69], [71].…”

Parallel, Proactive and Power Efficient Virtual Network Embedding in a Green and Distributed SD-ODCN Architecture

“…In this later layer, we address a Parallel and Proactive Virtual Network Embedding (PPVNE) sorting the total power efficiency of the whole network components and DCs' servers. Inspired by [11] and our previous complete survey study [12], we propose a mathematical MILP model maximizing total power efficiency in both DCs and inter cloud multi-domain networks components in the SD-ODCN architecture. Contributions in this paper are synthesized as follows: -After instantiating Virtual Network Requests (VNRs) in the SP layer, each user requiring multi-services VNRs at a fixed time stamp may benefit from a parallel and distributed processing in a timely manner.…”

“…Lines incorporate the ranking process. At this level, the algorithm calculates and stores in lines (11)(12)(13)(14)(15) the CER values of each DC (equation 9) and its associated network (equation 10). If the two obtained CER values are well below CER thresholds initialized at the beginning (V1=V2= 500 KgeqCO2) (line 16), indicating thus the DC with its associated network are green, their indices are therefore stored in the first class Q1 (lines [17][18].…”

“…Then, c  is the power gap between server's idle power and maximum power. Thereafter, we present just after in(12) the power consumption of their associated network path…”