Modern web services can see well over a billion requests per day. Data and services at such scale require advanced software and large amounts of computational resources to process requests in reasonable time. Advancements in cloud computing now allow us to acquire additional resources faster than in traditional capacity planning scenarios. Companies can scale systems up and down as required, allowing them to meet the demand of their customers without having to purchase their own expensive hardware. Unfortunately, these, now routine, scaling operations remain a primarily manual task. To solve this problem, we present CRAFTS (Cloud Resource Anticipation For Timing Scaling), a system for automatically identifying application throughput and predictive scaling of cloud computing resources based on historical data. We also present ARTS (Automated Request Trace Simulator), a request based workload generation tool for constructing diverse and realistic request patterns for modern web applications. ARTS allows us to evaluate CRAFTS' algorithms on a wide range of scenarios. In this thesis, we outline the design and implementation of both ARTS and CRAFTS and evaluate the effectiveness of various prediction algorithms applied to real-world request data and artificial workloads generated by ARTS.
","abstract_html":"<p>Modern web services can see well over a billion requests per day. Data and services at such scale require advanced software and large amounts of computational resources to process requests in reasonable time. Advancements in cloud computing now allow us to acquire additional resources faster than in traditional capacity planning scenarios. Companies can scale systems up and down as required, allowing them to meet the demand of their customers without having to purchase their own expensive hardware. Unfortunately, these, now routine, scaling operations remain a primarily manual task. To solve this problem, we present CRAFTS (Cloud Resource Anticipation For Timing Scaling), a system for automatically identifying application throughput and predictive scaling of cloud computing resources based on historical data. We also present ARTS (Automated Request Trace Simulator), a request based workload generation tool for constructing diverse and realistic request patterns for modern web applications. ARTS allows us to evaluate CRAFTS' algorithms on a wide range of scenarios. In this thesis, we outline the design and implementation of both ARTS and CRAFTS and evaluate the effectiveness of various prediction algorithms applied to real-world request data and artificial workloads generated by ARTS.</p>","abstract_has_math":false,"creators":["Guenther, Andrew"],"institution":null,"degree_name":"MS in Computer Science","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Alex Dekhtyar"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-06-01T07:00:00Z","date_published":"2014-06-01T07:00:00Z","updated_at":"2026-07-24T01:32:55Z","subjects":["scaling","distributed systems","cloud computing","web services"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2014.106"],"render_values":[{"text":"10.15368/theses.2014.106","href":"https://doi.org/10.15368/theses.2014.106","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/1261","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Alex Dekhtyar"]},{"key":"dc:creator","label":"Author","values":["Guenther, Andrew"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2014-06-17T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["scaling","distributed systems","cloud computing","web services"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/1261","10.15368/theses.2014.106"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Modern web services can see well over a billion requests per day. Data and services at such scale require advanced software and large amounts of computational resources to process requests in reasonable time. Advancements in cloud computing now allow us to acquire additional resources faster than in traditional capacity planning scenarios. Companies can scale systems up and down as required, allowing them to meet the demand of their customers without having to purchase their own expensive hardware. Unfortunately, these, now routine, scaling operations remain a primarily manual task. To solve this problem, we present CRAFTS (Cloud Resource Anticipation For Timing Scaling), a system for automatically identifying application throughput and predictive scaling of cloud computing resources based on historical data. We also present ARTS (Automated Request Trace Simulator), a request based workload generation tool for constructing diverse and realistic request patterns for modern web applications. ARTS allows us to evaluate CRAFTS' algorithms on a wide range of scenarios. In this thesis, we outline the design and implementation of both ARTS and CRAFTS and evaluate the effectiveness of various prediction algorithms applied to real-world request data and artificial workloads generated by ARTS.
"]},{"key":"dc:title","label":"Title","values":["ARTS and CRAFTS: Predictive Scaling for Request-Based Services in the Cloud"]}]}],"canonical_facts":{"dc:contributor":["Alex Dekhtyar"],"dc:creator":["Guenther, Andrew"],"dc:date.available":["2014-06-17T07:00:00Z"],"dc:description.abstract":["Modern web services can see well over a billion requests per day. Data and services at such scale require advanced software and large amounts of computational resources to process requests in reasonable time. Advancements in cloud computing now allow us to acquire additional resources faster than in traditional capacity planning scenarios. Companies can scale systems up and down as required, allowing them to meet the demand of their customers without having to purchase their own expensive hardware. Unfortunately, these, now routine, scaling operations remain a primarily manual task. To solve this problem, we present CRAFTS (Cloud Resource Anticipation For Timing Scaling), a system for automatically identifying application throughput and predictive scaling of cloud computing resources based on historical data. We also present ARTS (Automated Request Trace Simulator), a request based workload generation tool for constructing diverse and realistic request patterns for modern web applications. ARTS allows us to evaluate CRAFTS' algorithms on a wide range of scenarios. In this thesis, we outline the design and implementation of both ARTS and CRAFTS and evaluate the effectiveness of various prediction algorithms applied to real-world request data and artificial workloads generated by ARTS.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/1261","10.15368/theses.2014.106"],"dc:subject":["scaling","distributed systems","cloud computing","web services"],"dc:title":["ARTS and CRAFTS: Predictive Scaling for Request-Based Services in the Cloud"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["MS in Computer Science"]},"updated_at":"2026-07-24T01:32:55Z"}