Joint Computing and Electric Systems Optimization for Green Datacenters

This chapter presents an optimization framework to manage green datacenters using multilevel energy reduction techniques in a joint approach. A green datacenter exploits renewable energy sources and active Uninterruptible Power Supply (UPS) units to reduce the energy intake from the grid while improving its Quality of Service (QoS). At server level, the state-of-the-art correlation-aware Virtual Machines (VMs) consolidation technique allows to maximize server’s energy efficiency. At system level, heterogeneous Energy Storage Systems (ESS) replace standard UPSs, while a dedicated optimization strategy aims at maximizing the lifetime of the battery banks and to reduce the energy bill, considering the load of the servers. Results demonstrate, under different number of VMs in the system, up to 11.6% energy savings, 10.4% improvement of QoS compared to existing correlation-aware VM allocation schemes for datacenters and up to 96% electricity bill savings

Green Approach for Joint Management of Geo-Distributed Data Centers and Interconnection Networks

Every time an Internet user downloads a video, shares a picture, or sends an email, his/her device addresses a data center and often several of them. These complex systems feed the web and all Internet applications with their computing power and information storage, but they are very energy hungry. The energy consumed by Information and Communication Technology (ICT) infrastructures is currently more than 4\% of the worldwide consumption and it is expected to double in the next few years. Data centers and communication networks are responsible for a large portion of the ICT energy consumption and this has stimulated in the last years a research effort to reduce or mitigate their environmental impact. Most of the approaches proposed tackle the problem by separately optimizing the power consumption of the servers in data centers and of the network. However, the Cloud computing infrastructure of most providers, which includes traditional telcos that are extending their offer, is rapidly evolving toward geographically distributed data centers strongly integrated with the network interconnecting them. Distributed data centers do not only bring services closer to users with better quality, but also provide opportunities to improve energy efficiency exploiting the variation of prices in different time zones, the locally generated green energy, and the storage systems that are becoming popular in energy networks. In this paper, we propose an energy aware joint management framework for geo-distributed data centers and their interconnection network. The model is based on virtual machine migration and formulated using mixed integer linear programming. It can be solved using state-of-the art solvers such as CPLEX in reasonable time. The proposed approach covers various aspects of Cloud computing systems. Alongside, it jointly manages the use of green and brown energies using energy storage technologies. The obtained results show that significant energy cost savings can be achieved compared to a baseline strategy, in which data centers do not collaborate to reduce energy and do not use the power coming from renewable resources

Adapting Datacenter Capacity for Greener Datacenters and Grid

Cloud providers are adapting datacenter (DC) capacity to reduce carbon emissions. With hyperscale datacenters exceeding 100 MW individually, and in some grids exceeding 15% of power load, DC adaptation is large enough to harm power grid dynamics, increasing carbon emissions, power prices, or reduce grid reliability. To avoid harm, we explore coordination of DC capacity change varying scope in space and time. In space, coordination scope spans a single datacenter, a group of datacenters, and datacenters with the grid. In time, scope ranges from online to day-ahead. We also consider what DC and grid information is used (e.g. real-time and day-ahead average carbon, power price, and compute backlog). For example, in our proposed PlanShare scheme, each datacenter uses day-ahead information to create a capacity plan and shares it, allowing global grid optimization (over all loads, over entire day). We evaluate DC carbon emissions reduction. Results show that local coordination scope fails to reduce carbon emissions significantly (3.2%--5.4% reduction). Expanding coordination scope to a set of datacenters improves slightly (4.9%--7.3%). PlanShare, with grid-wide coordination and full-day capacity planning, performs the best. PlanShare reduces DC emissions by 11.6%--12.6%, 1.56x--1.26x better than the best local, online approach's results. PlanShare also achieves lower cost. We expect these advantages to increase as renewable generation in power grids increases. Further, a known full-day DC capacity plan provides a stable target for DC resource management.Comment: Published at e-Energy '23: Proceedings of the 14th ACM International Conference on Future Energy System

Dynamic Energy Management for Chip Multi-processors under Performance Constraints

We introduce a novel algorithm for dynamic energy management (DEM) under performance constraints in chip multi-processors (CMPs). Using the novel concept of delayed instructions count, performance loss estimations are calculated at the end of each control period for each core. In addition, a Kalman filtering based approach is employed to predict workload in the next control period for which voltage-frequency pairs must be selected. This selection is done with a novel dynamic voltage and frequency scaling (DVFS) algorithm whose objective is to reduce energy consumption but without degrading performance beyond the user set threshold. Using our customized Sniper based CMP system simulation framework, we demonstrate the effectiveness of the proposed algorithm for a variety of benchmarks for 16 core and 64 core network-on-chip based CMP architectures. Simulation results show consistent energy savings across the board. We present our work as an investigation of the tradeoff between the achievable energy reduction via DVFS when predictions are done using the effective Kalman filter for different performance penalty thresholds