Distributed Energy Efficient Clouds Over Core Networks

Abstract

In this paper, we introduce a framework for designing energy efficient cloud computing services over non-bypass IP/WDM core networks. We investigate network related factors including the centralization versus distribution of clouds and the impact of demand, content popularity and access frequency on the clouds placement, and cloud capability factors including the number of servers, switches and routers and amount of storage required in each cloud. We study the optimization of three cloud services: cloud content delivery, storage as a service (StaaS), and virtual machines (VMS) placement for processing applications. First, we develop a mixed integer linear programming (MILP) model to optimize cloud content delivery services. Our results indicate that replicating content into multiple clouds based on content popularity yields 43% total saving in power consumption compared to power un-aware centralized content delivery. Based on the model insights, we develop an energy efficient cloud content delivery heuristic, DEER-CD, with comparable power efficiency to the MILP results. Second, we extend the content delivery model to optimize StaaS applications. The results show that migrating content according to its access frequency yields up to 48% network power savings compared to serving content from a single central location. Third, we optimize the placement of VMs to minimize the total power consumption. Our results show that slicing the VMs into smaller VMs and placing them in proximity to their users saves 25% of the total power compared to a single virtualized cloud scenario. We also develop a heuristic for real time VM placement (DEER-VM) that achieves comparable power savings.

Energy Efficient Big Data Networks: Impact of Volume and Variety

“…We refer to the extracted information as Info. The structure of a PN is similar to the cloud structure presented in [16].PNs are located close to the user while data centers are typically in central locations in the network and may be far from the user. Therefore, PNs enable edge processing of big data, hence saving power.…”

“…Volume MILP Model In this section, we introduce a MILP model for EEBDN using a bypass IP over WDM network, (see [16] and [23] for details of MILP in IP over WDM networks). The PNs are attached to each core node of the NSFNET and consist of limited processing and storage resources, as depicted in Fig.…”

“…4) Number and locations of DCs constraints (14) (15) (16) Constraints (14) and (15) build a DC in location d if that location is selected to store the results of the processed big data traffic (i.e., Infos) or selected to process the incoming big data Chunks from PNs, where Z is a large enough unitless number to ensure that DCd = 1 when INF is greater than zero.…”

“…The authors in [15] developed a framework to perform a sequence of MapReduce jobs in Geo-distributed DCs where the processing of jobs is optimized according to time and monetary cost. In [16], the authors developed an energy efficient cloud computing framework in IP over WDM core networks.…”

Renewable energy in distributed energy efficient content delivery clouds

“…In [7], [8] we introduced a framework for designing energy efficient cloud content delivery over non-bypass IP/WDM core networks. We developed a Mixed Integer Linear Programming (MILP) model to optimize network related factors including the location of the cloud in the network and whether it should be centralized or distributed, and cloud capability factors including the number of servers, switches, routers and amount of storage required at each cloud location.…”