Scavenger

Javadi, Seyyed Ahmad; Suresh, A.; Wajahat, Muhammad; Gandhi, Anshul

doi:10.1145/3357223.3362734

Cited by 38 publications

(5 citation statements)

References 34 publications

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“…Moreover, even private clusters are shared by applications and tasks belonging to different organizations and users, making it hard to export and even harder to balance resource allocation across different application-level metrics. That led to the emergence of opaque-box approaches [16,17] that partition resources dynamically by reacting to demand changes in an online manner. Such techniques have been used to improve performance through better resource allocation.…”

Section: Why Consider Applications As Opaque Boxes?mentioning

confidence: 99%

“…In Figure 7 we saw that CPUJailing by itself improves the throughput of LS tasks by about 7%. This motivates us to explore its potential usage as a stand-alone mechanism for performance improvement, similar to Scavenger [17] and PerfIso [16]. First, we need to analyze the behavior of LS tasks under CPUJailing in more detail.…”

Section: Cpujailing As a Stand-alone Mechanismmentioning

confidence: 99%

“…CPU Isolation Power Opaque-Box PerfIso [16] ✓ ✗ ✗ Heracles [24] ✓ ✗ ✗ Pegasus [23] ✗ ✓ ✗ ixcp [31] ✗ ✓ ✗ Parties [7] ✗ ✓ ✗ Scavenger [17] ✓ ✗ ✓ PACT ✓ ✓ ✓ Table 1: PACT's functionality compared to existing power saving or CPU isolation approaches.…”

Section: Mechanismmentioning

confidence: 99%

“…Scavenger [17] is one of the first systems to adopt an opaque-box approach when assigning processor resources. It uses IPC data to track the effect BE tasks have on foreground VMs and regulate CPU, last-level cache, memory capacity, and network bandwidth.…”

Section: Isolation Mechanismsmentioning

confidence: 99%

“…Due to the significant savings it produces and the work done on mitigating interference, this approach is now a common practice in the industry [11,14,15,33,42]. Systems such as Per-fIso [16] and Scavenger [17] allow the co-location of LS and BE tasks and increase utilization while reducing adverse interference effects. Nevertheless, increasing utilization on its own cannot provide the power savings necessary to achieve our ambitious environmental goals; it is also necessary to further improve efficiency by reducing power consumption even at high utilization.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Leveraging application classes to save power in highly-utilized data centers

Kaffes

Sbîrlea

Lin

et al. 2020

Proceedings of the 11th ACM Symposium on Cloud Computing

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Data center energy consumption has become an increasingly significant contributor both to greenhouse emissions and costs. To increase utilization of individual hosts and improve efficiency, most modern data centers co-locate workloads belonging to different application classes, some being latency-sensitive (LS) and others best-effort (BE) which are more tolerant to performance variation. It is therefore necessary to design mechanisms that reduce power consumption even in the resulting high-utilization environment, while preserving LS task performance. Moreover, the abundance of different workloads and the security implications of public cloud make mechanisms that rely on extensive knowledge of workload characteristics or on application-exported metrics challenging to deploy. We present PACT, Per Application Class Turbo Controller, a system that leverages two novel mechanisms to reduce power consumption even in highly-utilized data centers. By treating applications like opaque boxes that do not need to provide application-specific performance signals, the first mechanism, Turbo Control, reduces power consumption by decreasing the operating frequency and throttling only BE tasks, without affecting performance-sensitive LS tasks. We identify the shortcomings of Turbo Control and increase its effectiveness by introducing CPUJailing, a mechanism that allocates different sets of cores to LS and BE applications. We deploy PACT (Turbo Control + CPUJailing) in production * Kostis Kaffes was an intern at Google during this work. † Christos Kozyrakis was partly at Google during this work.

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Section: Why Consider Applications As Opaque Boxes?mentioning

confidence: 99%

Section: Cpujailing As a Stand-alone Mechanismmentioning

confidence: 99%

Section: Mechanismmentioning

confidence: 99%

Section: Isolation Mechanismsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Leveraging application classes to save power in highly-utilized data centers

Kaffes

Sbîrlea

Lin

et al. 2020

Proceedings of the 11th ACM Symposium on Cloud Computing

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ScaleReactor: A graceful performance isolation agent with interference detection and investigation for container‐based scale‐out workloads

Zhu

et al. 2021

Concurrency and Computation

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Striking a balance between improved cluster utilization and guaranteed applicationQoS is a long-standing research problem in multi-tenants shared cluster. The typical solution is to detect performance degradation and investigate the root cause to conduct performance isolation. Existing efforts rely on lots of prior knowledge of applications and the assumption of interference-free workload placement is possible.Performance interference is usually mitigated through application-level approaches such as centralized rescheduling, which is usually an hindsight and a waste of resources.In this article, we present ScaleReactor, a graceful runtime agent on a per node basis that decouples the performance isolation from centralized resource management, and migrates the performance interference of scale-out workloads in container-based cluster using a lightweight black-box approach. ScaleReactor analyzes the degree of contention for multi-dimensional resources among co-located workloads to detect the performance degradation without additional prior information, and uses correlation analysis to locate the cause of contention, while isolating resources in a graceful manner to reduce system overhead and the performance degradation of intrusive workloads. Experiments have demonstrated that ScaleReactor effectively reduces the job completion time of scale-out applications in shared clusters, with the maximum value up to 36% and low system overhead against the existing isolation mechanism.

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Managing asynchronous workloads in a multi‐tenant microservice enterprise environment

Batista,

Morais,

Cavalcante

et al. 2023

Softw Pract Exp

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A multi‐tenant microservice architecture involving components with asynchronous interactions and batch jobs requires efficient strategies for managing asynchronous workloads. This article addresses this issue in the context of a leading company developing tax software solutions for many national and multi‐national corporations in Brazil. A critical process provided by the company's cloud‐based solutions encompasses tax integration, which includes coordinating complex tax calculation tasks and needs to be supported by asynchronous operations using a message broker to guarantee order correctness. We explored and implemented two approaches for managing asynchronous workloads related to tax integration within a multi‐tenant microservice architecture in the company's context: (i) a polling‐based approach that employs a queue as a distributed lock (DL) and (ii) a push‐based approach named single active consumer (SAC) that relies on the message broker's logic to deliver messages. These approaches aim to achieve efficient resource allocation when dealing with a growing number of container replicas and tenants. In this article, we evaluate the correctness and performance of the DL and SAC approaches to shed light on how asynchronous workloads impact the management of multi‐tenant microservice architectures from delivery and deployment perspectives.

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Scavenger

Cited by 38 publications

References 34 publications

Leveraging application classes to save power in highly-utilized data centers

Leveraging application classes to save power in highly-utilized data centers

ScaleReactor: A graceful performance isolation agent with interference detection and investigation for container‐based scale‐out workloads

Managing asynchronous workloads in a multi‐tenant microservice enterprise environment

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