Scheduling Frameworks for Cloud Container Services

Abstract

Compared with traditional virtual machines, cloud containers are more flexible and lightweight, emerging as the new norm of cloud resource provisioning. We exploit this new algorithm design space, and propose scheduling frameworks for cloud container services. Our offline and online schedulers permit partial execution, and allow a job to specify its job deadline, desired cloud containers, and inter-container dependence relations. We leverage the following classic and new techniques in our scheduling algorithm design. First, we apply the compact-exponential technique to express and handle nonconventional scheduling constraints. Second, we adopt the primal-dual framework that determines the primal solution based on its dual constraints in both the offline and online algorithms. The offline scheduling algorithm includes a new separation oracle to separate violated dual constraints, and works in concert with the randomized rounding technique to provide a near-optimal solution. The online scheduling algorithm leverages the online primal-dual framework with a learning-based scheme for obtaining dual solutions. Both theoretical analysis and trace-driven simulations validate that our scheduling frameworks are computationally efficient and achieve close-to-optimal aggregate job valuation.