IoNCloud: Exploring application affinity to improve utilization and predictability in datacenters

Abstract

The intra-cloud network is typically shared in a best-effort manner, which causes tenant applications to have no actual bandwidth guarantees. Recent proposals address this issue either by statically reserving a slice of the physical infrastructure for each application or by providing proportional sharing among flows. The former approach results in overprovisioned network resources, while the latter requires substantial management overhead. In this paper, we introduce a resource allocation strategy that aims at providing an efficient way to predictably share bandwidth among applications and at minimizing resource underutilization while maintaining low management overhead. To demonstrate the benefits of the strategy, we develop IoN-Cloud, a system that implements the proposed allocation scheme. IoNCloud employs the abstraction of attraction/repulsion among applications according to their temporal bandwidth demands in order to group them in virtual networks. In doing so, we explore the trade-off between high resource utilization (which is desired by providers to achieve economies of scale) and strict network guarantees (necessary for tenants to run jobs predictably). Evaluation results show that IoNCloud can (a) provide predictable network sharing; and (b) reduce allocated bandwidth, resource underutilization and management overhead when compared against state-of-the-art proposals. I. INTRODUCTIONCloud providers lack practical, efficient and reliable mechanisms to offer bandwidth guarantees for applications [1], [2]. The intra-cloud network is typically oversubscribed and shared in a best-effort manner, relying on TCP to achieve high network utilization and scalability. TCP, nonetheless, does not provide robust isolation among flows in the network [3]; in fact, long-lived flows with a large number of packets are privileged over small ones (which is typically called performance interference [4]) [5]. Moreover, recent studies [6], [7] show that bandwidth available for virtual machines (VMs) in the intracloud network can vary by a factor of five or more, resulting in poor and unpredictable overall application performance.The lack of network guarantees directly impacts both tenants and providers. Tenants are unable to enforce the allocation of network resources for their requests (which particularly hinders applications with strict bandwidth requirements) and can only deploy some specific enterprise applications in the cloud [8]. Moreover, costs are unpredictable due to high network variability (in many services, the subsequent computation depends on the data received from the network [9], [10]). Providers, in turn, may lose revenue, because performance interference ends up reducing datacenter throughput [1], [6]. Recent proposals [3], [6], [8], [11], [12] address this issue either by offering minimum guarantees or by providingproportional sharing. The former explicitly reserves a slice of the physical infrastructure for each application, which results in overprovisioned resources for tenants (since the temporal network...

Energy-aware load balancing in distributed data centers

“…Most of previous studies in energy consumption for cloud computing data centers have neglected the data center network [6,26,38,41]. Moreover, cloud networking has been addressed in recent works [15,23,24,28,33,34,42] due to the impact of network switches and links on cloud operations. In this dissertation, the network topology is considered as well as the traffic demands imposed by the virtual machines.…”