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Inspired by the shared infrastructure of colocation data centers and the growth of Mobile Edge Computing (MEC), colocation MEC businesses have thrived to offer an economical and low latency solution for MEC based tenants. In colocation MEC, a colocation service provider leases spaces at the base stations (BSs) to tenants for housing their servers. Although the tenants fully manage their own servers, they still need to purchase bandwidth from the network provider to via bandwidth purchasing market to serve their users. As a result, tenants should consider the trade-off between delay and energy consumption, which are related to bandwidth and server computing speed, respectively. In this paper, an auction framework is used to model the interaction between the network provider and tenants in the bandwidth market. The network provider determines the bandwidth amount and corresponding payment by tenants based on their submitted bids, in which the valuation of the bandwidth amount in each bid is based on the coupling between bandwidth and server computing speed. The networking provider also seeks winners in the social welfare maximization problem, which is NP-hard. To solve this problem, we propose a solution based on the randomized auction mechanism, which is proved to be approximately truthful, individually rational, and computationally efficient. Simulation results verify the effectiveness of our proposed randomized auction scheme in bandwidth trading. [ABSTRACT FROM AUTHOR]

Shared edge computing platforms deployed at the radio access network are expected to significantly improve the quality-of-service delivered by application service providers (ASPs) in a flexible and economic way. However, placing edge service in every possible edge site by an ASP is practically infeasible due to the ASP’s prohibitive budget requirement. In this paper, we investigate the edge service placement problem of an ASP under a limited budget, where the ASP dynamically rents computing/storage resources in edge sites to host its applications in close proximity to end users. Since the benefit of placing edge service in a specific site is usually unknown to the ASP a priori, optimal placement decisions must be made while learning this benefit. We pose this problem as a novel combinatorial contextual bandit learning problem. It is “combinatorial” because only a limited number of edge sites can be rented to provide the edge service given the ASP’s budget. It is “contextual” because we utilize user context information to enable finer-grained learning and decision-making. To solve this problem and optimize the edge computing performance, we propose SEEN, a Spatial-temporal Edge sErvice placemeNt algorithm. Furthermore, SEEN is extended to scenarios with overlapping service coverage by incorporating a disjunctively constrained knapsack problem. In both cases, we prove that our algorithm achieves a sublinear regret bound when it is compared with an Oracle algorithm that knows the exact benefit information. Simulations are carried out on a real-world dataset, whose results show that SEEN significantly outperforms benchmark solutions. [ABSTRACT FROM AUTHOR]

Emergency demand response (EDR), which is the last line of defense to avoid cascading failures during emergency events, has witnessed numerous crucial participants, including buildings and datacenters (DCs). However, even though the majority of DCs are physically located in mixed-use buildings (MUBs), the existing studies on EDR are non-coordinated approaches that separately focus on either buildings or DCs, hence ignoring that both DCs and non-DC (e.g., office) operations share the same MUB facilities (e.g., electricity supply). Furthermore, even when all MUB tenants (i.e., offices and DCs) are jointly considered, tenants will incur different costs to shed energy for EDR, thereby raising an issue of mis-aligned incentive for their participation. To overcome this non-coordinated energy shedding and mis-aligned incentives, we propose two incentive mechanisms in MUBs, such that the total incurred cost is minimized for energy shedding. The first mechanism, namely $\mathsf {MECH{\text -}NA}$ , is designed for non-strategic MUB tenants. In $\mathsf {MECH{\text -}NA}$ , the MUB operator provides a mechanism package, including reward rate and a commitment profile with deviation penalty, based on which the MUB tenants will shed energy to maximize the reward and minimize their energy-shedding and deviation costs. We also design a distributed algorithm to implement $\mathsf {MECH{\text -}NA}$ that can achieve the minimum MUB cost. The second mechanism, namely $\mathsf {MECH{\text -}SA}$ , is a VCG-Kelly-based mechanism tailored to handle strategic MUB tenants. In $\mathsf {MECH{\text -}SA}$ , the operator announces both reward and energy shedding rules, based on which the tenants strategically participate in an bidding game. For this game, we not only show that there exists an efficient Nash equilibrium at which the total MUB cost is achieved, but also design a distributed algorithm to implement $\mathsf {MECH{\text -}SA}$. Simulation results show that both $\mathsf {MECH{\text -}NA}$ and $\mathsf {MECH{\text -}SA}$ can obtain the optimal MUB cost, which outperforms partially or non-coordinated approaches. [ABSTRACT FROM AUTHOR]

