Anyesha Ghosh

dblp:392/8215 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Cloud and datacenter computing · 61% Distributed systems · 39%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cloud and datacenter computing
cluster resource management and scheduling
0.812024
Fast and Efficient Scaling for Microservices with SurgeGuard · SC 2024
Cloud and datacenter computing › microservices
microservice resource management
0.812024
Fast and Efficient Scaling for Microservices with SurgeGuard · SC 2024
Distributed systems › distributed resource management
qos-aware resource allocation
0.812024
Fast and Efficient Scaling for Microservices with SurgeGuard · SC 2024
Distributed systems
fault tolerance
0.212024
Fast and Efficient Scaling for Microservices with SurgeGuard · SC 2024

Methods — techniques the papers use, named apart from their topics

task-graph communication analysis · 0.8latency slack analysis · 0.8decentralized control · 0.8
YearPublicationVenuePosition
2024 Fast and Efficient Scaling for Microservices with SurgeGuard
abstract
The microservice architecture is increasingly popular for flexible, large-scale online applications. However, existing resource management mechanisms incur high latency in detecting Quality of Service (QoS) violations, and hence, fail to allocate resources effectively under commonly-observed varying load conditions. This results in over-allocation coupled with a late response that increase both the total cost of ownership and the magnitude of each QoS violation event. We present SurgeGuard, a decentralized resource controller for microservice applications specifically designed to guard application QoS during surges in load and network latency. SurgeGuard uses the key insight that for rapid detection and effective management of QoS violations, the controller must be aware of any available slack in latency and communication patterns between microservices within a task-graph. Our experiments show that for the workloads in DeathStarBench, SurgeGuard on average reduces the combined violation magnitude and duration by $61.1 \%$ and $93.7 \%$, respectively, compared to the well-known Parties and Caladan algorithms, and requires $8 \%$ fewer resources than Parties.
Anyesha Ghosh, Neeraja J. Yadwadkar, Mattan Erez
SC1