{"id":434100,"date":"2017-10-28T16:17:49","date_gmt":"2017-10-28T23:17:49","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=434100"},"modified":"2022-01-18T06:14:05","modified_gmt":"2022-01-18T14:14:05","slug":"geo-distribution-of-actor-based-services","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/geo-distribution-of-actor-based-services\/","title":{"rendered":"Geo-Distribution of Actor-Based Services"},"content":{"rendered":"

(Conference presentation slides are here (opens in new tab)<\/span><\/a>.)<\/p>\n

Many service applications use actors as a programming model for the middle tier, to simplify synchronization, fault-tolerance, and scalability. However, efficient operation of such actors in multiple, geographically distant datacenters is challenging, due to the very high communication latency. Caching and replication are essential to hide latency and exploit locality; but it is not a priori clear how to combine these techniques with the actor programming model.<\/p>\n

We present Geo, an open-source geo-distributed actor system that improves performance by caching actor states in one or more datacenters, yet guarantees the existence of a single latest version by virtue of a distributed cache coherence protocol. Geo’s programming model supports both volatile and persistent actors, and supports updates with a choice of linearizable and eventual consistency. Our evaluation on several workloads shows substantial performance benefits, and confirms the advantage of supporting both replicated and single-instance coherence protocols as configuration choices. For example, replication can provide fast, always-available reads and updates globally, while batching of linearizable storage accesses at a single location can boost the throughput of an order processing workload by 7x.<\/p>\n","protected":false},"excerpt":{"rendered":"

(Conference presentation slides are here.) Many service applications use actors as a programming model for the middle tier, to simplify synchronization, fault-tolerance, and scalability. However, efficient operation of such actors in multiple, geographically distant datacenters is challenging, due to the very high communication latency. Caching and replication are essential to hide latency and exploit locality; […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13560,13547],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-434100","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-programming-languages-software-engineering","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"ACM","msr_edition":"","msr_affiliation":"","msr_published_date":"2017-10-27","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"436263","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2017\/10\/Geo-Orleans-OOPSLA17.pdf","id":"436263","title":"Geo-Orleans-OOPSLA17","label_id":"243109","label":0},{"type":"doi","viewUrl":"false","id":"false","title":"10.1145\/3133931","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"user_nicename","value":"Phil Bernstein","user_id":33253,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Phil Bernstein"},{"type":"user_nicename","value":"Sebastian Burckhardt","user_id":33544,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Sebastian Burckhardt"},{"type":"user_nicename","value":"Sergey Bykov","user_id":33545,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Sergey Bykov"},{"type":"text","value":"Natacha Crooks","user_id":0,"rest_url":false},{"type":"text","value":"Jose M. Faleiro","user_id":0,"rest_url":false},{"type":"text","value":"Gabriel Kliot","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Alok Kumbhare","user_id":36086,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Alok Kumbhare"},{"type":"user_nicename","value":"Muntasir Raihan Rahman","user_id":41308,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Muntasir Raihan Rahman"},{"type":"text","value":"Vivek Shah","user_id":0,"rest_url":false},{"type":"text","value":"Adriana Szekeres","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Jorgen Thelin","user_id":32428,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Jorgen Thelin"}],"msr_impact_theme":[],"msr_research_lab":[199565],"msr_event":[],"msr_group":[144927,804652],"msr_project":[511400,170573],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":511400,"post_title":"Actor-Oriented Databases","post_name":"actor-oriented-databases","post_type":"msr-project","post_date":"2018-10-12 18:36:00","post_modified":"2018-10-12 18:37:49","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/actor-oriented-databases\/","post_excerpt":"Many of today\u2019s interactive server applications are implemented using actor-oriented programming frameworks. Such applications treat actors as a distributed in-memory object-oriented database. However, actor programming frameworks offer few if any database system features, leaving application developers to fend for themselves. It is challenging to add such features because the design space is different than traditional database systems. The system must be scalable to a large number of servers, it must work well with a variety…","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/511400"}]}},{"ID":170573,"post_title":"Orleans - Virtual Actors","post_name":"orleans-virtual-actors","post_type":"msr-project","post_date":"2010-10-14 17:19:52","post_modified":"2018-04-02 13:24:10","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/orleans-virtual-actors\/","post_excerpt":"Project \"Orleans\" invented the Virtual Actor abstraction, which provides a straightforward approach to building distributed interactive\u00a0applications, without the need to learn complex programming\u00a0patterns for handling concurrency, fault tolerance, and resource management. Orleans applications scale-up automatically and are meant to be deployed\u00a0in the cloud. 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