Cloud data centers are widely employed to offer reliable cloud services. However, low resource utilization and high power consumption have been great challenges for cloud providers. Moreover, the rapid increase in demand for affordable cloud services magnifies the obstacles for proficient resource management policies. In this paper, we investigate how to improve resource utilization and power consumption in cloud data centers when delivering services with statistically guaranteed Quality of Service (QoS). We assume that the service provider hosts different types of services, each of which has request classes with different QoS requirements. Different from the traditional approaches that distribute workloads with different QoS levels on different Virtual Machines (VMs), we introduce an approach to pack requests of the same service type, even with different QoS requirements, into the same VM, and to remove potential failure requests in time to improve resource usage and energy cost. We formally prove that our algorithm can statistically guarantee QoS conditions in terms of deadline miss ratios. We develop a cloud prototype to empirically validate our proposed methods and algorithm. Our experimental results demonstrate that our approach can significantly outperform other traditional approaches in terms of QoS guarantees, power consumption, resource demand and electricity cost. [ABSTRACT FROM PUBLISHER]

Even though demand response of data centers has attracted many studies, there are very limited attempts on an important segment: colocation datacenters. Unlike large-scale (Google-type) datacenters, the colocation operator lacks control over its tenant servers, which entails a special interest in a design of incentive mechanisms, such that the operator can coordinate tenants to reduce the power usage for demand response. However, most previous studies ignore the role of the demand response provider (DRP), who uses pricing signals as a guide for customer response and as a compensation for their cutting electricity usage. To address this oversight, we propose an incentive mechanism Reward-to-Reduce for colocation’s economic demand response, which shows an interaction between the DRP compensation to the colocation operator, and the colocation operator reward to tenants. Observing that this interaction contains strategic behaviors, we first formulate a two-stage Stackelberg game, where we show a unique competitive equilibrium of the operator strategy in the second stage, and a nonconvex problem of finding the optimal DRP compensation price in the first stage. We next analyze the second-stage equilibrium using an exact analysis and design an algorithm that can efficiently search the first-stage optimal DRP price with a reduced search space. Since the exact analysis can be impractical due to required tenants’ private information, we also propose an approximate approach with limited tenant information. Extensive case studies show that the approximate approach can have the same performance as the exact analysis in a wide array of case studies and the optimal DRP price can be determined effectively, with which the corresponding DRP individual cost is compared with the social cost. [ABSTRACT FROM PUBLISHER]

Data centers with high yet elastic energy demand are ideal candidates for participation in demand response programs. This paper studies emergency demand response (EDR) at multi-tenant colocation data centers (colocations). While the colocation has no direct control over tenants’ servers, we design online mechanisms to incentivize and coordinate tenants’ energy reduction. Our mechanism is online in nature, aiming to maximize not only social welfare but also tenant utility. Our main proposal is a truthful incentive auction that provides tenants with monetary remuneration for EDR energy reduction, minimizing social cost, which combines seamlessly with an online primal-dual framework for each tenant to schedule their delay-tolerant workloads. The online optimization at each tenant targets its utility maximization, concurrently reporting valuation functions for the tenant to participate in the auction. Our online algorithms achieve long-term performance guarantees in both tenants’ utility and social welfare maximization, while fulfilling the EDR requirement with minimal diesel generation. We validate the efficiency of our algorithms through both the theoretical analysis and real-world trace-driven simulations. [ABSTRACT FROM PUBLISHER]

