Cloud computing promises flexibility and high performance for users and high cost-efficiency for operators. Nevertheless, most cloud facilities operate at very low utilization, hurting both cost effectiveness and future scalability. We present Quasar, a cluster management system that increases resource utilization while providing consistently high application performance. Quasar employs three techniques. First, it does not rely on resource reservations, which lead to underutilization as users do not necessarily understand workload dynamics and physical resource requirements of complex codebases. Instead, users express performance constraints for each workload, letting Quasar determine the right amount of resources to meet these constraints at any point. Second, Quasar uses classification techniques to quickly and accurately determine the impact of the amount of resources (scale-out and scale-up), type of resources, and interference on performance for each workload and dataset. Third, it uses the classification results to jointly perform resource allocation and assignment, quickly exploring the large space of options for an efficient way to pack workloads on available resources. Quasar monitors workload performance and adjusts resource allocation and assignment when needed. We evaluate Quasar over a wide range of workload scenarios, including combinations of distributed analytics frameworks and low-latency, stateful services, both on a local cluster and a cluster of dedicated EC2 servers. At steady state, Quasar improves resource utilization by 47% in the 200-server EC2 cluster, while meeting performance constraints for workloads of all types.

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Quasar: resource-efficient and QoS-aware cluster management

Cloud computing environments allow customers to dynamically scale their applications. The key problem is how to lease the right amount of resources, on a pay-as-you-go basis. Application re-dimensioning can be implemented effortlessly, adapting the resources assigned to the application to the incoming user demand. However, the identification of the right amount of resources to lease in order to meet the required Service Level Agreement, while keeping the overall cost low, is not an easy task. Many techniques have been proposed for automating application scaling. We propose a classification of these techniques into five main categories: static threshold-based rules, control theory, reinforcement learning, queuing theory and time series analysis. Then we use this classification to carry out a literature review of proposals for auto-scaling in the cloud.

A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments

Cloud computing with its three key facets (i.e., Infrastructure-as-a-Service, Platform-as-a-Service, and Software-as-a-Service) and its inherent advantages (e.g., elasticity and scalability) still faces several challenges. The distance between the cloud and the end devices might be an issue for latency-sensitive applications such as disaster management and content delivery applications. Service level agreements (SLAs) may also impose processing at locations where the cloud provider does not have data centers. Fog computing is a novel paradigm to address such issues. It enables provisioning resources and services outside the cloud, at the edge of the network, closer to end devices, or eventually, at locations stipulated by SLAs. Fog computing is not a substitute for cloud computing but a powerful complement. It enables processing at the edge while still offering the possibility to interact with the cloud. This paper presents a comprehensive survey on fog computing. It critically reviews the state of the art in the light of a concise set of evaluation criteria. We cover both the architectures and the algorithms that make fog systems. Challenges and research directions are also introduced. In addition, the lessons learned are reviewed and the prospects are discussed in terms of the key role fog is likely to play in emerging technologies such as tactile Internet.

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A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges

Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers – Big Data and cloud computing – and reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i...

https://tandfonline.com/doi/pdf/10.1080/17538947.2016.1239771

Big Data and cloud computing: innovation opportunities and challenges

Resource management in a cloud environment is a hard problem, due to: the scale of modern data centers; the heterogeneity of resource types and their interdependencies; the variability and unpredictability of the load; as well as the range of objectives of the different actors in a cloud ecosystem. Consequently, both academia and industry began significant research efforts in this area. In this paper, we survey the recent literature, covering 250+ publications, and highlighting key results. We outline a conceptual framework for cloud resource management and use it to structure the state-of-the-art review. Based on our analysis, we identify five challenges for future investigation. These relate to: providing predictable performance for cloud-hosted applications; achieving global manageability for cloud systems; engineering scalable resource management systems; understanding economic behavior and cloud pricing; and developing solutions for the mobile cloud paradigm .

