Energy Efficient Cloud Networks

Cloud computing is expected to be a major factor that will dominate the future Internet service model. This paper summarizes our work on energy efficiency for cloud networks. We develop a framework for studying the energy efficiency of four cloud services in IP over WDM networks: cloud content delivery, storage as a service (StaaS), and virtual machines (VMS) placement for processing applications and infrastructure as a service (IaaS).Our approach is based on the co-optimization of both external network related factors such as whether to geographically centralize or distribute the clouds, the influence of users’ demand distribution, content popularity, access frequency and renewable energy availability and internal capability factors such as the number of servers, switches and routers as well as the amount of storage demanded in each cloud. Our investigation of the different energy efficient approaches is backed with Mixed Integer Linear Programming (MILP) models and real time heuristic

Patient-Centric HetNets Powered by Machine Learning and Big Data Analytics for 6G Networks

Having a cognitive and self-optimizing network that proactively adapts not only to channel conditions, but also according to its users' needs can be one of the highest forthcoming priorities of future 6G Heterogeneous Networks (HetNets). In this paper, we introduce an interdisciplinary approach linking the concepts of e-healthcare, priority, big data analytics (BDA) and radio resource optimization in a multi-tier 5G network. We employ three machine learning (ML) algorithms, namely, naïve Bayesian (NB) classifier, logistic regression (LR), and decision tree (DT), working as an ensemble system to analyze historical medical records of stroke out-patients (OPs) and readings from body-attached internet-of-things (IoT) sensors to predict the likelihood of an imminent stroke. We convert the stroke likelihood into a risk factor functioning as a priority in a mixed integer linear programming (MILP) optimization model. Hence, the task is to optimally allocate physical resource blocks (PRBs) to HetNet users while prioritizing OPs by granting them high gain PRBs according to the severity of their medical state. Thus, empowering the OPs to send their critical data to their healthcare provider with minimized delay. To that end, two optimization approaches are proposed, a weighted sum rate maximization (WSRMax) approach and a proportional fairness (PF) approach. The proposed approaches increased the OPs' average signal to interference plus noise (SINR) by 57% and 95%, respectively. The WSRMax approach increased the system's total SINR to a level higher than that of the PF approach, nevertheless, the PF approach yielded higher SINRs for the OPs, better fairness and a lower margin of error

Energy Efficient Disaggregated Servers for Future Data Centers

With the dawn of cloud computing, data centers’ power consumption has received increased attention. In this paper we evaluate the energy efficiency potential of exploiting the concept of Disaggregated Server (DS) design in data centers for efficient resource provisioning. A DS, is a new approach for future racks where servers are disaggregated and resources, such as processors, memory and IO ports are arranged in resource pools constructing processing pools, memory pools and IO pools. We developed a mixed integer linear programming (MILP) model for energy minimization of the virtual machine (VM) placement problem in data centres implementing DS approach. The results show that the average power savings are up to 49% for the different VM types considered

Energy Efficient Virtual Machines Placement in IP over WDM Networks

Virtual machines (VMs) offer an economic and scalable solution to efficiently utilize the physical resources. In this paper, we investigate the optimization of VM placement in IP over WDM core networks considering a VM workload that varies with the number of users served by the VM. Our results show that the optimal VM placement in distributed clouds yields up to 23% total power saving compared to a single cloud

Energy Efficient IoT Virtualization Framework with Passive Optical Access Networks

In this paper we design a framework for an energy efficient cloud computing platform for Internet of things (IoT) accompanied by a passive optical access network (PON). The design is evaluated using a Mixed Integer Linear Programming (MILP) model. IoT network consists of four layers. The first layer represents IoT objects and the three other layers host relays, the coordinator and the gateway, respectively. PON consists of two layers hosting the Optical Network Units (ONUs) and the Optical Line Terminal (OLT), respectively. Equipment at all layers, except the object layer, can aggregate and process the traffic generated by IoT objects. The processing is performed using distributed mini clouds that host different types of Virtual Machines (VMs). These mini clouds can be located at the three upper layers of the IoT network and the PON two layers. We aim to reduce the total power consumption resulting from the traffic delivery and data processing at the different layers. The energy efficiency can be achieved by optimizing the placement and number of the mini clouds and VMs and utilizing energy efficient routes. Our results indicate that up to 21% of total power can be saved utilizing energy efficient PONs and serving heterogeneous VMs

