Cost-efficient resource scheduling under QoS constraints for geo-distributed data centers

Maswood, Mirza Mohd Shahriar; Nasim, Robayet; Kassler, Andreas; Medhi, Deep

doi:10.1109/noms.2018.8406272

Cited by 7 publications

(2 citation statements)

References 21 publications

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“…This limits the flexibility of the assignment of a task to a queue, which now needs to ensure that the corresponding server is able to process the assigned task. In fact, the lack of flexibility also arises in much broader contexts such as due to a spatially constrained network architecture (e.g., in bike-sharing), see [10,21,29], or in the context of geographically distributed data centers [17,20]. An emerging line of work thus considers a bipartite graph between task types and servers; see for example [5,6,25,31,32,37].…”

Section: Introductionmentioning

confidence: 99%

Mean-field Analysis for Load Balancing on Spatial Graphs

Rutten¹,

Mukherjee²

2023

Preprint

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The analysis of large-scale, parallel-server load balancing systems has relied heavily on meanfield analysis. A pivotal assumption for this framework is that the servers are exchangeable. However, modern data-centers process a wide variety of task types and the data required to process tasks of a certain type is stored locally at servers. This gives rise to data locality constraints, where tasks of a particular type can only be routed to a small subset of servers. An emerging line of research, therefore, considers load balancing algorithms on bipartite graphs where vertices in the two partitions represent the task types and servers, respectively, and an edge represents the server's ability to process the corresponding task type. Due to the lack of exchangeability in this model, the mean-field techniques fundamentally break down. Recent progress has been made by considering graphs with strong edge-expansion properties, i.e., where any two large subsets of vertices are well-connected. However, data locality often leads to spatial constraints, where edges are local. As a result, these bipartite graphs do not have strong expansion properties.In this paper, we consider the power-of-d choices algorithm and develop a novel couplingbased approach to establish mean-field approximation for a large class of graphs that includes spatial graphs. As a corollary, we also show that, as the system size becomes large, the steadystate occupancy process for arbitrary regular bipartite graphs with diverging degrees, is indistinguishable from a fully flexible system on a complete bipartite graph. The method extends the scope of mean-field analysis far beyond the classical full-flexibility setup. En route, we prove that, starting from suitable states, the occupancy process becomes close to its steady state in a time that is independent of N . Such a large-scale mixing-time result might be of independent interest. Numerical experiments are conducted, which positively support the theoretical results.

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Section: Introductionmentioning

confidence: 99%

Mean-field Analysis for Load Balancing on Spatial Graphs

Rutten¹,

Mukherjee²

2023

Preprint

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“…The scheduling result of the proposed approach significantly improves the result of heuristic approaches. Maswood et al (2018) developed mixedinteger linear programming method to allocate resources by minimizing location-dependent costs. Using numerical evaluation, authors showed that their approach reduces the provisioning cost and energy consumption.…”

Section: Data Delivery and Qosmentioning

confidence: 99%

Towards a computing continuum: Enabling edge-to-cloud integration for data-driven workflows

Balouek-Thomert

Renart

Zamani

et al. 2019

The International Journal of High Performance Computing Applica

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Dramatic changes in the technology landscape marked by increasing scales and pervasiveness of compute and data have resulted in the proliferation of edge applications aimed at effectively processing data in a timely manner. As the levels and fidelity of instrumentation increases and the types and volumes of available data grow, new classes of applications are being explored that seamlessly combine real-time data with complex models and data analytics to monitor and manage systems of interest. However, these applications require a fluid integration of resources at the edge, the core, and along the data path to support dynamic and data-driven application workflows, that is, they need to leverage a computing continuum. In this article, we present our vision for enabling such a computing continuum and specifically focus on enabling edge-to-cloud integration to support data-driven workflows. The research is driven by an online data-driven tsunami warning use case that is supported by the deployment of large-scale national environment observation systems. This article presents our overall approach as well as current status and next steps.

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Optimizing Bandwidth Cost, Energy Consumption, Delay, and Load in a Cooperative Fog Environment

Manarat,

Maswood

2023

2023 6th International Conference on Electrical Information and Communication Technology (EICT)

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Cost-efficient resource scheduling under QoS constraints for geo-distributed data centers

Cited by 7 publications

References 21 publications

Mean-field Analysis for Load Balancing on Spatial Graphs

Mean-field Analysis for Load Balancing on Spatial Graphs

Towards a computing continuum: Enabling edge-to-cloud integration for data-driven workflows

Optimizing Bandwidth Cost, Energy Consumption, Delay, and Load in a Cooperative Fog Environment

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