Resource Management in Clouds: Survey and Research Challenges

Elastic resource scaling lets cloud systems meet application service level objectives (SLOs) with minimum resource provisioning costs. In this paper, we present CloudScale, a system that automates fine-grained elastic resource scaling for multi-tenant cloud computing infrastructures. CloudScale employs online resource demand prediction and prediction error handling to achieve adaptive resource allocation without assuming any prior knowledge about the applications running inside the cloud. CloudScale can resolve scaling conflicts between applications using migration, and integrates dynamic CPU voltage/frequency scaling to achieve energy savings with minimal effect on application SLOs. We have implemented CloudScale on top of Xen and conducted extensive experiments using a set of CPU and memory intensive applications (RUBiS, Hadoop, IBM System S). The results show that CloudScale can achieve significantly higher SLO conformance than other alternatives with low resource and energy cost. CloudScale is non-intrusive and light-weight, and imposes negligible overhead (

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CloudScale: elastic resource scaling for multi-tenant cloud systems

Dynamically adjusting the number of virtual machines (VMs) assigned to a cloud application to keep up with load changes and interference from other uses typically requires detailed application knowledge and an ability to know the future, neither of which are readily available to infrastructure service providers or application owners. The result is that systems need to be over-provisioned (costly), or risk missing their performance Service Level Objectives (SLOs) and have to pay penalties (also costly). AGILE deals with both issues: it uses wavelets to provide a medium-term resource demand prediction with enough lead time to start up new application server instances before performance falls short, and it uses dynamic VM cloning to reduce application startup times. Tests using RUBiS and Google cluster traces show that AGILE can predict varying resource demands over the medium-term with up to 3.42× better true positive rate and 0.34× the false positive rate than existing schemes. Given a target SLO violation rate, AGILE can efficiently handle dynamic application workloads, reducing both penalties and user dissatisfaction.

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AGILE: elastic distributed resource scaling for Infrastructure-as-a-Service

Large caches in storage servers have become essential for meeting service levels required by applications These caches need to be warmed with data often today due to various scenarios including dynamic creation of cache space and server restarts that clear cache contents When large storage caches are warmed at the rate of application I/O, warmup can take hours or even days, thus affecting both application performance and server load over a long period of timeWe have created Bonfire, a mechanism for accelerating cache warmup Bonfire monitors storage server workloads, logs important warmup data, and efficiently preloads storage-level caches with warmup data Bonfire is based on our detailed analysis of block-level data-center traces that provides insights into heuristics for warmup as well as the potential for efficient mechanisms We show through both simulation and trace replay that Bonfire reduces both warmup time and backend server load significantly, compared to a cache that is warmed up on demand

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Warming up storage-level caches with bonfire

A dynamic caching technique adaptively controls copies of data blocks stored within caches (“cached copies”) of a caching layer distributed among servers of a distributed data processing system. A cache coordinator of the distributed system implements the dynamic caching technique to increase the cached copies of the data blocks to improve processing performance of the servers. Alternatively, the technique may decrease the cached copies to reduce storage capacity of the servers. The technique may increase the cached copies when it detects local and/or remote cache bottleneck conditions at the servers, a data popularity condition at the servers, or a shared storage bottleneck condition at the storage system. Otherwise, the technique may decrease the cached copies at the servers.

Dynamic caching technique for adaptively controlling data block copies in a distributed data processing system

A method of performing a global deduplication may include: collecting a data chunk to be written to a backing storage of a storage system at a staging area in the storage system; generating a data fingerprint of the data chunk; sending the data fingerprint in batch along with other data fingerprints corresponding to data chunks collected at different times to a metadata server system in the storage system; receiving an indication, at the staging area, of whether the data fingerprint is unique in the storage system from the metadata server system; and discarding the data chunk when committing a data object containing the data chunk to the backing storage, when the indication indicates that the data chunk is not unique.

Sethuraman Subbiah

Papers

CloudScale: elastic resource scaling for multi-tenant cloud systems

AGILE: elastic distributed resource scaling for Infrastructure-as-a-Service

Warming up storage-level caches with bonfire

Dynamic caching technique for adaptively controlling data block copies in a distributed data processing system

Asynchronous backend global deduplication