Oversubscribing micro-clouds with energy-aware containers scheduling

Mendes, Seŕgio; Simão, José; Veiga, Luís

doi:10.1145/3297280.3297295

“…The scheduler allocates containers on the basis of latency and distance of user to edge node. Mendes [83] extend the functionalities of Docker Swarm to make it more flexible to overbooking. Overbooking is allocating more resources beyond the nominal capacity of the edge node.…”

Section: Kubernetes and Docker Swarm Container Schedulers For Edge Computingmentioning

confidence: 99%

“…Mendes [83] describe the use of a greedy algorithm for their energy-aware container placement scheduling model. Their algorithm uses the energy efficiency levels at the nodes to schedule container placement.…”

Section: B Algorithmsmentioning

confidence: 99%

Container Placement and Migration in Edge Computing: Concept and Scheduling Models

Oleghe

¹

2021

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Containers are a form of software virtualization, rapidly becoming the de facto way of providing edge computing services. Research on container-based edge computing is plentiful, and this has been buoyed by the increasing demand for single digit, milliseconds latency computations. A container scheduler is part of the architecture that is used to manage and orchestrate multiple container-based applications on heterogenous computing nodes. The scheduler decides how incoming computing requests are allocated to containers, which edge nodes the containers are placed on, and where already deployed containers are migrated to. This paper aims to clarify the concept of container placement and migration in edge servers and the scheduling models that have been developed for this purpose. The study illuminates the frameworks and algorithms upon which the scheduling models are built. To convert the problem to one that can be solved using an algorithm, the container placement problem in mostly abstracted using multi-objective optimization models or graph network models. The scheduling algorithms are predominantly heuristic-based algorithms, which are able to arrive at sub-optimal solutions very quickly. There is paucity of container scheduling models that consider distributed edge computing tasks. Research in decentralized scheduling systems is gaining momentum and the future outlook is in scheduling containers for mobile edge nodes.

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“…Numerous strategies are proposed for clusters [12], clouds [13], and more recently for cloud-edge (Fog) systems [14]. The aims of scheduling are load balancing [24], maximising resource utilisation [25] and energy efficiency [26], optimising execution costs [27], and maximising performance [28].…”

Section: Related Workmentioning

confidence: 99%

“…Energy efficiency is a parameter considered for scheduling. This is explored in the context of edge micro-clouds [26].…”

Section: Related Workmentioning

confidence: 99%

Priority-based Fair Scheduling in Edge Computing

Madej

¹

,

Wang

²

,

Αθανασόπουλος

³

et al. 2020

2020 IEEE 4th International Conference on Fog and Edge Computing (ICFEC)

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Scheduling is important in Edge computing. In contrast to the Cloud, Edge resources are hardware limited and cannot support workload-driven infrastructure scaling. Hence, resource allocation and scheduling for the Edge requires a fresh perspective. Existing Edge scheduling research assumes availability of all needed resources whenever a job request is made. This paper challenges that assumption, since not all job requests from a Cloud server can be scheduled on an Edge node. Thus, guaranteeing fairness among the clients (Cloud servers offloading jobs) while accounting for priorities of the jobs becomes a critical task. This paper presents four scheduling techniques, the first is a naive first come first serve strategy and further proposes three strategies, namely a client fair, priority fair, and hybrid that accounts for the fairness of both clients and job priorities. An evaluation on a target platform under three different scenarios, namely equal, random, and Gaussian job distributions is presented. The experimental studies highlight the low overheads and the distribution of scheduled jobs on the Edge node when compared to the naive strategy. The results confirm the superior performance of the hybrid strategy and showcase the feasibility of fair schedulers for Edge computing.

show abstract

“…Numerous strategies are proposed for clusters [12], clouds [13], and more recently for cloud-edge (Fog) systems [14]. The aims of scheduling are load balancing [24], maximising resource utilisation [25] and energy efficiency [26], optimising execution costs [27], and maximising performance [28].…”

Section: Related Workmentioning

confidence: 99%

Priority-based Fair Scheduling in Edge Computing

Madej¹,

Wang²,

Αθανασόπουλος³

et al. 2020

Preprint

0

View full text Add to dashboard Cite

Scheduling is important in Edge computing. In contrast to the Cloud, Edge resources are hardware limited and cannot support workload-driven infrastructure scaling. Hence, resource allocation and scheduling for the Edge requires a fresh perspective. Existing Edge scheduling research assumes availability of all needed resources whenever a job request is made. This paper challenges that assumption, since not all job requests from a Cloud server can be scheduled on an Edge node. Thus, guaranteeing fairness among the clients (Cloud servers offloading jobs) while accounting for priorities of the jobs becomes a critical task. This paper presents four scheduling techniques, the first is a naive first come first serve strategy and further proposes three strategies, namely a client fair, priority fair, and hybrid that accounts for the fairness of both clients and job priorities. An evaluation on a target platform under three different scenarios, namely equal, random, and Gaussian job distributions is presented. The experimental studies highlight the low overheads and the distribution of scheduled jobs on the Edge node when compared to the naive strategy. The results confirm the superior performance of the hybrid strategy and showcase the feasibility of fair schedulers for Edge computing.

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Oversubscribing micro-clouds with energy-aware containers scheduling

Cited by 5 publications

References 19 publications

Container Placement and Migration in Edge Computing: Concept and Scheduling Models

Container Placement and Migration in Edge Computing: Concept and Scheduling Models

Priority-based Fair Scheduling in Edge Computing

Priority-based Fair Scheduling in Edge Computing

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