Online Revenue Maximization for Server Pricing

Efficient and truthful mechanisms to price resources on remote servers/machines has been the subject of much work in recent years due to the importance of the cloud market. This paper considers revenue maximization in the online stochastic setting with non-preemptive jobs and a unit capacity server. One agent/job arrives at every time step, with parameters drawn from an underlying unknown distribution. We design a posted-price mechanism which can be efficiently computed, and is revenue-optimal in expectation and in retrospect, up to additive error. The prices are posted prior to learning the agent's type, and the computed pricing scheme is deterministic, depending only on the length of the allotted time interval and on the earliest time the server is available. If the distribution of agent's type is only learned from observing the jobs that are executed, we prove that a polynomial number of samples is sufficient to obtain a near-optimal truthful pricing strategy

Combinatorial Auction-Based Virtual Machine Provisioning And Allocation In Clouds

Current cloud providers use fixed-price based mechanisms to allocate Virtual Machine (VM) instances to their users. But economic theory states that when there are large amount of resources to be allocated to large number of users, auctions are the most efficient allocation mechanisms. Auctions achieve efficiency of allocation and also maximize the providers\u27 revenue, which a fixed-price based mechanism is unable to do. We argue that combinatorial auctions are best suited for the problem of VM provisioning and allocation in clouds, since they provide the users with the most flexible way to express their requirements. In combinatorial auctions, users bid for bundles of items rather than individual ones, therefore they are able to express whether the items they require are complementary to each other. The objective of this Ph.D. dissertation is to design, study, and implement combinatorial auction-based mechanisms for efficient provisioning and allocation of VM instances in clouds. The central hypothesis is that allocation efficiency and revenue maximization can be obtained by inducing users to fully express and truthfully report their preferences to the system. The rationale for our research is that, once efficient resource provisioning and allocation mechanisms that take into account the incentives of the users and cloud providers are developed and implemented, it will become more efficient to utilize cloud computing environments for solving challenging problems in business, science and engineering. In this dissertation, we present three combinatorial auction-based offline mechanisms to provision and allocation VM instances in clouds. We also present an online mechanism for dynamic provisioning of virtual machine instances in clouds. Finally, we designed an efficient bidding algorithm to assist users submitting bids to combinatorial auction-based mechanisms to execute parallel jobs the cloud. We outline our contribution and possible direction for future research in this field

Combining malleability and I/O control mechanisms to enhance the execution of multiple applications

This work presents a common framework that integrates CLARISSE, a cross-layer runtime for the I/O software stack, and FlexMPI, a runtime that provides dynamic load balancing and malleability capabilities for MPI applications. This integration is performed both at application level, as libraries executed within the application, as well as at central-controller level, as external components that manage the execution of different applications. We show that a cooperation between both runtimes provides important benefits for overall system performance: first, by means of monitoring, the CPU, communication and I/O performances of all executing applications are collected, providing a holistic view of the complete platform utilization. Secondly, we introduce a coordinated way of using CLARISSE and FlexMPI control mechanisms, based on two different optimization strategies, with the aim of improving both the application I/O and overall system performance. Finally, we present a detailed description of this proposal, as well as an empirical evaluation of the framework on a cluster showing significant performance improvements at both application and wide-platform levels. We demonstrate that with this proposal the overall I/O time of an application can be reduced by up to 49% and the aggregated FLOPS of all running applications can be increased by 10% with respect to the baseline case. (C) 2018 Elsevier Inc. All rights reserved.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been partially supported by the Spanish “Ministerio de Economia y Competitividad” under the project grant TIN2016-79637-P “Towards Unification of HPC and Big Data paradigms” and EU under the COST Program Action IC1305, Network for Sustainable Ultrascale Computing (NESUS)

Delay and price differentiation in cloud computing: a service model, supporting architectures, and performance

Many cloud service providers (CSPs) offer an on-demand service with a small delay. Motivated by the reality of cloud ecosystems, we study non-interruptible services and consider a differentiated service model to complement the existing market by offering multiple service level agreements (SLAs) to satisfy users with different delay tolerance. The model itself is incentive compatible by construction. Two typical architectures are considered to fulfill SLAs: (i) non-preemptive priority queues and (ii) multiple independent groups of servers. We leverage queueing theory to establish guidelines for the resultant market: (a) Under the first architecture, the service model can only improve the revenue marginally over the pure on-demand service model and (b) under the second architecture, we give a closed-form expression of the revenue improvement when a CSP offers two SLAs and derive a condition under which the market is viable. Additionally, under the second architecture, we give an exhaustive search procedure to find the optimal SLA delays and prices when a CSP generally offers multiple SLAs. Numerical results show that the achieved revenue improvement can be significant even if two SLAs are offered. Our results can help CSPs design optimal delay-differentiated services and choose appropriate serving architectures

