Voyager: Complete Container State Migration

Nadgowda, Shripad; Suneja, Sahil; Bila, Nilton; Isci, Canturk

doi:10.1109/icdcs.2017.91

Cited by 83 publications

(43 citation statements)

References 8 publications

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“…Their approach reduces the application downtime and container migration time, significantly. Nadgowda [48] have discussed containers live migration in more details; and proposed Voyager -which combines CRIU-based memory migration with data federation capabilities of union mounts to minimize migration downtime. "With a union view of data between the source and target hosts, Voyager containers can resume operation instantly on the target host, while performing disk state transfer lazily in the background" [48].…”

Section: Related Workmentioning

confidence: 99%

“…Nadgowda [48] have discussed containers live migration in more details; and proposed Voyager -which combines CRIU-based memory migration with data federation capabilities of union mounts to minimize migration downtime. "With a union view of data between the source and target hosts, Voyager containers can resume operation instantly on the target host, while performing disk state transfer lazily in the background" [48]. All of these techniques, with notable exception of [8], have focused on live migration of VMs and/or containers both, hence server consolidation, but cost recovery in migration, heterogeneity of datacenter resources and applications are not addressed.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An Energy and Performance Aware Consolidation Technique for Containerized Datacenters

Khan

Zakarya

Buyya

et al. 2021

IEEE Trans. Cloud Comput.

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Cloud datacenters have become a backbone for today's business and economy, which are the fastest-growing electricity consumers, globally. Numerous studies suggest that ∼30% of the US datacenters are comatose and the others are grossly lessutilized, which make it possible to save energy through resource consolidation techniques. However, consolidation comprises migrations that are expensive in terms of energy consumption and performance degradation, which is mostly not accounted for in many existing models, and, possibly, it could be more energy and performance efficient not to consolidate. In this paper, we investigate how migration decisions should be taken so that the migration cost is recovered, as only when migration cost has been recovered and performance is guaranteed, will energy start to be saved. We demonstrate through several experiments, using the Google workload data for 12,583 hosts and approximately one million tasks that belong to three different kinds of workload, how different allocation policies, combined with various migration approaches, will impact on datacenter's energy and performance efficiencies. Using several plausible assumptions for containerised datacenter setup , we suggest, that a combination of the proposed energy-performance-aware allocation (EPC-FU) and migration (CPER) techniques, and migrating relatively long-running containers only, offers for ideal energy and performance efficiencies.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Energy and Performance Aware Consolidation Technique for Containerized Datacenters

Khan

Zakarya

Buyya

et al. 2021

IEEE Trans. Cloud Comput.

View full text Add to dashboard Cite

show abstract

“…Evaluations from Ma et al [27] show 80% reduction of the handoff latency. Nadgowda et al [28] proposed a complete state container migration with Checkpoint/Restore In Userspace (CRIU)based memory migration, minimizing the overhead to be less than 3% of the actual workload.…”

Section: Related Workmentioning

confidence: 99%

Hardware Acceleration for Container Migration on Resource-Constrained Platforms

et al. 2020

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The computing capabilities of client devices are continuously increasing; at the same time, demands for ultra-low latency (ULL) services are increasing. These ULL services can be provided by migrating some micro-service container computations from the cloud and multi-access edge computing (MEC) to the client devices. The migration of a container image requires compression and decompression, which are computationally demanding. We quantitatively examine the hardware acceleration of container image compression and decompression on a client device. Specifically, we compare the Intel ® Quick Assist Technology (QAT) hardware acceleration with software compression/decompression. For scenarios with a local container image registry (i.e., without network bandwidth constraints), we find that QAT speeds up compression by a factor of over 7 compared to the single-core GZIP software and reduces the CPU core utilization by over 15% for large container images. These QAT benefits come at the expense of Input/Output (IO) memory access bitrates of up to 900 Mbyte/s (while the software compression/decompression does not require IO memory access). For scenarios with a remote container image registry, we find that the container push (compression) time savings increase with the network bandwidth, while the container pull (decompression) time savings level out for moderately high network bandwidths and slightly decrease for a very high network bandwidth. Furthermore, QAT acceleration achieves substantial power consumption reductions for container push compression for low to moderately high network bandwidths. Our evaluation results give reference performance benchmarks of the achievable latencies for container image instantiation and migration with and without hardware acceleration of the compression and decompression of container images.

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“…Moreover, the implementation is not optimized due to the transfer of root filesystem of the container across edge nodes. IBM proposes Voyager [26], a live container migration service designed in accordance with the Open Container Initiative (OCI) principles: the IBM solution implements a novel filesystem-agnostic and vendoragnostic migration service with consistency guarantees.…”

Section: B Container Service Migrationmentioning

confidence: 99%

Differentiated Service/Data Migration for Edge Services Leveraging Container Characteristics

et al. 2019

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The Multi-access Edge Computing (MEC) and Fog Computing paradigms are enabling the opportunity to have middleboxes either statically or dynamically deployed at network edges acting as local proxies with virtualized resources for supporting and enhancing service provisioning in edge localities. However, migration of edge-enabled services poses significant challenges in the edge computing environment. In this paper, we propose an edge computing platform architecture that supports service migration with different options of granularity (either entire service/data migration, or proactive applicationaware data migration) across heterogeneous edge devices (either MEC-based servers or resource-poor Fog devices) that host virtualized resources (Docker Containers). The most innovative elements of the technical contribution of our work include i) the possibility to select either an application-agnostic or an applicationaware approach, ii) the possibility to choose the appropriate application-aware approach (e.g., based on data access frequencies), iii) an automatic edge services placement support with the aim of finding a more effective placement with low energy consumption, and iv) the in-lab experimentation of the performance achieved over rapidly deployable environments with resource-limited edges such as Raspberry Pi devices.

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Voyager: Complete Container State Migration

Cited by 83 publications

References 8 publications

An Energy and Performance Aware Consolidation Technique for Containerized Datacenters

An Energy and Performance Aware Consolidation Technique for Containerized Datacenters

Hardware Acceleration for Container Migration on Resource-Constrained Platforms

Differentiated Service/Data Migration for Edge Services Leveraging Container Characteristics

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