COST-EFFICIENT RESOURCE PROVISIONING FOR CLOUD-ENABLED SCHEDULERS

Since the last decade, public cloud platforms are rapidly becoming de-facto computing platform for our society. To support the wide range of users and their diverse applications, public cloud platforms started to offer the same VMs under many purchasing options that differ across their cost, performance, availability, and time commitments. Popular purchasing options include on-demand, reserved, and transient VM types. Reserved VMs require long time commitments, whereas users can acquire and release the on-demand (and transient) VMs at any time. While transient VMs cost significantly less than on-demand VMs, platforms may revoke them at any time. In general, the stronger the commitment, i.e., longer and less flexible, the lower the price. However, longer and less flexible time commitments can increase cloud costs for users if future workloads cannot utilize the VMs they committed to buying. Interestingly, this wide range of purchasing options provide opportunities for cost savings. However, large cloud customers often find it challenging to choose the right mix of purchasing options to minimize their long-term costs while retaining the ability to adjust their capacity up and down in response to workload variations. Thus, optimizing the cloud costs requires users to select a mix of VM purchasing options based on their short- and long-term expectation of workload utilization. Notably, hybrid clouds combine multiple VM purchasing options or private clusters with public cloud VMs to optimize the cloud costs based on their workload expectations. In this thesis, we address the challenge of choosing a mix of different VM purchasing options in the context of large cloud customers and thereby optimizing their cloud costs. To this end, we make the following contributions: (i) design and implement a container orchestration platform (using Kubernetes) to optimize the cost of executing mixed interactive and batch workloads on cloud platforms using on-demand and transient VMs, (ii) develop simple analytical models for different straggler mitigation techniques to better understand the cost of synchronization in distributed machine learning workloads and compare their cost and performance on on-demand and transient VMs, (iii) design multiple policies to optimize long-term cloud costs by selecting a mix of VM purchasing options based on short- and long-term expectations of workload utilization (with no job waiting), (iv) introduce the concept of waiting policy for cloud-enabled schedulers, and show that provisioning long-term resources (e.g., reserved VMs) to optimize the cloud costs is dependent on it, and (v) design and implement speculative execution and ML-based waiting time predictions (for waiting policies) to show that optimizing job waiting in the cloud is possible without accurate job runtime predictions

Database Workload Management (Dagstuhl Seminar 12282)

This report documents the program and the outcomes of Dagstuhl Seminar 12282 "Database Workload Management". Dagstuhl Seminar 12282 was designed to provide a venue where researchers can engage in dialogue with industrial participants for an in-depth exploration of challenging industrial workloads, where industrial participants can challenge researchers to apply the lessons-learned from their large-scale experiments to multiple real systems, and that would facilitate the release of real workloads that can be used to drive future research, and concrete measures to evaluate and compare workload management techniques in the context of these workloads

Energy-efficient and thermal-aware resource management for heterogeneous datacenters

International audienceWe propose in this paper to study the energy-, thermal- and performance-aware resource management in heterogeneous datacenters. Witnessing the continuous development of heterogeneity in datacenters, we are confronted with their different behaviors in terms of performance, power consumption and thermal dissipation: indeed, heterogeneity at server level lies both in the computing infrastructure (computing power, electrical power consumption) and in the heat removal systems (different enclosure, fans, thermal sinks). Also the physical locations of the servers become important with heterogeneity since some servers can (over)heat others. While many studies address independently these parameters (most of the time performance and power or energy), we show in this paper the necessity to tackle all these aspects for an optimal resource management of the computing resources. This leads to improved energy usage in a heterogeneous datacenter including the cooling of the computer rooms. We build our approach on the concept of heat distribution matrix to handle the mutual influence of the servers, in heterogeneous environments, which is novel in this context. We propose a heuristic to solve the server placement problem and we design a generic greedy framework for the online scheduling problem. We derive several single-objective heuristics (for performance, energy, cooling) and a novel fuzzy-based priority mechanism to handle their tradeoffs. Finally, we show results using extensive simulations fed with actual measurements on heterogeneous servers