assumptions do not always hold. An especially compelling setting where this occurs is a virtualized data center, such as Amazon’s Elastic Compute Cloud (EC2). We show that Hadoop’s scheduler can cause severe performance degradation in heterogeneous environments. We design a new scheduling algorithm, Longest

and cost of cloud providers. CloudCmp measures the elastic computing, persistent storage, and networking services offered by a cloud along metrics that directly reflect their impact on the performance of customer applications. CloudCmp strives to ensure fairness, representativeness, and compliance

Traffic monitoring or crowd management systems produce large amounts of data in the form of events that need to be processed to detect relevant incidents. Rule-based pattern recognition is a promising approach for these applications, however, increasing amounts of data as well as large and complex

An attractive approach to leveraging the ability of cloudcomputing platforms to provide resources on demand is to build elastic applications, which can scale up or down based on resource requirements. To ease the development of elastic applications, it is useful for programmers to write

middleware framework to support autonomous workload elas-ticity and scalability based on application-level migration as a reconfiguration strategy. While other scalable frameworks (e.g., MapReduce or Google App Engine) force application developers to write programs following specific APIs, COS

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 finegrained elastic resource scaling for multi-tenant cloud computing infrastructures. Cloud

Cloud computing provides elastic computing infrastructure and resources which enable resource-on-demand and pay-as-you-go utility computing models. We believe that new applications can leverage these models to achieve new features that are not available for legacy applications. In our project we

This paper addresses “reflective ” control for applications that use server resources from a shared cloud infrastructure opportunistically. In this approach, an external reflective controller launches application functions based on knowledge of what resources are available from the cloud

Abstract—Cloud systems require elastic resource allocation to minimize resource provisioning costs while meeting service level objectives (SLOs). In this paper, we present a novel PRedictive Elastic reSource Scaling (PRESS) scheme for cloud systems. PRESS unobtrusively extracts fine-grained dynamic

Abstract. Next generation Cloud applications present elastic features and rapidly scale their comprised resources. Consequently, managing and monitoring Cloud applications is becoming a challenge. This paper showcases the functionality and novel features of: (i) c-Eclipse, a framework