TanGo: A Cost Optimization Framework for Tenant Task Placement in Geo-distributed Clouds

Cloud infrastructure has gradually displayed a tendency of geographical distribution in order to provide anywhere, anytime connectivity to tenants all over the world. The tenant task placement in geo-distributed clouds comes with three critical and coupled factors: regional diversity in electricity prices, access delay for tenants, and traffic demand among tasks. However, existing works disregard either the regional difference in electricity prices or the tenant requirements in geo-distributed clouds, resulting in increased operating costs or low user QoS. To bridge the gap, we design a cost optimization framework for tenant task placement in geo-distributed clouds, called TanGo. However, it is non-trivial to achieve an optimization framework while meeting all the tenant requirements. To this end, we first formulate the electricity cost minimization for task placement problem as a constrained mixed-integer non-linear programming problem. We then propose a near-optimal algorithm with a tight approximation ratio (1 − 1/e) using an effective submodular-based method. Results of in-depth simulations based on real-world datasets show the effectiveness of our algorithm as well as the overall 10%-30% reduction in electricity expenses compared to commonly-adopted alternatives.