Revenue Maximization with Optimal Capacity Control in Infrastructure as a Service Cloud Markets

Toosi, Adel Nadjaran; Vanmechelen, Kurt; Ramamohanarao, Kotagiri; Buyya, Rajkumar

doi:10.1109/tcc.2014.2382119

Cited by 54 publications

(36 citation statements)

References 20 publications

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“…Note that there is a rich literature on cloud resource allocation and pricing [34]. In [35], [36], the authors propose different profit maximization frameworks for cloud providers. References [37]- [39] study how to efficiently share resource and profit among cloud providers in a cloud federation.…”

Section: Related Workmentioning

confidence: 99%

Price-Based Resource Allocation for Edge Computing: A Market Equilibrium Approach

Nguyen

Bhargava

2021

IEEE Trans. Cloud Comput.

113

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The emerging edge computing paradigm promises to deliver superior user experience and enable a wide range of Internet of Things (IoT) applications. In this work, we propose a new market-based framework for efficiently allocating resources of heterogeneous capacity-limited edge nodes (EN) to multiple competing services at the network edge. By properly pricing the geographically distributed ENs, the proposed framework generates a market equilibrium (ME) solution that not only maximizes the edge computing resource utilization but also allocates optimal (i.e., utility-maximizing) resource bundles to the services given their budget constraints. When the utility of a service is defined as the maximum revenue that the service can achieve from its resource allotment, the equilibrium can be computed centrally by solving the Eisenberg-Gale (EG) convex program. drawn from the economics literature. We further show that the equilibrium allocation is Pareto-optimal and satisfies desired fairness properties including sharing incentive, proportionality, and envy-freeness. Also, two distributed algorithms are introduced, which efficiently converge to an ME. When each service aims to maximize its net profit (i.e., revenue minus cost) instead of the revenue, we derive a novel convex optimization problem and rigorously prove that its solution is exactly an ME. Extensive numerical results are presented to validate the effectiveness of the proposed techniques.

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Section: Related Workmentioning

confidence: 99%

Price-Based Resource Allocation for Edge Computing: A Market Equilibrium Approach

Nguyen

Bhargava

2021

IEEE Trans. Cloud Comput.

113

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“…Because these traces are user requests not representing VM instances demand, we need to make a mapping between request submissions from users to IaaS computing demand. We use the same technique used by Toosi et al for their workload generation to generate VM request traces …”

Section: Performance Evaluationmentioning

confidence: 99%

“…To derive VM request traces from Google traces, whenever a user submits a task, we check if there is already a VM instantiated by that user in the system with enough computing resources to run the new task. Otherwise, if there is no VM owned by the user with enough capacity to accommodate the new task, we instantiate a new VM to serve the user's task . We also terminate a VM when there is no task running on it.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…We use the same technique used by Toosi et al for their workload generation to generate VM request traces. 49 Google traces include record of users, each submitting several tasks, with specific resource requirements. Considering the fact that 93% of the Google cluster machines have the same computing capability, we assume all physical machines in the cluster have the same resources (in terms of CPU, memory, etc) and map our VM size to that of the physical machine.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Otherwise, if there is no VM owned by the user with enough capacity to accommodate the new task, we instantiate a new VM to serve the user's task. 49 We also terminate a VM when there is no task running on it. By this, we can create a trace of VM requests submitted from users.…”

Section: Performance Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Online virtual machine migration for renewable energy usage maximization in geographically distributed cloud data centers

Khosravi

Toosi

Buyya

2017

Concurrency and Computation

Self Cite

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Summary Energy consumption and its associated costs represent a huge part of cloud providers' operational costs. In this study, we explore how much energy cost savings can be made knowing the future level of renewable energy (solar/wind) available in data centers. Since renewable energy sources have intermittent nature, we take advantage of migrating virtual machines to the nearby data centers with excess renewable energy. In particular, we first devise an optimal offline algorithm with full future knowledge of renewable level in the system. Since in practice, accessing long‐term and exact future knowledge of renewable energy level is not feasible, we propose two online deterministic algorithms, one with no future knowledge called deterministic and one with limited knowledge of the future renewable availability called future‐aware. We show that the deterministic and future‐aware algorithms are 1+1/s and 1+1/s−ω/s.Tm competitive in comparison to the optimal offline algorithm, respectively, where s is the network to the brown energy cost, ω is the look‐ahead window‐size, and Tm is the migration time. The effectiveness of the proposed algorithms is analyzed through extensive simulation studies using real‐world traces of meteorological data and Google cluster workload.

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Revenue maximization approaches in IaaS clouds: Research challenges and opportunities

Badshah

Ghani

Daud

et al. 2022

Trans Emerging Tel Tech

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This study critically reviews the IaaS clouds development on revenue maximization since 2012 to answer these research queries; (i) What are the main influential factors towards revenue maximization in the cloud market? (ii) What are the main challenges and resistance towards revenue maximization in cloud computing? and (iii) What are the possible solutions and potentials to these hurdles in cloud computing? The data was analyzed and the influencing factors of revenue maximization were classified into seven distinct categories, that is, the performance of the services, service level agreement and penalties management, resources scalability, resources utilization and scheduling, customers' satisfaction, cost, and pricing management, as well as advertisement and auction. These parameters are investigated in detail and new dynamics for researchers in the field of the cloud are discovered. These studies are compared against each other for the seven distinct categories and solutions are proposed for the clouds' obstacles to revenue maximization. Furthermore, in the light of the findings and revenue maximization categories, the main limitations, challenges, true potential, and new directions towards revenue maximization are explored.

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Revenue Maximization with Optimal Capacity Control in Infrastructure as a Service Cloud Markets

Cited by 54 publications

References 20 publications

Price-Based Resource Allocation for Edge Computing: A Market Equilibrium Approach

Price-Based Resource Allocation for Edge Computing: A Market Equilibrium Approach

Online virtual machine migration for renewable energy usage maximization in geographically distributed cloud data centers

Revenue maximization approaches in IaaS clouds: Research challenges and opportunities

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