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Dynamic Resource Allocation in the Cloud with Near-Optimal Efficiency
Data Structures and Algorithms · Submitted 7 Sep 2018
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our main result is a simple and practical algorithm that achieves near-optimal performance on the above two objectives.
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The abstract, sentence by sentence
Cloud computing has motivated renewed interest in resource allocation problems with new consumption models. A common goal is to share a resource, such as CPU or I/O bandwidth, among distinct users with different demand patterns as well as different quality of service requirements. To ensure these service requirements, cloud offerings often come with a service level agreement (SLA) between the provider and the users. An SLA specifies the amount of a resource a user is entitled to utilize. In many cloud settings, providers would like to operate resources at high utilization while simultaneously respecting individual SLAs. There is typically a tradeoff between these two objectives; for example, utilization can be increased by shifting away resources from idle users to "scavenger" workload, but with the risk of the former then becoming active again. We study this fundamental tradeoff by formulating a resource allocation model that captures basic properties of cloud computing systems, including SLAs, highly limited feedback about the state of the system, and variable and unpredictable input sequences. Our main result is a simple and practical algorithm that achieves near-optimal performance on the above two objectives. First, we guarantee nearly optimal utilization of the resource even if compared to the omniscient offline dynamic optimum. Second, we simultaneously satisfy all individual SLAs up to a small error. The main algorithmic tool is a multiplicative weight update algorithm, and a primal-dual argument to obtain its guarantees. We also provide numerical validation on real data to demonstrate the performance of our algorithm in practical applications.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Cloud computing has motivated renewed interest in resource allocation problems with new consumption models. | background 1.00 | 0.04 | 0.01 | asserted 1.00 |
| s1 | A common goal is to share a resource, such as CPU or I/O bandwidth, among distinct users with different demand patterns as well as different quality of service requirements. | background 1.00 | 0.04 | 0.01 | asserted 0.93 |
| s2 | To ensure these service requirements, cloud offerings often come with a service level agreement (SLA) between the provider and the users. | background 1.00 | 0.04 | 0.01 | asserted 0.90 |
| s3 | An SLA specifies the amount of a resource a user is entitled to utilize. | background 1.00 | 0.04 | 0.01 | asserted 1.00 |
| s4 | In many cloud settings, providers would like to operate resources at high utilization while simultaneously respecting individual SLAs. | background 1.00 | 0.07 | 0.02 | hedged possibility 0.64 |
| s5 | There is typically a tradeoff between these two objectives; for example, utilization can be increased by shifting away resources from idle users to "scavenger" workload, but with the risk of the former then becoming active again. | background 0.99 | 0.07 | 0.02 | asserted 0.79 |
| s6 | We study this fundamental tradeoff by formulating a resource allocation model that captures basic properties of cloud computing systems, including SLAs, highly limited feedback about the state of the system, and variable and unpredictable input sequences. | contribution 0.67 | 0.92 | 0.04 | asserted 0.98 |
| s7 | Our main result is a simple and practical algorithm that achieves near-optimal performance on the above two objectives. | contribution 0.75 | 0.96 | 0.94 | asserted 1.00 |
| s8 | First, we guarantee nearly optimal utilization of the resource even if compared to the omniscient offline dynamic optimum. | result 0.96 | 0.94 | 0.32 | asserted 1.00 |
| s9 | Second, we simultaneously satisfy all individual SLAs up to a small error. | result 0.96 | 0.95 | 0.25 | asserted 1.00 |
| s10 | The main algorithmic tool is a multiplicative weight update algorithm, and a primal-dual argument to obtain its guarantees. | method 0.99 | 0.89 | 0.04 | asserted 0.99 |
| s11 | We also provide numerical validation on real data to demonstrate the performance of our algorithm in practical applications. | contribution 0.61 | 0.95 | 0.03 | asserted 1.00 |
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The paper
Original abstract
Cloud computing has motivated renewed interest in resource allocation problems with new consumption models. A common goal is to share a resource, such as CPU or I/O bandwidth, among distinct users with different demand patterns as well as different quality of service requirements. To ensure these service requirements, cloud offerings often come with a service level agreement (SLA) between the provider and the users. An SLA specifies the amount of a resource a user is entitled to utilize. In many cloud settings, providers would like to operate resources at high utilization while simultaneously respecting individual SLAs. There is typically a tradeoff between these two objectives; for example, utilization can be increased by shifting away resources from idle users to "scavenger" workload, but with the risk of the former then becoming active again. We study this fundamental tradeoff by formulating a resource allocation model that captures basic properties of cloud computing systems, including SLAs, highly limited feedback about the state of the system, and variable and unpredictable input sequences. Our main result is a simple and practical algorithm that achieves near-optimal performance on the above two objectives. First, we guarantee nearly optimal utilization of the resource even if compared to the omniscient offline dynamic optimum. Second, we simultaneously satisfy all individual SLAs up to a small error. The main algorithmic tool is a multiplicative weight update algorithm, and a primal-dual argument to obtain its guarantees. We also provide numerical validation on real data to demonstrate the performance of our algorithm in practical applications.
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3 arXiv papers cite this in 3 sentences
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Cites it as background 1 paper
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The ability to dynamically increase and decrease the level of resources (CPU, memory, storage, etc.) allocated to an application running in cloud-based systems [ref] .
Systemic Risk and Vulnerability Analysis of Multi-cloud Environments · Difference in Management · 0.88
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