[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-150954-en":3,"doc-seo-150954-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},150954,549768064622,"Lucas Vance","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","ARA - Adaptive Resource Allocation for Cloud Computing Environments under Bursty Workloads","Cloud computing users rely on scalable resources, yet bursty demand patterns can sharply degrade application performance and threaten service level agreement (SLA) targets. Conventional load balancers often ignore bursty arrivals, causing load imbalance and significant response-time losses. This paper introduces ARA, a burstiness-aware adaptive resource allocation framework with prediction-driven on-the-fly switching between greedy and random site selection. Simulation and EC2 experiments demonstrate fast adaptation and improved system performance across bursty and non-bursty workloads.","ARA: Adaptive Resource Allocation for Cloud Computing Environments under Bursty Workloads  \nJianzhe Tai Juemin Zhang Jun Li Waleed Meleis Ningfang Mi  \nNortheastern University, Boston, MA 02115, USA  \n{jtai, jzhang, junli, meleis, [ningfang](ningfang}@ece.neu.edu)[}](ningfang}@ece.neu.edu)[@ece.neu.edu](ningfang}@ece.neu.edu)  \nAbstract—Cloud computing nowadays becomes quite popular among a community of cloud users by offering a variety of resources. However, burstiness in user demands often dramatically degrades the application performance. In order to satisfy peak user demands and meet Service Level Agreement (SLA), ef􀀂cient resource allocation schemes are highly demanded in the cloud. However, we 􀀂nd that conventional load balancers unfortunately neglect cases of bursty arrivals and thus experience signi􀀂cant performance degradation. Motivated by this problem, we propose new burstiness-aware algorithms to balance bursty workloads across all computing sites, and thus to improve overall system performance. We present a smart load balancer, which leverages the knowledge of burstiness to predict the changes in user demands and on-the-􀀃y shifts between the schemes that are“greedy” (i.e., always select the best site) and “random” (i.e., randomly select one) based on the predicted information. Both simulation and real experimental results show that this new load balancer can adapt quickly to the changes in user demands and thus improve performance by making a smart site selection for cloud users under both bursty and non-bursty workloads.  \nI. INTRODUCTION  \nCloud computing nowadays becomes quite popular among a community of cloud users by offering a variety of resources. Cloud computing platforms, such as those provided by Microsoft, Amazon, Google, IBM, and Hewlett-Packard, let developers deploy applications across computers hosted by a central organization. These applications can access a large network of computing resources that are deployed and managed by a cloud computing provider. Developers obtain the advantages of a managed computing platform, without having to commit resources to design, build and maintain the network. Yet, an important problem that must be addressed effectively in the cloud is how to manage QoS and maintain SLA for cloud users that share cloud resources.  \nIn cloud platforms, resource allocation (or load balancing) takes place at two levels. First, when an application is uploaded to the cloud, the load balancer assigns the requested instances to physical computers, attempting to balance the computational load of multiple applications across physical computers. Second, when an application receives multiple incoming requests, these requests should be each assigned to a speci􀀂c application instance to balance the computational load across a set of instances of the same application. For example, Amazon EC2 uses elastic load balancing (ELB) to control how incoming requests are handled. Application designers can direct requests to instances in speci􀀂c availability zones, to speci􀀂c instances, or to instances demonstrating the shortest response times.  \nBursty workloads are often found in multi-tier architectures, large storage systems, and grid services [1], [2], [3] . Internet 􀀃ash-crowds and traf􀀂c surges are familiar examples of burstytraf􀀂c, where bursts of requests are aggressively clustered together during short periods and thus create spikes with extremely high arrival rate. We argue that the presence of burstiness can cause load unbalancing in clouds and consequently degrade the overall system performance. In cloud systems, many applications are no longer single-programsingle-execution applications. These applications involve a large number of concurrent and dependent jobs, which can be executed either in parallel or sequentially. Simultaneously launching jobs from different applications during a short time period can immediately cause a signi􀀂cant arrival peak, which further aggravates resource compe","cbCaimCnJehT8F2U","https://ap.wps.com/l/cbCaimCnJehT8F2U","pdf",432844,1,"English","en",105,"# Abstract\n# Introduction\n## Resource allocation and load balancing in cloud platforms\n## Impact of burstiness on QoS and SLA\n## Contributions of ARA\n## Simulation and real EC2 evaluation","[{\"question\":\"What problem does ARA address in cloud computing environments?\",\"answer\":\"ARA addresses performance degradation caused by bursty user demand patterns that conventional load balancers fail to handle, leading to load imbalance and SLA risk.\"},{\"question\":\"How does ARA predict and react to burstiness changes?\",\"answer\":\"ARA uses an on-off prediction approach to forecast changes in user demands based on burstiness, then adapts by shifting load-balancing behavior accordingly.\"},{\"question\":\"What are the two load-balancing schemes ARA switches between?\",\"answer\":\"ARA switches on-the-fly between a greedy scheme (always selecting the best site) and a random scheme (randomly selecting among available sites) using predicted information.\"}]","ARA - 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