The massive energy consumption of data centers worldwide has resulted in a large carbon footprint, raising serious concerns to sustainable IT initiatives and attracting a great amount of research attention. Nonetheless, the current efforts to date, despite encouraging, have been primarily centered around owner-operated data centers (e.g., Google data center), leaving out another major segment of data center industry-colocation data centers-much less explored. As a major hindrance to carbon efficiency desired by the operator, colocation suffers from “split incentive”: tenants may not be willing to manage their servers for carbon efficiency. In this paper, we aim at minimizing the carbon footprint of geo-distributed colocation data centers, while ensuring that the operator's cost meets a long-term budget constraint. We overcome the “split incentive” hurdle by devising a novel online carbon-aware incentive mechanism, called GreenColo, in which tenants voluntarily bid for energy reduction at self-determined prices and will receive financial rewards if their bids are accepted at runtime. Using trace based simulation we show that GreenColo results in a carbon footprint fairly close (23 versus 18 percent) to the optimal offline solution with future information, while being able to satisfy the colocation operator's long-term budget constraint. We demonstrate the effectiveness of GreenColo in practical scenarios via both simulation studies and scaled-down prototype experiments. Our results show that GreenColo can reduce the carbon footprint by up to 24 percent without incurring any additional cost for the colocation operator (compared to the no-incentive baseline case), while tenants receive financial rewards for “free” without violating service level agreement.

The growing environmental and sustainability concerns have made energy efficiency a pressing issue for data center operation. Governments, as well as various organizations, are urging data centers to cap the increasing energy consumption. Naturally, achieving long term energy capping involves deciding energy usage over a long timescale (without accurately foreseeing the far future) and hence, we call this process “energy budgeting”. In this paper, we introduce an online resource management solution, called eBud (energy Budgeting), for a virtualized data center. eBud determines the number of servers, resource allocation to virtual machines and corresponding workload distribution to minimize data center operational cost while satisfying a long term energy cap. We prove that eBud achieves a close-to-minimum cost compared to the optimal offline algorithm with future information, while bounding the potential violation of energy budget constraint, in an almost arbitrarily random environment. We also perform a trace-based simulation study to complement the performance analysis. The simulation results show that eBud reduces the cost by more than$\mathrm{16}$percent (compared to state-of-the-art prediction-based algorithm) while resulting in a zero energy budget deficit. We also perform an experimental study based on RUBiS, demonstrating that in a real life scenario, eBud can achieve energy capping with a negligible increase in operational cost.

Power-hungry data centers face an urgent pressure on reducing the energy cost. The existing efforts, despite being numerous, have primarily centered around owner-operated data centers (e.g., Google), leaving another critical data center segment - colocation data center (e.g., Equinix) which rents out physical space to multiple tenants for housing their own servers - much less explored. Colocations have a major barrier to achieve cost efficiency: server power management by individual tenants is uncoordinated. This paper proposes RECO (REward for COst reduction), which shifts tenants' power management from uncoordinated to coordinated, using financial reward as a lever. RECO pays (voluntarily participating) tenants for energy reduction such that the colocation operator's overall cost is minimized. RECO incorporates the time-varying operation environment (e.g., cooling efficiency, intermittent renewables), addresses the peak power demand charge, and also proactively learns tenants' unknown responses to the offered reward. RECO includes a new feedback-based online algorithm to optimize the reward without far future offline information. We evaluate RECO using both scaled-down prototype experiments and simulations. Our results show that RECO is "win-win" and can successfully reduce the colocation operator's overall cost, by up to 27% compared to the no-incentive baseline case. Further, tenants receive financial rewards (up to 15% of their colocation costs) for "free" without violating Service Level Agreements.

Autoscaling has become an integral feature of cloud computing services, allowing users to dynamically scale the cloud resources on demand for both performance and cost. Moreover, recent survey shows the importance of satisfying long-term budget constraints (e.g., monthly or yearly) for cloud users. However, meeting such constraints while optimizing delay performance is challenging: it requires the knowledge of complete offline information such as workload demand over the entire budgeting period, which is difficult to predict accurately. This paper proposes a new autoscaling system, BATS, which optimizes delay performance while meeting long-term budget constraints using only past and instantaneous workload information. Analytically, we prove that, for arbitrary workload arrival, the autoscaling algorithm of BATS achieves close-to-optimal performance even compared to the optimal solution that has complete offline information. Empirically, we build BATS autoscaler as a user-friendly service for running applications on Windows Azure. The experimental results show that BATS achieves both lower cost and less delay compared with the state-of-art threshold-based autoscaling solutions. We also run simulation studies to complement the implementation results, demonstrating the effectiveness, scalability and robustness of BATS for reducing both average and tail latency under various workload scenarios.

No abstract available.

In light of the growing trend of data center carbon emission that has raised serious sustainability concerns, data center operators are aggressively seeking ways to minimize the overall energy consumption by using more on-site renewable energy and ultimately achieving carbon neutrality (or "net"). In this paper, we propose a dynamic server provisioning algorithm, called SPAN (Server Provisioning for carbon Neutrality), to control the number of active servers for minimizing the data center operational cost (defined as a weighted sum of electricity cost and delay cost) while achieving carbon neutrality without requiring long-term future information. Leveraging the recently-developed Lyapunov optimization technique, it is rigorously proved that SPAN achieves a close-to-minimum operational cost compared to the optimal offline algorithm with future information, while bounding the potential violation of carbon neutrality. The simulation result is also consistent with our analysis, showing that SPAN can reduce the average operational cost by 20% while maintaining a carbon neutral data center.