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 \mathrm16 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. [ABSTRACT FROM PUBLISHER]

We study the demand response (DR) of geo-distributed data centers (DCs) using smart grid’s pricing signals set by local electric utilities. The geo-distributed DCs are suitable candidates for the DR programs due to their huge energy consumption and flexibility to distribute their energy demand across time and location, whereas the price signal is well-known for DR programs to reduce the peak-to-average load ratio. There are two dependencies that make the pricing design difficult: 1) dependency among utilities; and 2) dependency between DCs and their local utilities. Our proposed pricing scheme is constructed based on a two-stage Stackelberg game in which each utility sets a real-time price to maximize its own profit in Stage I and based on these prices, the DCs’ service provider minimizes its cost via workload shifting and dynamic server allocation in Stage II. For the first dependency, we show that there exists a unique Nash equilibrium. For the second dependency, we propose an iterative and distributed algorithm that can converge to this equilibrium, where the “right prices” are set for the “right demands.” We also verify our proposal by trace-based simulations, and results show that our pricing scheme significantly outperforms other baseline schemes in terms of flattening the power demand over time and space. [ABSTRACT FROM PUBLISHER]

Demand response programs have been considered critical for power grid reliability and efficiency. Especially, the demand response of datacenters has recently received encouraging efforts due to huge demands and flexible power control knobs of datacenters. However, most current efforts focus on owner-operated datacenters, omitting another critical segment of datacenter business: multitenant colocation. In colocation datacenters, while there exist multiple tenants who manage their own servers, the colocation operator only provides facilities such as cooling, reliable power, and network connectivity. Therefore, colocation has a unique feature that challenges any attempts to design a demand response program: uncoordinated power management among tenants. To tackle this challenge, two incentive mechanisms are proposed to coordinate tenant power consumption for demand response under two different scenarios. First, in the case of economic demand response where the operator can adjust an elastic energy reduction target, we show that there is an interaction between the operator and tenant strategies, where each side maximizes its own benefit. Hence, we apply a two-stage Stackelberg game to analyze this scenario and derive this game’s equilibria. However, computing these equilibria can be intractable with exhaustive search; therefore, we propose an algorithm to find the Stackelberg equilibria with linear complexity. Second, in the case of emergency demand response where a fixed energy reduction target must be fulfilled, we devise two incentive schemes with the distributed algorithms that can achieve the same optimal social cost. While the first algorithm is based on the dual-decomposition method that is suitable for nonstrategic tenants, the second one is designed for strategic tenants to achieve a unique Nash equilibrium of a bidding game. Finally, trace-based simulations are also provided to illustrate the efficacy of our proposed incentive schemes. [ABSTRACT FROM PUBLISHER]

Online television (TV) market has been expanding rapidly over the last few years and provided TV studios with a cost-effective and reliable channel for the delivery of high-quality TV content. To maximize profit by setting up an online TV content platform, two major challenges are faced by the platform owner: what is the optimal investment (e.g., how many hosting servers, bandwidth acquisition) and how to price TV content producers who utilize the platform as a channel to distribute their content. To address these two challenges, we first derive the optimal pricing policy based on the widely-adopted “pay-per-usage” model, and then formalize and solve the optimal investment decision problem. Rationality of self-interested TV content producers and audiences is also taken into account. Specifically, we first use a model with a representative content viewer to determine how many times a TV content with a certain quality is watched. Then, by modeling the content providers as self-interested agents making independent production decisions, we show that for any price charged by the platform, there always exists a unique equilibrium in the content production stage, which makes it possible for the platform owner to maximize its profit without uncertainties because of the unique outcome in the content producers' decision stage. Finally, we develop an algorithm to derive the optimal price and then formalize the investment decision problem to maximize the platform's profit. [ABSTRACT FROM PUBLISHER]