Future Energy Efficient Data Centers With Disaggregated Servers

The popularity of the Internet and the demand for 24/7 services uptime is driving system performance and reliability requirements to levels that today's data centers can no longer support. This paper examines the traditional monolithic conventional server (CS) design and compares it to a new design paradigm: the disaggregated server (DS) data center design. The DS design arranges data centers resources in physical pools, such as processing, memory, and IO module pools, rather than packing each subset of such resources into a single server box. In this paper, we study energy efficient resource provisioning and virtual machine (VM) allocation in DS-based data centers compared to CS-based data centers. First, we present our new design for the photonic DS-based data center architecture, supplemented with a complete description of the architectural components. Second, we develop a mixed integer linear programming (MILP) model to optimize VM allocation for the DS-based data center, including the data center communication fabric power consumption. Our results indicate that, in DS data centers, the optimum allocation of pooled resources and their communication power yields up to 42% average savings in total power consumption when compared with the CS approach. Due to the MILP high computational complexity, we developed an energy efficient resource provisioning heuristic for DS with communication fabric (EERP-DSCF), based on the MILP model insights, with comparable power efficiency to the MILP model. With EERP-DSCF, we can extend the number of served VMs, where the MILP model scalability for a large number of VMs is challenging. Furthermore, we assess the energy efficiency of the DS design under stringent conditions by increasing the CPU to memory traffic and by including high noncommunication power consumption to determine the conditions at which the DS and CS designs become comparable in power consumption. Finally, we present a complete analysis of the communication patterns in our new DS design and some recommendations for design and implementation challenges

Energy Efficient Tapered Data Networks for Big Data Processing in IP/WDM Networks

Classically the data produced by Big Data applications is transferred through the access and core networks to be processed in data centers where the resulting data is stored. In this work we investigate improving the energy efficiency of transporting Big Data by processing the data in processing nodes of limited processing and storage capacity along its journey through the core network to the data center. The amount of data transported over the core network will be significantly reduced each time the data is processed therefore we refer to such a network as an Energy Efficient Tapered Data Network. The results of a Mixed Integer linear Programming (MILP), developed to optimize the processing of Big Data in the Energy Efficient Tapered Data Networks, show significant reduction in network power consumption up to 76%

Energy Efficient Big Data Networks: Impact of Volume and Variety

In this article, we study the impact of big data’s volume and variety dimensions on Energy Efficient Big Data Networks (EEBDN) by developing a Mixed Integer Linear Programming (MILP) model to encapsulate the distinctive features of these two dimensions. Firstly, a progressive energy efficient edge, intermediate, and central processing technique is proposed to process big data’s raw traffic by building processing nodes (PNs) in the network along the way from the sources to datacenters. Secondly, we validate the MILP operation by developing a heuristic that mimics, in real time, the behaviour of the MILP for the volume dimension. Thirdly, we test the energy efficiency limits of our green approach under several conditions where PNs are less energy efficient in terms of processing and communication compared to data centers. Fourthly, we test the performance limits in our energy efficient approach by studying a “software matching” problem where different software packages are required to process big data. The results are then compared to the Classical Big Data Networks (CBDN) approach where big data is only processed inside centralized data centers. Our results revealed that up to 52% and 47% power saving can be achieved by the EEBDN approach compared to the CBDN approach, under the impact of volume and variety scenarios, respectively. Moreover, our results identify the limits of the progressive processing approach and in particular the conditions under which the CBDN centralized approach is more appropriate given certain PNs energy efficiency and software availability levels

Resilient Service Embedding in IoT Networks

The Internet of Things (IoT) can support a significant number of services including those in smart homes and the automation of industries and public utilities. However, the growth of these deployments has posed a significant challenge especially in terms of how to build such deployments in a highly resilient manner. The IoT devices are prone to unpredicted failures and cyber-attacks, i.e. various types of damage, unreliable wireless connections, limited transmission power, computing ability, and storage space. Thus resilience is essential in IoT networks and in the services they support. In this paper, we introduce a new approach to resilience in IoT service embedding, based on traffic splitting. Our study assesses the power consumption associated with the services embedded and the data delivery time. The results are compared to recent approaches in resilience including redundancy and replication approaches. We constructed an optimization model whose goal is to determine the optimum physical resources to be used to embed the IoT virtual topology, where the latter is derived from a business process (BP). The embedding process makes use of the service-oriented architecture (SOA) paradigm. The physical resources of interest include IoT links and devices. The model made use of mixed integer linear programming (MILP) with an objective function that aimed to minimize both the total power consumption and the traffic latency. The optimization results show that the power consumption is reduced and the data delivery time is reduced in the service embedding approach where the proposed traffic splitting approach is employed resulting in the selection of energy efficient nodes and routes in the IoT network. Our methods resulted in up to 35% power saving compared to current methods and reduced the average traffic latency by up to 37% by selecting energy-efficient nodes and routes in IoT networks and by optimizing traffic flow to minimize latency

Renewable energy in distributed energy efficient content delivery clouds

In this paper, we develop a Mixed Integer Linear Programming (MILP) model to study the impact of renewable energy availability, represented by wind farms, on the location of clouds and the content replication schemes of cloud content over IP/WDM networks. In our analysis, we assume that renewable energy is only available to power clouds while the IP/WDM network is powered by non-renewable energy. Our results show that popularity based replication in clouds is the most energy efficient content replication scheme when the clouds are powered only by non-renewable energy sources or when renewable energy availability is limited. With abundant renewable energy, a cloud with a full copy of the content can be built at each node. However, the model should achieve a trade-off between the transmission power losses to deliver renewable energy from wind farms to clouds and the non-renewable power consumption of the IP/WDM network. We discuss this trade-off and show how to optimize the transmission power losses of renewable energy while minimizing the non-renewable network power consumption