62 research outputs found
Greening Multi-Tenant Data Center Demand Response
Data centers have emerged as promising resources for demand response,
particularly for emergency demand response (EDR), which saves the power grid
from incurring blackouts during emergency situations. However, currently, data
centers typically participate in EDR by turning on backup (diesel) generators,
which is both expensive and environmentally unfriendly. In this paper, we focus
on "greening" demand response in multi-tenant data centers, i.e., colocation
data centers, by designing a pricing mechanism through which the data center
operator can efficiently extract load reductions from tenants during emergency
periods to fulfill energy reduction requirement for EDR. In particular, we
propose a pricing mechanism for both mandatory and voluntary EDR programs,
ColoEDR, that is based on parameterized supply function bidding and provides
provably near-optimal efficiency guarantees, both when tenants are price-taking
and when they are price-anticipating. In addition to analytic results, we
extend the literature on supply function mechanism design, and evaluate ColoEDR
using trace-based simulation studies. These validate the efficiency analysis
and conclude that the pricing mechanism is both beneficial to the environment
and to the data center operator (by decreasing the need for backup diesel
generation), while also aiding tenants (by providing payments for load
reductions).Comment: 34 pages, 6 figure
Robust Bandit Learning with Imperfect Context
A standard assumption in contextual multi-arm bandit is that the true context
is perfectly known before arm selection. Nonetheless, in many practical
applications (e.g., cloud resource management), prior to arm selection, the
context information can only be acquired by prediction subject to errors or
adversarial modification. In this paper, we study a contextual bandit setting
in which only imperfect context is available for arm selection while the true
context is revealed at the end of each round. We propose two robust arm
selection algorithms: MaxMinUCB (Maximize Minimum UCB) which maximizes the
worst-case reward, and MinWD (Minimize Worst-case Degradation) which minimizes
the worst-case regret. Importantly, we analyze the robustness of MaxMinUCB and
MinWD by deriving both regret and reward bounds compared to an oracle that
knows the true context. Our results show that as time goes on, MaxMinUCB and
MinWD both perform as asymptotically well as their optimal counterparts that
know the reward function. Finally, we apply MaxMinUCB and MinWD to online edge
datacenter selection, and run synthetic simulations to validate our theoretical
analysis
Extending Demand Response to Tenants in Cloud Data Centers via Non-intrusive Workload Flexibility Pricing
Participating in demand response programs is a promising tool for reducing
energy costs in data centers by modulating energy consumption. Towards this
end, data centers can employ a rich set of resource management knobs, such as
workload shifting and dynamic server provisioning. Nonetheless, these knobs may
not be readily available in a cloud data center (CDC) that serves cloud
tenants/users, because workloads in CDCs are managed by tenants themselves who
are typically charged based on a usage-based or flat-rate pricing and often
have no incentive to cooperate with the CDC operator for demand response and
cost saving. Towards breaking such "split incentive" hurdle, a few recent
studies have tried market-based mechanisms, such as dynamic pricing, inside
CDCs. However, such mechanisms often rely on complex designs that are hard to
implement and difficult to cope with by tenants. To address this limitation, we
propose a novel incentive mechanism that is not dynamic, i.e., it keeps pricing
for cloud resources unchanged for a long period. While it charges tenants based
on a Usage-based Pricing (UP) as used by today's major cloud operators, it
rewards tenants proportionally based on the time length that tenants set as
deadlines for completing their workloads. This new mechanism is called
Usage-based Pricing with Monetary Reward (UPMR). We demonstrate the
effectiveness of UPMR both analytically and empirically. We show that UPMR can
reduce the CDC operator's energy cost by 12.9% while increasing its profit by
4.9%, compared to the state-of-the-art approaches used by today's CDC operators
to charge their tenants
Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees
Many problems, such as online ad display, can be formulated as online
bipartite matching. The crucial challenge lies in the nature of
sequentially-revealed online item information, based on which we make
irreversible matching decisions at each step. While numerous expert online
algorithms have been proposed with bounded worst-case competitive ratios, they
may not offer satisfactory performance in average cases. On the other hand,
reinforcement learning (RL) has been applied to improve the average
performance, but it lacks robustness and can perform arbitrarily poorly. In
this paper, we propose a novel RL-based approach to edge-weighted online
bipartite matching with robustness guarantees (LOMAR), achieving both good
average-case and worst-case performance. The key novelty of LOMAR is a new
online switching operation which, based on a judicious condition to hedge
against future uncertainties, decides whether to follow the expert's decision
or the RL decision for each online item. We prove that for any ,
LOMAR is -competitive against any given expert online algorithm. To
improve the average performance, we train the RL policy by explicitly
considering the online switching operation. Finally, we run empirical
experiments to demonstrate the advantages of LOMAR compared to existing
baselines. Our code is available at: https://github.com/Ren-Research/LOMARComment: Accepted by ICML 202
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