We consider the problem of service hosting where a service provider can dynamically rent edge resources via short term contracts to ensure better quality of service to its customers. The service can also be partially hosted at the edge, in which case, customers’ requests can be partially served at the edge. The total cost incurred by the system is modeled as a combination of the rent cost, the service cost incurred due to latency in serving customers, and the fetch cost incurred as a result of the bandwidth used to fetch the code/databases of the service from the cloud servers to host the service at the edge. In this paper, we compare multiple hosting policies with regret as a metric, defined as the difference in the cost incurred by the policy and the optimal policy over some time horizon T. In particular we consider the Retro Renting (RR) and Follow The Perturbed Leader (FTPL) policies proposed in the literature and provide performance guarantees on the regret of these policies. We show that under i.i. d stochastic arrivals, RR policy has linear regret while FTPL policy has constant regret. Next, we propose a variant of FTPL, namely Wait then FTPL (W-FTPL), which also has constant regret while demonstrating much better dependence on the fetch cost. We also show that under adversarial arrivals, RR policy has linear regret while both FTPL and W-FTPL have regret O($\sqrt{T}$) which is orderoptimal.

This work focuses on designing caching policies for transient data, i.e., data which can be used to serve requests only for a finite duration of time after which it becomes redundant. We first characterize the fundamental limit on the performance of caching policies for transient data and characterize the performance of traditional caching policies like LRU for this setting. Traditional caching policies often make decisions based on the popularity of the data being cached. We propose a new caching policy which uses both the popularity and the residual life-time (time remaining before the data becomes redundant) to make caching decisions. We show that in the setting where data being cached is transient, our policy outperforms traditional caching policies.

Motivated by applications like Information Centric Networking for the Internet of Things, we study caching policies for the setting where the data being cached is heterogeneous in nature. This heterogeneity is in two aspects, namely, the lifetime of the data and the size of the data. We propose a caching policy which divides the cache into sub-caches, such that each sub-cache is reserved for data of a specific size and lifetime. Via analytical results and simulations, we show that our policy outperforms existing caching policies for heterogeneous data.