Tackling heterogeneous traffic in multi-access systems via erasure coded servers Article Swipe
Tuhinangshu Choudhury
,
Weina Wang
,
Gauri Joshi
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1145/3492866.3549713
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1145/3492866.3549713
Most data generated by modern applications is stored in the cloud, and there is an exponential growth in the volume of jobs to access these data and perform computations using them. The volume of data access or computing jobs can be heterogeneous across different job types and can unpredictably change over time. Cloud service providers cope with this demand heterogeneity and unpredictability by over-provisioning the number of servers hosting each job type. In this paper, we propose the addition of erasure-coded servers that can flexibly serve multiple job types without additional storage cost. We analyze the service capacity region and the response time of such erasure-coded systems and compare them with standard uncoded replication-based systems currently used in the cloud. We show that coding expands the service capacity region, thus enabling the system to handle variability in demand for different data types. Moreover, we characterize the response time of the coded system in various arrival rate regimes. This analysis reveals that adding even a small number of coded servers can significantly reduce the mean response time, with a drastic reduction in regimes where the demand is skewed across different job types.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1145/3492866.3549713
- https://dl.acm.org/doi/pdf/10.1145/3492866.3549713
- OA Status
- gold
- Cited By
- 4
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4285019769
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4285019769Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3492866.3549713Digital Object Identifier
- Title
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Tackling heterogeneous traffic in multi-access systems via erasure coded serversWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-09-21Full publication date if available
- Authors
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Tuhinangshu Choudhury, Weina Wang, Gauri JoshiList of authors in order
- Landing page
-
https://doi.org/10.1145/3492866.3549713Publisher landing page
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-
https://dl.acm.org/doi/pdf/10.1145/3492866.3549713Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://dl.acm.org/doi/pdf/10.1145/3492866.3549713Direct OA link when available
- Concepts
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Server, Computer science, Erasure code, Provisioning, Cloud computing, Distributed computing, Response time, Computer network, Erasure, Replication (statistics), Operating system, Decoding methods, Algorithm, Mathematics, Programming language, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 2, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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21Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.capacity | 97, 127 |
| abstract_inverted_index.enabling | 130 |
| abstract_inverted_index.flexibly | 84 |
| abstract_inverted_index.multiple | 86 |
| abstract_inverted_index.regimes. | 156 |
| abstract_inverted_index.response | 101, 146, 174 |
| abstract_inverted_index.standard | 111 |
| abstract_inverted_index.Moreover, | 142 |
| abstract_inverted_index.computing | 37 |
| abstract_inverted_index.currently | 115 |
| abstract_inverted_index.different | 43, 139, 188 |
| abstract_inverted_index.generated | 2 |
| abstract_inverted_index.providers | 54 |
| abstract_inverted_index.reduction | 179 |
| abstract_inverted_index.additional | 90 |
| abstract_inverted_index.exponential | 15 |
| abstract_inverted_index.variability | 135 |
| abstract_inverted_index.applications | 5 |
| abstract_inverted_index.characterize | 144 |
| abstract_inverted_index.computations | 28 |
| abstract_inverted_index.erasure-coded | 80, 105 |
| abstract_inverted_index.heterogeneity | 59 |
| abstract_inverted_index.heterogeneous | 41 |
| abstract_inverted_index.significantly | 170 |
| abstract_inverted_index.unpredictably | 48 |
| abstract_inverted_index.unpredictability | 61 |
| abstract_inverted_index.over-provisioning | 63 |
| abstract_inverted_index.replication-based | 113 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/8 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Decent work and economic growth |
| citation_normalized_percentile.value | 0.68227102 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |