Resource Adjustment and Intervention Scheduling in the Availability-Aware Cloud

Abstract

Server instances, also known as virtual machines (VMs), with various computing and storage capabilities (i.e. different combinations of CPU, memory, storage, and networking), are commonly offered by cloud computing service providers such as Amazon Web Services (AWS), Google, Microsoft, Rackspace, and SalesForce. The clients, ranging from individuals and small institutions to large companies, can rent and pay only for a set of VMs that are actually needed as a usagebased (pay-as-you-go) posted pricing model. This model alleviates the clients’ concerns about the risks of capacity under-utilization and demand non-fulfillment associated with in-house facilities. These VMs are mapped onto physical servers within cloud. Such datacenters are known to be susceptible to different types of failures from frequent small-scale failures (such as disk failures) to less frequent but more catastrophic failures (such as power distribution unit failures) (Dean 2009). Failures at the physical infrastructure level inevitably render the mapped VMs unavailable due to loss of connectivity, software bugs, human errors, etc.