The Inter-cloud meta-scheduling

Inter-cloud is a recently emerging approach that expands cloud elasticity. By facilitating an adaptable setting, it purposes at the realization of a scalable resource provisioning that enables a diversity of cloud user requirements to be handled efficiently. This study’s contribution is in the inter-cloud performance optimization of job executions using metascheduling concepts. This includes the development of the inter-cloud meta-scheduling (ICMS) framework, the ICMS optimal schemes and the SimIC toolkit. The ICMS model is an architectural strategy for managing and scheduling user services in virtualized dynamically inter-linked clouds. This is achieved by the development of a model that includes a set of algorithms, namely the Service-Request, Service-Distribution, Service-Availability and Service-Allocation algorithms. These along with resource management optimal schemes offer the novel functionalities of the ICMS where the message exchanging implements the job distributions method, the VM deployment offers the VM management features and the local resource management system details the management of the local cloud schedulers. The generated system offers great flexibility by facilitating a lightweight resource management methodology while at the same time handling the heterogeneity of different clouds through advanced service level agreement coordination. Experimental results are productive as the proposed ICMS model achieves enhancement of the performance of service distribution for a variety of criteria such as service execution times, makespan, turnaround times, utilization levels and energy consumption rates for various inter-cloud entities, e.g. users, hosts and VMs. For example, ICMS optimizes the performance of a non-meta-brokering inter-cloud by 3%, while ICMS with full optimal schemes achieves 9% optimization for the same configurations. The whole experimental platform is implemented into the inter-cloud Simulation toolkit (SimIC) developed by the author, which is a discrete event simulation framework

Fair, responsive scheduling of engineering workflows on computing grids

This thesis considers scheduling in the context of a grid computing system used in engineering design. Users desire responsiveness and fairness in the treatment of the workflows they submit. Submissions outstrip the available computing capacity during the work day, and the queue is only caught up on overnight and at weekends. The execution times observed span a wide range of 10^0 to 10^7 core-minutes. The Projected Schedule Length Ratio (P-SLR) list scheduling policy is designed to use execution time estimates and the structure of the dependency graph to improve on the existing industrial FairShare policy. P-SLR aims to minimise the worst-case SLR of jobs and keep SLR fair across the space of job execution times. P-SLR is shown to equal or surpass all other evaluated policies in responsiveness and fairness across the spectra of load and networking delays. P-SLR is also dominant where execution time estimates are within an order of magnitude of the real value. Such estimates are considered achievable using user knowledge or automated profiling. Outside this range, the Shortest Remaining Time First (SRTF) policy achieved better responsiveness and fairness. The Projected Value Remaining (PVR) policy considers the case where a curve specifying the value of a job over time is given. PVR aims to maximise total workload value, even under overload, by maximising the worst-case job value in a workload. PVR is shown to be dominant across the load and networking spectra. Where execution time estimates are coarser than the nearest power of 2, SRTF delivers higher value than PVR. SRTF is also shown to have responsiveness, fairness and value close behind P-SLR and PVR throughout the range of load and network delays considered. However, the kinds of starvation under overload incurred by SRTF would almost certainly be undesirable if implemented in a production system

Temporally Designing the Consumer Experience: Three Essays Examining the Influence of Time Architecture on Consumer Behavior

How can the temporal aspects of the consumer experience be strategically constructed and communicated to improve consumer behavior and decision-making? This dissertation advocates for the explicit and systematic integration of time as a determining factor in consumer experiences, presenting three essays investigating different dimensions of time architecture, the temporal design of a consumer experience: temporal sequencing of planning prompt nudges (Essay 1), temporal partitioning of initial charitable contributions (Essay 2), and temporal duration of contemporary online promotions (Essay 3). Essay 1 explores how the timing of planning nudge delivery impacts intervention effectiveness in tasks containing an optimal “early bird” deadline (i.e., after which benefits of task completion diminish). Results from three studies find that planning prompt nudge reminders delivered after the optimal deadline are significantly more effective than control reminders but offer little benefit when implemented before the optimal deadline. These findings call for 1) strategic temporal management of planning prompts and 2) increased research exploring the ideal timing of nudge delivery. Essay 2 investigates how temporal aspects of giving perpetuate donor support. Consistent with an anchoring account, results from five studies demonstrate that prior donors who initially give a recurring time-dispersed gift (e.g., monthly $10 gift for 12 months) subsequently donate less than those who initially give a one-time lump-sum gift of the equivalent total amount (e.g., single$120 gift). Several approaches for offsetting recurring donors’ later reduced giving are tested and implications for charities are discussed. Essay 3 questions the degree to which contemporary instantiations of online time scarcity promotions (e.g., one-hour flash sales with countdown timers) can be presumed to operate in ways theoretically and empirically consistent with foundational demonstrations of time scarcity marketing tactics, which largely predate modern online retailing and predominantly involve offline contexts (e.g., printed newspaper ad). Results from 26 new studies find that present-day online time scarcity promotions may not be as effective as generally assumed, consistent with the argument that these promotions represent a novel theoretical and empirical phenomenon. Together, these essays demonstrate that the temporal design of a consumer experience can promote or undermine traditionally accepted marketing practices, thereby warranting systematic investigation and proactive management